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Source author record

Bo Li

Bo Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Machine LearningComputer Visionastro-ph.SRArtificial IntelligenceCryptography and SecurityComputation and Languagequant-phcond-mat.mtrl-scieess.ASComputer Science and Game Theorycond-mat.mes-hallSoundeess.IVDistributed, Parallel, and Cluster ComputingApplicationsGraphicsRoboticscond-mat.stat-mechcond-mat.str-elcond-mat.supr-conMultimediaphysics.opticsphysics.space-phcond-mat.softeess.SPmath.OCMethodologyNetworking and Internet Architecturephysics.ins-detphysics.soc-phBiological Physicscs.CYInformation Retrievalastro-phcond-mat.dis-nnecon.THGenomicsInformation Theorymath.COmath.ITmath.NAMolecular NetworksNumerical Analysisphysics.class-phphysics.flu-dynphysics.plasm-phQuantitative Methodsastro-ph.HECell BehaviorComputational Engineering, Finance, and Sciencecond-mat.quant-gasecon.GNhep-phHuman-Computer Interactionmath-phmath.CAmath.CVmath.DSmath.MPMathematical SoftwareMultiagent Systemsnucl-thPerformancephysics.comp-phphysics.med-phq-fin.CPq-fin.ECSocial and Information NetworksSoftware EngineeringSymbolic Computation

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Published work

302 published item(s)

preprint2026arXiv

CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models

Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Current LLM-assisted modeling workflows face a refinement-routing problem: prediction discrepancies are observed through executable implementations, but the relevant revision may involve the modeling assumption, representation design, implementation, or task constraint. Without structured feedback propagation across these levels, iterative refinement may repair code while failing to revise the assumption responsible for the discrepancy. We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space. CellScientist represents modeling decisions as structured states, realizes them as admissible programs under task and interface constraints, and routes execution discrepancies back to targeted hypothesis or implementation updates. This enables a closed Hypothesis -> Implementation -> Hypothesis loop where failures become structured signals for model refinement rather than debugging events. Across morphology and transcriptomic benchmarks, with additional single-cell perturbation evaluations, the final executable models selected by CellScientist improve over reference baselines under fixed split and evaluation protocols, while the workflow produces auditable refinement traces.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Evolving Idea Graphs with Learnable Edits-and-Commits for Multi-Agent Scientific Ideation

LLM-empowered multi-agent systems offer new potential to accelerate scientific discovery by generating novel research ideas. However, existing methods typically coordinate agents through temporary texts, such as drafts or chat logs; it is difficult to pinpoint the weaknesses in the generated ideas and how the agents refine them. To this end, we introduce \textbf{Evolving Idea Graphs} (EIG), a graph-based multi-agent scientific ideation framework that can generate high-performance research ideas across various benchmark-native metrics, such as novelty, feasibility, and clarity. Instead of coordinating solely through texts, EIG represents a partially formed proposal as an evolving idea graph, where nodes capture scientific claims and edges encode relations (e.g., support and conflict), enabling unresolved weaknesses to remain identifiable throughout the idea evolving process. Specifically, a learned two-head controller operates over the evolving graph to guide the ideation: one head selects graph edits for agents to execute, while the other decides when the graph is ready for commit as final proposal synthesis. On AI Idea Bench 2025 and LiveIdeaBench, EIG outperforms all compared systems on both automatic benchmark scores and blind expert ratings. Ablations further show that explicit graph state provides the main performance gains, and learned edit-and-commit control adds consistent improvements.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion

Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive biases. Therefore, we propose a Retrieval-Augmented Discrete Diffusion (RADD) framework to decouple retrieve and reranking for MMKGC. A relation-aware multimodal KGE retriever serves as both global retriever and distillation teacher, while a conditional discrete denoiser performs shortlist-level entity-identity generation for reranking. Training combines KGE supervision, denoising cross-entropy, and temperature-scaled distillation from the retriever to the denoiser. At inference, the designed Diff-Rerank first forms a top-$K$ shortlist with the retriever and then reranks it with the denoiser, ensuring that recall is a strict prerequisite for precision. Experiments on three MMKGC benchmarks show that RADD achieves the best performance and consistent gains over strong unimodal, multimodal, and LLM-based baselines, while ablations further verify the contribution of each component.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Holistic Evaluation of Multimodal LLMs on Spatial Intelligence

Multimodal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, the very capability that anchors artificial general intelligence in the physical world. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models (GPT, Gemini, Grok, Seed, Qwen, and Intern) stand on the path toward spatial intelligence (SI). We thus propose EASI for holistic Evaluation of multimodAl LLMs on Spatial Intelligence. EASI conceptualizes a comprehensive taxonomy of spatial tasks that unifies existing benchmarks and a growing collection of newly curated ones, enabling systematic evaluation of state-of-the-art models. In this report, we conduct the study across eight key benchmarks, at a cost exceeding ten billion total tokens. Our empirical study then reveals that (1) GPT-5 demonstrates unprecedented strength in SI, yet (2) still falls short of human performance significantly across a broad spectrum of SI-tasks. Moreover, we (3) show that SI-tasks expose greater model capability deficiency than non-SI tasks, to the extent that (4) proprietary models do not exhibit a decisive advantage when facing the most difficult ones. In addition, we conduct a qualitative evaluation across a diverse set of scenarios that are intuitive for humans, yet fail the most advanced multimodal models. EASI is an ongoing community effort: we have open-sourced the EASI codebase that provides a one-stop and reproducible solution with standardized interfaces, integrated protocols and prompts that significantly reduce the friction of configuring and running multiple benchmarks; we have also launched an accompanying EASI leaderboard to provide a continually updated snapshot of model performance across the full SI spectrum, accelerating collective progress toward robust SI.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

CBD: A Certified Backdoor Detector Based on Local Dominant Probability

Backdoor attack is a common threat to deep neural networks. During testing, samples embedded with a backdoor trigger will be misclassified as an adversarial target by a backdoored model, while samples without the backdoor trigger will be correctly classified. In this paper, we present the first certified backdoor detector (CBD), which is based on a novel, adjustable conformal prediction scheme based on our proposed statistic local dominant probability. For any classifier under inspection, CBD provides 1) a detection inference, 2) the condition under which the attacks are guaranteed to be detectable for the same classification domain, and 3) a probabilistic upper bound for the false positive rate. Our theoretical results show that attacks with triggers that are more resilient to test-time noise and have smaller perturbation magnitudes are more likely to be detected with guarantees. Moreover, we conduct extensive experiments on four benchmark datasets considering various backdoor types, such as BadNet, CB, and Blend. CBD achieves comparable or even higher detection accuracy than state-of-the-art detectors, and it in addition provides detection certification. Notably, for backdoor attacks with random perturbation triggers bounded by $\ell_2\leq0.75$ which achieves more than 90\% attack success rate, CBD achieves 100\% (98\%), 100\% (84\%), 98\% (98\%), and 72\% (40\%) empirical (certified) detection true positive rates on the four benchmark datasets GTSRB, SVHN, CIFAR-10, and TinyImageNet, respectively, with low false positive rates.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification

Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness. Recent studies show that even advanced attacks cannot break such defenses effectively, since the purification process induces an extremely deep computational graph which poses the potential problem of gradient obfuscation, high memory cost, and unbounded randomness. In this paper, we propose a unified framework DiffAttack to perform effective and efficient attacks against diffusion-based purification defenses, including both DDPM and score-based approaches. In particular, we propose a deviated-reconstruction loss at intermediate diffusion steps to induce inaccurate density gradient estimation to tackle the problem of vanishing/exploding gradients. We also provide a segment-wise forwarding-backwarding algorithm, which leads to memory-efficient gradient backpropagation. We validate the attack effectiveness of DiffAttack compared with existing adaptive attacks on CIFAR-10 and ImageNet. We show that DiffAttack decreases the robust accuracy of models compared with SOTA attacks by over 20% on CIFAR-10 under $\ell_\infty$ attack $(ε=8/255)$, and over 10% on ImageNet under $\ell_\infty$ attack $(ε=4/255)$. We conduct a series of ablations studies, and we find 1) DiffAttack with the deviated-reconstruction loss added over uniformly sampled time steps is more effective than that added over only initial/final steps, and 2) diffusion-based purification with a moderate diffusion length is more robust under DiffAttack.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Towards Code Watermarking with Dual-Channel Transformations

The expansion of the open source community and the rise of large language models have raised ethical and security concerns on the distribution of source code, such as misconduct on copyrighted code, distributions without proper licenses, or misuse of the code for malicious purposes. Hence it is important to track the ownership of source code, in which watermarking is a major technique. Yet, drastically different from natural languages, source code watermarking requires far stricter and more complicated rules to ensure the readability as well as the functionality of the source code. Hence we introduce SrcMarker, a watermarking system to unobtrusively encode ID bitstrings into source code, without affecting the usage and semantics of the code. To this end, SrcMarker performs transformations on an AST-based intermediate representation that enables unified transformations across different programming languages. The core of the system utilizes learning-based embedding and extraction modules to select rule-based transformations for watermarking. In addition, a novel feature-approximation technique is designed to tackle the inherent non-differentiability of rule selection, thus seamlessly integrating the rule-based transformations and learning-based networks into an interconnected system to enable end-to-end training. Extensive experiments demonstrate the superiority of SrcMarker over existing methods in various watermarking requirements.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Bertrand duopoly game with differentiated products reconsidered

In this paper, we explore a dynamic Bertrand duopoly game with differentiated products, where firms are boundedly rational and consumers are assumed to possess an underlying CES utility function. We mainly focus on two distinct degrees of product substitutability. Several tools based on symbolic computations such as the triangular decomposition method and the PCAD method are employed in the analytical investigation of the model. The uniqueness of the non-vanishing equilibrium is proved and rigorous conditions for the local stability of this equilibrium are established for the first time. Most importantly, we find that increasing the substitutability degree or decreasing the product differentiation has an effect of destabilization for our Bertrand model, which is in contrast with the relative conclusions for the Cournot models. This finding could be conducive to the revelation of the essential difference between dynamic Cournot and Bertrand oligopolies with differentiated goods. In the special case of identical marginal costs, we derive that lower degrees of product differentiation mean lower prices, higher supplies, lower profits, and lower social welfare. Furthermore, complex dynamics such as periodic orbits and chaos are reported through our numerical simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Defending SDN against packet injection attacks using deep learning

The (logically) centralised architecture of the software-defined networks makes them an easy target for packet injection attacks. In these attacks, the attacker injects malicious packets into the SDN network to affect the services and performance of the SDN controller and overflow the capacity of the SDN switches. Such attacks have been shown to ultimately stop the network functioning in real-time, leading to network breakdowns. There have been significant works on detecting and defending against similar DoS attacks in non-SDN networks, but detection and protection techniques for SDN against packet injection attacks are still in their infancy. Furthermore, many of the proposed solutions have been shown to be easily by-passed by simple modifications to the attacking packets or by altering the attacking profile. In this paper, we develop novel Graph Convolutional Neural Network models and algorithms for grouping network nodes/users into security classes by learning from network data. We start with two simple classes - nodes that engage in suspicious packet injection attacks and nodes that are not. From these classes, we then partition the network into separate segments with different security policies using distributed Ryu controllers in an SDN network. We show in experiments on an emulated SDN that our detection solution outperforms alternative approaches with above 99\% detection accuracy on various types (both old and new) of injection attacks. More importantly, our mitigation solution maintains continuous functions of non-compromised nodes while isolating compromised/suspicious nodes in real-time. All code and data are publicly available for reproducibility of our results.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

MixGen: A New Multi-Modal Data Augmentation

Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-language representation learning to further improve data efficiency. It generates new image-text pairs with semantic relationships preserved by interpolating images and concatenating text. It's simple, and can be plug-and-played into existing pipelines. We evaluate MixGen on four architectures, including CLIP, ViLT, ALBEF and TCL, across five downstream vision-language tasks to show its versatility and effectiveness. For example, adding MixGen in ALBEF pre-training leads to absolute performance improvements on downstream tasks: image-text retrieval (+6.2% on COCO fine-tuned and +5.3% on Flicker30K zero-shot), visual grounding (+0.9% on RefCOCO+), visual reasoning (+$0.9% on NLVR2), visual question answering (+0.3% on VQA2.0), and visual entailment (+0.4% on SNLI-VE).

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Product Ranking for Revenue Maximization with Multiple Purchases

Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $Õ(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Toward Reliability in the NISQ Era: Robust Interval Guarantee for Quantum Measurements on Approximate States

Near-term quantum computation holds potential across multiple application domains. However, imperfect preparation and evolution of states due to algorithmic and experimental shortcomings, characteristic in the near-term implementation, would typically result in measurement outcomes deviating from the ideal setting. It is thus crucial for any near-term application to quantify and bound these output errors. We address this need by deriving robustness intervals which are guaranteed to contain the output in the ideal setting. The first type of interval is based on formulating robustness bounds as semi-definite programs, and uses only the first moment and the fidelity to the ideal state. Furthermore, we consider higher statistical moments of the observable and generalize bounds for pure states based on the non-negativity of Gram matrices to mixed states, thus enabling their applicability in the NISQ era where noisy scenarios are prevalent. Finally, we demonstrate our results in the context of the variational quantum eigensolver (VQE) on noisy and noiseless simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks

Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use cases makes it challenging to use in practice. In addition, selecting an appropriate FL framework for a specific use case can be a daunting task. In this work, we present UniFed, the first unified platform for standardizing existing open-source FL frameworks. The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks. In particular, to address the substantial variations in workflows and data formats, UniFed introduces a configuration-based schema-enforced task specification, offering 20 editable fields. UniFed also provides functionalities such as distributed execution management, logging, and data analysis. With UniFed, we evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance, through conducting developer surveys and code-level investigation. We collect 15 diverse FL scenario setups (e.g., horizontal and vertical settings) for FL framework evaluation. This comprehensive evaluation allows us to analyze both model and system performance, providing detailed comparisons and offering recommendations for framework selection. UniFed simplifies the process of selecting and utilizing the appropriate FL framework for specific use cases, while enabling standardized distributed experimentation and deployment. Our results and analysis based on experiments with up to 178 distributed nodes provide valuable system design and deployment insights, aiming to empower practitioners in their pursuit of effective FL solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks

Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users. As local training data comes from different users who may not be trustworthy, several studies have shown that FL is vulnerable to poisoning attacks. Meanwhile, to protect the privacy of local users, FL is usually trained in a differentially private way (DPFL). Thus, in this paper, we ask: What are the underlying connections between differential privacy and certified robustness in FL against poisoning attacks? Can we leverage the innate privacy property of DPFL to provide certified robustness for FL? Can we further improve the privacy of FL to improve such robustness certification? We first investigate both user-level and instance-level privacy of FL and provide formal privacy analysis to achieve improved instance-level privacy. We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels. Theoretically, we provide the certified robustness of DPFL based on both criteria given a bounded number of adversarial users or instances. Empirically, we conduct extensive experiments to verify our theories under a range of poisoning attacks on different datasets. We find that increasing the level of privacy protection in DPFL results in stronger certified attack inefficacy; however, it does not necessarily lead to a stronger certified prediction. Thus, achieving the optimal certified prediction requires a proper balance between privacy and utility loss.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

"Help! Can You Hear Me?": Understanding How Help-Seeking Posts are Overwhelmed on Social Media during a Natural Disaster

Posting help-seeking requests on social media has been broadly adopted by victims during natural disasters to look for urgent rescue and supplies. The help-seeking requests need to get sufficient public attention and be promptly routed to the intended target(s) for timely responses. However, the huge volume and diverse types of crisis-related posts on social media might limit help-seeking requests to receive adequate engagement and lead to their overwhelm. To understand this problem, this work proposes a mixed-methods approach to figure out the overwhelm situation of help-seeking requests, and individuals' and online communities' strategies to cope. We focused on the 2021 Henan Floods in China and collected 141,674 help-seeking posts with the keyword "Henan Rainstorm Mutual Aid" on a popular Chinese social media platform Weibo. The findings indicate that help-seeking posts confront critical challenges of both external overwhelm (i.e., an enormous number of non-help-seeking posts with the help-seeking-related keyword distracting public attention) and internal overwhelm (i.e., attention inequality with 5% help-seeking posts receiving more than 95% likes, comments, and shares). We discover linguistic and non-linguistic help-seeking strategies that could help to prevent the overwhelm, such as including contact information, disclosing situational vulnerabilities, using subjective narratives, and structuring help-seeking posts to a normalized syntax. We also illustrate how community members spontaneously work to prevent the overwhelm with their collective wisdom (e.g., norm development through discussion) and collaborative work (e.g., cross-community support). We reflect on how the findings enrich the literature in crisis informatics and raise design implications that facilitate effective help-seeking on social media during natural disasters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Language Agnostic Multilingual Streaming On-Device ASR System

On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency. E2E models have also shown promising results for multilingual automatic speech recognition (ASR). In this paper, we extend our previous capacity solution to streaming applications and present a streaming multilingual E2E ASR system that runs fully on device with comparable quality and latency to individual monolingual models. To achieve that, we propose an Encoder Endpointer model and an End-of-Utterance (EOU) Joint Layer for a better quality and latency trade-off. Our system is built in a language agnostic manner allowing it to natively support intersentential code switching in real time. To address the feasibility concerns on large models, we conducted on-device profiling and replaced the time consuming LSTM decoder with the recently developed Embedding decoder. With these changes, we managed to run such a system on a mobile device in less than real time.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models

Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. However, recent studies reveal that the robustness of these models can be challenged by carefully crafted textual adversarial examples. While several individual datasets have been proposed to evaluate model robustness, a principled and comprehensive benchmark is still missing. In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations. Our findings are summarized as follows. (i) Most existing adversarial attack algorithms are prone to generating invalid or ambiguous adversarial examples, with around 90% of them either changing the original semantic meanings or misleading human annotators as well. Therefore, we perform a careful filtering process to curate a high-quality benchmark. (ii) All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy. We hope our work will motivate the development of new adversarial attacks that are more stealthy and semantic-preserving, as well as new robust language models against sophisticated adversarial attacks. AdvGLUE is available at https://adversarialglue.github.io.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

Machine learning (ML) robustness and domain generalization are fundamentally correlated: they essentially concern data distribution shifts under adversarial and natural settings, respectively. On one hand, recent studies show that more robust (adversarially trained) models are more generalizable. On the other hand, there is a lack of theoretical understanding of their fundamental connections. In this paper, we explore the relationship between regularization and domain transferability considering different factors such as norm regularization and data augmentations (DA). We propose a general theoretical framework proving that factors involving the model function class regularization are sufficient conditions for relative domain transferability. Our analysis implies that ``robustness" is neither necessary nor sufficient for transferability; rather, regularization is a more fundamental perspective for understanding domain transferability. We then discuss popular DA protocols (including adversarial training) and show when they can be viewed as the function class regularization under certain conditions and therefore improve generalization. We conduct extensive experiments to verify our theoretical findings and show several counterexamples where robustness and generalization are negatively correlated on different datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Algorithmic Fair Allocation of Indivisible Items: A Survey and New Questions

The theory of algorithmic fair allocation is within the center of multi-agent systems and economics in the last decade due to its industrial and social importance. At a high level, the problem is to assign a set of items that are either goods or chores to a set of agents so that every agent is happy with what she obtains. Particularly, in this survey, we focus on indivisible items, for which absolute fairness such as envy-freeness and proportionality cannot be guaranteed. One main theme in the recent research agenda is about designing algorithms that approximately achieve the fairness criteria. We aim at presenting a comprehensive survey of recent progresses through the prism of algorithms, highlighting the ways to relax fairness notions and common techniques to design algorithms, as well as the most interesting questions for future research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Approximate Group Fairness for Clustering

We incorporate group fairness into the algorithmic centroid clustering problem, where $k$ centers are to be located to serve $n$ agents distributed in a metric space. We refine the notion of proportional fairness proposed in [Chen et al., ICML 2019] as {\em core fairness}, and $k$-clustering is in the core if no coalition containing at least $n/k$ agents can strictly decrease their total distance by deviating to a new center together. Our solution concept is motivated by the situation where agents are able to coordinate and utilities are transferable. A string of existence, hardness and approximability results is provided. Particularly, we propose two dimensions to relax core requirements: one is on the degree of distance improvement, and the other is on the size of deviating coalition. For both relaxations and their combination, we study the extent to which relaxed core fairness can be satisfied in metric spaces including line, tree and general metric space, and design approximation algorithms accordingly.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition

Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it is an art rather than science to find valid IVs in many real-world scenes. Moreover, the predefined hand-made IVs could be weak or erroneous by violating the conditions of valid IVs. These thorny facts hinder the application of the IV-based counterfactual prediction methods. In this paper, we propose a novel Automatic Instrumental Variable decomposition (AutoIV) algorithm to automatically generate representations serving the role of IVs from observed variables (IV candidates). Specifically, we let the learned IV representations satisfy the relevance condition with the treatment and exclusion condition with the outcome via mutual information maximization and minimization constraints, respectively. We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome. The IV and confounder representations compete for the information with their constraints in an adversarial game, which allows us to get valid IV representations for IV-based counterfactual prediction. Extensive experiments demonstrate that our method generates valid IV representations for accurate IV-based counterfactual prediction.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the tracking development in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bayesian Changepoint Estimation for Spatially Indexed Functional Time Series

We propose a Bayesian hierarchical model to simultaneously estimate mean based changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak and strong spatial correlation, and weak and strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion

Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Can pruning improve certified robustness of neural networks?

With the rapid development of deep learning, the sizes of neural networks become larger and larger so that the training and inference often overwhelm the hardware resources. Given the fact that neural networks are often over-parameterized, one effective way to reduce such computational overhead is neural network pruning, by removing redundant parameters from trained neural networks. It has been recently observed that pruning can not only reduce computational overhead but also can improve empirical robustness of deep neural networks (NNs), potentially owing to removing spurious correlations while preserving the predictive accuracies. This paper for the first time demonstrates that pruning can generally improve certified robustness for ReLU-based NNs under the complete verification setting. Using the popular Branch-and-Bound (BaB) framework, we find that pruning can enhance the estimated bound tightness of certified robustness verification, by alleviating linear relaxation and sub-domain split problems. We empirically verify our findings with off-the-shelf pruning methods and further present a new stability-based pruning method tailored for reducing neuron instability, that outperforms existing pruning methods in enhancing certified robustness. Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training, and up to 24.5% under adversarial training on the CIFAR10 dataset. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models across different datasets. Our findings offer a new angle to study the intriguing interaction between sparsity and robustness, i.e. interpreting the interaction of sparsity and certified robustness via neuron stability. Codes are available at: https://github.com/VITA-Group/CertifiedPruning.

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preprint2022arXiv

CARE: Certifiably Robust Learning with Reasoning via Variational Inference

Despite great recent advances achieved by deep neural networks (DNNs), they are often vulnerable to adversarial attacks. Intensive research efforts have been made to improve the robustness of DNNs; however, most empirical defenses can be adaptively attacked again, and the theoretically certified robustness is limited, especially on large-scale datasets. One potential root cause of such vulnerabilities for DNNs is that although they have demonstrated powerful expressiveness, they lack the reasoning ability to make robust and reliable predictions. In this paper, we aim to integrate domain knowledge to enable robust learning with the reasoning paradigm. In particular, we propose a certifiably robust learning with reasoning pipeline (CARE), which consists of a learning component and a reasoning component. Concretely, we use a set of standard DNNs to serve as the learning component to make semantic predictions, and we leverage the probabilistic graphical models, such as Markov logic networks (MLN), to serve as the reasoning component to enable knowledge/logic reasoning. However, it is known that the exact inference of MLN (reasoning) is #P-complete, which limits the scalability of the pipeline. To this end, we propose to approximate the MLN inference via variational inference based on an efficient expectation maximization algorithm. In particular, we leverage graph convolutional networks (GCNs) to encode the posterior distribution during variational inference and update the parameters of GCNs (E-step) and the weights of knowledge rules in MLN (M-step) iteratively. We conduct extensive experiments on different datasets and show that CARE achieves significantly higher certified robustness compared with the state-of-the-art baselines. We additionally conducted different ablation studies to demonstrate the empirical robustness of CARE and the effectiveness of different knowledge integration.

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preprint2022arXiv

Cell Polarity and Movement with Reaction-Diffusion and Moving Boundary: Rigorous Modeling and Robust Simulations

Cell polarity and movement are fundamental to many biological functions. Experimental and theoretically studies have indicated that interactions of certain proteins lead to the cell polarization which plays a key role in controlling the cell movement. We study the cell polarity and movement based on a class of biophysical models that consist of reaction-diffusion equations for different proteins and the dynamics of moving cell boundary. Such a moving boundary is often simulated by a phase-filed model. We first apply the matched asymptotic analysis to give a rigorous derivation of the sharp-interface model of the cell boundary from a phase-field model. We then develop a robust numerical approach that combines the level-set method to track the sharp boundary of a moving cell and accurate discretization techniques for solving the reaction-diffusion equations on the moving cell region. Our extensive numerical simulations predict the cell polarization under various kinds of stimulus, and capture both the linear and circular trajectories of a moving cell for a long period of time. In particular, we have identified some key parameters controlling different cell trajectories that are less accurately predicted by reduced models. Our work has linked different models and also developed tools that can be adapted for the challenging three-dimensional simulations.

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preprint2022arXiv

Certifying Out-of-Domain Generalization for Blackbox Functions

Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. However, existing techniques either make strong assumptions on the model class and loss functions that can be certified, such as smoothness expressed via Lipschitz continuity of gradients, or require to solve complex optimization problems. As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss. In this paper, we focus on the problem of certifying distributional robustness for blackbox models and bounded loss functions, and propose a novel certification framework based on the Hellinger distance. Our certification technique scales to ImageNet-scale datasets, complex models, and a diverse set of loss functions. We then focus on one specific application enabled by such scalability and flexibility, i.e., certifying out-of-domain generalization for large neural networks and loss functions such as accuracy and AUC. We experimentally validate our certification method on a number of datasets, ranging from ImageNet, where we provide the first non-vacuous certified out-of-domain generalization, to smaller classification tasks where we are able to compare with the state-of-the-art and show that our method performs considerably better.

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preprint2022arXiv

Characterizing Attacks on Deep Reinforcement Learning

Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations. However, some attacks assume full availability of the victim model, and some require a huge amount of computation, making them less feasible for real world applications. In this work, we make further explorations of the vulnerabilities of DRL by studying other aspects of attacks on DRL using realistic and efficient attacks. First, we adapt and propose efficient black-box attacks when we do not have access to DRL model parameters. Second, to address the high computational demands of existing attacks, we introduce efficient online sequential attacks that exploit temporal consistency across consecutive steps. Third, we explore the possibility of an attacker perturbing other aspects in the DRL setting, such as the environment dynamics. Finally, to account for imperfections in how an attacker would inject perturbations in the physical world, we devise a method for generating a robust physical perturbations to be printed. The attack is evaluated on a real-world robot under various conditions. We conduct extensive experiments both in simulation such as Atari games, robotics and autonomous driving, and on real-world robotics, to compare the effectiveness of the proposed attacks with baseline approaches. To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.

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preprint2022arXiv

Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

The lifestyles of urban dwellers could reveal important insights regarding the dynamics and complexity of cities. Despite growing research on analysis of lifestyle patterns in cities, little is known about the characteristics of people's lifestyles patterns at urban scale. This limitation is primarily due to challenges in characterizing lifestyle patterns when human movement data is aggregated to protect the privacy of users. In this study, we model cities based on aggregated human visitation data to construct a network of places. We then examine the subgraph signatures in the networks of places to map and characterize lifestyle patterns at city scale. Location-based data from Harris County, Dallas County, New York County, and Broward County in the United States were examined to reveal lifestyle signatures in cities. For the motif analysis, two-node, three-node, and four-node motifs without location attributes were extracted from human visitation networks. Second, homogenized nodes in motifs were encoded with location categories from NAICS codes. Multiple statistical measures, including network metrics and motif properties, were quantified to characterize lifestyle signatures. The results show that: people's lifestyles in urban environments can be well depicted and quantified based on distribution and attributes of motifs in networks of places; motifs in networks of places show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities; human visitation networks and lifestyle patterns show similarities across different metropolitan areas implying the universality of lifestyle signatures across cities. The findings provide deeper insights into urban lifestyles signatures in urban studies and provide important insights for data-informed urban planning and management.

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preprint2022arXiv

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications. Previous primal-dual style approaches suffer from instability issues and lack optimality guarantees. This paper overcomes the issues from the perspective of probabilistic inference. We introduce a novel Expectation-Maximization approach to naturally incorporate constraints during the policy learning: 1) a provable optimal non-parametric variational distribution could be computed in closed form after a convex optimization (E-step); 2) the policy parameter is improved within the trust region based on the optimal variational distribution (M-step). The proposed algorithm decomposes the safe RL problem into a convex optimization phase and a supervised learning phase, which yields a more stable training performance. A wide range of experiments on continuous robotic tasks shows that the proposed method achieves significantly better constraint satisfaction performance and better sample efficiency than baselines. The code is available at https://github.com/liuzuxin/cvpo-safe-rl.

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preprint2022arXiv

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks in RL and corresponding defenses, its robustness against training-time (poisoning) attacks remains largely unanswered. In this work, we focus on certifying the robustness of offline RL in the presence of poisoning attacks, where a subset of training trajectories could be arbitrarily manipulated. We propose the first certification framework, COPA, to certify the number of poisoning trajectories that can be tolerated regarding different certification criteria. Given the complex structure of RL, we propose two certification criteria: per-state action stability and cumulative reward bound. To further improve the certification, we propose new partition and aggregation protocols to train robust policies. We further prove that some of the proposed certification methods are theoretically tight and some are NP-Complete problems. We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties. All experimental results are available at https://copa-leaderboard.github.io.

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preprint2022arXiv

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains challenging. In this paper, we present the first unified framework CROP (Certifying Robust Policies for RL) to provide robustness certification on both action and reward levels. In particular, we propose two robustness certification criteria: robustness of per-state actions and lower bound of cumulative rewards. We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification. Empirically, we apply CROP to evaluate several existing empirically robust RL algorithms, including adversarial training and different robust regularization, in four environments (two representative Atari games, Highway, and CartPole). Furthermore, by evaluating these algorithms against adversarial attacks, we demonstrate that our certification are often tight. All experiment results are available at website https://crop-leaderboard.github.io.

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preprint2022arXiv

Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines

Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which training examples are 'guilty' in making the trained ML model predictions inaccurate or unfair?" Modeling data influence for ML training has attracted intensive interest over the last decade, and one popular framework is to compute the Shapley value of each training example with respect to utilities such as validation accuracy and fairness of the trained ML model. Unfortunately, despite recent intensive interest and research, existing methods only consider a single ML model "in isolation" and do not consider an end-to-end ML pipeline that consists of data transformations, feature extractors, and ML training. We present DataScope (ease.ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training. To this end, we first develop a novel algorithmic framework that computes Shapley value over a specific family of ML pipelines that we call canonical pipelines: a positive relational algebra query followed by a K-nearest-neighbor (KNN) classifier. We show that, for many subfamilies of canonical pipelines, computing Shapley value is in PTIME, contrasting the exponential complexity of computing Shapley value in general. We then put this to practice -- given an sklearn pipeline, we approximate it with a canonical pipeline to use as a proxy. We conduct extensive experiments illustrating different use cases and utilities. Our results show that DataScope is up to four orders of magnitude faster over state-of-the-art Monte Carlo-based methods, while being comparably, and often even more, effective in data debugging.

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preprint2022arXiv

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

Recent success of deep neural networks (DNNs) hinges on the availability of large-scale dataset; however, training on such dataset often poses privacy risks for sensitive training information. In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS. Comparing with the standard PATE privacy-preserving framework which allows teachers to vote on one-dimensional predictions, voting on the high dimensional gradient vectors is challenging in terms of privacy preservation. As dimension reduction techniques are required, we need to navigate a delicate tradeoff space between (1) the improvement of privacy preservation and (2) the slowdown of SGD convergence. To tackle this, we take advantage of communication efficient learning and propose a novel noise compression and aggregation approach TOPAGG by combining top-k compression for dimension reduction with a corresponding noise injection mechanism. We theoretically prove that the DATALENS framework guarantees differential privacy for its generated data, and provide analysis on its convergence. To demonstrate the practical usage of DATALENS, we conduct extensive experiments on diverse datasets including MNIST, Fashion-MNIST, and high dimensional CelebA, and we show that, DATALENS significantly outperforms other baseline DP generative models. In addition, we adapt the proposed TOPAGG approach, which is one of the key building blocks in DATALENS, to DP SGD training, and show that it is able to achieve higher utility than the state-of-the-art DP SGD approach in most cases. Our code is publicly available at https://github.com/AI-secure/DataLens.

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preprint2022arXiv

Determinants of local chemical environments and magnetic moments of high-entropy alloys

High-entropy alloys (HEAs) such as CrMnFeCoNi exhibit unconventional mechanical properties due to their compositional disorder. However, it remains a formidable challenge to estimate the local chemical-environment and magnetic effects of HEAs. Herein we identify the state-associated cohesive energy and band filling originated from the tight-binding and Friedel models as descriptors to quantify the site-to-site chemical bonding and magnetic moments of HEAs. We find that the s-state cohesive energy is indispensable in determining the bonding-strength trend of CrMnFeCoNi that differs from the bonding characteristics of precious and refractory HEAs, while the s-band filling is effective in determining the magnetic moments. This unusual behavior stems from the unique chemical and magnetic nature of Cr atoms and is essentially due to the localized and transferred itinerant electrons. Our study establishes a fundamental physical picture of chemical bonding and magnetic interactions of HEAs and provides a rational guidance for designing advanced structural alloys.

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preprint2022arXiv

Domain Generalization using Pretrained Models without Fine-tuning

Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks. However, fine-tuning is usually computationally expensive due to the ever-growing size of pretrained models. More importantly, it may cause over-fitting on source domain and compromise their generalization ability as shown in recent works. Generally, pretrained models possess some level of generalization ability and can achieve decent performance regarding specific domains and samples. However, the generalization performance of pretrained models could vary significantly over different test domains even samples, which raises challenges for us to best leverage pretrained models in DG tasks. In this paper, we propose a novel domain generalization paradigm to better leverage various pretrained models, named specialized ensemble learning for domain generalization (SEDGE). It first trains a linear label space adapter upon fixed pretrained models, which transforms the outputs of the pretrained model to the label space of the target domain. Then, an ensemble network aware of model specialty is proposed to dynamically dispatch proper pretrained models to predict each test sample. Experimental studies on several benchmarks show that SEDGE achieves significant performance improvements comparing to strong baselines including state-of-the-art method in DG tasks and reduces the trainable parameters by ~99% and the training time by ~99.5%.

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preprint2022arXiv

Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN

The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction. Furthermore, the generative adversarial networks (GAN) is applied to the super-resolution field, which effectively improves the visual quality of the reconstructed image. However, under the condtion of high upscaling factors, the excessive abnormal reasoning of the network produces some distorted structures, so that there is a certain deviation between the reconstructed image and the ground-truth image. In order to fundamentally improve the quality of reconstructed images, this paper proposes a effective method called Dual Perceptual Loss (DP Loss), which is used to replace the original perceptual loss to solve the problem of single image super-resolution reconstruction. Due to the complementary property between the VGG features and the ResNet features, the proposed DP Loss considers the advantages of learning two features simultaneously, which significantly improves the reconstruction effect of images. The qualitative and quantitative analysis on benchmark datasets demonstrates the superiority of our proposed method over state-of-the-art super-resolution methods.

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preprint2022arXiv

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.

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preprint2022arXiv

Eliciting Truthful Reports with Partial Signals in Repeated Games

We consider a repeated game where a player self-reports her usage of a service and is charged a payment accordingly by a center. The center observes a partial signal, representing part of the player's true consumption, which is generated from a publicly known distribution. The player can report any value that does not contradict the signal and the center issues a payment based on the reported information. Such problems find application in net metering billing in the electricity market, where a customer's actual consumption of the electricity network is masked and complete verification is impractical. When the underlying true value is relatively constant, we propose a penalty mechanism that elicits truthful self-reports. Namely, besides charging the player the reported value, the mechanism charges a penalty proportional to her inconsistent reports. We show how fear of the uncertainty in the future incentivizes the player to be truthful today. For Bernoulli distributions, we give the complete analysis and optimal strategies given any penalty. Since complete truthfulness is not possible for continuous distributions, we give approximate truthful results by a reduction from Bernoulli distributions. We also extend our mechanism to a multi-player cost sharing setting and give equilibrium results.

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preprint2022arXiv

Equalized Focal Loss for Dense Long-Tailed Object Detection

Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more prevalent in the industry because they have a simple and fast pipeline that is easy to deploy. However, in the long-tailed scenario, this line of work has not been explored so far. In this paper, we investigate whether one-stage detectors can perform well in this case. We discover the primary obstacle that prevents one-stage detectors from achieving excellent performance is: categories suffer from different degrees of positive-negative imbalance problems under the long-tailed data distribution. The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem. To address this issue, we propose the Equalized Focal Loss (EFL) that rebalances the loss contribution of positive and negative samples of different categories independently according to their imbalance degrees. Specifically, EFL adopts a category-relevant modulating factor which can be adjusted dynamically by the training status of different categories. Extensive experiments conducted on the challenging LVIS v1 benchmark demonstrate the effectiveness of our proposed method. With an end-to-end training pipeline, EFL achieves 29.2% in terms of overall AP and obtains significant performance improvements on rare categories, surpassing all existing state-of-the-art methods. The code is available at https://github.com/ModelTC/EOD.

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preprint2022arXiv

FILTRA: Rethinking Steerable CNN by Filter Transform

Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation of data and reduce overfitting. It has been an intuitive and widely used technique to construct a steerable filter by augmenting a filter with its transformed copies in the past decades, which is named as filter transform in this paper. Recently, the problem of steerable CNN has been studied from aspect of group representation theory, which reveals the function space structure of a steerable kernel function. However, it is not yet clear on how this theory is related to the filter transform technique. In this paper, we show that kernel constructed by filter transform can also be interpreted in the group representation theory. This interpretation help complete the puzzle of steerable CNN theory and provides a novel and simple approach to implement steerable convolution operators. Experiments are executed on multiple datasets to verify the feasibility of the proposed approach.

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preprint2022arXiv

Game of Trojans: A Submodular Byzantine Approach

Machine learning models in the wild have been shown to be vulnerable to Trojan attacks during training. Although many detection mechanisms have been proposed, strong adaptive attackers have been shown to be effective against them. In this paper, we aim to answer the questions considering an intelligent and adaptive adversary: (i) What is the minimal amount of instances required to be Trojaned by a strong attacker? and (ii) Is it possible for such an attacker to bypass strong detection mechanisms? We provide an analytical characterization of adversarial capability and strategic interactions between the adversary and detection mechanism that take place in such models. We characterize adversary capability in terms of the fraction of the input dataset that can be embedded with a Trojan trigger. We show that the loss function has a submodular structure, which leads to the design of computationally efficient algorithms to determine this fraction with provable bounds on optimality. We propose a Submodular Trojan algorithm to determine the minimal fraction of samples to inject a Trojan trigger. To evade detection of the Trojaned model, we model strategic interactions between the adversary and Trojan detection mechanism as a two-player game. We show that the adversary wins the game with probability one, thus bypassing detection. We establish this by proving that output probability distributions of a Trojan model and a clean model are identical when following the Min-Max (MM) Trojan algorithm. We perform extensive evaluations of our algorithms on MNIST, CIFAR-10, and EuroSAT datasets. The results show that (i) with Submodular Trojan algorithm, the adversary needs to embed a Trojan trigger into a very small fraction of samples to achieve high accuracy on both Trojan and clean samples, and (ii) the MM Trojan algorithm yields a trained Trojan model that evades detection with probability 1.

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preprint2022arXiv

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a ground metric that recognizes the difference between ECG signals based on physiologically determined features. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate improvements in accuracy and robustness, reflecting the effectiveness of our data augmentation method.

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preprint2022arXiv

Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks.

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preprint2022arXiv

Graph Contrastive Learning with Personalized Augmentation

Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augmented views of each graph via data augmentation. Existing GCL models mainly focus on applying \textit{identical augmentation strategies} for all graphs within a given scenario. However, real-world graphs are often not monomorphic but abstractions of diverse natures. Even within the same scenario (e.g., macromolecules and online communities), different graphs might need diverse augmentations to perform effective GCL. Thus, blindly augmenting all graphs without considering their individual characteristics may undermine the performance of GCL arts.To deal with this, we propose the first principled framework, termed as \textit{G}raph contrastive learning with \textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL by allowing each graph to choose its own suitable augmentation operations.In essence, GPA infers tailored augmentation strategies for each graph based on its topology and node attributes via a learnable augmentation selector, which is a plug-and-play module and can be effectively trained with downstream GCL models end-to-end. Extensive experiments across 11 benchmark graphs from different types and domains demonstrate the superiority of GPA against state-of-the-art competitors.Moreover, by visualizing the learned augmentation distributions across different types of datasets, we show that GPA can effectively identify the most suitable augmentations for each graph based on its characteristics.

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preprint2022arXiv

How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection

Model stealing attacks present a dilemma for public machine learning APIs. To protect financial investments, companies may be forced to withhold important information about their models that could facilitate theft, including uncertainty estimates and prediction explanations. This compromise is harmful not only to users but also to external transparency. Model stealing defenses seek to resolve this dilemma by making models harder to steal while preserving utility for benign users. However, existing defenses have poor performance in practice, either requiring enormous computational overheads or severe utility trade-offs. To meet these challenges, we present a new approach to model stealing defenses called gradient redirection. At the core of our approach is a provably optimal, efficient algorithm for steering an adversary's training updates in a targeted manner. Combined with improvements to surrogate networks and a novel coordinated defense strategy, our gradient redirection defense, called GRAD${}^2$, achieves small utility trade-offs and low computational overhead, outperforming the best prior defenses. Moreover, we demonstrate how gradient redirection enables reprogramming the adversary with arbitrary behavior, which we hope will foster work on new avenues of defense.

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preprint2022arXiv

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes, and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.

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preprint2022arXiv

Improving the fusion of acoustic and text representations in RNN-T

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR). To estimate the output distributions over subword units, RNN-T uses a fully connected layer as the joint network to fuse the acoustic representations extracted using the acoustic encoder with the text representations obtained using the prediction network based on the previous subword units. In this paper, we propose to use gating, bilinear pooling, and a combination of them in the joint network to produce more expressive representations to feed into the output layer. A regularisation method is also proposed to enable better acoustic encoder training by reducing the gradients back-propagated into the prediction network at the beginning of RNN-T training. Experimental results on a multilingual ASR setting for voice search over nine languages show that the joint use of the proposed methods can result in 4%--5% relative word error rate reductions with only a few million extra parameters.

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preprint2022arXiv

Impulsively Generated Kink Wave Trains in Solar Coronal Slabs

We numerically follow the response of density-enhanced slabs to impulsive, localized, transverse velocity perturbations by working in the framework of ideal magnetohydrodynamics (MHD). Both linear and nonlinear regimes are addressed. Kink wave trains are seen to develop along the examined slabs, sharing the characteristics that more oscillatory patterns emerge with time and that the apparent wavelength increases with distance at a given instant. Two features nonetheless arise due to nonlinearity, one being a density cavity close to the exciter and the other being the appearance of shocks both outside and inside the nominal slab. These features may be relevant for understanding the interaction between magnetic structures and such explosive events as coronal mass ejections. Our numerical findings on kink wave trains in solar coronal slabs are discussed in connection with typical measurements of streamer waves.

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preprint2022arXiv

Invariant Information Bottleneck for Domain Generalization

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers and the original optimization objective could fail when pseudo-invariant features and geometric skews exist. Inspired by IRM, in this paper we propose a novel formulation for domain generalization, dubbed invariant information bottleneck (IIB). IIB aims at minimizing invariant risks for nonlinear classifiers and simultaneously mitigating the impact of pseudo-invariant features and geometric skews. Specifically, we first present a novel formulation for invariant causal prediction via mutual information. Then we adopt the variational formulation of the mutual information to develop a tractable loss function for nonlinear classifiers. To overcome the failure modes of IRM, we propose to minimize the mutual information between the inputs and the corresponding representations. IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM. Furthermore, experiments on DomainBed show that IIB outperforms $13$ baselines by $0.9\%$ on average across $7$ real datasets.

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preprint2022arXiv

Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques.

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preprint2022arXiv

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked. In this work, we aim to enhance the ML robustness from a different perspective by leveraging domain knowledge: We propose a Knowledge Enhanced Machine Learning Pipeline (KEMLP) to integrate domain knowledge (i.e., logic relationships among different predictions) into a probabilistic graphical model via first-order logic rules. In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task. Theoretically, we provide convergence results and prove that, under mild conditions, the prediction of KEMLP is more robust than that of the main DNN model. Empirically, we take road sign recognition as an example and leverage the relationships between road signs and their shapes and contents as domain knowledge. We show that compared with adversarial training and other baselines, KEMLP achieves higher robustness against physical attacks, $\mathcal{L}_p$ bounded attacks, unforeseen attacks, and natural corruptions under both whitebox and blackbox settings, while still maintaining high clean accuracy.

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preprint2022arXiv

Large cavitation bubbles in the tube with a conical-frustum shaped closed end during a transient process

The transient process accompanied by extreme acceleration in the conical sections of hydraulic systems (e.g., draft tube, diffuser) can induce large cavitation bubbles both at the closed ends and in the bulk liquid. The collapses of the large cavitation bubbles can cause severe damage to the solid walls. We conduct experiments in the tubes with different conical-frustum shaped closed ends with the `tube-arrest' method and observe bubbles generated at these two locations. For the bubbles generated at the close end of the tube, we propose the onset criteria, consisting of two universal non-dimensional parameters $Ca_1$ and $Ca_2$, of large cavitation bubbles separating the water column. We investigate their dynamics including the collapse time and speed. The results indicate that the larger the conical angle, the faster the bubbles collapse. For the bubbles generated in the bulk liquid, we numerically study the collapse time, the jet characteristics and the pressure pulse at bubble collapse. We observe a much stronger jet and pressure pulse of bubbles in tubes, comparing with a bubble near an infinite plate. Our results can provide guidance in the design and safe operation of hydraulic machinery with complex geometries, considering the cavitation during the transient process.

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preprint2022arXiv

Location Intelligence Reveals the Extent, Timing, and Spatial Variation of Hurricane Preparedness

Improving hurricane preparedness is essential to reduce hurricane impacts. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the CBG level using high-resolution location intelligence data. Anonymized cell phone data on visits to POIs for each CBG before 2017 Hurricane Harvey were used to examine hurricane preparedness. Four categories of POI, grocery stores, gas stations, pharmacies and home improvement stores, were identified as having close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage, whereas the peak of visits to gas stations happened closer to landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. The study advances data-driven understanding of human protective actions and provide emergency response managers with novel insights to proactively monitor disaster preparedness, facilitating identifying under-prepared areas and better allocating resources timely.

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preprint2022arXiv

Machine Learning Empowered Intelligent Data Center Networking: A Survey

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To address these challenges, both academia and industry turn to artificial intelligence technology to realize network intelligence. To this end, a considerable number of novel and creative machine learning-based (ML-based) research works have been put forward in recent few years. Nevertheless, there are still enormous challenges faced by the intelligent optimization of data center networks (DCNs), especially in the scenario of online real-time dynamic processing of massive heterogeneous services and traffic data. To best of our knowledge, there is a lack of systematic and original comprehensively investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we comprehensively investigate the application of machine learning to data center networking, and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control. In order to provide a multi-dimensional and multi-perspective comparison of various solutions, we design a quality assessment criteria called REBEL-3S to impartially measure the strengths and weaknesses of these research works. Moreover, we also present unique insights into the technology evolution of the fusion of data center network and machine learning, together with some challenges and potential future research opportunities.

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preprint2022arXiv

MHMS: Multimodal Hierarchical Multimedia Summarization

Multimedia summarization with multimodal output can play an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. In this work, we propose a multimodal hierarchical multimedia summarization (MHMS) framework by interacting visual and language domains to generate both video and textual summaries. Our MHMS method contains video and textual segmentation and summarization module, respectively. It formulates a cross-domain alignment objective with optimal transport distance which leverages cross-domain interaction to generate the representative keyframe and textual summary. We evaluated MHMS on three recent multimodal datasets and demonstrated the effectiveness of our method in producing high-quality multimodal summaries.

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preprint2022arXiv

On the Certified Robustness for Ensemble Models and Beyond

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend against such attacks, both empirical and theoretical defense approaches have been extensively studied for a single ML model. In this work, we aim to analyze and provide the certified robustness for ensemble ML models, together with the sufficient and necessary conditions of robustness for different ensemble protocols. Although ensemble models are shown more robust than a single model empirically; surprisingly, we find that in terms of the certified robustness the standard ensemble models only achieve marginal improvement compared to a single model. Thus, to explore the conditions that guarantee to provide certifiably robust ensemble ML models, we first prove that diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption. We then provide the bounded model-smoothness analysis based on the proposed Ensemble-before-Smoothing strategy. We also prove that an ensemble model can always achieve higher certified robustness than a single base model under mild conditions. Inspired by the theoretical findings, we propose the lightweight Diversity Regularized Training (DRT) to train certifiably robust ensemble ML models. Extensive experiments show that our DRT enhanced ensembles can consistently achieve higher certified robustness than existing single and ensemble ML models, demonstrating the state-of-the-art certified L2-robustness on MNIST, CIFAR-10, and ImageNet datasets.

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preprint2022arXiv

On the Price of Fairness of Allocating Contiguous Blocks

In this work, we revisit the problem of fairly allocating a number of indivisible items that are located on a line to multiple agents. A feasible allocation requires that the allocated items to each agent are connected on the line. The items can be goods on which agents have non-negative utilities, or chores on which the utilities are non-positive. Our objective is to understand the extent to which welfare is inevitably sacrificed by enforcing the allocations to be fair, i.e., price of fairness (PoF). We study both egalitarian and utilitarian welfare. Previous works by Suksompong [Discret. Appl. Math., 2019] and Höhne and van Stee [Inf. Comput., 2021] have studied PoF regarding the notions of envy-freeness and proportionality. However, these fair allocations barely exist for indivisible items, and thus in this work, we focus on the relaxations of maximin share fairness and proportionality up to one item, which are guaranteed to be satisfiable. For most settings, we give (almost) tight ratios of PoF and all the upper bounds are proved by designing polynomial time algorithms.

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preprint2022arXiv

Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion.

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preprint2022arXiv

PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status is challenging. Even in clinical settings, HRV is sensitive to acute stressors such as physical activity, mental stress, hydration, alcohol, and sleep. Wearable devices provide convenient HRV measurements, but the irregularity of measurements and uncaptured stressors can bias conventional analytical methods. To better interpret HRV measurements for downstream healthcare applications, we learn a personalized diurnal rhythm as an accurate physiological indicator for each individual. We develop Physiological Multitask-Learning (PhysioMTL) by harnessing Optimal Transport theory within a Multitask-learning (MTL) framework. The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task. Our model outperforms competing MTL methodologies on unobserved predictive tasks for synthetic and two real-world datasets. Specifically, our method provides remarkable prediction results on unseen held-out subjects given only $20\%$ of the subjects in real-world observational studies. Furthermore, our model enables a counterfactual engine that generates the effect of acute stressors and chronic conditions on HRV rhythms.

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preprint2022arXiv

Pisces: Efficient Federated Learning via Guided Asynchronous Training

Federated learning (FL) is typically performed in a synchronous parallel manner, where the involvement of a slow client delays a training iteration. Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration. However, this is not always possible in practice, and the selection strategy often has to navigate an unpleasant trade-off between the speed and the data quality of clients. In this paper, we present Pisces, an asynchronous FL system with intelligent participant selection and model aggregation for accelerated training. To avoid incurring excessive resource cost and stale training computation, Pisces uses a novel scoring mechanism to identify suitable clients to participate in a training iteration. It also adapts the pace of model aggregation to dynamically bound the progress gap between the selected clients and the server, with a provable convergence guarantee in a smooth non-convex setting. We have implemented Pisces in an open-source FL platform called Plato, and evaluated its performance in large-scale experiments with popular vision and language models. Pisces outperforms the state-of-the-art synchronous and asynchronous schemes, accelerating the time-to-accuracy by up to 2.0x and 1.9x, respectively.

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preprint2022arXiv

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today's methods cannot achieve without sacrificing performance on other safety axes. For instance, adversarial training improves adversarial robustness but sharply degrades other classifier performance metrics. Similarly, strong data augmentation and regularization techniques often improve OOD robustness but harm anomaly detection, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and roundly improves safety measures.

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preprint2022arXiv

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions

Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encouraged the development of a number of ML algorithms from perception to prediction to planning. However, training AVs usually requires a large amount of training data collected from different driving environments (e.g., cities) as well as different types of personal information (e.g., working hours and routes). Such collected large data, treated as the new oil for ML in the data-centric AI era, usually contains a large amount of privacy-sensitive information which is hard to remove or even audit. Although existing privacy protection approaches have achieved certain theoretical and empirical success, there is still a gap when applying them to real-world applications such as autonomous vehicles. For instance, when training AVs, not only can individually identifiable information reveal privacy-sensitive information, but also population-level information such as road construction within a city, and proprietary-level commercial secrets of AVs. Thus, it is critical to revisit the frontier of privacy risks and corresponding protection approaches in AVs to bridge this gap. Following this goal, in this work, we provide a new taxonomy for privacy risks and protection methods in AVs, and we categorize privacy in AVs into three levels: individual, population, and proprietary. We explicitly list out recent challenges to protect each of these levels of privacy, summarize existing solutions to these challenges, discuss the lessons and conclusions, and provide potential future directions and opportunities for both researchers and practitioners. We believe this work will help to shape the privacy research in AV and guide the privacy protection technology design.

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preprint2022arXiv

Probing dissipation process via Fano resonance and collective effect in the X-ray cavity

In the absence of time-reversal symmetry, the asymmetric parameter q of the Fano resonance is extended into the complex space, where its trajectory can be utilized to investigate the decoherence process. By embedding the ensemble of M$\ddot{\rm{o}}$ssbauer nuclei in the thin-film planar cavity in this work, the trajectories of asymmetric parameter q are studied via the collective effect of the nuclear ensemble, which is regulated by the nuclear abundance and angle offset. Due to the diverse controllable methods of the collective resonant strength, there are different straight lines and arc-shape trajectories in the complex plane, in which the slopes and the radius can be respectively adjusted by the angle offset and nuclear abundance. It is demonstrated that the dissipation process can be suppressed equivalently by the strong energy exchange between the cavity and nuclear ensemble. The present results could enrich the behaviors of the asymmetric parameter q in the complex plane and would provide new possibility for the decoherence research through the thin-film planar cavity.

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preprint2022arXiv

Provable Domain Generalization via Invariant-Feature Subspace Recovery

Domain generalization asks for models trained over a set of training environments to perform well in unseen test environments. Recently, a series of algorithms such as Invariant Risk Minimization (IRM) has been proposed for domain generalization. However, Rosenfeld et al. (2021) shows that in a simple linear data model, even if non-convexity issues are ignored, IRM and its extensions cannot generalize to unseen environments with less than $d_s+1$ training environments, where $d_s$ is the dimension of the spurious-feature subspace. In this paper, we propose to achieve domain generalization with Invariant-feature Subspace Recovery (ISR). Our first algorithm, ISR-Mean, can identify the subspace spanned by invariant features from the first-order moments of the class-conditional distributions, and achieve provable domain generalization with $d_s+1$ training environments under the data model of Rosenfeld et al. (2021). Our second algorithm, ISR-Cov, further reduces the required number of training environments to $O(1)$ using the information of second-order moments. Notably, unlike IRM, our algorithms bypass non-convexity issues and enjoy global convergence guarantees. Empirically, our ISRs can obtain superior performance compared with IRM on synthetic benchmarks. In addition, on three real-world image and text datasets, we show that both ISRs can be used as simple yet effective post-processing methods to improve the worst-case accuracy of (pre-)trained models against spurious correlations and group shifts.

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preprint2022arXiv

Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas

The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can help transportation agencies incorporate resilience considerations into project development proactively and to understand the resilience performance of current road networks effectively. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on critical dimensions of road network resilience, and two integrated metrics were proposed to combine all metrics to show the overall resilience performance of road segments. A case study was conducted on the Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. Since the data used in this study is available to other states and countries, the framework presented in this study can be adopted by other transportation agencies across the globe for regional transportation resilience assessments.

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preprint2022arXiv

Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.

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preprint2022arXiv

Robustifying Reinforcement Learning Policies with $\mathcal{L}_1$ Adaptive Control

A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations. Existing robust methods try to obtain a fixed policy for all envisioned dynamic variation scenarios through robust or adversarial training. These methods could lead to conservative performance due to emphasis on the worst case, and often involve tedious modifications to the training environment. We propose an approach to robustifying a pre-trained non-robust RL policy with $\mathcal{L}_1$ adaptive control. Leveraging the capability of an $\mathcal{L}_1$ control law in the fast estimation of and active compensation for dynamic variations, our approach can significantly improve the robustness of an RL policy trained in a standard (i.e., non-robust) way, either in a simulator or in the real world. Numerical experiments are provided to validate the efficacy of the proposed approach.

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preprint2022arXiv

Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning

The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters. However, existing D-KFAC algorithms require to compute and communicate a large volume of second-order information, i.e., Kronecker factors (KFs), before preconditioning gradients, resulting in large computation and communication overheads as well as a high memory footprint. In this paper, we propose DP-KFAC, a novel distributed preconditioning scheme that distributes the KF constructing tasks at different DNN layers to different workers. DP-KFAC not only retains the convergence property of the existing D-KFAC algorithms but also enables three benefits: reduced computation overhead in constructing KFs, no communication of KFs, and low memory footprint. Extensive experiments on a 64-GPU cluster show that DP-KFAC reduces the computation overhead by 1.55x-1.65x, the communication cost by 2.79x-3.15x, and the memory footprint by 1.14x-1.47x in each second-order update compared to the state-of-the-art D-KFAC methods.

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preprint2022arXiv

SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination

Transfer learning through the use of pre-trained models has become a growing trend for the machine learning community. Consequently, numerous pre-trained models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: "Can we effectively recover private information from these pre-trained models? What are the sufficient conditions to retrieve such sensitive information?" We first explore different statistical information which can discriminate the private training distribution from other distributions. Based on our observations, we propose a novel private data reconstruction framework, SecretGen, to effectively recover private information. Compared with previous methods which can recover private data with the ground true prediction of the targeted recovery instance, SecretGen does not require such prior knowledge, making it more practical. We conduct extensive experiments on different datasets under diverse scenarios to compare SecretGen with other baselines and provide a systematic benchmark to better understand the impact of different auxiliary information and optimization operations. We show that without prior knowledge about true class prediction, SecretGen is able to recover private data with similar performance compared with the ones that leverage such prior knowledge. If the prior knowledge is given, SecretGen will significantly outperform baseline methods. We also propose several quantitative metrics to further quantify the privacy vulnerability of pre-trained models, which will help the model selection for privacy-sensitive applications. Our code is available at: https://github.com/AI-secure/SecretGen.

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preprint2022arXiv

Sparse Black-box Video Attack with Reinforcement Learning

Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To overcome this drawback, a few methods try to select some key frames and then perform attacks based on them. Unfortunately, their selection strategy is independent of the attacking step, therefore the resulting performance is limited. Instead, we argue the frame selection phase is closely relevant with the attacking phase. The key frames should be adjusted according to the attacking results. For that, we formulate the black-box video attacks into a Reinforcement Learning (RL) framework. Specifically, the environment in RL is set as the recognition model, and the agent in RL plays the role of frame selecting. By continuously querying the recognition models and receiving the attacking feedback, the agent gradually adjusts its frame selection strategy and adversarial perturbations become smaller and smaller. We conduct a series of experiments with two mainstream video recognition models: C3D and LRCN on the public UCF-101 and HMDB-51 datasets. The results demonstrate that the proposed method can significantly reduce the adversarial perturbations with efficient query times.

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preprint2022arXiv

Standing Sausage Perturbations in solar coronal loops with diffuse boundaries: An initial-value-problem perspective

Working in pressureless magnetohydrodynamics, we examine the consequences of some peculiar dispersive properties of linear fast sausage modes (FSMs) in one-dimensional cylindrical equilibria with a continuous radial density profile ($ρ_0(r)$). As recognized recently on solid mathematical grounds, cutoff axial wavenumbers may be absent for FSMs when $ρ_0(r)$ varies sufficiently slowly outside the nominal cylinder. Trapped modes may therefore exist for arbitrary axial wavenumbers and density contrasts, their axial phase speeds in the long-wavelength regime differing little from the external Alfv$\acute{\rm e}$n speed. If these trapped modes indeed show up in the solutions to the associated initial value problem (IVP), then FSMs have a much better chance to be observed than expected with classical theory, and can be invoked to account for a considerably broader range of periodicities than practiced. However, with axial fundamentals in active region loops as an example, we show that this long-wavelength expectation is not seen in our finite-difference solutions to the IVP, the reason for which is then explored by superposing the necessary eigenmodes to re-solve the IVP. At least for the parameters we examine, the eigenfunctions of trapped modes are characterized by a spatial extent well exceeding the observationally reasonable range of the spatial extent of initial perturbations, meaning a negligible fraction of energy that a trapped mode can receive. We conclude that the absence of cutoff wavenumbers for FSMs in the examined equilibrium does not guarantee a distinct temporal behavior.

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preprint2022arXiv

Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification

Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task. In this paper, we propose to modify the structure of the cascaded-encoder-based recurrent neural network transducer (RNN-T) model by integrating a per-frame language identifier (LID) predictor. RNN-T with cascaded encoders can achieve streaming ASR with low latency using first-pass decoding with no right-context, and achieve lower word error rates (WERs) using second-pass decoding with longer right-context. By leveraging such differences in the right-contexts and a streaming implementation of statistics pooling, the proposed method can achieve accurate streaming LID prediction with little extra test-time cost. Experimental results on a voice search dataset with 9 language locales shows that the proposed method achieves an average of 96.2% LID prediction accuracy and the same second-pass WER as that obtained by including oracle LID in the input.

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preprint2022arXiv

Streaming Intended Query Detection using E2E Modeling for Continued Conversation

In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations. Toavoid repeating a hotword, we propose a streaming end-to-end(E2E) intended query detector that identifies the utterancesdirected towards the device and filters out other utterancesnot directed towards device. The proposed approach incor-porates the intended query detector into the E2E model thatalready folds different components of the speech recognitionpipeline into one neural network.The E2E modeling onspeech decoding and intended query detection also allows us todeclare a quick intended query detection based on early partialrecognition result, which is important to decrease latencyand make the system responsive. We demonstrate that theproposed E2E approach yields a 22% relative improvement onequal error rate (EER) for the detection accuracy and 600 mslatency improvement compared with an independent intendedquery detector. In our experiment, the proposed model detectswhether the user is talking to the device with a 8.7% EERwithin 1.4 seconds of median latency after user starts speaking.

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preprint2022arXiv

Superfluid Spin Transistor

We propose to use the Hall response of topological defects, such as merons and antimerons, to spin currents in 2D magnetic insulator with in-plane anisotropy for identification of the Berezinskii-Kosterlitz-Thouless (BKT) transition in a transistor-like geometry. Our numerical results relying on a combination of Monte Carlo and spin dynamics simulations show transition from spin superfluidity to conventional spin transport, accompanied by the universal jump of the spin stiffness and exponential growth of the transverse vorticity current. We propose a superfluid spin transistor in which the spin and vorticity currents are modulated by tuning the in-plane magnet across BKT transition, e.g., by changing the exchange interaction, magnetic anisotropy, or temperature.

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preprint2022arXiv

Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness.

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preprint2022arXiv

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

Evaluating the performance of autonomous vehicles (AV) and their complex subsystems to high precision under naturalistic circumstances remains a challenge, especially when failure or dangerous cases are rare. Rarity does not only require an enormous sample size for a naive method to achieve high confidence estimation, but it also causes dangerous underestimation of the true failure rate and it is extremely hard to detect. Meanwhile, the state-of-the-art approach that comes with a correctness guarantee can only compute an upper bound for the failure rate under certain conditions, which could limit its practical uses. In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative. Our high-dimensional experiment estimating the misclassification rate of one of the state-of-the-art traffic sign classifiers further reveals that this efficiency still holds true even when the target is very small, achieving over 600 times efficiency boost. This highlights the potential of Deep IS in providing a precise estimate even against high-dimensional uncertainties.

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preprint2022arXiv

The Confluence of Blockchain and 6G Network: Scenarios Analysis and Performance Assessment

Emerging advanced applications, such as smart cities, healthcare, and virtual reality, demand more challenging requirements on sixth-generation (6G) mobile networks, including the need for improved secrecy, greater integrity, non-repudiation, authentication, and access control. While blockchain, with its intrinsic features, is generally regarded as one of the most disruptive technological enablers for 6G functional standards, there is no comprehensive study of whether, when, and how blockchain will be used in 6G scenarios. Existing research lacks performance assessment methodology for the use of blockchain in 6G scenarios. Therefore, we abstract seven fine-grained 6G possibilities from the application layer and investigate the why, what, and when issues for 6G scenarios in this work. Moreover, we provide a methodology for evaluating the performance and scalability of blockchain-based 6G scenarios. In conclusion, we undertake comprehensive experimental to assess the performance of the Quorum blockchain and 6G scenarios. The experimental results show that a consortium blockchain with the proper settings may satisfy the performance and scalability requiremen

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preprint2022arXiv

The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining

In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase their data from a trained model. Existing machine unlearning techniques focus on centralized training, where access to all holders' training data is a must for the server to conduct the unlearning process. It remains largely underexplored about how to achieve unlearning when full access to all training data becomes unavailable. One noteworthy example is Federated Learning (FL), where each participating data holder trains locally, without sharing their training data to the central server. In this paper, we investigate the problem of machine unlearning in FL systems. We start with a formal definition of the unlearning problem in FL and propose a rapid retraining approach to fully erase data samples from a trained FL model. The resulting design allows data holders to jointly conduct the unlearning process efficiently while keeping their training data locally. Our formal convergence and complexity analysis demonstrate that our design can preserve model utility with high efficiency. Extensive evaluations on four real-world datasets illustrate the effectiveness and performance of our proposed realization.

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preprint2022arXiv

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data. Given an acceptable level of privacy guarantee, the goal of FL is to minimize the time-to-accuracy of model training. Compared with distributed ML in data centers, there are four distinct challenges to achieving short time-to-accuracy in FL training, namely the lack of information for optimization, the tradeoff between statistical and system utility, client heterogeneity, and large configuration space. In this paper, we survey recent works in addressing these challenges and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review system works including measurement studies and benchmarking tools that aim to support FL developers.

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preprint2022arXiv

Towards Practical Certifiable Patch Defense with Vision Transformer

Patch attacks, one of the most threatening forms of physical attack in adversarial examples, can lead networks to induce misclassification by modifying pixels arbitrarily in a continuous region. Certifiable patch defense can guarantee robustness that the classifier is not affected by patch attacks. Existing certifiable patch defenses sacrifice the clean accuracy of classifiers and only obtain a low certified accuracy on toy datasets. Furthermore, the clean and certified accuracy of these methods is still significantly lower than the accuracy of normal classification networks, which limits their application in practice. To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS). Specifically, we propose a progressive smoothed image modeling task to train Vision Transformer, which can capture the more discriminable local context of an image while preserving the global semantic information. For efficient inference and deployment in the real world, we innovatively reconstruct the global self-attention structure of the original ViT into isolated band unit self-attention. On ImageNet, under 2% area patch attacks our method achieves 41.70% certified accuracy, a nearly 1-fold increase over the previous best method (26.00%). Simultaneously, our method achieves 78.58% clean accuracy, which is quite close to the normal ResNet-101 accuracy. Extensive experiments show that our method obtains state-of-the-art clean and certified accuracy with inferring efficiently on CIFAR-10 and ImageNet.

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preprint2022arXiv

Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM

In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method. We take the first step towards solving the above problem by theoretically exploring the effect of epsilon (the parameter of differential privacy that determines the strength of privacy guarantee) on utility of the learning model. We trace the change of utility with modification of epsilon and reveal an established relationship between epsilon and utility. We then formalize this relationship and propose a practical approach for estimating the utility under an arbitrary value of epsilon. Both theoretical analysis and experimental results demonstrate high estimation accuracy and broad applicability of our approach in practical applications. As providing algorithms with strong utility guarantees that also give privacy when possible becomes more and more accepted, our approach would have high practical value and may be likely to be adopted by companies and organizations that would like to preserve privacy but are unwilling to compromise on utility.

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preprint2022arXiv

Trustworthy AI: From Principles to Practices

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment of various systems based on it. However, many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection. These shortcomings degrade user experience and erode people's trust in all AI systems. In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, and accountability. To unify currently available but fragmented approaches toward trustworthy AI, we organize them in a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to system development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items for practitioners and societal stakeholders (e.g., researchers, engineers, and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges for the future development of trustworthy AI systems, where we identify the need for a paradigm shift toward comprehensively trustworthy AI systems.

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preprint2022arXiv

Turn-Taking Prediction for Natural Conversational Speech

While a streaming voice assistant system has been used in many applications, this system typically focuses on unnatural, one-shot interactions assuming input from a single voice query without hesitation or disfluency. However, a common conversational utterance often involves multiple queries with turn-taking, in addition to disfluencies. These disfluencies include pausing to think, hesitations, word lengthening, filled pauses and repeated phrases. This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task. To better model the conversational interaction, it is critical to discriminate disfluencies and end of query in order to allow the user to hold the floor for disfluencies while having the system respond as quickly as possible when the user has finished speaking. In this paper, we present a turntaking predictor built on top of the end-to-end (E2E) speech recognizer. Our best system is obtained by jointly optimizing for ASR task and detecting when the user is paused to think or finished speaking. The proposed approach demonstrates over 97% recall rate and 85% precision rate on predicting true turn-taking with only 100 ms latency on a test set designed with 4 types of disfluencies inserted in conversational utterances.

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preprint2022arXiv

Ultrafast Dynamics of Defect-Assisted Auger process in PdSe2 films: Synergistic Interaction Between Defect Trapping and Auger Effect

Strong Coulomb interactions in two-dimensional systems, together with quantum confinement, make many-body processes particularly effective for carrier dynamics, which plays a crucial role in determining carrier lifetime, photoconductivity, and emission yield of the materials. Hereby, by using optical pump and terahertz probe spectroscopy, we have investigated the photocarrier dynamics in the PdSe2 films with different thickness. The experimental results reveal that the photocarrier relaxation consists of two components: a fast component of 2.5 ps that shows the layer-thickness independence, and a slow component has typical lifetime of 7.3 ps decreasing with the layer thickness. Surprisingly, the relaxation times for both fast and slow components are exhibited both pump fluence and temperature independence, which suggests that synergistic interactions between defect trapping and Auger effect dominate the photocarrier dynamics in PdSe2 films. A model involving defect-assisted Auger process is proposed, which can reproduce the experimental results well. The fitting results reveal that the layer dependent lifetime is determined by the defect density rather than carrier occupancy rate after photoexcitation. Our results underscore the interplay between Auger process and defects in two-dimensional semiconductors.

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preprint2022arXiv

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond

The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way. Gradual domain adaptation (GDA), on the other hand, assumes a path of $(T-1)$ unlabeled intermediate domains bridging the source and target, and aims to provide better generalization in the target domain by leveraging the intermediate ones. Under certain assumptions, Kumar et al. (2020) proposed a simple algorithm, Gradual Self-Training, along with a generalization bound in the order of $e^{O(T)} \left(\varepsilon_0+O\left(\sqrt{log(T)/n}\right)\right)$ for the target domain error, where $\varepsilon_0$ is the source domain error and $n$ is the data size of each domain. Due to the exponential factor, this upper bound becomes vacuous when $T$ is only moderately large. In this work, we analyze gradual self-training under more general and relaxed assumptions, and prove a significantly improved generalization bound as $\varepsilon_0+ O \left(TΔ+ T/\sqrt{n}\right) + \widetilde{O}\left(1/\sqrt{nT}\right)$, where $Δ$ is the average distributional distance between consecutive domains. Compared with the existing bound with an exponential dependency on $T$ as a multiplicative factor, our bound only depends on $T$ linearly and additively. Perhaps more interestingly, our result implies the existence of an optimal choice of $T$ that minimizes the generalization error, and it also naturally suggests an optimal way to construct the path of intermediate domains so as to minimize the accumulative path length $TΔ$ between the source and target. To corroborate the implications of our theory, we examine gradual self-training on multiple semi-synthetic and real datasets, which confirms our findings. We believe our insights provide a path forward toward the design of future GDA algorithms.

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preprint2022arXiv

Unsupervised Learning of Accurate Siamese Tracking

Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial supervision from template-search pairs and are still unable to track objects with strong variation over a long time span. As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward. We present a novel unsupervised tracking framework, in which we can learn temporal correspondence both on the classification branch and regression branch. Specifically, to propagate reliable template feature in the forward propagation process so that the tracker can be trained in the cycle, we first propose a consistency propagation transformation. We then identify an ill-posed penalty problem in conventional cycle training in backward propagation process. Thus, a differentiable region mask is proposed to select features as well as to implicitly penalize tracking errors on intermediate frames. Moreover, since noisy labels may degrade training, we propose a mask-guided loss reweighting strategy to assign dynamic weights based on the quality of pseudo labels. In extensive experiments, our tracker outperforms preceding unsupervised methods by a substantial margin, performing on par with supervised methods on large-scale datasets such as TrackingNet and LaSOT. Code is available at https://github.com/FlorinShum/ULAST.

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preprint2022arXiv

VeriFi: Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning.

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preprint2022arXiv

What Would Jiminy Cricket Do? Towards Agents That Behave Morally

When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong. By contrast, artificial agents are currently not endowed with a moral sense. As a consequence, they may learn to behave immorally when trained on environments that ignore moral concerns, such as violent video games. With the advent of generally capable agents that pretrain on many environments, it will become necessary to mitigate inherited biases from environments that teach immoral behavior. To facilitate the development of agents that avoid causing wanton harm, we introduce Jiminy Cricket, an environment suite of 25 text-based adventure games with thousands of diverse, morally salient scenarios. By annotating every possible game state, the Jiminy Cricket environments robustly evaluate whether agents can act morally while maximizing reward. Using models with commonsense moral knowledge, we create an elementary artificial conscience that assesses and guides agents. In extensive experiments, we find that the artificial conscience approach can steer agents towards moral behavior without sacrificing performance.

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preprint2022arXiv

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

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preprint2022arXiv

Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e.g., face parsers and face landmark detectors. These models provide the flexibility to construct various criterions with very concrete and interpretable semantic meanings (e.g., change face shape or change skin color) to restrict latent subspace disentanglement. Rich latent space controls unknown previously can be discovered using the constructed criterions. Second, we propose a new perspective to explain the behavior of a CNN classifier by generating counterfactuals in the interpretable latent subspaces we discovered. This explanation helps reveal whether the classifier learns semantics as intended. Experiments on various disentanglement criterions demonstrate the effectiveness of our approach. We believe this approach contributes to both areas of image manipulation and counterfactual explainability of CNNs. The code is available at \url{https://github.com/prclibo/ice}.

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preprint2022arXiv

Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One

Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the intrinsic characteristics of chain-style conditional modeling (e.g., exposure bias or lack of long-range coherence), severely limiting their ability to model distributions properly. In this paper, we propose a unique method termed E-ARM for training autoregressive generative models that takes advantage of a well-designed energy-based learning objective. By leveraging the extra degree of freedom of the softmax operation, we are allowed to make the autoregressive model itself be an energy-based model for measuring the likelihood of input without introducing any extra parameters. Furthermore, we show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem and increase temporal coherence for autoregressive generative models. Extensive empirical results, covering benchmarks like language modeling, neural machine translation, and image generation, demonstrate the effectiveness of the proposed approach.

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preprint2021arXiv

A Better and Faster End-to-End Model for Streaming ASR

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by word error rate (WER)) and endpointer latency [2]. However, the model still tends to delay the predictions towards the end and thus has much higher partial latency compared to a conventional ASR model. To address this issue, we look at encouraging the E2E model to emit words early, through an algorithm called FastEmit [3]. Naturally, improving on latency results in a quality degradation. To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR. Secondly, we also explore running a 2nd-pass beam search to improve quality. In order to ensure the 2nd-pass completes quickly, we explore non-causal Conformer layers that feed into the same 1st-pass RNN-T decoder, an algorithm called Cascaded Encoders [5]. Overall, we find that the Conformer RNN-T with Cascaded Encoders offers a better quality and latency tradeoff for streaming ASR.

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preprint2021arXiv

AI-GAN: Attack-Inspired Generation of Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets \eg MNIST and CIFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets \eg CIFAR-100 with around $90\%$ success rates among all classes.

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preprint2021arXiv

Anomalous Example Detection in Deep Learning: A Survey

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.

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preprint2021arXiv

Application-driven Privacy-preserving Data Publishing with Correlated Attributes

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes -- which may be hidden and can be inferred by machine learning algorithms -- while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

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preprint2021arXiv

Attack-Resistant Federated Learning with Residual-based Reweighting

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, we present a novel aggregation algorithm with residual-based reweighting to defend federated learning. Our aggregation algorithm combines repeated median regression with the reweighting scheme in iteratively reweighted least squares. Our experiments show that our aggregation algorithm outperforms other alternative algorithms in the presence of label-flipping and backdoor attacks. We also provide theoretical analysis for our aggregation algorithm.

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preprint2021arXiv

Back-n White Neutron Source at CSNS and its Applications

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source (CSNS) that emit through the incoming proton channel were exploited to build a white neutron beam facility (the so-called Back-n white neutron source), which was completed in March 2018. The Back-n neutron beam is very intense, at approximately 2*10^7 n/cm^2/s at 55 m from the target, and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices. In addition, the excellent energy spectrum spanning from 0.5 eV to 200 MeV, and a good time resolution related to the time-of-flight measurements make it a typical white neutron source for nuclear data measurements; its overall performance is among that of the best white neutron sources in the world. Equipped with advanced spectrometers, detectors, and application utilities, the Back-n facility can serve wide applications, with a focus on neutron-induced cross-section measurements. This article presents an overview of the neutron beam characteristics, the experimental setups, and the ongoing applications at Back-n.

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preprint2021arXiv

Distributed Algorithms that Solve Boolean Equations with Local and Differential Privacies

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a {\em private} equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable using existing distributed linear equation algorithms as a subroutine. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i.d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability. Furthermore, we present an algorithm for distributed verification of the satisfiability of Boolean equations, and prove its correctness. Finally, we show that by utilizing linear equation solvers with differential privacy to replace the in-network computing routines, the overall distributed Boolean equation algorithms can be made differentially private. Under the standard Laplace mechanism, we prove an explicit level of noises that can be injected in the linear equation steps for ensuring a prescribed level of differential privacy.

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preprint2021arXiv

Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses. In this work, we propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation during the training. The Dual-mode ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks. We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and a large-scale dataset MultiDomain. Experiments and ablation studies demonstrate that Dual-mode ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR. With Dual-mode ASR, we achieve new state-of-the-art streaming ASR results on both LibriSpeech and MultiDomain in terms of accuracy and latency.

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preprint2021arXiv

ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.

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preprint2021arXiv

Fair Allocation with Interval Scheduling Constraints

We study a fair resource scheduling problem, where a set of interval jobs are to be allocated to heterogeneous machines controlled by agents. Each job is associated with release time, deadline, and processing time such that it can be processed if its complete processing period is between its release time and deadline. The machines gain possibly different utilities by processing different jobs, and all jobs assigned to the same machine should be processed without overlap. We consider two widely studied solution concepts, namely, maximin share fairness and envy-freeness. For both criteria, we discuss the extent to which fair allocations exist and present constant approximation algorithms for various settings.

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preprint2021arXiv

FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measured by word error rate (WER), is highly challenging. Existing approaches including Early and Late Penalties and Constrained Alignments penalize emission delay by manipulating per-token or per-frame probability prediction in sequence transducer models. While being successful in reducing delay, these approaches suffer from significant accuracy regression and also require additional word alignment information from an existing model. In this work, we propose a sequence-level emission regularization method, named FastEmit, that applies latency regularization directly on per-sequence probability in training transducer models, and does not require any alignment. We demonstrate that FastEmit is more suitable to the sequence-level optimization of transducer models for streaming ASR by applying it on various end-to-end streaming ASR networks including RNN-Transducer, Transformer-Transducer, ConvNet-Transducer and Conformer-Transducer. We achieve 150-300 ms latency reduction with significantly better accuracy over previous techniques on a Voice Search test set. FastEmit also improves streaming ASR accuracy from 4.4%/8.9% to 3.1%/7.5% WER, meanwhile reduces 90th percentile latency from 210 ms to only 30 ms on LibriSpeech.

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preprint2021arXiv

G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differentially private data generator that preserves high generated data utility. Our approach leverages generative adversarial nets to generate data, combined with private aggregation among different discriminators to ensure strong privacy guarantees. Compared to existing approaches, G-PATE significantly improves the use of privacy budgets. In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator. In addition, with random projection and gradient discretization, the proposed gradient aggregation mechanism is able to effectively deal with high-dimensional gradient vectors. Theoretically, we prove that G-PATE ensures differential privacy for the data generator. Empirically, we demonstrate the superiority of G-PATE over prior work through extensive experiments. We show that G-PATE is the first work being able to generate high-dimensional image data with high data utility under limited privacy budgets ($ε\le 1$). Our code is available at https://github.com/AI-secure/G-PATE.

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preprint2021arXiv

Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p(Δt_(i+1) |G_i) where Δt_(i+1) represents the duration of the (i+1)-th transaction, G_i represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.

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preprint2021arXiv

Interaction between optical pulse and tumor using finite element analysis

Photoacoustic imaging is an emerging technology based on the photoacoustic effect that has developed rapidly in recent years. It combines the high contrast of optical imaging and the high penetration and high resolution of acoustic imaging. As a non-destructive biological tissue imaging technology, photoacoustic imaging has important application value in the field of biomedicine. With its high efficiency bi-oimaging capabilities and excellent biosafety performance, it has been favored by researchers. The visualization of photoacoustic imaging has great research signifi-cance in the early diagnosis of some diseases, especially tumors. In photoacoustic imaging, light transmission and thermal effects are important processes. This article is based on COMSOL software and uses finite element analysis to construct a physi-cal model for simulation. Through laser pulses into the stomach tissue containing tumor, the physical process of light transmission and biological heat transfer was studied, and a photothermal model composed of two physical fields was built, and finally a series of visualization graphics were obtained. This work has certain theo-retical guiding significance for further promoting the application of photoacoustic imaging in the field of biomedicine.

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preprint2021arXiv

Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.

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preprint2021arXiv

MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks (DNNs) on computer clusters. With the increase of computational power, network communications generally limit the system scalability. Wait-free backpropagation (WFBP) is a popular solution to overlap communications with computations during the training process. In this paper, we observe that many DNNs have a large number of layers with only a small amount of data to be communicated at each layer in distributed training, which could make WFBP inefficient. Based on the fact that merging some short communication tasks into a single one can reduce the overall communication time, we formulate an optimization problem to minimize the training time in pipelining communications and computations. We derive an optimal solution that can be solved efficiently without affecting the training performance. We then apply the solution to propose a distributed training algorithm named merged-gradient WFBP (MG-WFBP) and implement it in two platforms Caffe and PyTorch. Extensive experiments in three GPU clusters are conducted to verify the effectiveness of MG-WFBP. We further exploit trace-based simulations of 4 to 2048 GPUs to explore the potential scaling efficiency of MG-WFBP. Experimental results show that MG-WFBP achieves much better scaling performance than existing methods.

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preprint2021arXiv

Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5\% clean training data without causing obvious performance degradation on clean examples. Code is available in https://github.com/bboylyg/NAD.

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preprint2021arXiv

ORDNet: Capturing Omni-Range Dependencies for Scene Parsing

Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap between long-range and short-range dependencies, which largely reduces the models' flexibility in application to diverse spatial scales and relationships in complicated natural scene images. To fill such a gap, we develop a Middle-Range (MR) branch to capture middle-range dependencies by restricting self-attention into local patches. Also, we observe that the spatial regions which have large correlations with others can be emphasized to exploit long-range dependencies more accurately, and thus propose a Reweighed Long-Range (RLR) branch. Based on the proposed MR and RLR branches, we build an Omni-Range Dependencies Network (ORDNet) which can effectively capture short-, middle- and long-range dependencies. Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images. Extensive experiments show that our proposed ORDNet outperforms previous state-of-the-art methods on three scene parsing benchmarks including PASCAL Context, COCO Stuff and ADE20K, demonstrating the superiority of capturing omni-range dependencies in deep models for scene parsing task.

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preprint2021arXiv

Population of Bright Plume Threads in Solar Polar Coronal Holes

Coronal holes are well accepted to be source regions of the fast solar wind. As one of the common structures in coronal holes, coronal plumes might contribute to the origin of the nascent solar wind. To estimate the contribution of coronal plumes to the nascent solar wind, we make the first attempt to estimate their populations in solar polar coronal holes. By comparing the observations viewed from two different angles taken by the twin satellites of STEREO and the results of Monte Carlo simulations, we estimate about 16--27 plumes rooted in an area of $4\times10^4$ arcsec$^2$ of the polar coronal holes near the solar minimum, which occupy about 2--3.4% of the area. Based on these values, the contribution of coronal plumes to the nascent solar wind has also been discussed. A further investigation indicates that more precise number of coronal plumes can be worked out with observations from three or more viewing angles.

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preprint2021arXiv

Rescuing Deep Hashing from Dead Bits Problem

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e.g. $\operatorname{sigmoid}(\cdot)$ or $\operatorname{tanh}(\cdot)$, and minimize a quantization loss to approximate discrete values. However, this paradigm may make more and more hash bits stuck into the wrong saturated area of the activation functions and never escaped. We call this problem "Dead Bits Problem~(DBP)". Besides, the existing quantization loss will aggravate DBP as well. In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. Moreover, we devise an error-aware quantization loss to further alleviate DBP. It avoids the negative effect of quantization loss based on the similarity between two images. The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods. Experimental results on three datasets demonstrate the efficiency of the proposed gradient amplifier and the error-aware quantization loss.

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preprint2021arXiv

Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks. As ML systems are increasingly being used in a variety of mission-critical applications, improving the robustness of ML systems has become of utmost importance. In this paper, we propose the first robust AutoML framework, Robusta--based on reinforcement learning (RL)--to perform feature selection, aiming to select features that lead to both accurate and robust ML systems. We show that a variation of the 0-1 robust loss can be directly optimized via an RL-based combinatorial search in the feature selection scenario. In addition, we employ heuristics to accelerate the search procedure based on feature scoring metrics, which are mutual information scores, tree-based classifiers feature importance scores, F scores, and Integrated Gradient (IG) scores, as well as their combinations. We conduct extensive experiments and show that the proposed framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples compared with other feature selection methods.

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preprint2021arXiv

Segmentation of Breast Microcalcifications: A Multi-Scale Approach

Accurate characterization of microcalcifications (MCs) in 2D full-field digital screening mammography is a necessary step towards reducing diagnostic uncertainty associated with the callback of women with suspicious MCs. Quantitative analysis of MCs has the potential to better identify MCs that have a higher likelihood of corresponding to invasive cancer. However, automated identification and segmentation of MCs remains a challenging task with high false positive rates. We present Hessian Difference of Gaussians Regression (HDoGReg), a two stage multi-scale approach to MC segmentation. Candidate high optical density objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with higher response near MCs, chooses the objects which constitute actual MCs. The method is trained and validated on 435 mammograms from two separate datasets. HDoGReg achieved a mean intersection over the union of 0.670$\pm$0.121 per image, intersection over the union per MC object of 0.607$\pm$0.250 and true positive rate of 0.744 at 0.4 false positive detections per $cm^2$. The results of HDoGReg perform better when compared to state-of-the-art MC segmentation and detection methods.

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preprint2021arXiv

The First 3D Coronal Loop Model Heated by MHD Waves against Radiative Losses

In the quest to solve the long-standing coronal heating problem, it has been suggested half a century ago that coronal loops could be heated by waves. Despite the accumulating observational evidence of the possible importance of coronal waves, still no 3D MHD simulations exist that show significant heating by MHD waves. Here we report on the first 3D coronal loop model heating the plasma against radiative cooling. The coronal loop is driven at the footpoint by transverse oscillations and subsequently the induced Kelvin-Helmholtz instability deforms the loop cross-section and generates small-scale structures. Wave energy is transfered to smaller scales where it is dissipated, overcoming the internal energy losses by radiation. These results open up a new avenue to address the coronal heating problem.

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preprint2021arXiv

Understanding Robustness in Teacher-Student Setting: A New Perspective

Adversarial examples have appeared as a ubiquitous property of machine learning models where bounded adversarial perturbation could mislead the models to make arbitrarily incorrect predictions. Such examples provide a way to assess the robustness of machine learning models as well as a proxy for understanding the model training process. Extensive studies try to explain the existence of adversarial examples and provide ways to improve model robustness (e.g. adversarial training). While they mostly focus on models trained on datasets with predefined labels, we leverage the teacher-student framework and assume a teacher model, or oracle, to provide the labels for given instances. We extend Tian (2019) in the case of low-rank input data and show that student specialization (trained student neuron is highly correlated with certain teacher neuron at the same layer) still happens within the input subspace, but the teacher and student nodes could differ wildly out of the data subspace, which we conjecture leads to adversarial examples. Extensive experiments show that student specialization correlates strongly with model robustness in different scenarios, including student trained via standard training, adversarial training, confidence-calibrated adversarial training, and training with robust feature dataset. Our studies could shed light on the future exploration about adversarial examples, and enhancing model robustness via principled data augmentation.

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preprint2021arXiv

What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space

Deep neural networks (DNNs) are increasingly deployed in different applications to achieve state-of-the-art performance. However, they are often applied as a black box with limited understanding of what knowledge the model has learned from the data. In this paper, we focus on image classification and propose a method to visualize and understand the class-wise knowledge (patterns) learned by DNNs under three different settings including natural, backdoor and adversarial. Different to existing visualization methods, our method searches for a single predictive pattern in the pixel space to represent the knowledge learned by the model for each class. Based on the proposed method, we show that DNNs trained on natural (clean) data learn abstract shapes along with some texture, and backdoored models learn a suspicious pattern for the backdoored class. Interestingly, the phenomenon that DNNs can learn a single predictive pattern for each class indicates that DNNs can learn a backdoor even from clean data, and the pattern itself is a backdoor trigger. In the adversarial setting, we show that adversarially trained models tend to learn more simplified shape patterns. Our method can serve as a useful tool to better understand the knowledge learned by DNNs on different datasets under different settings.

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preprint2020arXiv

A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehension

Referring expression comprehension aims to localize the object instance described by a natural language expression. Current referring expression methods have achieved good performance. However, none of them is able to achieve real-time inference without accuracy drop. The reason for the relatively slow inference speed is that these methods artificially split the referring expression comprehension into two sequential stages including proposal generation and proposal ranking. It does not exactly conform to the habit of human cognition. To this end, we propose a novel Realtime Cross-modality Correlation Filtering method (RCCF). RCCF reformulates the referring expression comprehension as a correlation filtering process. The expression is first mapped from the language domain to the visual domain and then treated as a template (kernel) to perform correlation filtering on the image feature map. The peak value in the correlation heatmap indicates the center points of the target box. In addition, RCCF also regresses a 2-D object size and 2-D offset. The center point coordinates, object size and center point offset together to form the target bounding box. Our method runs at 40 FPS while achieving leading performance in RefClef, RefCOCO, RefCOCO+ and RefCOCOg benchmarks. In the challenging RefClef dataset, our methods almost double the state-of-the-art performance (34.70% increased to 63.79%). We hope this work can arouse more attention and studies to the new cross-modality correlation filtering framework as well as the one-stage framework for referring expression comprehension.

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preprint2020arXiv

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this paper, we review the latest single-source deep unsupervised domain adaptation methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different domain adaptation strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised domain adaptation methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions.

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preprint2020arXiv

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.

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preprint2020arXiv

Adversarial Mutual Information for Text Generation

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower bound. In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. Within this framework, the forward and backward networks are able to iteratively promote or demote each other's generated instances by comparing the real and synthetic data distributions. We also develop a latent noise sampling strategy that leverages random variations at the high-level semantic space to enhance the long term dependency in the generation process. Extensive experiments based on different text generation tasks demonstrate that the proposed AMI framework can significantly outperform several strong baselines, and we also show that AMI has potential to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem.

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preprint2020arXiv

Bayesian Auctions with Efficient Queries

Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately, this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players' value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be of any constant approximation to the optimal revenue. For single-item auctions and multi-item auctions with unit-demand or additive valuation functions, we prove tight upper-bounds via efficient query schemes, without requiring the distributions to be regular or have monotone hazard rate. Thus, in those auction settings the seller needs to access much less than the full distributions in order to achieve approximately optimal revenue.

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preprint2020arXiv

Chirality and magnetic configuration associated with two-ribbon solar flares: AR 10930 versus AR 11158

The structural property of the magnetic field in flare-bearing solar active regions (ARs) is one of the key aspects for understanding and forecasting solar flares. In this paper, we make a comparative analysis on the chirality and magnetic configurations associated with two X-class two-ribbon flares happening in AR 10930 and AR 11158. The photospheric magnetic fields of the two ARs were observed by space-based instruments, and the corresponding coronal magnetic fields were calculated based on the nonlinear force-free field model. The analysis shows that the electric current in the two ARs was distributed mostly around the main polarity inversion lines (PILs) where the flares happened, and the magnetic chirality (indicated by the signs of force-free factor $α$) along the main PILs is opposite for the two ARs, i.e., left-handed ($α<0$) for AR 10930 and right-handed ($α>0$) for AR 11158. It is found that, for both the flare events, a prominent magnetic connectivity (featured by co-localized strong $α$ and strong current density distributions) was formed along the main PIL before flare and was totally broken after flare eruption. The two branches of the broken magnetic connectivity, combined with the prominent magnetic connectivity before flare, compose the opposite magnetic configurations in the two ARs owing to their opposite chirality, i.e., Z-shaped configuration in AR 10930 with left-handed chirality and inverse Z-shaped configuration in AR 11158 with right-handed chirality. It is speculated that two-ribbon flares can be generally classified to these two magnetic configurations by chirality in the flare source regions of ARs.

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preprint2020arXiv

Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach

Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model and valuable for uncertainty quantification. However, Bayesian approaches to multi-model ensembles have been criticized for making oversimplified assumptions about bias and variability, as well as treating different models as statistically independent. This paper extends the Bayesian hierarchical approach of Sansom et al. (2017) by explicitly accounting for spatial variability and inter-model dependence. We propose a Bayesian hierarchical model that accounts for bias between climate models and observations, spatial and inter-model dependence, the emergent relationship between historical and future periods, and natural variability. Extensive simulations show that our model provides better estimates and uncertainty quantification than the commonly used simple model mean. These results are illustrated using data from the CMIP5 model archive. As examples, for Central North America our projected mean temperature for 2070--2100 is about 0.8 K lower than the simple model mean, while for East Asia it is about 0.5 K higher; however, in both cases, the widths of the 90% credible intervals are of the order 3--6 K, so the uncertainties overwhelm the relatively small differences in projected mean temperatures.

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preprint2020arXiv

Controllability and Accessibility on Graphs for Bilinear Systems over Lie Groups

This paper presents graph theoretic conditions for the controllability and accessibility of bilinear systems over the special orthogonal group, the special linear group and the general linear group, respectively, in the presence of drift terms. Such bilinear systems naturally induce two interaction graphs: one graph from the drift, and another from the controlled dynamics. As a result, the system controllability or accessibility becomes a property of the two graphs in view of the classical Lie algebra rank condition. We establish a systemic way of transforming the Lie bracket operations in the underlying Lie algebra, into specific operations of removing or creating links over the drift and controlled interaction graphs. As a result, we establish a series of graphical conditions for the controllability and accessibility of such bilinear systems, which rely only on the connectivity of the union of the drift and controlled interaction graphs. We present examples to illustrate the validity of the established results, and show that the proposed conditions are in fact considerably tight.

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preprint2020arXiv

Controllable Orthogonalization in Training DNNs

Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in representation. This paper proposes a computationally efficient and numerically stable orthogonalization method using Newton's iteration (ONI), to learn a layer-wise orthogonal weight matrix in DNNs. ONI works by iteratively stretching the singular values of a weight matrix towards 1. This property enables it to control the orthogonality of a weight matrix by its number of iterations. We show that our method improves the performance of image classification networks by effectively controlling the orthogonality to provide an optimal tradeoff between optimization benefits and representational capacity reduction. We also show that ONI stabilizes the training of generative adversarial networks (GANs) by maintaining the Lipschitz continuity of a network, similar to spectral normalization (SN), and further outperforms SN by providing controllable orthogonality.

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preprint2020arXiv

Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection

With the increased applications of automatic speech recognition (ASR) in recent years, it is essential to automatically insert punctuation marks and remove disfluencies in transcripts, to improve the readability of the transcripts as well as the performance of subsequent applications, such as machine translation, dialogue systems, and so forth. In this paper, we propose a Controllable Time-delay Transformer (CT-Transformer) model that jointly completes the punctuation prediction and disfluency detection tasks in real time. The CT-Transformer model facilitates freezing partial outputs with controllable time delay to fulfill the real-time constraints in partial decoding required by subsequent applications. We further propose a fast decoding strategy to minimize latency while maintaining competitive performance. Experimental results on the IWSLT2011 benchmark dataset and an in-house Chinese annotated dataset demonstrate that the proposed approach outperforms the previous state-of-the-art models on F-scores and achieves a competitive inference speed.

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preprint2020arXiv

Correlating surface energy with adsorption energy by means of intrinsic characteristics of substrates

Surface energy is fundamental in controlling surface properties and surface-driven processes like heterogeneous catalysis, as adsorption energy is. It is thus crucial to establish an effective scheme to determine surface energy and its relation with adsorption energy. Herein, we propose a model to quantify the effects of the intrinsic characteristics of materials on the material-dependent property and anisotropy of surface energy, based on the period number and group number of bulk atoms, and the valence-electron number, electronegativity and coordination of surface atoms. Our scheme holds for elemental crystals in both solid and liquid phases, body-centered-tetragonal intermetallics, fluorite-structure intermetallics, face-centered-cubic intermetallics, Mg-based surface alloys and semiconductor compounds, which further identifies a quantitative relation between surface energy and adsorption energy and rationalizes the material-dependent error of first-principle methods in calculating the two quantities. This model is predictive with easily accessible parameters and thus allows the rapid screening of materials for targeted properties.

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preprint2020arXiv

Damping of slow surface kink modes in solar photospheric waveguides modeled by one-dimensional inhomogeneities

Given the recent interest in magnetohydrodynamic (MHD) waves in pores and sunspot umbrae, we examine the damping of slow surface kink modes (SSKMs) by modeling solar photospheric waveguides with a cylindrical inhomogeneity comprising a uniform interior, a uniform exterior, and a continuous transition layer (TL) in between. Performing an eigen-mode analysis in linear, resistive, gravity-free MHD, our approach is idealized in that, among other things, our equilibrium is structured only in the radial direction. We can nonetheless address two damping mechanisms simultaneously, one being the Ohmic resistivity, and the other being the resonant absorption of SSKMs in the cusp and Alfv$\acute{\rm e}$n continua. We find that the relative importance of the two mechanisms depends sensitively on the magnetic Reynolds number ($R_{\rm m}$). Resonant absorption is the sole damping mechanism for realistically large values of $R_{\rm m}$, and the cusp resonance in general dominates the Alfv$\acute{\rm e}$n one unless the axial wavenumbers are at the lower end of the observationally relevant range. We also find that the thin-boundary approximation holds only when the TL-width-to-radius ratios are much smaller than nominally expected. The Ohmic resistivity is far more important for realistically small $R_{\rm m}$. Even in this case, SSKMs are only marginally damped, with damping-time-to-period-ratios reaching $\sim 10$ in the parameter range we examine.

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preprint2020arXiv

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem. In this work, we first characterize this phenomenon from the information-theoretic perspective and show that under certain conditions, the mutual information between the output after $l$ layers and the input of GCN converges to 0 exponentially with respect to $l$. We also show that, on the other hand, graph decomposition can potentially weaken the condition of such convergence rate, which enabled our analysis for GraphCNN. While different graph structures can only benefit from the corresponding decomposition, in practice, we propose an automatic connectivity-aware graph decomposition algorithm, DeGNN, to improve the performance of general graph neural networks. Extensive experiments on widely adopted benchmark datasets demonstrate that DeGNN can not only significantly boost the performance of corresponding GNNs, but also achieves the state-of-the-art performances.

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preprint2020arXiv

Discouraging Pool Block Withholding Attacks in Bitcoins

The arisen of Bitcoin has led to much enthusiasm for blockchain research and block mining, and the extensive existence of mining pools helps its participants (i.e., miners) gain reward more frequently. Recently, the mining pools are proved to be vulnerable for several possible attacks, and pool block withholding attack is one of them: one strategic pool manager sends some of her miners to other pools and these miners pretend to work on the puzzles but actually do nothing. And these miners still get reward since the pool manager can not recognize these malicious miners. In this work, we revisit the game-theoretic model for pool block withholding attacks and propose a revised approach to reallocate the reward to the miners. Fortunately, in the new model, the pool managers have strong incentive to not launch such attacks. We show that for any number of mining pools, no-pool-attacks is always a Nash equilibrium. Moreover, with only two minority mining pools participating, no-pool-attacks is actually the unique Nash equilibrium.

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preprint2020arXiv

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

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preprint2020arXiv

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Given a data set $\mathcal{D}$ containing millions of data points and a data consumer who is willing to pay for \$$X$ to train a machine learning (ML) model over $\mathcal{D}$, how should we distribute this \$$X$ to each data point to reflect its "value"? In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all $N$ data points, it requires $O(2^N)$ model evaluations for exact computation and $O(N\log N)$ for $(ε, δ)$-approximation. In this paper, we focus on one popular family of ML models relying on $K$-nearest neighbors ($K$NN). The most surprising result is that for unweighted $K$NN classifiers and regressors, the Shapley value of all $N$ data points can be computed, exactly, in $O(N\log N)$ time -- an exponential improvement on computational complexity! Moreover, for $(ε, δ)$-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity $O(N^{h(ε,K)}\log N)$ when $ε$ is not too small and $K$ is not too large. We empirically evaluate our algorithms on up to $10$ million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further. We then extend our algorithms to other scenarios such as (1) weighed $K$NN classifiers, (2) different data points are clustered by different data curators, and (3) there are data analysts providing computation who also requires proper valuation.

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preprint2020arXiv

Elastic depths for detecting shape anomalies in functional data

We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth's detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data. Supplementary materials, including additional simulations, data examples, and an R-package are available online.

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preprint2020arXiv

Evaluating proxy influence in assimilated paleoclimate reconstructions -- Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. We apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

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preprint2020arXiv

Fluid Guided CVD Growth for Large-scale Monolayer Two-dimensional Materials

Atmospheric pressure chemical vapor deposition (APCVD) has been used extensively for synthesizing two-dimensional (2D) materials, due to its low cost and promise for high-quality monolayer crystal synthesis. However, the understanding of the reaction mechanism and the key parameters affecting the APCVD processes is still in its embryonic stage. Hence, the scalability of the APCVD method in achieving large scale continuous film remains very poor. Here, we use MoSe2 as a model system and present a fluid guided growth strategy for understanding and controlling the growth of 2D materials. Through the integration of experiment and computational fluid dynamics (CFD) analysis in the full-reactor scale, we identified three key parameters: precursor mixing, fluid velocity and shear stress, which play a critical role in the APCVD process. By modifying the geometry of the growth setup, to enhance precursor mixing and decrease nearby velocity shear rate and adjusting flow direction, we have successfully obtained inch-scale monolayer MoSe2. This unprecedented success of achieving scalable 2D materials through fluidic design lays the foundation for designing new CVD systems to achieve the scalable synthesis of nanomaterials.

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preprint2020arXiv

GAPS: Generator for Automatic Polynomial Solvers

Minimal problems in computer vision raise the demand of generating efficient automatic solvers for polynomial equation systems. Given a polynomial system repeated with different coefficient instances, the traditional Gröbner basis or normal form based solution is very inefficient. Fortunately the Gröbner basis of a same polynomial system with different coefficients is found to share consistent inner structure. By precomputing such structures offline, Gröbner basis as well as the polynomial system solutions can be solved automatically and efficiently online. In the past decade, several tools have been released to generate automatic solvers for a general minimal problems. The most recent tool autogen from Larsson et al. is a representative of these tools with state-of-the-art performance in solver efficiency. GAPS wraps and improves autogen with more user-friendly interface, more functionality and better stability. We demonstrate in this report the main approach and enhancement features of GAPS. A short tutorial of the software is also included.

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preprint2020arXiv

Hardcore bosonic domain walls on honeycomb lattice

Linelike hardcore bosonic domain walls in a staggered potential on honeycomb lattice are studied using quantum Monte Carlo simulations. The phase diagrams of ribbons with zigzag and armchair domain walls are mapped, which contain superfluid and insulator phases at various fillings. In the $ρ=\frac{1}{2}$ insulator, the domain wall separates two charge-density-wave (CDW) regions with opposite Berry curvatures. Associated with the change of topological properties, superfluid transport occurs down the domain wall. The superfluid density associated with a zigzag domain wall is much larger than that of an armchair domain wall due to the different arrangements of occupied and unoccupied sites along the domain wall. Our results provide a concrete context to study bosonic topological phenomena, which may be simulated experimentally using bosonic cold atoms trapped in optical lattices.

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preprint2020arXiv

Heating at the remote footpoints as a brake on jet flows along loops in the solar atmosphere

We report on observations of a solar jet propagating along coronal loops taken by the Solar Dynamics Observatory (SDO), the Interface Region Imaging Spectragraph (IRIS) and 1-m New Vacuum Solar Telescope (NVST). The ejecta of the jet consist of multi-thermal components and propagate with a speed greater than 100 km/s. Brightenings are found in the remote footpoints of the coronal loops having compact and round-shape in the Halpha images. The emission peak of the remote brightening in the Atmospheric Imaging Assembly (AIA) 94 Åpassband lags 60 s behind that in the jet base. The brightenings in the remote footpoints are believed to be consequences of heating by nonthermal electrons, MHD waves and/or conduction front generated by the magnetic reconnection processes of the jet. The heating in the remote footpoints leads to extension of the brightening along the loops toward the jet base, which is believed to be the chromospheric evaporation. This apparently acts as a brake on the ejecta, leading to a deceleration in the range from 1.5 to 3 km s$^{-2}$ with an error of $\sim1.0$\,km s$^{-2}$ when the chromospheric evaporation and the ejecta meet at locations near the loop apexes. The dynamics of this jet allows a unique opportunity to diagnose the chromospheric evaporation from the remote footpoints, from which we deduce a velocity in the range of 330--880 km/s.

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preprint2020arXiv

How eruptions of a small filament feed materials to a nearby larger-scaled filament

As one of the most common features in the solar atmosphere, filaments are significant not only in the solar physics but also in the stellar and laboratory plasma physics. With the New Vacuum Solar Telescope and the Solar Dynamics Observatory, here we report on multi-wavelength observations of eruptions of a small (30\arcsec) filament (SF) and its consequences while interacting with the ambient magnetic features including a large (300\arcsec) filament (LF). The eruptions of the SF drive a two-side-loop jet that is a result of magnetic reconnection between the SF threads and an over-lying magnetic channel. As a consequence of the eruption, the heating in the footpoints of the SF destabilises the barbs of the LF rooted nearby. Supersonic chromospheric plasma flows along the barbs of the LF are then observed in the \halpha\ passband and they apparently feed materials to the LF. We suggest they are shock-driven plasma flows or chromospheric evaporations, which both can be the consequences of the heating in the chromosphere by nonthermal particles generated in the magnetic reconnection associated with the two-side-loop jet. Our observations demonstrate that the destabilisation in the vicinity of the footpoints of a barb can drive chromospheric plasma feeding to the filament.

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preprint2020arXiv

Identification and Validation of the SNV Biomarkers Based on Multi-Dimensional Patterns

Background: Single nucleotide variants (SNVs) are detected as different distributions of DNA samples of distinct types of cancer patients. Even though, it is an exacting task to select the appropriate method to identify cancer to the greatest extent of SNVs. Results: In this paper, we proposed a biomarker concept based on SNV patterns in different feature dimensions. Raw dataset (2761 samples) consisting of twelve different cancers was obtained from TCGA (The Cancer Genome Atlas). After preliminary screening of 562,321 DNA mutation sites in the samples, the mutation sites were extracted and characterized by cancer types in six different SNV feature dimensions. In this study, we found that the extracted features showed similar distribution in the cluster center of the disease type of the samples. After the initial processing of the raw data, the sample was more focused on the subtype distribution of the cancer or the cancer at the SNV level. We used k-nearest neighbors (KNN) to classify the extracted features and Leave-One-Out cross verified them. The accuracy of classifying is stable at around 97% and reached 97.43% at the highest. During the validation phase, we found validated oncogenes in the loci of the features with the highest importance among nine cancers. Conclusions: In summary, the samples showed consistent patterns according to the cancer in which it belongs. It is feasible to classify the cancer of the sample by the distribution of different dimensions of the SNVs and has a high accuracy. And has potential implications for the discovery of cancer-causing genes.

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preprint2020arXiv

Impossibility of masking a set of quantum states of nonzero measure

We study the quantum information masking based on isometric linear operators that distribute the information encoded in pure states to the correlations in bipartite states. It is shown that a isometric linear operator can not mask any nonzero measure set of pure states. We present a geometric characterization of the maskable sets, and show that any maskable set must be on a spherical circle in certain Euclidean spaces. Detailed examples and potential applications in such as secret sharing and quantum cryptography are analyzed.

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preprint2020arXiv

Improving Robustness of Deep-Learning-Based Image Reconstruction

Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks questionable. Use of deep neural networks as inverse problem solvers has generated much excitement for medical imaging including CT and MRI, but recently a similar vulnerability has also been demonstrated for these tasks. We show that for such inverse problem solvers, one should analyze and study the effect of adversaries in the measurement-space, instead of the signal-space as in previous work. In this paper, we propose to modify the training strategy of end-to-end deep-learning-based inverse problem solvers to improve robustness. We introduce an auxiliary network to generate adversarial examples, which is used in a min-max formulation to build robust image reconstruction networks. Theoretically, we show for a linear reconstruction scheme the min-max formulation results in a singular-value(s) filter regularized solution, which suppresses the effect of adversarial examples occurring because of ill-conditioning in the measurement matrix. We find that a linear network using the proposed min-max learning scheme indeed converges to the same solution. In addition, for non-linear Compressed Sensing (CS) reconstruction using deep networks, we show significant improvement in robustness using the proposed approach over other methods. We complement the theory by experiments for CS on two different datasets and evaluate the effect of increasing perturbations on trained networks. We find the behavior for ill-conditioned and well-conditioned measurement matrices to be qualitatively different.

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preprint2020arXiv

Large Deviation Analysis of Function Sensitivity in Random Deep Neural Networks

Mean field theory has been successfully used to analyze deep neural networks (DNN) in the infinite size limit. Given the finite size of realistic DNN, we utilize the large deviation theory and path integral analysis to study the deviation of functions represented by DNN from their typical mean field solutions. The parameter perturbations investigated include weight sparsification (dilution) and binarization, which are commonly used in model simplification, for both ReLU and sign activation functions. We find that random networks with ReLU activation are more robust to parameter perturbations with respect to their counterparts with sign activation, which arguably is reflected in the simplicity of the functions they generate.

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preprint2020arXiv

Large-scale dynamics of winds originated from black hole accretion flows: (I) Hydrodynamics

Winds from black hole accretion flows are ubiquitous. Previous works mainly focus on the launching of wind in the accretion flow scale. It still remains unclear how far the winds can propagate outward and what is their large-scale dynamics. As the first paper of this series, we study the large-scale dynamics of thermal wind beyond accretion scales via analytical and numerical methods. Boundary conditions, which are crucial to our problem, are analyzed and presented based on the small-scale simulations combined with observations of winds. Both black hole and galaxy potential are taken into account. For winds originated from hot accretion flows, we find that the wind can reach to large scales. The radial profiles of velocity, density, and temperature can be approximated by $v_r\approx v_{r0}, ρ\approx ρ_{0}(r/r_0)^{-2}$, and $T\approx T_0 (r/r_0)^{-2(γ-1)}$, where $v_{r0}, ρ_0, T_0$ are the velocity, density, and temperature of winds at the boundary $r_0(\equiv 10^3 r_g)$, $γ$ is the polytropic index. During the outward propagation, the enthalpy and the rotational energy compensate the increase of gravitational potential. For thin disks, we find that because the Bernoulli parameter is smaller, winds cannot propagate as far as the hot winds, but stop at a certain radius where the Bernoulli parameter is equal to the potential energy. Before the winds stop, the profiles of dynamical quantities can also be approximated by the above relations. In this case the rotational energy alone compensates the increase of the potential energy.

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preprint2020arXiv

MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation

Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Since the labeled data may be collected from multiple sources, multi-source domain adaptation (MDA) has attracted increasing attention. Recent MDA methods do not consider the pixel-level alignment between sources and target or the misalignment across different sources. In this paper, we propose a novel MDA framework to address these challenges. Specifically, we design an end-to-end Multi-source Adversarial Domain Aggregation Network (MADAN). First, an adapted domain is generated for each source with dynamic semantic consistency while aligning towards the target at the pixel-level cycle-consistently. Second, sub-domain aggregation discriminator and cross-domain cycle discriminator are proposed to make different adapted domains more closely aggregated. Finally, feature-level alignment is performed between the aggregated domain and the target domain while training the task network. For the segmentation adaptation, we further enforce category-level alignment and incorporate context-aware generation, which constitutes MADAN+. We conduct extensive MDA experiments on digit recognition, object classification, and simulation-to-real semantic segmentation. The results demonstrate that the proposed MADAN and MANDA+ models outperform state-of-the-art approaches by a large margin.

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preprint2020arXiv

Magnon Landau Levels and Spin Responses in Antiferromagnets

We study gauge fields produced by gradients of the Dzyaloshinskii-Moriya interaction and propose a model of AFM topological insulator of magnons. In the long wavelength limit, the Landau levels induced by the inhomogeneous Dzyaloshinskii-Moriya interaction exhibit relativistic physics described by the Klein-Gordon equation. The spin Nernst response due to the formation of magnonic Landau levels is compared to similar topological responses in skyrmion and vortex-antivortex crystal phases of AFM insulators. Our studies show that AFM insulators exhibit rich physics associated with topological magnon excitations.

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preprint2020arXiv

Measurement of the neutron beam profile of the Back-n white neutron facility at CSNS with a Micromegas detector

The Back-n white neutron beam line, which uses back-streaming white neutrons from the spallation target of the China Spallation Neutron Source, is used for nuclear data measurements. A Micromegas-based neutron detector with two variants was specially developed to measure the beam spot distribution for this beam line. In this article, the design, fabrication, and characterization of the detector are described. The results of the detector performance tests are presented, which include the relative electron transparency, the gain and the gain uniformity, and the neutron beam profile reconstruction capability. The result of the first measurement of the Back-n neutron beam spot distribution is also presented.

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preprint2020arXiv

Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning

Efficient navigation and precise localization of Brownian micro/nano self-propelled motor particles within complex landscapes could enable future high-tech applications involving for example drug delivery, precision surgery, oil recovery, and environmental remediation. Here we employ a model-free deep reinforcement learning algorithm based on bio-inspired neural networks to enable different types of micro/nano motors to be continuously controlled to carry out complex navigation and localization tasks. Micro/nano motors with either tunable self-propelling speeds or orientations or both, are found to exhibit strikingly different dynamics. In particular, distinct control strategies are required to achieve effective navigation in free space and obstacle environments, as well as under time constraints. Our findings provide fundamental insights into active dynamics of Brownian particles controlled using artificial intelligence and could guide the design of motor and robot control systems with diverse application requirements.

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preprint2020arXiv

Mixed properties of magnetohydrodynamic waves undergoing resonant absorption in the cusp continuum

Observations of magnetohydrodynamic (MHD) waves in the structured solar atmosphere have shown that these waves are damped and can thus contribute to atmospheric heating. In this paper, we focus on the damping mechanism of resonant absorption in the cusp continuum. This process takes places when waves travel through an inhomogeneous plasma. Our aim is to determine the properties of MHD waves undergoing resonant absorption in the cusp continuum in the transition layer of a cylindrical solar atmospheric structure, such as a photospheric pore or a coronal loop. Depending on which quantities dominate, one can assess what type of classical MHD wave the modes in question resemble most. In order to study the properties of these waves, we analytically determine the spatial profiles of compression, displacement, and vorticity for waves with frequencies in the cusp continuum, which undergo resonant absorption. We confirm these analytical derivations via numerical calculations of the profiles in the resistive MHD framework. We show that the dominant quantities for the modes in the cusp continuum are the displacement parallel to the background magnetic field and the vorticity component in the azimuthal direction (i.e. perpendicular to the background magnetic field and along the loop boundary).

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preprint2020arXiv

Multi-layer quantum search and inclusion of NP into BQP

In this work, we present a multi-layer quantum search method that generates an exponential speedup of the standard Grover's algorithm. As direct applications, any NP problems can be solved efficiently on a quantum circuit with only polynomial gate complexity. In particular, such multi-layer search can solve the factoring problem with an exponential speedup, providing an alternative to Shor's algorithm. Our results show that the exponential speedup of quantum circuits is ubiquitous, and Grover's search is much more powerful than that has been demonstrated. With no contradiction to the quadratic optimality of single-layer query complexity, the great potential of Grover's search is fully released by such multi-layer search design.

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preprint2020arXiv

Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey

In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) addresses this problem by minimizing the impact of domain shift between the source and target domains. Multi-source domain adaptation (MDA) is a powerful extension in which the labeled data may be collected from multiple sources with different distributions. Due to the success of DA methods and the prevalence of multi-source data, MDA has attracted increasing attention in both academia and industry. In this survey, we define various MDA strategies and summarize available datasets for evaluation. We also compare modern MDA methods in the deep learning era, including latent space transformation and intermediate domain generation. Finally, we discuss future research directions for MDA.

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preprint2020arXiv

Multi-Task Learning Enhanced Single Image De-Raining

Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people. In this paper, we address a non-trivial issue of removing visual effect of rain streak from a single image. Differing from existing work, our method combines various semantic constraint task in a proposed multi-task regression model for rain removal. These tasks reinforce the model's capabilities from the content, edge-aware, and local texture similarity respectively. To further improve the performance of multi-task learning, we also present two simple but powerful dynamic weighting algorithms. The proposed multi-task enhanced network (MENET) is a powerful convolutional neural network based on U-Net for rain removal research, with a specific focus on utilize multiple tasks constraints and exploit the synergy among them to facilitate the model's rain removal capacity. It is noteworthy that the adaptive weighting scheme has further resulted in improved network capability. We conduct several experiments on synthetic and real rain images, and achieve superior rain removal performance over several selected state-of-the-art (SOTA) approaches. The overall effect of our method is impressive, even in the decomposition of heavy rain and rain streak accumulation.The source code and some results can be found at:https://github.com/SumiHui/MENET.

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preprint2020arXiv

Naive Gabor Networks for Hyperspectral Image Classification

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set.

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preprint2020arXiv

Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System

The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are, however, expensively available in the real scenario. In addition, the empirical distribution of WER for most ASR systems usually tends to put a significant mass near zero, making it difficult to simulate with a single continuous distribution. In order to address the two issues of ASR quality estimation (QE), we propose a novel neural zero-inflated model to predict the WER of the ASR result without transcripts. We design a neural zero-inflated beta regression on top of a bidirectional transformer language model conditional on speech features (speech-BERT). We adopt the pre-training strategy of token level mask language modeling for speech-BERT as well, and further fine-tune with our zero-inflated layer for the mixture of discrete and continuous outputs. The experimental results show that our approach achieves better performance on WER prediction in the metrics of Pearson and MAE, compared with most existed quality estimation algorithms for ASR or machine translation.

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preprint2020arXiv

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.

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preprint2020arXiv

On the Dirichlet problem for the Schrödinger equation with boundary value in BMO space

Let $(X,d,μ)$ be a metric measure space satisfying a $Q$-doubling condition, $Q>1$, and an $L^2$-Poincaré inequality. Let $\mathscr{L}=\mathcal{L}+V$ be a Schrödinger operator on $X$, where $\mathcal{L}$ is a non-negative operator generalized by a Dirichlet form, and $V$ is a non-negative Muckenhoupt weight that satisfies a reverse Hölder condition $RH_q$ for some $q\ge (Q+1)/2$. We show that a solution to $(\mathscr{L}-\partial_t^2)u=0$ on $X\times \mathbb{R}_+$ satisfies the Carleson condition, $$\sup_{B(x_B,r_B)}\frac{1}{μ(B(x_B,r_B))} \int_{0}^{r_B} \int_{B(x_B,r_B)} |t\nabla u(x,t)|^2 \frac{\mathrm{d}μ\mathrm{d} t}{t}<\infty,$$ if and only if, $u$ can be represented as the Poisson integral of the Schrödinger operator $\mathscr{L}$ with trace in the BMO space associated with $\mathscr{L}$.

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preprint2020arXiv

On uncertainty estimation in active learning for image segmentation

Uncertainty estimation is important for interpreting the trustworthiness of machine learning models in many applications. This is especially critical in the data-driven active learning setting where the goal is to achieve a certain accuracy with minimum labeling effort. In such settings, the model learns to select the most informative unlabeled samples for annotation based on its estimated uncertainty. The highly uncertain predictions are assumed to be more informative for improving model performance. In this paper, we explore uncertainty calibration within an active learning framework for medical image segmentation, an area where labels often are scarce. Various uncertainty estimation methods and acquisition strategies (regions and full images) are investigated. We observe that selecting regions to annotate instead of full images leads to more well-calibrated models. Additionally, we experimentally show that annotating regions can cut 50% of pixels that need to be labeled by humans compared to annotating full images.

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preprint2020arXiv

Optimal approximations of available states and a triple uncertainty relation

We investigate the optimal convex approximation of the quantum state with respect to a set of available states. By isometric transformation, we have presented the general mathematical model and its solutions together with a triple uncertainty equality relation. Meanwhile, we show a concise inequality criterion for decomposing qubit mixed states. The new results include previous ones as special cases. Our model and method may be applied to solve similar problems in high-dimensional and multipartite scenarios

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preprint2020arXiv

QEBA: Query-Efficient Boundary-Based Blackbox Attack

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.

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preprint2020arXiv

Recapture as You Want

With the increasing prevalence and more powerful camera systems of mobile devices, people can conveniently take photos in their daily life, which naturally brings the demand for more intelligent photo post-processing techniques, especially on those portrait photos. In this paper, we present a portrait recapture method enabling users to easily edit their portrait to desired posture/view, body figure and clothing style, which are very challenging to achieve since it requires to simultaneously perform non-rigid deformation of human body, invisible body-parts reasoning and semantic-aware editing. We decompose the editing procedure into semantic-aware geometric and appearance transformation. In geometric transformation, a semantic layout map is generated that meets user demands to represent part-level spatial constraints and further guides the semantic-aware appearance transformation. In appearance transformation, we design two novel modules, Semantic-aware Attentive Transfer (SAT) and Layout Graph Reasoning (LGR), to conduct intra-part transfer and inter-part reasoning, respectively. SAT module produces each human part by paying attention to the semantically consistent regions in the source portrait. It effectively addresses the non-rigid deformation issue and well preserves the intrinsic structure/appearance with rich texture details. LGR module utilizes body skeleton knowledge to construct a layout graph that connects all relevant part features, where graph reasoning mechanism is used to propagate information among part nodes to mine their relations. In this way, LGR module infers invisible body parts and guarantees global coherence among all the parts. Extensive experiments on DeepFashion, Market-1501 and in-the-wild photos demonstrate the effectiveness and superiority of our approach. Video demo is at: \url{https://youtu.be/vTyq9HL6jgw}.

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preprint2020arXiv

Reducing urban traffic congestion due to localized routing decisions

Balancing traffic flow by influencing drivers' route choices to alleviate congestion is becoming increasingly more appealing in urban traffic planning. Here, we introduce a discrete dynamical model comprising users who make their own routing choices on the basis of local information and those who consider routing advice based on localized inducement. We identify the formation of traffic patterns, develop a scalable optimization method for identifying control values used for user guidance, and test the effectiveness of these measures on synthetic and real-world road networks.

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preprint2020arXiv

Reinforcement Learning with Perturbed Rewards

Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in practice (e.g., when rewards are collected through sensors), and is therefore not credible. In addition, for applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors by receiving corrupted rewards. In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix. We develop a robust RL framework that enables agents to learn in noisy environments where only perturbed rewards are observed. Our solution framework builds on existing RL/DRL algorithms and firstly addresses the biased noisy reward setting without any assumptions on the true distribution (e.g., zero-mean Gaussian noise as made in previous works). The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that trained policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 84.6% and 80.8% improvements on average score for five Atari games, with error rates as 10% and 30% respectively.

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preprint2020arXiv

Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.

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preprint2020arXiv

Relative Pose Estimation of Calibrated Cameras with Known $\mathrm{SE}(3)$ Invariants

The $\mathrm{SE}(3)$ invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total. These problems reduces the minimal number of point pairs for relative pose estimation and improves the estimation efficiency and robustness. The $\mathrm{SE}(3)$ invariant constraints can come from extra sensor measurements or motion assumption. Different from conventional relative pose estimation with extra constraints, no extrinsic calibration is required to transform the constraints to the camera frame. This advantage comes from the invariance of $\mathrm{SE}(3)$ invariants cross different coordinate systems on a rigid body and makes the solvers more convenient and flexible in practical applications. Besides proposing the concept of relative pose estimation constrained by $\mathrm{SE}(3)$ invariants, we present a comprehensive study of existing polynomial formulations for relative pose estimation and discover their relationship. Different formulations are carefully chosen for each proposed problems to achieve best efficiency. Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods.

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preprint2020arXiv

Rethinking Distributional Matching Based Domain Adaptation

Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distributional shift between domains. Currently, most popular DA algorithms are based on distributional matching (DM). However in practice, realistic domain shifts (RDS) may violate their basic assumptions and as a result these methods will fail. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods. We further propose InstaPBM, a novel Instance-based Predictive Behavior Matching method for robust DA. Extensive experiments on both conventional and RDS benchmarks demonstrate both the limitations of DM methods and the efficacy of InstaPBM: Compared with the best baselines, InstaPBM improves the classification accuracy respectively by $4.5\%$, $3.9\%$ on Digits5, VisDA2017, and $2.2\%$, $2.9\%$, $3.6\%$ on DomainNet-LDS, DomainNet-ILDS, ID-TwO. We hope our intuitive yet effective method will serve as a useful new direction and increase the robustness of DA in real scenarios. Code will be available at anonymous link: https://github.com/pikachusocute/InstaPBM-RobustDA.

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preprint2020arXiv

SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing

Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples which are manipulated instances targeting to mislead DNNs to make incorrect predictions. Currently, most such adversarial examples try to guarantee "subtle perturbation" by limiting the $L_p$ norm of the perturbation. In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples". In particular, we propose an algorithm \emph{SemanticAdv} which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets. We conduct extensive experiments to show that the semantic based adversarial examples can not only fool different learning tasks such as face verification and landmark detection, but also achieve high targeted attack success rate against \emph{real-world black-box} services such as Azure face verification service based on transferability. To further demonstrate the applicability of \emph{SemanticAdv} beyond face recognition domain, we also generate semantic perturbations on street-view images. Such adversarial examples with controlled semantic manipulation can shed light on further understanding about vulnerabilities of DNNs as well as potential defensive approaches.

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preprint2020arXiv

Shape retrieval of non-rigid 3d human models

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.

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preprint2020arXiv

Simulation of topological phases with color center arrays in phononic crystals

We propose an efficient scheme for simulating the topological phases of matter based on silicon-vacancy (SiV) center arrays in phononic crystals. This phononic band gap structure allows for long-range spin-spin interactions with a tunable profile. Under a particular periodic microwave driving, the band-gap mediated spin-spin interaction can be further designed with the form of the Su-Schrieffer-Heeger (SSH) Hamiltonian. In momentum space, we investigate the topological characters of the SSH model, and show that the topological nontrivial phase can be obtained through modulating the periodic driving fields. Furthermore, we explore the zero-energy topological edge states at the boundary of the color center arrays, and study the robust quantum information transfer via the topological edge states. This setup provides a scalable and promising platform for studying topological quantum physics and quantum information processing with color centers and phononic crystals.

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preprint2020arXiv

Spin superfluidity in noncollinear antiferromagnets

We explore the spin superfluid transport in exchange interaction dominated three-sublattice antiferromagnets. The system in the long-wavelength regime is described by an $SO(3)$ invariant field theory. Additional corrections from Dzyaloshinskii-Moriya interactions or anisotropies can break the symmetry; however, the system still approximately holds a $U(1)$-rotation symmetry. Thus, the power-law spatial decay signature of spin superfluidity is identified in a nonlocal-measurement setup where the spin injection is described by the generalized spin-mixing conductance. We suggest iron jarosites as promising material candidates for realizing our proposal.

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preprint2020arXiv

Stable Prediction via Leveraging Seed Variable

In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution is agnostic and might be totally different from the training one. In such a case, previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. Those spurious correlations are changeable across data, leading to instability of prediction across data. By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction. By assuming the independence between causal and non-causal variables, we show, both theoretically and with empirical experiments, that our algorithm can precisely separate causal and non-causal variables for stable prediction across test data. Extensive experiments on both synthetic and real-world datasets demonstrate that our algorithm outperforms state-of-the-art methods for stable prediction.

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preprint2020arXiv

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test data and on the underlying true model. Under model misspecification, agnostic distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. To address these problems, we propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimizes a variable decorrelation regularizer and a weighted regression model. The variable decorrelation regularizer estimates a weight for each sample such that variables are decorrelated on the weighted training data. Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data. Extensive experiments clearly demonstrate that our DWR algorithm can significantly improve the accuracy of parameter estimation and stability of prediction with model misspecification and agnostic distribution shift.

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preprint2020arXiv

The high helium abundance and charge states of the interplanetary CME and its material source on the Sun

Identifying the source of the material within coronal mass ejections (CMEs) and understanding CME onset mechanisms are fundamental issues in solar and space physics. Parameters relating to plasma composition, such as charge states and He abundance (\ahe), may be different for plasmas originating from differing processes or regions on the Sun. Thus, it is crucial to examine the relationship between in-situ measurements of CME composition and activity on the Sun. We study the CME that erupted on 2014 September 10, in association with an X1.6 flare, by analyzing AIA imaging and IRIS spectroscopic observations and its in-situ signatures detected by Wind and ACE. We find that during the slow expansion and intensity increase of the sigmoid, plasma temperatures of 9 MK, and higher, first appear at the footpoints of the sigmoid, associated with chromospheric brightening. Then the high-temperature region extends along the sigmoid. IRIS observations confirm that this extension is caused by transportation of hot plasma upflow. Our results show that chromospheric material can be heated to 9 MK, and above, by chromospheric evaporation at the sigmoid footpoints before flare onset. The heated chromospheric material can transport into the sigmoidal structure and supply mass to the CME. The aforementioned CME mass supply scenario provides a reasonable explanation for the detection of high charge states and elevated \ahe\ in the associated ICME. The observations also demonstrate that the quasi-steady evolution in the precursor phase is dominated by magnetic reconnection between the rising flux rope and the overlying magnetic field structure.

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preprint2020arXiv

The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks

This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have raised serious concerns given that training data usually contain privacy-sensitive information. Thus far, successful model-inversion attacks have only been demonstrated on simple models, such as linear regression and logistic regression. Previous attempts to invert neural networks, even the ones with simple architectures, have failed to produce convincing results. We present a novel attack method, termed the generative model-inversion attack, which can invert deep neural networks with high success rates. Rather than reconstructing private training data from scratch, we leverage partial public information, which can be very generic, to learn a distributional prior via generative adversarial networks (GANs) and use it to guide the inversion process. Moreover, we theoretically prove that a model's predictive power and its vulnerability to inversion attacks are indeed two sides of the same coin---highly predictive models are able to establish a strong correlation between features and labels, which coincides exactly with what an adversary exploits to mount the attacks. Our extensive experiments demonstrate that the proposed attack improves identification accuracy over the existing work by about 75\% for reconstructing face images from a state-of-the-art face recognition classifier. We also show that differential privacy, in its canonical form, is of little avail to defend against our attacks.

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preprint2020arXiv

The Steiner $k$-eccentricity on trees

We study the Steiner $k$-eccentricity on trees, which generalizes the previous one in the paper [X.~Li, G.~Yu, S.~Klavžar, On the average Steiner 3-eccentricity of trees, arXiv:2005.10319, 2020]. To support the algorithm, we achieve much stronger properties for the Steiner $k$-ecc tree than that in the previous paper. Based on this, a linear time algorithm is devised to calculate the Steiner $k$-eccentricity of a vertex in a tree. On the other hand, the lower and upper bounds of the average Steiner $k$-eccentricity index of a tree on order $n$ are established based on a novel technique which is quite different from that in the previous paper but much easier to follow.

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preprint2020arXiv

Towards Evaluating the Robustness of Chinese BERT Classifiers

Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks. Meanwhile, character-level Chinese NLP models, including BERT for Chinese, have also demonstrated that they can outperform the existing models. In this paper, we show that, however, such BERT-based models are vulnerable under character-level adversarial attacks. We propose a novel Chinese char-level attack method against BERT-based classifiers. Essentially, we generate "small" perturbation on the character level in the embedding space and guide the character substitution procedure. Extensive experiments show that the classification accuracy on a Chinese news dataset drops from 91.8% to 0% by manipulating less than 2 characters on average based on the proposed attack. Human evaluations also confirm that our generated Chinese adversarial examples barely affect human performance on these NLP tasks.

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preprint2020arXiv

Towards Fast and Accurate Streaming End-to-End ASR

End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parameters than a conventional ASR system, thus making it suitable for on-device applications. For example, recurrent neural network transducer (RNN-T) as a streaming E2E model has shown promising potential for on-device ASR. For such applications, quality and latency are two critical factors. We propose to reduce E2E model's latency by extending the RNN-T endpointer (RNN-T EP) model with additional early and late penalties. By further applying the minimum word error rate (MWER) training technique, we achieved 8.0% relative word error rate (WER) reduction and 130ms 90-percentile latency reduction over on a Voice Search test set. We also experimented with a second-pass Listen, Attend and Spell (LAS) rescorer . Although it did not directly improve the first pass latency, the large WER reduction provides extra room to trade WER for latency. RNN-T EP+LAS, together with MWER training brings in 18.7% relative WER reduction and 160ms 90-percentile latency reductions compared to the original proposed RNN-T EP model.

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preprint2020arXiv

Unrestricted Adversarial Examples via Semantic Manipulation

Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation. Such adversarial perturbations are usually restricted by bounding their $\mathcal{L}_p$ norm such that they are imperceptible, and thus many current defenses can exploit this property to reduce their adversarial impact. In this paper, we instead introduce "unrestricted" perturbations that manipulate semantically meaningful image-based visual descriptors - color and texture - in order to generate effective and photorealistic adversarial examples. We show that these semantically aware perturbations are effective against JPEG compression, feature squeezing and adversarially trained model. We also show that the proposed methods can effectively be applied to both image classification and image captioning tasks on complex datasets such as ImageNet and MSCOCO. In addition, we conduct comprehensive user studies to show that our generated semantic adversarial examples are photorealistic to humans despite large magnitude perturbations when compared to other attacks.

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preprint2020arXiv

Wave pressure and energy cascade rate of kink waves computed with Elsasser variables

Numerical simulations have revealed a new type of turbulence of unidirectional waves in a plasma that is perpendicularly structured (Magyar et al. 2017), named uniturbulence. For this new type of turbulence, the transverse structuring modifies the upward propagating wave to have both Elsasser variables, leading to the well-known perpendicular cascade. In this paper, we study an analytical description of the non-linear evolution of kink waves in a cylindrical flux tube, which are prone to uniturbulence. We show that they lead to a non-linear cascade for both propagating and standing waves. We calculate explicit expressions for the wave pressure and energy cascade rate. The computed damping rate τ/P depends on the density contrast of the flux tube and the background plasma and is inversely proportional to the amplitude of the kink wave. The dependence on the density contrast shows that it plays a role especially in the lower solar corona. These expressions may be added in Alfven wave driven models of the solar atmosphere (such as AWSOM, van der Holst et al. 2014), modifying it to UAWSOM (Uniturbulence and Alfven Wave Solar Model).

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preprint2020arXiv

Weighted Lasso Estimates for Sparse Logistic Regression: Non-asymptotic Properties with Measurement Error

When we are interested in high-dimensional system and focus on classification performance, the $\ell_{1}$-penalized logistic regression is becoming important and popular. However, the Lasso estimates could be problematic when penalties of different coefficients are all the same and not related to the data. We proposed two types of weighted Lasso estimates depending on covariates by the McDiarmid inequality. Given sample size $n$ and dimension of covariates $p$, the finite sample behavior of our proposed methods with a diverging number of predictors is illustrated by non-asymptotic oracle inequalities such as $\ell_{1}$-estimation error and squared prediction error of the unknown parameters. We compare the performance of our methods with former weighted estimates on simulated data, then apply these methods to do real data analysis.

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preprint2019arXiv

A Pyramid Scheme Model Based on "Consumption Rebate" Frauds

There are various types of pyramid schemes which have inflicted or are inflicting losses on many people in the world. We propose a pyramid scheme model which has the principal characters of many pyramid schemes appeared in recent years: promising high returns, rewarding the participants recruiting the next generation of participants, and the organizer will take all the money away when he finds the money from the new participants is not enough to pay the previous participants interest and rewards. We assume the pyramid scheme carries on in the tree network, ER random network, SW small-world network or BA scale-free network respectively, then give the analytical results of how many generations the pyramid scheme can last in these cases. We also use our model to analyse a pyramid scheme in the real world and we find the connections between participants in the pyramid scheme may constitute a SW small-world network.

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preprint2019arXiv

Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units

Deep neural networks with rectified linear units (ReLU) are getting more and more popular due to their universal representation power and successful applications. Some theoretical progress regarding the approximation power of deep ReLU network for functions in Sobolev space and Korobov space have recently been made by [D. Yarotsky, Neural Network, 94:103-114, 2017] and [H. Montanelli and Q. Du, SIAM J Math. Data Sci., 1:78-92, 2019], etc. In this paper, we show that deep networks with rectified power units (RePU) can give better approximations for smooth functions than deep ReLU networks. Our analysis bases on classical polynomial approximation theory and some efficient algorithms proposed in this paper to convert polynomials into deep RePU networks of optimal size with no approximation error. Comparing to the results on ReLU networks, the sizes of RePU networks required to approximate functions in Sobolev space and Korobov space with an error tolerance $\varepsilon$, by our constructive proofs, are in general $\mathcal{O}(\log\frac{1}{\varepsilon})$ times smaller than the sizes of corresponding ReLU networks constructed in most of the existing literature. Comparing to the classical results of Mhaskar [Mhaskar, Adv. Comput. Math. 1:61-80, 1993], our constructions use less number of activation functions and numerically more stable, they can be served as good initials of deep RePU networks and further trained to break the limit of linear approximation theory. The functions represented by RePU networks are smooth functions, so they naturally fit in the places where derivatives are involved in the loss function.

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preprint2019arXiv

Comprehensive Characterization of Solar Eruptions With Remote and In-Situ Observations, and Modeling: The Major Solar Events on 4 November 2015

Solar energetic particles (SEPs) are an important product of solar activity. They are connected to solar active regions and flares, coronal mass ejections (CMEs), EUV waves, shocks, Type II and III radio emissions, and X-ray bursts. These phenomena are major probes of the partition of energy in solar eruptions, as well as for the organization, dynamics, and relaxation of coronal and interplanetary magnetic fields. Many of these phenomena cause terrestrial space weather, posing multiple hazards for humans and their technology from space to the ground. Since particular flares, shocks, CMEs, and EUV waves produce SEP events but others do not, since propagation effects from the low corona to 1 AU appear important for some events but not others, and since Type II and III radio emissions and X-ray bursts are sometimes produced by energetic particles leaving these acceleration sites, it is necessary to study the whole system with a multi-frequency and multi-instrument perspective that combines both in-situ and remote observations with detailed modelling of phenomena. This article demonstrates this comprehensive approach, and shows its necessity, by analysing a trio of unusual and striking solar eruptions, radio and X-ray bursts, and SEP events that occurred on 4 November 2015. These events show both strong similarities and differences from standard events and each other, despite having very similar interplanetary conditions and only two are sites and CME genesis regions. They are therefore major targets for further in-depth observational studies, and for testing both existing and new theories and models. Based on the very limited modelling available we identify the aspects that are and are not understood, and we discuss ideas that may lead to improved understanding of the SEP, radio, and space-weather events.

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preprint2019arXiv

Intrinsic spin Nernst effect of magnons in a noncollinear antiferromagnet

We investigate the intrinsic magnon spin current in a noncollinear antiferromagnetic insulator. We introduce a definition of the magnon spin current in a noncollinear antiferromagnet and find that it is in general non-conserved, but for certain symmetries and spin polarizations the averaged effect of non-conserving terms can vanish. We formulate a general linear response theory for magnons in noncollinear antiferromagnets subject to a temperature gradient and analyze the effect of symmetries on the response tensor. We apply this theory to single-layer potassium iron jarosite KFe$_3$(OH)$_6$(SO$_4$)$_2$ and predict a measurable spin current response.

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preprint2019arXiv

Magnonic Analogue of Edelstein Effect in Antiferromagnetic Insulators

We investigate the nonequilibrium spin polarization due to a temperature gradient in antiferromagnetic insulators, which is the magnonic analogue of the inverse spin-galvanic effect of electrons. We derive a linear response theory of a temperature-gradient-induced spin polarization for collinear and noncollinear antiferromagnets, which comprises both extrinsic and intrinsic contributions. We apply our theory to several noncentrosymmetric antiferromagnetic insulators, i.e., to a one-dimensional antiferromagnetic spin chain, a single layer of kagome noncollinear antiferromagnet, e.g., $\text{KFe}_3(\text{OH})_6(\text{SO}_4)_2$, and a noncollinear breathing pyrochlore antiferromagnet, e.g., LiGaCr$_4$O$_8$. The shapes of our numerically evaluated response tensors agree with those implied by the magnetic symmetry. Assuming a realistic temperature gradient of $10 \text{K}/\text{mm}$, we find two-dimensional spin densities of up to $\sim 10^6\hbar/\text{cm}^2$ and three-dimensional bulk spin densities of up to $\sim 10^{14}\hbar/\text{cm}^3$, encouraging an experimental detection.

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preprint2019arXiv

Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

Differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li, $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions have been measured at CSNS Back-n white neutron source. Two enriched (90%) $^{10}$B samples 5.0 cm in diameter and ~85.0 $μ$g/cm$^{2}$ in thickness each with an aluminum backing were prepared, and back-to-back mounted at the sample holder. The charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array (LPDA) system. The neutron energy E$_{n}$ was determined by TOF (time-of-flight) method, and the valid $α$ events were extracted from the E$_{n}$-Amplitude two-dimensional spectrum. With 15 silicon detectors, the differential cross sections of $α$-particles were measured from 19.2° to 160.8°. Fitted with the Legendre polynomial series, the ($n, α$) cross sections were obtained through integration. The absolute cross sections were normalized using the standard cross sections of the $^{10}$B($n, α$)$^{7}$Li reaction in the 0.3 - 0.5 MeV neutron energy region. The measurement neutron energy range for the $^{10}$B($n, α$)$^{7}$Li reaction is 1.0 eV $\le$ En < 2.5 MeV (67 energy points), and for the $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions is 1.0 eV $\le$ En < 1.0 MeV (59 energy points). The present results have been analyzed by the resonance reaction mechanism and the level structure of the $^{11}$B compound system, and compared with existing measurements and evaluations.

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preprint2019arXiv

Preparing squeezed spin states in a spin-mechanical hybrid system with silicon-vacancy centers

We present and analyze an effective scheme for preparing squeezed spin states in a novel spin-mechanical hybrid device, which is realized by a single crystal diamond waveguide with built-in silicon-vacancy (SiV) centers. After studying the strain couplings between the SiV spins and the propagating phonon modes, we show that long-range spin-spin interactions can be achieved under large detuning condition. We model these nonlinear spin-spin couplings with an effective one-axis twisting Hamiltonian, and find that the system can be steered to the squeezed spin states in the practical situations. This work may have interesting applications in high-precision metrology and quantum information.

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preprint2019arXiv

Real-Space Investigation of the Charge Density Wave in VTe2 Monolayer with Rotational and Mirror Symmetries Broken

Recently the charge density wave (CDW) in vanadium dichalcogenides have attracted increasing research interests, but a real-space investigation on the symmetry breaking of the CDW state in VTe2 monolayer is still lacking. We have investigated the CDW of VTe2 monolayer by low energy electron diffraction (LEED) and scanning tunneling microscope (STM). While the LEED experiments revealed a (4X4) CDW transition at 192+-2 K, our low-temperature STM experiments resolved the (4X4) lattice distortions and charge-density modulation in real space, and further unveiled a 1D modulation that breaks the three-fold rotational and mirror symmetries in the CDW state. In accordance with the CDW state at low temperature, a CDW gap of 12 meV was detected by scanning tunneling spectroscopy (STS) at 4.9 K. Our work provides real-space evidence on the symmetry breaking of the (4X4) CDW state in VTe2 monolayer, and implies there is a certain mechanism, beyond the conventional Fermi surface nesting or the q-dependent electron-phonon coupling, is responsible for the formation of CDW state in VTe2 monolayer.

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preprint2019arXiv

Rule-Guided Compositional Representation Learning on Knowledge Graphs

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

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preprint2019arXiv

Transition region loops in the very late phase of flux-emergence in IRIS sit-and-stare observations

Loops are one of the fundamental structures that trace the geometry of the magnetic field in the solar atmosphere. Their evolution and dynamics provide a crucial proxy for studying how the magnetized structures are formed and heated in the solar atmosphere. Here, we report on spectroscopic observations of a set of transition region loops taken by the Interface Region Imaging Spectrograph (IRIS) at Si IV 1394 Å with a sit-and-stare mode. The loops are corresponding to the flux emergence at its very late phase when the emerged magentic features in the photosphere have fully developed. We find the transition region loops are still expanding and moving upward with a velocity of a few kilometers per second ($\lesssim$10 km/s) at this stage. The expansion of the loops leads to interactions between themselves and the ambient field, which can drive magnetic reconnection evidenced by multiple intense brightenings, including transition region explosive events and IRIS bombs in the footpoint region associated with the moving polarity. A set of quasi-periodic brightenings with a period of about 130 s is found at the loop apex, from which the Si IV 1394 Å profiles are significantly non-Gaussian with enhancements at both blue and red wings at Doppler velocities of about 50 km/s. We suggest that the transition region loops in the very late phase of flux emergence can be powered by heating events generated by the interactions between the expanding loops and the ambient fields and also by (quasi-)periodic processes, such as oscillation-modulated braiding reconnection.

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preprint2019arXiv

Variational implicit-solvent predictions of the dry-wet transition pathways for ligand-receptor binding and unbinding kinetics

Ligand-receptor binding and unbinding are fundamental biomolecular processes and particularly essential to drug efficacy. Environmental water fluctuations, however, impact the corresponding thermodynamics and kinetics and thereby challenge theoretical descriptions. Here, we devise a holistic, implicit-solvent, multi-method approach to predict the (un)binding kinetics for a generic ligand-pocket model. We use the variational implicit-solvent model (VISM) to calculate the solute-solvent interfacial structures and the corresponding free energies, and combine the VISM with the string method to obtain the minimum energy paths and transition states between the various metastable ('dry' and 'wet') hydration states. The resulting dry-wet transition rates are then used in a spatially-dependent multi-state continuous-time Markov chain Brownian dynamics simulations, and the related Fokker-Planck equation calculations, of the ligand stochastic motion, providing the mean first-passage times for binding and unbinding. We find the hydration transitions to significantly slow down the binding process, in semi-quantitative agreement with existing explicit-water simulations, but significantly accelerate the unbinding process. Moreover, our methods allow the characterization of non-equilibrium hydration states of pocket and ligand during the ligand movement, for which we find substantial memory and hysteresis effects for binding versus unbinding. Our study thus provides a significant step forward towards efficient, physics-based interpretation and predictions of the complex kinetics in realistic ligand-receptor systems.

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preprint2017arXiv

Reply to "Failure to replicate long-range tunable attractions in colloidal system"

An arxiv paper, ref. [1] by Cao et al., claimed that the tunable attraction reported in our ref. [2] could not be detected. Ref. [1] was submitted to Nature in Apr. 2016 as a Comment on our ref. [2]. Our reply in May 2016 responded to ref. [1] and was reviewed by the editor of Nature and an external referee. Ref. [1] was rejected by Nature in Aug. 2016. We provide our reply, which answers all of the criticisms in ref. [1], in Appendix I. The main text contains a brief reply to the main criticisms.

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preprint2016arXiv

A General Retraining Framework for Scalable Adversarial Classification

Traditional classification algorithms assume that training and test data come from similar distributions. This assumption is violated in adversarial settings, where malicious actors modify instances to evade detection. A number of custom methods have been developed for both adversarial evasion attacks and robust learning. We propose the first systematic and general-purpose retraining framework which can: a) boost robustness of an \emph{arbitrary} learning algorithm, in the face of b) a broader class of adversarial models than any prior methods. We show that, under natural conditions, the retraining framework minimizes an upper bound on optimal adversarial risk, and show how to extend this result to account for approximations of evasion attacks. Extensive experimental evaluation demonstrates that our retraining methods are nearly indistinguishable from state-of-the-art algorithms for optimizing adversarial risk, but are more general and far more scalable. The experiments also confirm that without retraining, our adversarial framework dramatically reduces the effectiveness of learning. In contrast, retraining significantly boosts robustness to evasion attacks without significantly compromising overall accuracy.

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preprint2016arXiv

A Time Projection Chamber with GEM-Based Readout

For the International Large Detector concept at the planned International Linear Collider, the use of time projection chambers (TPC) with micro-pattern gas detector readout as the main tracking detector is investigated. In this paper, results from a prototype TPC, placed in a 1 T solenoidal field and read out with three independent GEM-based readout modules, are reported. The TPC was exposed to a 6 GeV electron beam at the DESY II synchrotron. The efficiency for reconstructing hits, the measurement of the drift velocity, the space point resolution and the control of field inhomogeneities are presented.

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preprint2016arXiv

Damping and power spectra of quasi-periodic intensity disturbances above a solar polar coronal hole

We study intensity disturbances above a solar polar coronal hole seen in the AIA 171 Å and 193 Å passbands, aiming to provide more insights into their physical nature. The damping and power spectra of the intensity disturbances with frequencies from 0.07 mHz to 10.5 mHz are investigated. The damping of the intensity disturbances tends to be stronger at lower frequencies, and their damping behavior below 980" (for comparison, the limb is at 945") is different from what happens above. No significant difference is found between the damping of the intensity disturbances in the AIA 171 Å and that in the AIA 193 Å. The indices of the power spectra of the intensity disturbances are found to be slightly smaller in the AIA 171 Å than in the AIA 193 Å, but the difference is within one sigma deviation. An additional enhanced component is present in the power spectra in a period range of 8--40 minutes at lower heights. While the power spectra of spicule is highly correlated with its associated intensity disturbance, it suggests that the power spectra of the intensity disturbances might be a mixture of spicules and wave activities. We suggest that each intensity disturbance in the polar coronal hole is possibly a series of independent slow magnetoacoustic waves triggered by spicular activities.

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preprint2016arXiv

Data Poisoning Attacks on Factorization-Based Collaborative Filtering

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the industry, their outputs could affect business decision making, introducing incentives for an adversarial party to compromise the availability or integrity of such systems. We introduce a data poisoning attack on collaborative filtering systems. We demonstrate how a powerful attacker with full knowledge of the learner can generate malicious data so as to maximize his/her malicious objectives, while at the same time mimicking normal user behavior to avoid being detected. While the complete knowledge assumption seems extreme, it enables a robust assessment of the vulnerability of collaborative filtering schemes to highly motivated attacks. We present efficient solutions for two popular factorization-based collaborative filtering algorithms: the \emph{alternative minimization} formulation and the \emph{nuclear norm minimization} method. Finally, we test the effectiveness of our proposed algorithms on real-world data and discuss potential defensive strategies.

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preprint2016arXiv

Dynamics of a prominence-horn structure during its evaporation in the solar corona

The physical connection among and formation mechanisms of various components of the prominence-horn cavity system remain elusive. Here we present observations of such a system, focusing on a section of the prominence that rises and separates gradually from the main body. This forms a configuration sufficiently simple to yield clues to the above issues. It is characterized by embedding horns, oscillations, and a gradual disappearance of the separated material. The prominence-horn structure exhibits a large amplitude longitudinal oscillation with a period of ~150 minutes and an amplitude of ~30 Mm along the trajectory defined by the concave horn structure. The horns also experience a simultaneous transverse oscillation with a much smaller amplitude (~3 Mm) and shorter period (~10-15 minutes), likely representative of a global mode of the large-scale magnetic structure. The gradual disappearance of the structure indicates that the horn, an observational manifestation of the field-aligned transition region separating the cool and dense prominence from the hot and tenuous corona, is formed due to the heating and diluting process of the central prominence mass, while most previous studies suggest that it is the opposite process, i.e., the cooling and condensation of coronal plasmas, to form the horn. This study also demonstrates how the prominence transports magnetic flux to the upper corona, a process essential for the gradual build-up of pre-eruption magnetic energy.

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preprint2016arXiv

Effects of single- and multi-substituted Zn ions in doped-122 type iron-based superconductors

Recent experiments on Zn-substituted 122-type iron-based superconductors (FeSCs) at electron- and hole- doped region provide us with a testing ground for understanding the effect of Zn impurities in these systems. Our first-principle calculations of the electronic structure reveal that the Zn 3d orbitals are far below the Fermi level and chemically inactive, while the Zn 4s-orbital is partially occupied and its wave function overlapping with those 3d-orbitals of neighboring Fe-ions. This suggests that the impurity effect is originating in the Zn 4s-orbital, not its 3d-orbitals. Employing a phenomenological two-orbital lattice model for 122-FeSCs and the self-consistent Bogoliubov-de Gennes equations, we study how the Zn-impurities suppress the superconductivity in electron- and hole- doped compounds. Our obtained results qualitatively agree with the experimental measurements.

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preprint2016arXiv

Fast sausage modes in magnetic tubes with continuous transverse profiles: effects of a finite plasma beta

While standing fast sausage modes in flare loops are often invoked to interpret quasi-periodic pulsations (QPPs) in solar flares, it is unclear as to how they are influenced by the combined effects of a continuous transverse structuring and a finite internal plasma beta ($β_{\rm i}$). We derive a generic dispersion relation (DR) governing linear sausage waves in straight magnetic tubes for which plasma pressure is not negligible and the density and temperature inhomogeneities of essentially arbitrary form take place in a layer of arbitrary width. Focusing on fast modes, we find that $β_{\rm i}$ only weakly influences $k_{\rm c}$, the critical longitudinal wavenumber separating the leaky from trapped modes. Likewise, for both trapped and leaky modes, the periods $P$ in units of the transverse fast time depend only weakly on $β_{\rm i}$, which is compatible with the fact that the effective wavevectors of fast sausage modes are largely perpendicular to the background magnetic field. However, a weak $β_{\rm i}$ dependence of the damping times $τ$ is seen only when the length-to-radius ratio $L/R$ is $\sim 50\%$ larger than some critical value $π/(k_{\rm c} R)$, which itself rather sensitively depends on the density contrast, profile steepness as well as on how the transverse structuring is described. In the context of QPPs, we conclude that the much simpler zero-beta theory can be employed for trapped modes, as long as one sees the deduced internal Alfvén speed as actually being the fast speed. In contrast, effects due to a finite beta in flare loops should be considered when leaky modes are exploited.

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preprint2016arXiv

Global Theory to Understand Toroidal Drift Waves in Steep Gradient

Toroidal drift waves with unconventional mode structures and non-ground eigenstates, which differ from typical ballooning structure mode, are found to be important recently by large scale global gyrokinetic simulations and especially become dominant at strong gradient edge plasmas [cf., Xie and Xiao, Phys. Plasmas, 22, 090703 (2015)]. The global stability and mode structures of drift wave in this steep edge density and temperature gradients are examined by both direct numerical solutions of a model two-dimensional eigen equation and analytical theory employing WKB-ballooning approach. Theory agrees with numerical solutions quite well. Our results indicate that (i) non-ground eigenstates and unconventional mode structures generally exist and can be roughly described by two parameters `quantum number' $l$ and ballooning angle $\vartheta_k$, (ii) local model can overestimate the growth rate largely, say, $>50\%$, and (iii) the narrow steep equilibrium profile leads to twisting (triangle-like) radial mode structures. With velocity space integral, semi-local theory predicts that the critical jump gradient of the most unstable ion temperature gradient mode from ground state $l=0$ to non-ground state $l=1$ is $L_T^{-1}R\sim50$. These features can have important consequences to turbulent transport.

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preprint2016arXiv

Impulsively generated sausage waves in coronal tubes with transversally continuous structuring

The frequency dependence of the longitudinal group speeds of trapped sausage waves plays an important role in determining impulsively generated wave trains, which have often been invoked to account for quasi-periodic signals in coronal loops. We examine how the group speeds ($v_{\rm gr}$) depend on angular frequency ($ω$) for sausage modes in pressureless coronal tubes with continuous transverse density distributions by solving the dispersion relation pertinent to the case where the density inhomogeneity of arbitrary form takes place in a transition layer of arbitrary thickness. We find that in addition to the transverse lengthscale $l$ and density contrast $ρ_{\rm i}/ρ_{\rm e}$, the group speed behavior depends also on the detailed form of the density inhomogeneity. For parabolic profiles, $v_{\rm gr}$ always decreases with $ω$ first before increasing again, as happens for the much studied top-hat profiles. For linear profiles, however, the behavior of the $ω-v_{\rm gr}$ curves is more complex. When $ρ_{\rm i}/ρ_{\rm e} \lesssim 6$, the curves become monotonical for large values of $l$. On the other hand, for higher density contrasts, a local maximum $v_{\rm gr}^{\rm max}$ exists in addition to a local minimum $v_{\rm gr}^{\rm min}$ when coronal tubes are diffuse. With time-dependent computations, we show that the different behavior of group speed curves, the characteristic speeds $v_{\rm gr}^{\rm min}$ and $v_{\rm gr}^{\rm max}$ in particular, is reflected in the temporal evolution and Morlet spectra of impulsively generated wave trains. We conclude that the observed quasi-periodic wave trains not only can be employed to probe such key parameters as density contrasts and profile steepness, but also have the potential to discriminate between the unknown forms of the transverse density distribution.

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preprint2016arXiv

Label distribution based facial attractiveness computation by deep residual learning

Two challenges lie in the facial attractiveness computation research: the lack of true attractiveness labels (scores), and the lack of an accurate face representation. In order to address the first challenge, this paper recasts facial attractiveness computation as a label distribution learning (LDL) problem rather than a traditional single-label supervised learning task. In this way, the negative influence of the label incomplete problem can be reduced. Inspired by the recent promising work in face recognition using deep neural networks to learn effective features, the second challenge is expected to be solved from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. Integrating these two ideas, an end-to-end deep learning framework is established. Our approach achieves the best results on a standard benchmark SCUT-FBP dataset compared with other state-of-the-art work.

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preprint2016arXiv

Observation of an MHD Alfvén vortex in the slow solar wind

In the solar wind, magnetic field power spectra usually show several power-laws. In this paper, magnetic field data from the Cluster mission during an undisturbed interval of slow solar wind is analyzed at 0.28Hz, near the spectral break point between the ion inertial and dissipation/dispersion ranges. Assuming Taylor's frozen-in condition, it corresponds to a proton kinetic scale of $kv_A/Ω_p \sim 0.38$, where $v_A$ and $Ω_p$ are the Alfvén speed and proton angular gyrofrequency, respectively. Data show that the Cluster spacecraft passed through a series of wavepackets. A strong isolated wavepacket is found to be in accordance with the four Cluster satellites crossing an Alfvén vortex, a nonlinear solution to the incompressible MHD equations. A strong agreement is seen between the data from four satellites and a model vortex with a radius of the order of $40$ times the local proton gyro-radii. The polarization at different spacecraft is compared and is found to agree with the vortex model, whereas it cannot be explained solely by the linear plane wave approach.

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preprint2016arXiv

Period ratios for standing kink and sausage modes in magnetized structures with siphon flow on the Sun

Standing oscillations with multiple periods were found in a number of atmospheric structures on the Sun. The ratio of the period of the fundamental to twice the one of its first overtone, $P_1/2P_2$, is important in applications of solar magneto-seismology. We examine how field-aligned flows impact $P_1/2P_2$ of standing modes in solar magnetic cylinders. For coronal loops, the flow effects are significant for both fast kink and sausage modes. For kink ones, they reduce $P_1/2P_2$ by up to 17\% relative to the static case even when the density contrast between the loop and its surroundings approaches infinity. For sausage modes, the reduction in $P_1/2P_2$ due to flow is typically $\lesssim 5.5\%$ compared with the static case. However, the threshold aspect ratio, only above which can trapped sausage modes be supported, may increase dramatically with the flow magnitude. For photospheric tubes, the flow effect on $P_1/2P_2$ is not as strong. However, when applied to sausage modes, introducing field-aligned flows offers more possibilities in interpreting the multiple periods recently measured. We conclude that field-aligned flows should be taken into account to help better understand what causes the departure of $P_1/2P_2$ from unity.

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preprint2016arXiv

Reaching Agreement in Quantum Hybrid Networks

We consider a basic quantum hybrid network model consisting of a number of nodes each holding a qubit, for which the aim is to drive the network to a consensus in the sense that all qubits reach a common state. Projective measurements are applied serving as control means, and the measurement results are exchanged among the nodes via classical communication channels. We show how to carry out centralized optimal path planning for this network with all-to-all classical communications, in which case the problem becomes a stochastic optimal control problem with a continuous action space. To overcome the computation and communication obstacles facing the centralized solutions, we also develop a distributed Pairwise Qubit Projection (PQP) algorithm, where pairs of nodes meet at a given time and respectively perform measurements at their geometric average. We show that the qubit states are driven to a consensus almost surely along the proposed PQP algorithm, and that the expected qubit density operators converge to the average of the network's initial values.

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preprint2016arXiv

Recognizing Car Fluents from Video

Physical fluents, a term originally used by Newton [40], refers to time-varying object states in dynamic scenes. In this paper, we are interested in inferring the fluents of vehicles from video. For example, a door (hood, trunk) is open or closed through various actions, light is blinking to turn. Recognizing these fluents has broad applications, yet have received scant attention in the computer vision literature. Car fluent recognition entails a unified framework for car detection, car part localization and part status recognition, which is made difficult by large structural and appearance variations, low resolutions and occlusions. This paper learns a spatial-temporal And-Or hierarchical model to represent car fluents. The learning of this model is formulated under the latent structural SVM framework. Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents. In experiments, the proposed method outperforms several highly related baseline methods in terms of car fluent recognition and car part localization.

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preprint2016arXiv

Robust High-Dimensional Linear Regression

The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of learning has made it a natural target for adversarial manipulation of training data, which we term poisoning attacks. Prior approaches to dealing with robust supervised learning rely on strong assumptions about the nature of the feature matrix, such as feature independence and sub-Gaussian noise with low variance. We propose an integrated method for robust regression that relaxes these assumptions, assuming only that the feature matrix can be well approximated by a low-rank matrix. Our techniques integrate improved robust low-rank matrix approximation and robust principle component regression, and yield strong performance guarantees. Moreover, we experimentally show that our methods significantly outperform state of the art both in running time and prediction error.

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preprint2016arXiv

Secondary fast magnetoacoustic waves trapped in randomly structured plasmas

Fast magnetoacoustic wave is an important tool for inferring solar atmospheric parameters. We numerically simulate the propagation of fast wave pulses in randomly structured plasmas mimicking the highly inhomogeneous solar corona. A network of secondary waves is formed by a series of partial reflections and transmissions. These secondary waves exhibit quasi-periodicities in both time and space. Since the temporal and spatial periods are related simply through the fast wave speed, we quantify the properties of secondary waves by examining the dependence of the average temporal period ($\bar{p}$) on the initial pulse width ($w_0$) as well as the density contrast ($δ_ρ$) and correlation length ($L_c$) that characterize the randomness of the equilibrium density profiles. For small-amplitude pulses, $δ_ρ$ does not alter $\bar{p}$ significantly. Large-amplitude pulses, on the other hand, enhance the density contrast when $δ_ρ$ is small but have a smoothing effect when $δ_ρ$ is sufficiently large. We found that $\bar{p}$ scales linearly with $L_c$ and that the scaling factor is larger for a narrower pulse. However, in terms of the absolute values of $\bar{p}$, broader pulses generate secondary waves with longer periods, and this effect is stronger in random plasmas with shorter correlation lengths. Secondary waves carry the signatures of both the leading wave pulse and background plasma, our study may find applications in MHD seismology by exploiting the secondary waves detected in the dimming regions after CMEs or EUV waves.

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preprint2016arXiv

Vehicle Detection from 3D Lidar Using Fully Convolutional Network

Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.

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preprint2016arXiv

Vortex Laser at Exceptional Point

The optical vortices carrying orbital angular momentum (OAM) are commonly generated by modulating the available conventional light beam. This article shows that a micro-laser operates at the exceptional point (EP) of the non-Hermitian quantum system can directly emit vortex laser with well-defined OAM at will. Two gratings (the refractive index modulation and along azimuthal direction and the grating protruding from the micro-ring cavity) modulate the eigenmode of a micro-ring cavity to be a vortex laser mode. The phase-matching condition ensures that we can tune the OAM of the vortex beam to be arbitrary orders by changing the grating protruding from the micro-ring cavity while the system is kept at EP. The results are obtained by analytical analysis and confirmed by 3D full wave simulations.

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preprint2015arXiv

A Much better replacement of the Michaelis-Menten equation and its application

Michaelis-Menten equation is a basic equation of enzyme kinetics and gives an acceptable approximation of real chemical reaction processes. Analyzing the derivation of this equation yields the fact that its good performance of approximating real reaction processes is due to Michaelis-Menten curve (15). This curve is derived from Quasi-Steady-State Assumption(QSSA), which has been proved always true and called Quasi-Steady-State Law by Banghe Li et al [19]. Here, we found a quartic equation A(S,E)=0 (22), which gives more accurate approximation of the reaction process in two aspects: during the quasi-steady state of a reaction, Michaelis-Menten curve approximates the reaction well, while our quartic equation $A(S,E)=0$ gives better approximation; near the end of the reaction, our equation approaches the end of the reaction with a tangent line same to that of the reaction, while Michaelis-Menten curve does not. In addition, our quartic equation A(S,E)=0 differs to Michaelis-Menten curve less than the order of $1/S^3$ as S approaches $+\infty$. By considering the above merits of A(S,E)=0, we suggest it as a replacement of Michaelis-Menten curve. Intuitively, this new equation is more complex and harder to understand. But, just because its complexity, it provides more information about the rate constants than Michaelis-Menten curve does. Finally, we get a better replacement of the Michaelis-Menten equation by combing A(S,E)=0 and the equation $dP/dt=k_2C(t)$.

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preprint2015arXiv

A unified description of superconducting pairing symmetry in electron-doped Fe-based-122 compounds

The pairing symmetry is examined in highly electron-doped Ba(Fe$_{1-x}$Co$_x$As)$_2$ and A$_y$Fe$_2$Se$_2$ (with A=K, Cs) compounds, with similar crystallographic and electronic band structures. Starting from a phenomenological two-orbital model, we consider nearest-neighbor and next-nearest-neighbor intraorbital pairing interactions on the Fe square lattice. In this model, we find a unified description of the evolution from $s_\pm$-wave pairing ($2.0 < n \lesssim 2.4$) to $d$-wave pairing ($2.4 \lesssim n \lesssim 2.5$) as a function of electron filling. In the crossover region a novel time-reversal symmetry breaking state with $s_\pm+id$ pairing symmetry emerges. This minimal model offers an overall picture of the evolution of superconductivity with electron doping for both $s_\pm$-wave [Ba(Fe$_{1-x}$Co$_x$As)$_2$] and $d$-wave [A$_y$Fe$_2$Se$_2$] pairing, as long as the dopants only play the role of a charge reservoir. However, the situation is more complicated for Ba(Fe$_{1-x}$Co$_x$As)$_2$. A real-space study further shows that when the impurity scattering effects of Co dopants are taken into account, the superconductivity is completely suppressed for $n > 2.4$. This preempts any observation of $d$-wave pairing in this compound, in contrast to A$_y$Fe$_2$Se$_2$.

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preprint2015arXiv

Bifunctional Metamaterials with Simultaneous and Independent Manipulation of Thermal and Electric Fields

Metamaterials offer a powerful way to manipulate a variety of physical fields ranging from wave fields (electromagnetic field, acoustic field, elastic wave, etc.), static fields (static magnetic field, static electric field) to diffusive fields (thermal field, diffusive mass). However, the relevant reports and studies are usually conducted on a single physical field or functionality. In this study, we proposed and experimentally demonstrated a bifunctional metamaterial which can manipulate thermal and electric fields simultaneously and independently. Specifically, a composite with independently controllable thermal and electric conductivity was introduced, on the basis of which a bifunctional device capable of shielding thermal flux and concentrating electric current simultaneously was designed, fabricated and characterized. This work provides an encouraging example of metamaterials transcending their natural limitations, which offers a promising future in building a broad platform for manipulation of multi-physics field.

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preprint2015arXiv

Concentrating Electric and Thermal Fields Simultaneously Using Fan-shaped Structure

Recently, considerable attention has been focused on the transformation optics and metamaterial due to their fascinating phenomena and potential applications. Concentrator is one of the most representative ones, which however is limited in single physical domain. Here we propose and give the experimental demonstration of bifunctional concentrator that can concentrate electric and thermal fields into a given region simultaneously while keeping the external fields undistorted. Fan-shaped structure composed of alternating wedges made of two kinds of natural materials is proposed to achieve this goal. The simulation and experimental results show good agreement, thereby confirming the feasibility of our scheme.

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preprint2015arXiv

Cool transition region loops observed by the Interface Region Imaging Spectrograph

We report on the first Interface Region Imaging Spectrograph (IRIS) study of cool transition region loops. This class of loops has received little attention in the literature. A cluster of such loops was observed on the solar disk in active region NOAA11934, in the Si IV 1402.8 Å spectral raster and 1400 Å slit-jaw (SJ) images. We divide the loops into three groups and study their dynamics and interaction. The first group comprises relatively stable loops, with 382--626\,km cross-sections. Observed Doppler velocities are suggestive of siphon flows, gradually changing from -10 km/s at one end to 20 km/s at the other end of the loops. Nonthermal velocities from 15 to 25 km/s were determined. These physical properties suggest that these loops are impulsively heated by magnetic reconnection occurring at the blue-shifted footpoints where magnetic cancellation with a rate of $10^{15}$ Mx/s is found. The released magnetic energy is redistributed by the siphon flows. The second group corresponds to two footpoints rooted in mixed-magnetic-polarity regions, where magnetic cancellation occurred at a rate of $10^{15}$ Mx/s and line profiles with enhanced wings of up to 200 km/s were observed. These are suggestive of explosive-like events. The Doppler velocities combined with the SJ images suggest possible anti-parallel flows in finer loop strands. In the third group, interaction between two cool loop systems is observed. Evidence for magnetic reconnection between the two loop systems is reflected in the line profiles of explosive events, and a magnetic cancellation rate of $3\times10^{15}$ Mx/s observed in the corresponding area. The IRIS observations have thus opened a new window of opportunity for in-depth investigations of cool transition region loops. Further numerical experiments are crucial for understanding their physics and their role in the coronal heating processes.

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preprint2015arXiv

Coronal sources and in situ properties of the solar winds sampled by ACE during 1999-2008

We identify the coronal sources of the solar winds sampled by the ACE spacecraft during 1999-2008, and examine the in situ solar wind properties as a function of wind sources. The standard two-step mapping technique is adopted to establish the photospheric footpoints of the magnetic flux tubes along which the ACE winds flow. The footpoints are then placed in the context of EIT 284~Å images and photospheric magnetograms, allowing us to categorize the sources into four groups: coronal holes (CHs), active regions (ARs), the quiet Sun (QS), and "Undefined". This practice also enables us to establish the response to solar activity of the fractions occupied by each kind of solar winds, and of their speeds and O$^{7+}$/O$^{6+}$ ratios measured in situ. We find that during the maximum phase, the majority of ACE winds originate from ARs. During the declining phase, CHs and ARs are equally important contributors to the ACE solar winds. The QS contribution increases with decreasing solar activity, and maximizes in the minimum phase when QS appear to be the primary supplier of the ACE winds. With decreasing activity, the winds from all sources tend to become cooler, as represented by the increasingly low O$^{7+}$/O$^{6+}$ ratios. On the other hand, during each activity phase, the AR winds tend to be the slowest and associated with the highest O$^{7+}$/O$^{6+}$ ratios, and the CH winds correspond to the other extreme, with the QS winds lying in between. Applying the same analysis method to the slow winds only, here defined as the winds with speeds lower than 500 km s$^{-1}$, we find basically the same overall behavior, as far as the contributions of individual groups of sources are concerned. This statistical study indicates that QS regions are an important source of the solar wind during the minimum phase.

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preprint2015arXiv

Direct detection of dark matter with resonant annihilation

In the scenario where the dark matter (DM) particles $χ\barχ$ pair annihilate through a resonance particle $R$, the constraint from DM relic density makes the corresponding cross section for DM-nuclei elastic scattering extremely small, and can be below the neutrino background induced by the coherent neutrino-nuclei scattering, which makes the DM particle beyond the reach of the conventional DM direct detection experiments. We present an improved analytical calculation of the DM relic density in the case of resonant DM annihilation for $s$- and $p$-wave cases and invesitgate the condition for the DM-nuclei scattering cross section to be above the neutrino background. We show that in Higgs-portal type models, for DM particles with $s$-wave annihilation, the spin-independent DM-nucleus scattering cross section is proportional to $Γ_{R}/m_{R}$, the ratio of the decay width and the mass of $R$. For a typical DM particle mass $\sim50$ GeV, the condition leads to $Γ_{R}/m_{R} \gtrsim \mathcal{O}(10^{-4})$. In $p$-wave annihilation case, the spin-independent scattering cross section is insensitive to $Γ_{R}/m_{R}$, and is always above the neutrino background, as long as the DM particle is lighter than the top quark. The real singlet DM model is discussed as a concrete example.

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preprint2015arXiv

Electrostatic Field Invisibility Cloak

Invisibility cloak is drawing much attention due to its special camouflage when exposed to physical field varing from wave (electromagnetic field, acoustic field, elastic wave, etc.) to scalar field (thermal field, static magnetic field, dc electric field and mass diffusion). Here, an electrostatic field invisibility cloak has been theoretically investigated, and experimentally demonstrated for the first time to perfectly hide a certain region from sight without disturbing the external electrostatic field. The desired cloaking effect has been achieved via both scattering cancelling technology and transformation optics (TO).This present work will pave a novel way for manipulating of electrostatic field where would enable a wide range of potential applications and sustainable products made available.

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preprint2015arXiv

Finite-time Convergent Gossiping

Gossip algorithms are widely used in modern distributed systems, with applications ranging from sensor networks and peer-to-peer networks to mobile vehicle networks and social networks. A tremendous research effort has been devoted to analyzing and improving the asymptotic rate of convergence for gossip algorithms. In this work we study finite-time convergence of deterministic gossiping. We show that there exists a symmetric gossip algorithm that converges in finite time if and only if the number of network nodes is a power of two, while there always exists an asymmetric gossip algorithm with finite-time convergence, independent of the number of nodes. For $n=2^m$ nodes, we prove that a fastest convergence can be reached in $nm=n\log_2 n$ node updates via symmetric gossiping. On the other hand, under asymmetric gossip among $n=2^m+r$ nodes with $0\leq r<2^m$, it takes at least $mn+2r$ node updates for achieving finite-time convergence. It is also shown that the existence of finite-time convergent gossiping often imposes strong structural requirements on the underlying interaction graph. Finally, we apply our results to gossip algorithms in quantum networks, where the goal is to control the state of a quantum system via pairwise interactions. We show that finite-time convergence is never possible for such systems.

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preprint2015arXiv

Independent Manipulation of Electric and Thermal Fields with Bilayer Structure

Recently, increasing attention has been focused on the employment of transformation and metamaterial for manipulation of various physical fields, which requires complicated configuration and usually limits in single field. Here, for the first time, we propose and experimentally demonstrated bilayer structure to achieve simultaneously independent manipulation of multi-physics field (dc electric fields and thermal) by directly solving the dc electric/ thermal field equations. This structure is composed of two layers: the outer layer is made of isotropic and homogeneous material, while the inner layer is fan-shape layer. Since it is not based on TO, it can be readily experimentally fabricated with naturally occurring materials. Experimentally, we has designed, fabricated and characterized two structures simultaneously behaving as dc electric cloak/ thermal concentrator and dc electric concentrator/ thermal cloak, respectively. The simulation results agree well with the experiment ones, thus confirming the feasibility of our methodology. This job provides a novel avenue to manipulation of multiphysics fields, thus might find potential applications in various areas.

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preprint2015arXiv

Inferring flare loop parameters with measurements of standing sausage modes

Standing fast sausage modes in flare loops were suggested to account for a considerable number of quasi-periodic pulsations (QPPs) in the light curves of solar flares. This study continues our investigation into the possibility to invert the measured periods $P$ and damping times $τ$ of sausage modes to deduce the transverse Alfvén time $R/v_{\rm Ai}$, density contrast $ρ_{\rm i}/ρ_{\rm e}$, and the steepness of the density distribution transverse to flare loops. A generic dispersion relation (DR) governing linear sausage modes is derived for pressureless cylinders where density inhomogeneity of arbitrary form takes place within the cylinder. We show that in general the inversion problem is under-determined for QPP events where only a single sausage mode exists, be the measurements spatially resolved or unresolved. While $R/v_{\rm Ai}$ can be inferred to some extent, the range of possible steepness parameters may be too broad to be useful. However, for spatially resolved measurements where an additional mode is present, it is possible to deduce self-consistently $ρ_{\rm i}/ρ_{\rm e}$, the profile steepness, and the internal Alfvén speed $v_{\rm Ai}$. We show that at least for a recent QPP event that involves a fundamental kink mode in addition to a sausage one, flare loop parameters are well constrained, even if the specific form of the transverse density distribution remains unknown. We conclude that spatially resolved, multi-mode QPP measurements need to be pursued for inferring flare loop parameters.

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preprint2015arXiv

Isotropic Negative Thermal Expansion Metamaterials

Negative thermal expansion materials are important and desirable in science and engineering applications. However, natural materials with isotropic negative thermal expansion are rare and usually unsatisfied in performance. Here, we propose a novel method to achieve negative thermal expansion via a metamaterial approach. The metamaterial is constructed with unit cells that combine bi-material strips and anti-chiral structures. Both experimental and simulation results display isotropic negative thermal expansion properties. The coefficient of negative thermal expansion of the metamaterials is demonstrated to be dependent on the difference between the thermal expansion coefficients of two component materials in the bi-material strips, as well as on the circular node radius and the ligament length in the anti-chiral structures. The measured value of the linear negative thermal expansion coefficient reaches -68.1X10-6 1/K in an operating temperature range from 303.15 K to 773.15 K, which is among the largest achieved in experiments to date. Our findings provide a novel and practical approach to obtaining materials with tunable isotropic negative thermal expansion on any scale.

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preprint2015arXiv

Kink and Sausage Modes in Nonuniform Magnetic Slabs with Continuous Transverse Density Distributions

We examine the influence of a continuous density structuring transverse to coronal slabs on the dispersive properties of fundamental standing kink and sausage modes supported therein. We derive generic dispersion relations (DRs) governing linear fast waves in pressureless straight slabs with general transverse density distributions, and focus on the cases where the density inhomogeneity takes place in a layer of arbitrary width and in arbitrary form. The physical relevance of the solutions to the DRs is demonstrated by the corresponding time-dependent computations. For all profiles examined, the lowest-order kink modes are trapped regardless of longitudinal wavenumber $k$. A continuous density distribution introduces a difference to their periods of $\lesssim 13\%$ when $k$ is the observed range, relative to the case where the density profile takes a step-function form. Sausage modes and other branches of kink modes are leaky at small $k$, and their periods and damping times are heavily influenced by how the transverse density profile is prescribed, the lengthscale in particular. These modes have sufficiently high quality to be observable only for physical parameters representative of flare loops. We conclude that while the simpler DR pertinent to a step-function profile can be used for the lowest-order kink modes, the detailed information on the transverse density structuring needs to be incorporated into studies of sausage modes and higher-order kink modes.

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preprint2015arXiv

Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation

This paper presents a method for learning And-Or models to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). Firstly, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on car 3D CAD simulation. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Secondly, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets --- the KITTI dataset \cite{Geiger12}, the PASCAL VOC2007 car dataset~\cite{pascal}, and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation --- the PASCAL VOC2006 car dataset~\cite{pascal}, the 3D car dataset~\cite{savarese}, and the PASCAL3D+ car dataset~\cite{xiang_wacv14}. Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.

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preprint2015arXiv

Low Degree Nullstellensatz Certificates for 3-Colorability

In a seminal paper, De Loera et. al introduce the algorithm NulLA (Nullstellensatz Linear Algebra) and use it to measure the difficulty of determining if a graph is not 3-colorable. The crux of this relies on a correspondence between 3-colorings of a graph and solutions to a certain system of polynomial equations over a field $\mathbb{k}$. In this article, we give a new direct combinatorial characterization of graphs that can be determined to be non-3-colorable in the first iteration of this algorithm when $\mathbb{k}=GF(2)$. This greatly simplifies the work of De Loera et. al, as we express the combinatorial characterization directly in terms of the graphs themselves without introducing superfluous directed graphs. Furthermore, for all graphs on at most $12$ vertices, we determine at which iteration NulLA detects a graph is not 3-colorable when $\mathbb{k}=GF(2)$.

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preprint2015arXiv

Magnetic flux supplement to coronal bright points

Coronal bright points (BPs) are associated with magnetic bipolar features (MBFs) and magnetic cancellation. Here, we investigate how BP-associated MBFs form and how the consequent magnetic cancellation occurs. We analyse longitudinal magnetograms from the Helioseismic and Magnetic Imager to investigate the photospheric magnetic flux evolution of 70 BPs. From images taken in the 193 A passband of the Atmospheric Imaging Assembly (AIA) we dermine that the BPs' lifetimes vary from 2.7 to 58.8 hours. The formation of the BP MBFs is found to involve three processes, namely emergence, convergence and local coalescence of the magnetic fluxes. The formation of a MBF can involve more than one of these processes. Out of the 70 cases, flux emergence is the main process of a MBF buildup of 52 BPs, mainly convergence is seen in 28, and 14 cases are associated with local coalescence. For MBFs formed by bipolar emergence, the time difference between the flux emergence and the BP appearance in the AIA 193 Å passband varies from 0.1 to 3.2 hours with an average of 1.3 hours. While magnetic cancellation is found in all 70 BPs, it can occur in three different ways: (I) between a MBF and small weak magnetic features (in 33 BPs); (II) within a MBF with the two polarities moving towards each other from a large distance (34 BPs); (III) within a MBF whose two main polarities emerge in the same place simultaneously (3 BPs). While a MBF builds up the skeleton of a BP, we find that the magnetic activities responsible for the BP heating may involve small weak fields.

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preprint2015arXiv

Optimum Design for Coexistence Between Matrix Completion Based MIMO Radars and a MIMO Communication System

Recently proposed multiple input multiple output radars based on matrix completion (MIMO-MC) employ sparse sampling to reduce the amount of data that need to be forwarded to the radar fusion center, and as such enable savings in communication power and bandwidth. This paper proposes designs that optimize the sharing of spectrum between a MIMO-MC radar and a communication system, so that the latter interferes minimally with the former. First, the communication system transmit covariance matrix is designed to minimize the effective interference power (EIP) to the radar receiver, while maintaining certain average capacity and transmit power for the communication system. Two approaches are proposed, namely a noncooperative and a cooperative approach, with the latter being applicable when the radar sampling scheme is known at the communication system. Second, a joint design of the communication transmit covariance matrix and the MIMO-MC radar sampling scheme is proposed, which achieves even further EIP reduction.

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preprint2015arXiv

Periods and damping rates of fast sausage oscillations in multi-shelled coronal loops

Standing sausage modes are important in interpreting quasi-periodic pulsations in the lightcurves of solar flares. Their periods and damping times play an important role in seismologically diagnosing key parameters like the magnetic field strength in regions where flare energy is released. Usually such applications are based on theoretical results neglecting unresolved fine structures in magnetized loops. However, the existence of fine structuring is suggested on both theoretical and observational grounds. Adopting the framework of cold magnetohydrodynamics (MHD), we model coronal loops as magnetized cylinders with a transverse equilibrium density profile comprising a monolithic part and a modulation due to fine structuring in the form of concentric shells. The equation governing the transverse velocity perturbation is solved with an initial-value-problem approach, and the effects of fine structuring on the periods $P$ and damping times $τ$ of global, leaky, standing sausage modes are examined. A parameter study shows that fine structuring, be it periodically or randomly distributed, brings changes of only a few percent to $P$ and $τ$ when there are more than about ten shells. The monolithic part, its steepness in particular, plays a far more important role in determining $P$ and $τ$. We conclude that when measured values of $P$ and $τ$ of sausage modes are used for seismological purposes, it is justified to use theoretical results where the effects due to fine structuring are neglected.

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preprint2015arXiv

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-based Model

The explosion of cloud services on the Internet brings new challenges in service discovery and selection. Particularly, the demand for efficient quality-of-service (QoS) evaluation is becoming urgently strong. To address this issue, this paper proposes neighborhood-based approach for QoS prediction of cloud services by taking advantages of collaborative intelligence. Different from heuristic collaborative filtering and matrix factorization, we define a formal neighborhood-based prediction framework which allows an efficient global optimization scheme, and then exploit different baseline estimate component to improve predictive performance. To validate the proposed methods, a large-scale QoS-specific dataset which consists of invocation records from 339 service users on 5,825 web services on a world-scale distributed network is used. Experimental results demonstrate that the learned neighborhood-based models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than state-of-the-art prediction methods.

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preprint2015arXiv

Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models

We produce new reconstructions of Northern Hemisphere annually averaged temperature anomalies back to 1000 AD, and explore the effects of including external climate forcings within the reconstruction and of accounting for short-memory and long-memory features. Our reconstructions are based on two linear models, with the first linking the latent temperature series to three main external forcings (solar irradiance, greenhouse gas concentration and volcanism), and the second linking the observed temperature proxy data (tree rings, sediment record, ice cores, etc.) to the unobserved temperature series. Uncertainty is captured with additive noise, and a rigorous statistical investigation of the correlation structure in the regression errors is conducted through systematic comparisons between reconstructions that assume no memory, short-memory autoregressive models, and long-memory fractional Gaussian noise models. We use Bayesian estimation to fit the model parameters and to perform separate reconstructions of land-only and combined land-and-marine temperature anomalies. For model formulations that include forcings, both exploratory and Bayesian data analysis provide evidence against models with no memory. Model assessments indicate that models with no memory underestimate uncertainty. However, no single line of evidence is sufficient to favor short-memory models over long-memory ones, or to favor the opposite choice. When forcings are not included, the long-memory models appear to be necessary. While including external climate forcings substantially improves the reconstruction, accurate reconstructions that exclude these forcings are vital for testing the fidelity of climate models used for future projections.

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preprint2015arXiv

Region-Based Rate-Control for H.264/AVC for Low Bit-Rate Applications

Rate-control plays an important role in video coding. However, in the conventional rate-control algorithms, the number and position of Macroblocks (MBs) inside one basic unit for rate-control is inflexible and predetermined. The different characteristics of the MBs are not fully considered. Also, there is no overall optimization of the coding of basic units. This paper proposes a new region-based rate-control scheme for H.264/AVC to improve the coding efficiency. The inter-frame information is explored to objectively divide one frame into multiple regions based on their rate-distortion behaviors. The MBs with the similar characteristics are classified into the same region, and the entire region instead of a single MB or a group of contiguous MBs is treated as a basic unit for rate-control. A linear rate-quantization stepsize model and a linear distortion-quantization stepsize model are proposed to accurately describe the rate-distortion characteristics for the region-based basic units. Moreover, based on the above linear models, an overall optimization model is proposed to obtain suitable Quantization Parameters (QPs) for the region-based basic units. Experimental results demonstrate that the proposed region-based rate-control approach can achieve both better subjective and objective quality by performing the rate-control adaptively with the content, compared to the conventional rate-control approaches.

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preprint2015arXiv

Sources of quasi-periodic propagating disturbances above a solar polar coronal hole

Quasi-periodic propagating disturbances (PDs) are ubiquitous in polar coronal holes on the Sun. It remains unclear as to what generates PDs. In this work, we investigate how the PDs are generated in the solar atmosphere by analyzing a fourhour dataset taken by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). We find convincing evidence that spicular activities in the solar transition region as seen in the AIA 304 Å passband are responsible for PDs in the corona as revealed in the AIA 171 Å images. We conclude that spicules are an important source that triggers coronal PDs.

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preprint2015arXiv

Spatial damping of propagating sausage waves in coronal cylinders

Sausage modes are important in coronal seismology. Spatially damped propagating sausage waves were recently observed in the solar atmosphere. We examine how wave leakage influences the spatial damping of sausage waves propagating along coronal structures modeled by a cylindrical density enhancement embedded in a uniform magnetic field. Working in the framework of cold magnetohydrodynamics, we solve the dispersion relation (DR) governing sausage waves for complex-valued longitudinal wavenumber $k$ at given real angular frequencies $ω$. For validation purposes, we also provide analytical approximations to the DR in the low-frequency limit and in the vicinity of $ω_{\rm c}$, the critical angular frequency separating trapped from leaky waves. In contrast to the standing case, propagating sausage waves are allowed for $ω$ much lower than $ω_{\rm c}$. However, while able to direct their energy upwards, these low-frequency waves are subject to substantial spatial attenuation. The spatial damping length shows little dependence on the density contrast between the cylinder and its surroundings, and depends only weakly on frequency. This spatial damping length is of the order of the cylinder radius for $ω\lesssim 1.5 v_{\rm Ai}/a$, where $a$ and $v_{\rm Ai}$ are the cylinder radius and the Alfvén speed in the cylinder, respectively. We conclude that if a coronal cylinder is perturbed by symmetric boundary drivers (e.g., granular motions) with a broadband spectrum, wave leakage efficiently filters out the low-frequency components.

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preprint2015arXiv

Standing Sausage Modes In Nonuniform Magnetic Tubes: An Inversion Scheme For Inferring Flare Loop Parameters

Standing sausage modes in flare loops are important for interpreting quasi-periodic pulsations (QPPs) in solar flare lightcurves. We propose an inversion scheme that consistently uses their periods $P$ and damping times $τ$ to diagnose flare loop parameters. We derive a generic dispersion relation governing linear sausage waves in pressure-less straight tubes, for which the transverse density inhomogeneity takes place in a layer of arbitrary width $l$ and is of arbitrary form. We find that $P$ and $τ$ depend on the combination of $[R/v_{\rm Ai}, L/R, l/R, ρ_{\rm i}/ρ_{\rm e}]$, where $R$ is the loop radius, $L$ is the looplength, $v_{\rm Ai}$ is the internal Alfvén speed, and $ρ_{\rm i}/ρ_{\rm e}$ is the density contrast. For all the density profiles examined, $P$ and $τ$ experience saturation when $L/R \gg 1$, yielding an inversion curve in the $[R/v_{\rm Ai}, l/R, ρ_{\rm i}/ρ_{\rm e}]$ space with a specific density profile when $L/R$ is sufficiently large. When applied to a spatially unresolved QPP event, the scheme yields that $R/v_{\rm Ai}$ is the best constrained, whereas $l/R$ corresponds to the other extreme. For spatially resolved QPPs, while $L/R \gg 1$ cannot be assumed beforehand, an inversion curve remains possible due to additional geometrical constraints. When a spatially resolved QPP event involves another mode, as is the case for a recent event, the full set of $[v_{\rm Ai}, l, ρ_{\rm i}/ρ_{\rm e}]$ can be inferred. We conclude that the proposed scheme provides a useful tool for magneto-seismologically exploiting QPPs.

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preprint2015arXiv

The Least-core and Nucleolus of Path Cooperative Games

Cooperative games provide an appropriate framework for fair and stable profit distribution in multiagent systems. In this paper, we study the algorithmic issues on path cooperative games that arise from the situations where some commodity flows through a network. In these games, a coalition of edges or vertices is successful if it enables a path from the source to the sink in the network, and lose otherwise. Based on dual theory of linear programming and the relationship with flow games, we provide the characterizations on the CS-core, least-core and nucleolus of path cooperative games. Furthermore, we show that the least-core and nucleolus are polynomially solvable for path cooperative games defined on both directed and undirected network.

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preprint2015arXiv

Waterproofed Photomultiplier Tube Assemblies for the Daya Bay Reactor Neutrino Experiment

In the Daya Bay Reactor Neutrino Experiment 960 20-cm-diameter waterproof photomultiplier tubes are used to instrument three water pools as Cherenkov detectors for detecting cosmic-ray muons. Of these 960 photomultiplier tubes, 341 are recycled from the MACRO experiment. A systematic program was undertaken to refurbish them as waterproof assemblies. In the context of passing the water leakage check, a success rate better than 97% was achieved. Details of the design, fabrication, testing, operation, and performance of these waterproofed photomultiplier-tube assemblies are presented.

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preprint2014arXiv

Computing the Least-core and Nucleolus for Threshold Cardinality Matching Games

Cooperative games provide a framework for fair and stable profit allocation in multi-agent systems. \emph{Core}, \emph{least-core} and \emph{nucleolus} are such solution concepts that characterize stability of cooperation. In this paper, we study the algorithmic issues on the least-core and nucleolus of threshold cardinality matching games (TCMG). A TCMG is defined on a graph $G=(V,E)$ and a threshold $T$, in which the player set is $V$ and the profit of a coalition $S\subseteq V$ is 1 if the size of a maximum matching in $G[S]$ meets or exceeds $T$, and 0 otherwise. We first show that for a TCMG, the problems of computing least-core value, finding and verifying least-core payoff are all polynomial time solvable. We also provide a general characterization of the least core for a large class of TCMG. Next, based on Gallai-Edmonds Decomposition in matching theory, we give a concise formulation of the nucleolus for a typical case of TCMG which the threshold $T$ equals $1$. When the threshold $T$ is relevant to the input size, we prove that the nucleolus can be obtained in polynomial time in bipartite graphs and graphs with a perfect matching.

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preprint2014arXiv

Coverage versus Supply Cost in Facility Location: Physics of Frustrated Spin Systems

A comprehensive coverage is crucial for communication, supply and transportation networks, yet it is limited by the requirement of extensive infrastructure and heavy energy consumption. Here we draw an analogy between spins in antiferromagnet and outlets in supply networks, and apply techniques from the studies of disordered systems to elucidate the effects of balancing the coverage and supply costs on the network behavior. A readily applicable, coverage optimization algorithm is derived. Simulation results show that magnetized and antiferromagnetic domains emerge and coexist to balance the need for coverage and energy saving. The scaling of parameters with system size agrees with the continuum approximation in two dimensions and the tree approximation in random graphs. Due to frustration caused by the competition between coverage and supply cost, a transition between easy and hard computation regimes is observed. We further suggest a local expansion approach to greatly simplify the message updates which shed light on simplifications in other problems.

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preprint2014arXiv

Cross-Layer Software-Defined 5G Network

In the past few decades, the world has witnessed a rapid growth in mobile communication and reaped great benefits from it. Even though the fourth generation (4G) mobile communication system is just being deployed worldwide, proliferating mobile demands call for newer wireless communication technologies with even better performance. Consequently, the fifth generation (5G) system is already emerging in the research field. However, simply evolving the current mobile networks can hardly meet such great expectations, because over the years the infrastructures have generally become ossified, closed, and vertically constructed. Aiming to establish a new paradigm for 5G mobile networks, in this article, we propose a cross-layer software-defined 5G network architecture. By jointly considering both the network layer and the physical layer together, we establish the two software-defined programmable components, the control plane and the cloud computing pool, which enable an effective control of the mobile network from the global perspective and benefit technological innovations. Specifically, by the cross-layer design for software-defining, the logically centralized and programmable control plane abstracts the control functions from the network layer down to the physical layer, through which we achieve the fine-grained controlling of mobile network, while the cloud computing pool provides powerful computing capability to implement the baseband data processing of multiple heterogeneous networks. We discuss the main challenges of our architecture, including the fine-grained control strategies, network virtualization, and programmability. The architecture significantly benefits the convergence towards heterogeneous networks and it enables much more controllable, programmable and evolvable mobile networks.

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preprint2014arXiv

Floquet Quantum Spin Hall Insulator in Cold Atomic Systems

For cold atomic systems, varying the optical lattice potential periodically provides a general and simple way to drive the system into phases with nontrivial topology. Besides its simplicity, this driving approach, compared to the usual driving approach by exerting an external electromagnetic field to the static system, has the merit that it does not break the original static system's time-reversal symmetry at any given time. Based on this approach, we find that a trivial insulator with time-reversal symmetry can be driven into a Floquet quantum spin Hall insulator. This novel state of matter can stably host one or two pair of gapless helical states on the same boundary, which suggests this state is not a simple analog of the quantum spin Hall insulator. The effect of a time-reversal-symmetry-breaking periodic perturbation, the stability of the novel states, and this new driving approach to a system without time-reversal symmetry are discussed.

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preprint2014arXiv

Measurements of Outflow Velocities in On-Disk Plumes from EIS Hinode Observations

The contribution of plumes to the solar wind has been subject to hot debate in the past decades. The EUV Imaging Spectrometer (EIS) on board Hinode provides a unique means to deduce outflow velocities at coronal heights via direct Doppler shift measurements of coronal emission lines. Such direct Doppler shift measurements were not possible with previous spectrometers. We measure the outflow velocity at coronal heights in several on-disk long-duration plumes, which are located in coronal holes and show significant blue shifts throughout the entire observational period. In one case, a plume is measured 4 hours apart. The deduced outflow velocities are consistent, suggesting that the flows are quasi-steady. Furthermore, we provide an outflow velocity profile along the plumes, finding that the velocity corrected for the line-of-sight effect can reach 10 km s$^{-1}$ at 1.02 $R_{\odot}$, 15 km s$^{-1}$ at 1.03 $R_{\odot}$, and 25 km s$^{-1}$ at 1.05 $R_{\odot}$. This clear signature of steady acceleration, combined with the fact that there is no significant blue shift at the base of plumes, provides an important constraint on plume models. At the height of 1.03 $R_{\odot}$, EIS also deduced a density of 1.3$\times10^{8}$ cm$^{-3}$, resulting in a proton flux of about 4.2$\times10^9$ cm$^{-2}$s$^{-1}$ scaled to 1AU, which is an order of magnitude higher than the proton input to a typical solar wind if a radial expansion is assumed. This suggests that, coronal hole plumes may be an important source of the solar wind.

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preprint2014arXiv

Non-zero total correlation means non-zero quantum correlation

We investigated the super quantum discord based on weak measurements. The super quantum discord is an extension of the standard quantum discord defined by projective measurements and also describes the quantumness of correlations. We provide some equivalent conditions for zero super quantum discord by using quantum discord, classical correlation and mutual information. In particular, we find that the super quantum discord is zero only for product states, which have zero mutual information. This result suggests that non-zero correlations can always be detected using the quantum correlation with weak measurements. As an example, we present the assisted state-discrimination method.

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preprint2014arXiv

Oscillations in a sunspot with light bridges

Solar Optical Telescope onboard Hinode observed a sunspot (AR 11836) with two light bridges (LBs) on 31 Aug 2013. We analysed a 2-hour \ion{Ca}{2} H emission intensity data set and detected strong 5-min oscillation power on both LBs and in the inner penumbra. The time-distance plot reveals that 5-min oscillation phase does not vary significantly along the thin bridge, indicating that the oscillations are likely to originate from the underneath. The slit taken along the central axis of the wide light bridge exhibits a standing wave feature. However, at the centre of the wide bridge, the 5-min oscillation power is found to be stronger than at its sides. Moreover, the time-distance plot across the wide bridge exhibits a herringbone pattern that indicates a counter-stream of two running waves originated at the bridge sides. Thus, the 5-min oscillations on the wide bridge also resemble the properties of running penumbral waves. The 5-min oscillations are suppressed in the umbra, while the 3-min oscillations occupy all three cores of the sunspot's umbra, separated by the LBs. The 3-min oscillations were found to be in phase at both sides of the LBs. It may indicate that either LBs do not affect umbral oscillations, or umbral oscillations at different umbral cores share the same source. Also, it indicates that LBs are rather shallow objects situated in the upper part of the umbra. We found that umbral flashes follow the life cycles of umbral oscillations with much larger amplitudes. They cannot propagate across LBs. Umbral flashes dominate the 3-min oscillation power within each core, however, they do not disrupt the phase of umbral oscillation.

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preprint2014arXiv

Parallel-cascade-based mechanisms for heating solar coronal loops: test against observations

The heating of solar coronal loops is at the center of the problem of coronal heating. Given that the origin of the fast solar wind has been tracked down to atmospheric layers with transition region or even chromospheric temperatures, it is worthy attempting to address whether the mechanisms proposed to provide the basal heating of the solar wind apply to coronal loops as well. We extend the loop studies based on a classical parallel-cascade scenario originally proposed in the solar wind context by considering the effects of loop expansion, and perform a parametric study to directly contrast the computed loop densities and electron temperatures with those measured by TRACE and YOHKOH/SXT. This comparison yields that with the wave amplitudes observationally constrained by SUMER measurements, while the computed loops may account for a significant fraction of SXT loops, they seem too hot when compared with TRACE loops. Lowering the wave amplitudes does not solve this discrepancy, introducing magnetic twist will make the comparison even less desirable. We conclude that the nanoflare heating scenario better explains ultraviolet loops, while turbulence-based steady heating mechanisms may be at work in heating a fraction of soft X-ray loops.

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preprint2014arXiv

Quantum advantage by weak measurements

Weak measurements may result in extra quantity of quantumness of correlations compared with standard projective measurement on a bipartite quantum state. We show that the quantumness of correlations by weak measurements can be consumed for information encoding which is only accessible by coherent quantum interactions. Then it can be considered as a resource for quantum information processing and can quantify this quantum advantage. We conclude that weak measurements can create more valuable quantum correlation.

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preprint2014arXiv

Quantum correlation induced by the average distance between the reduced states

A new quantum correlation in terms of the average distance between the reduced state and the $i$-th output reduced states under local von Neumann measurements is proposed. It is shown that only the product states do not contain this quantum correlation and thus it is different from both the quantum discord (QD) [Phys. Rev. Lett. \textbf{88}, 017901 (2001)] and the measurement-induced nonlocality (MIN) [Phys. Rev. Lett. \textbf{106}, 120401(2011)]. For pure states, it is twice of the quantity MIN, and is smaller than QD and entanglement of formation (EOF). A general analytical formula is given and a lower bound for the two qubits case is obtained. Furthermore, we compare it with EOF and QD through the Werner state and the isotropic state respectively.

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preprint2014arXiv

Standing sausage modes in coronal loops with plasma flow

Magnetohydrodynamic waves are important for diagnosing the physical parameters of coronal plasmas. Field-aligned flows appear frequently in coronal loops.We examine the effects of transverse density and plasma flow structuring on standing sausage modes trapped in coronal loops, and examine their observational implications. We model coronal loops as straight cold cylinders with plasma flow embedded in a static corona. An eigen-value problem governing propagating sausage waves is formulated, its solutions used to construct standing modes. Two transverse profiles are distinguished, one being the generalized Epstein distribution (profile E) and the other (N) proposed recently in Nakariakov et al.(2012). A parameter study is performed on the dependence of the maximum period $P_\mathrm{max}$ and cutoff length-to-radius ratio $(L/a)_{\mathrm{cutoff}}$ in the trapped regime on the density parameters ($ρ_0/ρ_\infty$ and profile steepness $p$) and flow parameters (magnitude $U_0$ and profile steepness $u$). For either profile, introducing a flow reduces $P_\mathrm{max}$ relative to the static case. $P_\mathrm{max}$ depends sensitively on $p$ for profile N but is insensitive to $p$ for profile E. By far the most important effect a flow introduces is to reduce the capability for loops to trap standing sausage modes: $(L/a)_{\mathrm{cutoff}}$ may be substantially reduced in the case with flow relative to the static one. If the density distribution can be described by profile N, then measuring the sausage mode period can help deduce the density profile steepness. However, this practice is not feasible if profile E better describes the density distribution. Furthermore, even field-aligned flows with magnitudes substantially smaller than the ambient Alfvén speed can make coronal loops considerably less likely to support trapped standing sausage modes.

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preprint2014arXiv

Temperature evolution of magnetic flux rope in a failed solar eruption

In this presentation, we report for the first time the detailed temperature evolution process of the magnetic flux rope in a failed solar eruption. Occurred on January 05, 2013, the flux rope was impulsively accelerated to a speed of ~ 400 km/s in the first minute, then decelerated and came to a complete stop in two minutes. The failed eruption resulted in a large-size high-lying (~ 100 Mm above the surface) high-temperature "fire ball" sitting in the corona for more than two hours. The time evolution of the thermal structure of the flux rope was revealed through the differential emission measure analysis technique, which produced temperature maps using observations of the Atmospheric Imaging Assembly on board Solar Dynamic Observatory. The average temperature of the flux rope steadily increased from ~ 5 MK to ~ 10 MK during the first nine minutes of the evolution, which was much longer than the rise time (about three minutes) of the associated soft X-ray flare. We suggest that the flux rope be heated by the energy release of the continuing magnetic reconnection, different from the heating of the low-lying flare loops, which is mainly produced by the chromospheric plasma evaporation. The loop arcade overlying the flux rope was pushed up by ~ 10 Mm during the attempted eruption. The pattern of the velocity variation of the loop arcade strongly suggests that the failure of the eruption be caused by the strapping effect of the overlying loop arcade.

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preprint2013arXiv

3D model retrieval using global and local radial distances

3D model retrieval techniques can be classified as histogram-based, view-based and graph-based approaches. We propose a hybrid shape descriptor which combines the global and local radial distance features by utilizing the histogram-based and view-based approaches respectively. We define an area-weighted global radial distance with respect to the center of the bounding sphere of the model and encode its distribution into a 2D histogram as the global radial distance shape descriptor. We then uniformly divide the bounding cube of a 3D model into a set of small cubes and define their centers as local centers. Then, we compute the local radial distance of a point based on the nearest local center. By sparsely sampling a set of views and encoding the local radial distance feature on the rendered views by color coding, we extract the local radial distance shape descriptor. Based on these two shape descriptors, we develop a hybrid radial distance shape descriptor for 3D model retrieval. Experiment results show that our hybrid shape descriptor outperforms several typical histogram-based and view-based approaches.

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preprint2013arXiv

4-Dimensional Geometry Lens: A Novel Volumetric Magnification Approach

We present a novel methodology that utilizes 4-Dimensional (4D) space deformation to simulate a magnification lens on versatile volume datasets and textured solid models. Compared with other magnification methods (e.g., geometric optics, mesh editing), 4D differential geometry theory and its practices are much more flexible and powerful for preserving shape features (i.e., minimizing angle distortion), and easier to adapt to versatile solid models. The primary advantage of 4D space lies at the following fact: we can now easily magnify the volume of regions of interest (ROIs) from the additional dimension, while keeping the rest region unchanged. To achieve this primary goal, we first embed a 3D volumetric input into 4D space and magnify ROIs in the 4th dimension. Then we flatten the 4D shape back into 3D space to accommodate other typical applications in the real 3D world. In order to enforce distortion minimization, in both steps we devise the high dimensional geometry techniques based on rigorous 4D geometry theory for 3D/4D mapping back and forth to amend the distortion. Our system can preserve not only focus region, but also context region and global shape. We demonstrate the effectiveness, robustness, and efficacy of our framework with a variety of models ranging from tetrahedral meshes to volume datasets.

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preprint2013arXiv

A New 3D Geometric Approach to Focus and Context Lens Effect Simulation

We present a novel methodology based on geometric approach to simulate magnification lens effects. Our aim is to promote new applications of powerful geometric modeling techniques in visual computing. Conventional image processing/visualization methods are computed in two dimensional space (2D). We examine this conventional 2D manipulation from a completely innovative perspective of 3D geometric processing. Compared with conventional optical lens design, 3D geometric method are much more capable of preserving shape features and minimizing distortion. We magnify an area of interest to better visualize the interior details, while keeping the rest of area without perceivable distortion. We flatten the mesh back into 2D space for viewing, and further applications in the screen space. In both steps, we devise an iterative deformation scheme to minimize distortion around both focus and context region, while avoiding the noncontinuous transition region between the focus and context areas. Particularly, our method allows the user to flexibly modify the ROI shapes to accommodate complex feature. The user can also easily specify a spectrum of metrics for different visual effects. Various experimental results demonstrate the effectiveness, robustness, and efficiency of our framework.

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preprint2013arXiv

A Spline-based Volumetric Data Modeling Framework and Its Applications

In this dissertation, we concentrate on the challenging research issue of developing a spline-based modeling framework, which converts the conventional data (e.g., surface meshes) to tensor-product trivariate splines. This methodology can represent both boundary/volumetric geometry and real volumetric physical attributes in a compact and continuous fashion. The regular tensor-product structure enables our new developed methods to be embedded into the industry standard seamlessly. These properties make our techniques highly preferable in many physically-based applications including mechanical analysis, shape deformation and editing, virtual surgery training, etc.

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preprint2013arXiv

A Survey of Spline-based Volumetric Data Modeling Framework and Its Applications

The rapid advances in 3D scanning and acquisition techniques have given rise to the explosive increase of volumetric digital models in recent years. This dissertation systematically trailblazes a novel volumetric modeling framework to represent 3D solids. The need to explore more efficient and robust 3D modeling framework has gained the prominence. Although the traditional surface representation (e.g., triangle mesh) has many attractive properties, it is incapable of expressing the interior space and materials. Such a serious drawback overshadows many potential modeling and analysis applications. Consequently volumetric modeling techniques become the well-known solution to this problem. Nevertheless, many unsolved research issues remain when developing an efficient modeling paradigm for existing 3D models: complex geometry (fine details and extreme concaveness), arbitrary topology, heterogenous materials, large-scale data storage and processing, etc.

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preprint2013arXiv

Kalman-filter-based track fitting in non-uniform magnetic field with segment-wise helical track model

In the future International Linear Collider (ILC) experiment, high performance tracking is essential to its physics program including precision Higgs studies. One of major challenges for a detector such as the proposed International Large Detector (ILD) is to provide excellent momentum resolution in a magnetic filed with small (but non-negligible) non-uniformity. The non-uniform magnetic field implies deviation from a helical track and hence requires the extension of a helical track model used for track fitting in a uniform magnetic field. In this paper, a segment-wise helical track model is introduced as such an extension. The segment-wise helical track model approximates the magnetic field between two nearby measurement sites to be uniform and steps between the two sites along a helix. The helix frame is then transformed according to the new magnetic field direction for the next step, so as to take into account the non-uniformity of the magnetic field. Details of the algorithm and mathematical aspects of the segment-wise helical track model in a Kalman-filter-based track fitting in the non-uniform magnetic field are elaborated. The new track model is implemented and successfully tested in the framework of the Kalman filter tracking software package, KalTest, which was originally developed for tracking in a uniform magnetic field.

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preprint2013arXiv

Kinetic plasma turbulence in the fast solar wind measured by Cluster

The k-filtering technique and wave polarization analysis are applied to Cluster magnetic field data to study plasma turbulence at the scale of the ion gyroradius in the fast solar wind. Waves are found propagating in directions nearly perpendicular to the background magnetic field at such scales. The frequencies of these waves in the solar wind frame are much smaller than the proton gyro-frequency. After the wave vector ${\bf k}$ is determined at each spacecraft frequency $f_{sc}$, wave polarization property is analyzed in the plane perpendicular to ${\bf k}$. Magnetic fluctuations have $δB_\perp>δB_\parallel$ (here the $\parallel$ and $\perp$ refer to the background magnetic field ${\bf B}_0$). The wave magnetic field has right-handed polarization at propagation angles $θ_{\bf kB}<90^\circ$ and $>90^\circ$. The magnetic field in the plane perpendicular to ${\bf B}_0$ however has no clear sense of a dominant polarization but local rotations. We discuss the merits and limitations of linear kinetic Aflvén waves (KAWs) and coherent Alfvén vortices in the interpretation of the data. We suggest that the fast solar wind turbulence may be populated with KAWs, small scale current sheets and Alfvén vortices at ion kinetic scales.

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preprint2013arXiv

Magnetic domain walls induced by twin boundaries in low doped Fe-pnictides

Inspired by experimental observations of the enhancement of superconductivity at the twin-boundary (TB) in slightly electron doped Ba(Ca)(FeAs)$_2$ where a strong $2\times1$ antiferromagnetic (AF) collinear order is in presence, we investigate theoretically the effects of TBs on the complex interplay between magnetism and superconductivity using a minimum phenomenological two-orbital model. The magnetic order can be simulated from an onsite Coulomb interaction $U$ and the Hund's coupling $J_H$, while the effective pairing interaction yields the superconductivity with $s\pm$ wave symmetry. The spatial distributions of the magnetic, superconducting and charge density orders near two different types of TBs are calculated. Each of the TBs has two different orientations. We find that the first type TBs, which corresponds to a $90^\circ$ lattice rotation in the $a-b$ plane, enable magnetic domain walls (DWs) to be pinned at them, and that superconductivity is enhanced at such TBs or DWs. This result is consistent with experiments for a TB with an orientation of $45^\circ$ from the x-axis. Contrastingly, we predict that superconductivity is suppressed at the second type of TBs which correspond to an asymmetrical placement of As atoms on the opposite sides of the TB. Furthermore, the lattice-mismatch effect across the TBs is investigated. The comparison of our results with the observations from the nuclear-magnetic-resonance (NMR) experiments are also discussed.

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preprint2013arXiv

Monogamy deficit for quantum correlation in multipartite quantum system

We introduce the concept of monogamy deficit for quantum correlation by combining together two types of monogamy inequalities depending on different measurement sides. For tripartite pure state, we demonstrate a relation which connects two types of monogamy inequalities for quantum discord and provide the difference between them. By using this relation, we obtain an unified physical interpretation for these two monogamy deficit. In addition, we find an interesting fact that there is a general monogamy condition for several quantum correlations for tripartite pure states. We then provide a necessary and sufficient condition for the establishment of one kind of monogamy inequality for tripartite mixed state and generalize it to multipartite quantum state.

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preprint2013arXiv

Pattern Recognition and Revealing using Parallel Coordinates Plot

Parallel coordinates plot (PCP) is an excellent tool for multivariate visualization and analysis, but it may fail to reveal inherent structures for datasets with a large number of items. In this paper, we propose a suite of novel clustering, dimension ordering and visualization techniques based on PCP, to reveal and highlight hidden structures. First, we propose a continuous spline based polycurves design to extract and classify different cluster aspects of the data. Then, we provide an efficient and optimal correlation based sorting technique to reorder coordinates, as a helpful visualization tool for data analysis. Various results generated by our framework visually represent much structure, trend and correlation information to guide the user, and improve the efficacy of analysis, especially for complex and noisy datasets.

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preprint2013arXiv

Periodic migration in a physical model of cells on micropatterns

We extend a model for the morphology and dynamics of a crawling eukaryotic cell to describe cells on micropatterned substrates. This model couples cell morphology, adhesion, and cytoskeletal flow in response to active stresses induced by actin and myosin. We propose that protrusive stresses are only generated where the cell adheres, leading to the cell's effective confinement to the pattern. Consistent with experimental results, simulated cells exhibit a broad range of behaviors, including steady motion, turning, bipedal motion, and periodic migration, in which the cell crawls persistently in one direction before reversing periodically. We show that periodic motion emerges naturally from the coupling of cell polarization to cell shape by reducing the model to a simplified one-dimensional form that can be understood analytically.

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preprint2013arXiv

The electron-ion streaming instabilities driven by drift velocities of the order of electron thermal velocity in a nonmagnetized plasma

We examine the electron-ion streaming instabilities driven by drift velocities of the order of the electron thermal velocity in a nonmagnetized plasma by one-dimensional electrostatic particle-in-cell code which adopts an ion-to-electron mass ratio of 1600. An initial state is set up where the ion bulk speed is zero while the electrons drift relative to ions, and where electrons are much hotter. We examine in detail four runs where drift velocity is systematically varied from lower than to larger than the electron thermal velocity. In all runs the Langmuir waves with Doppler-shifted frequencies dominate early on when streaming instabilities are too weak to discern. And then intense ion-acoustic waves or Buneman instabilities appear, which tend to be accompanied by localized electron and ion beams. Ion-acoustic modes and Buneman modes co-exist in the system when the initial drift velocity is just over the electron thermal speed. Beam modes are excited when the localized beams with large enough velocities appear. In the developed stage of instabilities, the direction in which density depressions propagate is always opposite to that of the localized ion beams. When the initial drift velocity is close to the electron thermal speed, categorizing the relevant instabilities is not easy, and one needs to examine in detail the wave dispersion diagrams at various stages of the evolution of the system.

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preprint2013arXiv

The Heine-Stieltjes correspondence and a new angular momentum projection for many-particle systems

A new angular momentum projection for systems of particles with arbitrary spins is formulated based on the Heine-Stieltjes correspondence, which can be regarded as the solutions of the mean-field plus pairing model in the strong pairing interaction G ->Infinity limit. Properties of the Stieltjes zeros of the extended Heine-Stieltjes polynomials, of which the roots determine the projected states, and the related Van Vleck zeros are discussed. The electrostatic interpretation of these zeros is presented. As examples, applications to n nonidentical particles of spin-1/2 and to identical bosons or fermions are made to elucidate the procedure and properties of the Stieltjes zeros and the related Van Vleck zeros. It is shown that the new angular momentum projection for n identical bosons or fermions can be simplified with the branching multiplicity formula of U(N) supset O(3) and the special choices of the parameters used in the projection. Especially, it is shown that the solutions for identical bosons can always be expressed in terms of zeros of Jacobi polynomials. However, unlike non-identical particle systems, the n-coupled states of identical particles are non-orthogonal with respect to the multiplicity label after the projection.

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preprint2013arXiv

The One-Way Information Deficit and Geometry for a Class of Two-qubit States

The work deficit, as introduced by Jonathan Oppenheim \emph{et al}[Phys. Rev. Lett. \textbf{89}, 180402 (2002)] is a good measure of the quantum correlations in a state and provides a new standpoint for understanding quantum non-locality. In this paper, we analytically evaluate the one-way information deficit (OWID) for the Bell-diagonal states and a class of two-qubit states and further give the geometry picture for OWID. The dynamic behavior of the OWID under decoherence channel is investigated and it is shown that the OWID of some classes of $X$ states is more robust against the decoherence than the entanglement.

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preprint2013arXiv

The period ratio for standing kink and sausage modes in solar structures with siphon flow. I. magnetized slabs

In the applications of solar magneto-seismology(SMS), employing the ratio of the period of the fundamental mode to twice the one of its first overtone, $P_1/2P_2$, plays an important role. We examine how field-aligned flows affect the dispersion properties, and hence the period ratios, of standing modes supported by magnetic slabs in the solar atmosphere. We numerically solve the dispersion relations and devise a graphic means to construct standing modes. For coronal slabs, we find that the flow effects are significant, for the fast kink and sausage modes alike. For the kink ones, they may reduce $P_1/2P_2$ by up to 23% compared with the static case, and the minimum allowed $P_1/2P_2$ can fall below the lower limit analytically derived for static slabs. For the sausage modes, while introducing the flow reduces $P_1/2P_2$ by typically $\lesssim 5$ % relative to the static case, it significantly increases the threshold aspect ratio only above which standing sausage modes can be supported, meaning that their detectability is restricted to even wider slabs. In the case of photospheric slabs, the flow effect is not as strong. However, standing modes are distinct from the coronal case in that standing kink modes show a $P_1/2P_2$ that deviates from unity even for a zero-width slab, while standing sausage modes no longer suffer from a threshold aspect ratio. We conclude that transverse structuring in plasma density and flow speed should be considered in seismological applications of multiple periodicities to solar atmospheric structures.

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preprint2013arXiv

The witness of sudden change of geometric quantum correlation

In this paper, we give a sufficient and necessary condition (witness) for the sudden change of geometric quantum discord by considering mathematical definition of the discontinuity of a function. Based on the witness, we can find out various sudden changes of quan- tum correlation by considering both the Markovian and the non-Markovian cases. In particular, we can accurately find out critical points of the sudden changes even though they are not quite obvious in the graphical representation. In addition, one can also find that sudden change of quantum correlation, like the frozen quantum correlation, strongly depends on the choice of the quantum correlation measure.

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preprint2013arXiv

vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing

As an important application in the busy world today, mobile video conferencing facilitates virtual face-to-face communication with friends, families and colleagues, via their mobile devices on the move. However, how to provision high-quality, multi-party video conferencing experiences over mobile devices is still an open challenge. The fundamental reason behind is the lack of computation and communication capacities on the mobile devices, to scale to large conferencing sessions. In this paper, we present vSkyConf, a cloud-assisted mobile video conferencing system to fundamentally improve the quality and scale of multi-party mobile video conferencing. By novelly employing a surrogate virtual machine in the cloud for each mobile user, we allow fully scalable communication among the conference participants via their surrogates, rather than directly. The surrogates exchange conferencing streams among each other, transcode the streams to the most appropriate bit rates, and buffer the streams for the most efficient delivery to the mobile recipients. A fully decentralized, optimal algorithm is designed to decide the best paths of streams and the most suitable surrogates for video transcoding along the paths, such that the limited bandwidth is fully utilized to deliver streams of the highest possible quality to the mobile recipients. We also carefully tailor a buffering mechanism on each surrogate to cooperate with optimal stream distribution. We have implemented vSkyConf based on Amazon EC2 and verified the excellent performance of our design, as compared to the widely adopted unicast solutions.

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preprint2012arXiv

An anisotropic-Alfvenic-turbulence-based solar wind model with proton temperature anisotropy

How the solar wind is accelerated to its supersonic speed is intimately related to how it is heated. Mechanisms based on ion-cyclotron resonance have been successful in explaining a large number of observations, those concerning the significant ion temperature anisotropy above coronal holes in particular. However, they suffer from the inconsistency with turbulence theory which says that the turbulent cascade in a low-beta medium like the solar corona should proceed in the perpendicular rather than the parallel direction, meaning that there is little energy in the ion gyro-frequency range for ions to absorb via ion-cyclotron resonance. Recently a mechanism based on the interaction between the solar wind particles and the anisotropic turbulence has been proposed, where the perpendicular proton energy addition is via the stochastic heating (Chandran et al. 2011). We extend this promising mechanism by properly accounting for the effect of proton temperature anisotropy on the propagation of Alfven waves, for the radiative losses of electron energy, and for the field line curvature that naturally accompanies solar winds in the corona. While this mechanism was shown in previous studies to apply to the polar fast solar wind, we demonstrate here for the first time that it applies also to the slow wind flowing along field lines bordering streamer helmets.

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preprint2012arXiv

Assisted state discrimination without entanglement

It is shown that the dissonance, a quantum correlation which is equal to quantum discord for separable state, is required for assisted optimal state discrimination. We find that only one side discord is required in the optimal process of assisted state discrimination, while another side discord and entanglement is not necessary. We confirm that the quantum discord, which is asymmetric depending on local measurements, is a resource for assisted state discrimination. With the absence of entanglement, we give the necessary and sufficient condition for vanishing one side discord in assisted state discrimination for a class of $d$ nonorthogonal states. As a byproduct, we find that the positive-partial-transposition (PPT) condition is the necessary and sufficient condition for the separability of a class of $2\times d$ states.

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preprint2012arXiv

Detecting genuine multipartite correlations in terms of the rank of coefficient matrix

We propose a method to detect genuine quantum correlation for arbitrary quantum state in terms of the rank of coefficient matrices associated with the pure state. We then derive a necessary and sufficient condition for a quantum state to possess genuine correlation, namely that all corresponding coefficient matrices have rank larger than one. We demonstrate an approach to decompose the genuine quantum correlated state with high rank coefficient matrix into the form of product states with no genuine quantum correlation for pure state.

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preprint2012arXiv

Interpretation of the coronal magnetic field configuration of the Sun

The origin of the heliospheric magnetic flux on the Sun, and hence the origin of the solar wind, is a topic of hot debate.While the prevailing view is that the solar wind originates from outside coronal streamer helmets, there also exists the suggestion that the open magnetic field spans a far wider region.Without the definitive measurement of the coronal magnetic field, it is difficult to resolve the conflict between the two scenarios without doubt.We present two 2-dimensional, Alfvénic-turbulence-based models of the solar corona and solar wind, one with and the other without a closed magnetic field region in the inner corona.The purpose of the latter model is to test whether it is possible to realize a picture suggested by polarimetric measurements of the corona using the FeXIII 10747Å line, where open magnetic field lines seem to penetrate the streamer base.The boundary conditions at the coronal base are able to account for important observational constraints, especially those on the magnetic flux distribution.Interestingly, the two models provide similar polarized brightness (pB) distributions in the field of view (FOV) of SOHO/LASCO C2 and C3 coronagraphs.In particular, a dome-shaped feature is present in the C2 FOV even for the model without any closed magnetic field.Moreover, both models fit equally well the Ulysses data scaled to 1 AU.We suggest that: 1) The pB observations cannot be safely taken as a proxy for the magnetic field topology, as often implicitly assumed.2) The Ulysses measurements, especially the one showing a nearly uniform distribution with heliocentric latitude of the radial magnetic field, do not rule out the ubiquity of open magnetic fields on the Sun.

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preprint2012arXiv

Magnetic and superconducting structures near twin boundaries in low doped Fe-pnictides

The effects of twin boundaries (TBs) on the complex interaction between magnetism and superconductivity in slightly electron-doped Ba(Ca)(FeAs)$_2$ superconductors are investigated. The spatial distributions of the magnetic, superconducting and charge density orders near two different types of TBs are calculated. We find that TBs corresponding to a 90$^\circ$ lattice rotation in the a-b plane enable magnetic domain walls to form with only a small effective Coulomb interaction between valance electrons, and that superconductivity is enhanced at such TBs. Contrastingly, we find that superconductivity is suppressed at TBs corresponding to an asymmetrical placement of As atoms with respect to the Fe atoms in the a-b plane.

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preprint2012arXiv

Non-local correlations in the Haldane phase for an XXZ spin-1 chain: A perspective from infinite matrix product state representation

String correlations are investigated in an infinite-size XXZ spin-1 chain. By using the infinite matrix product state representation, we calculate a long-range string order. In the XY phase, the string correlations decay within a relatively very large lattice distance, which makes a finite-size study difficult to verify the non-existence of the string order. Thus, in the Haldane phase, the non-vanishing string correlations in the limit of a very large distance allow to characterize the phase boundaries to the XY phase and the Neel phase, which implies that the transverse long-range string order is the order parameter for the Haldane phase. In addition, the singular behaviors of the von Neumann entropy and the fidelity per lattice site are shown to capture clearly the phase transition points that are consistent with the results from the string order. The estimated critical points including a BKT transition from the XY phase to the Haldane phase agree well with the previous results: $Δ_{c2} = 0$ for the XY-Haldane phase transition and $Δ_{c3} = 1.185$ for the Haldane-N'eel phase transition from the density renormalization group. From a finite-entanglement scaling of the von Neumann entropy with respect to the truncation dimension, the central charges are found to be $c \simeq 1.0$ at $Δ_{c2} = 0$ and $c \simeq 0.5$ at $Δ_{c3} = 1.185$, respectively, which shows that the XY-Haldane phase transition at $Δ_{c2} = 0$ belongs to the Heisenberg universality class, while the Haldane-Neel phase transition at $Δ_{c2} = 1.185$ belongs to the two-dimensional classical Ising universality class. It is also shown that, the long-range order parameters and the von Neumann entropy, as well as the fidelity per site approach, can be applied to characterize quantum phase transitions as a universal phase transition indicator for one-dimensional lattice many-body systems.

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preprint2012arXiv

Resource Availability-Aware Advance Reservation for Parallel Jobs with Deadlines

Advance reservation is important to guarantee the quality of services of jobs by allowing exclusive access to resources over a defined time interval on resources. It is a challenge for the scheduler to organize available resources efficiently and to allocate them for parallel AR jobs with deadline constraint appropriately. This paper provides a slot-based data structure to organize available resources of multiprocessor systems in a way that enables efficient search and update operations, and formulates a suite of scheduling policies to allocate resources for dynamically arriving AR requests. The performance of the scheduling algorithms were investigated by simulations with different job sizes and durations, system loads and scheduling flexibilities. Simulation results show that job sizes and durations, system load and the flexibility of scheduling will impact the performance metrics of all the scheduling algorithms, and the PE-Worst-Fit algorithm becomes the best algorithm for the scheduler with the highest acceptance rate of AR requests, and the jobs with the First-Fit algorithm experience the lowest average slowdown. The data structure and scheduling policies can be used to organize and allocate resources for parallel AR jobs with deadline constraint in large-scale computing systems.

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preprint2012arXiv

Splitting of self-collimated beams in two-dimensional sonic crystals

An easy-to-implement scheme to split self-collimated acoustic beams in sonic crystals (SCs) is proposed by introducing line-defects into SCs, by which an incoming self-collimated beam can be split into a 90 degree-bended one and a transmitted one with an arbitrary power ratio by adjusting the value of defect size. An all-angle and wide-band splitting instrument is demonstrated with nearly perfect efficiency (more than 90%) for Gaussian beams at a wide range of incident angles. Splitting effect for a point source as input is also realized, in which two subwavelength images of the source, i.e., a bended one and a transmitted one, are formed by our designed splitting structure. Finally, the proposed one-to-two splitting scheme is generalized to one-to-N (N>2) cases by inserting more rows of line defects into the SC.

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preprint2012arXiv

Sudden Change of Quantum Discord under Single Qubit Noise

We show that the sudden change of quantum correlation can occur even when only one part of the composite entangled state is exposed to a noisy environment. Our results are illustrated through the action of different noisy environments individually on a single qubit of quantum system. Composite noise on the whole of the quantum system is thus not the necessarily condition for the occurrence of sudden transition for quantum correlation.

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preprint2012arXiv

Topological quantum phase transition in bond-alternating spin-1/2 Heisenberg chains

We investigate string correlations in an infinite-size spin-1/2 bond-alternating Heisenberg chain. By employing the infinite matrix product state representation with the infinite time evolving block decimation method, a finite string correlation for extremely large lattice distances is directly observed, contrast to an extrapolated extreme value for finite size chains. We find that a topological quantum phase transition occurs between two different phases separated and characterized by two different long-range string orders in the space of bond-alternating interactions. Also, the critical exponent $β$ from the long-range string orders is obtained as $β=1/12$ and the central charge at the critical point is obtained as $c \simeq 1$, which shows that the topological phase transition belongs to the Gussian universality class. In addition, it is shown that, for the topological quantum phase transition, the phase boundary can be captured by the singular behavior of the von Neumann entropy and the pinch point of the fidelity per site.

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preprint2011arXiv

A Statistical Study on the Morphology of Rays and Dynamics of Blobs in the Wake of Coronal Mass Ejections

In this paper, with a survey through the Large Angle and Spectrometric Coronagraph (LASCO) data from 1996 to 2009, we present 11 events with plasma blobs flowing outwards sequentially along a bright coronal ray in the wake of a coronal mass ejection. The ray is believed to be associated with the current sheet structure that formed as a result of solar eruption, and the blobs are products of magnetic reconnection occurring along the current sheet. The ray morphology and blob dynamics are investigated statistically. It is found that the apparent angular widths of the rays at a fixed time vary in a range of 2.1-6.6 (2.0-4.4) degrees with an average of 3.5 (2.9) degrees at 3 (4) Rs, respectively, and the observed durations of the events vary from 12 h to a few days with an average of 27 h. It is also found, based on the analysis of blob motions, that 58% (26) of the blobs were accelerated, 20% (9) were decelerated, and 22% (10) moved with a nearly-constant speed. Comparing the dynamics of our blobs and those that are observed above the tip of a helmet streamer, we find that the speeds and accelerations of the blobs in these two cases differ significantly. It is suggested that these differences of the blob dynamics stem from the associated magnetic reconnection involving different magnetic field configurations and triggering processes.

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preprint2011arXiv

Acoustic guiding and subwavelength imaging with sharp bending by sonic crystal

A sharp bending scheme for the self-collimation of acoustic waves is proposed by simply truncating the sonic crystals. An all-angle and wide-band 90°-bending wave guide is demonstrated with nearly perfect transmissions for Gaussian beams at a wide range of incident angles. A 90°-bended imaging for a point source with a subwavelength resolution of 0 0.37λ is also realized by the proposed structure. These results will find applicability in the manipulation of acoustic waves by sonic crystals.

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preprint2011arXiv

Ground state fidelity in bond-alternative Ising chains with Dzyaloshinskii-Moriya interactions

A systematic analysis is performed for quantum phase transitions in a bond-alternative one-dimensional Ising model with a Dzyaloshinskii-Moriya (DM) interaction by using the fidelity of ground state wave functions based on the infinite matrix product states algorithm. For an antiferromagnetic phase, the fidelity per lattice site exhibits a bifurcation, which shows spontaneous symmetry breaking in the system. A critical DM interaction is inversely proportional to an alternating exchange coupling strength for a quantum phase transition. Further, a finite-entanglement scaling of von Neumann entropy with respect to truncation dimensions gives a central charge c = 0.5 at the critical point.

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preprint2011arXiv

Ground-State Fidelity and Kosterlitz-Thouless Phase Transition for Spin 1/2 Heisenberg Chain with Next-to-the-Nearest-Neighbor Interaction

The Kosterlitz-Thouless transition for the spin 1/2 Heisenberg chain with the next-to-the-nearest-neighbor interaction is investigated in the context of an infinite matrix product state algorithm, which is a generalization of the infinite time-evolving block decimation algorithm [G. Vidal, Phys. Rev. Lett. \textbf{98}, 070201 (2007)] to accommodate both the next-to-the-nearest-neighbor interaction and spontaneous dimerization. It is found that, in the critical regime, the algorithm automatically leads to infinite degenerate ground-state wave functions, due to the finiteness of the truncation dimension. This results in \textit{pseudo} symmetry spontaneous breakdown, as reflected in a bifurcation in the ground-state fidelity per lattice site. In addition, this allows to introduce a pseudo-order parameter to characterize the Kosterlitz-Thouless transition.

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preprint2011arXiv

Nonlocality of two-qubit and three-qubit Schmidt-Correlated states

We investigate the nonlocality of Schmidt-correlated (SC) states, and present analytical expressions of the maximum violation value of Bell inequalities. It is shown that the violation of Clauser-Horne-Shimony-Holt (CHSH) inequality is necessary and sufficient for the nonlocality of two-qubit SC states, whereas the violation of the Svetlichny inequality is only a sufficient condition for the genuine nonlocality of three-qubit SC states. Furthermore, the relations among the maximum violation values, concurrence and relative entropy entanglement are discussed.

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preprint2011arXiv

Performance Guarantee under Longest-Queue-First Schedule in Wireless Networks

Efficient link scheduling in a wireless network is challenging. Typical optimal algorithms require solving an NP-hard sub-problem. To meet the challenge, one stream of research focuses on finding simpler sub-optimal algorithms that have low complexity but high efficiency in practice. In this paper, we study the performance guarantee of one such scheduling algorithm, the Longest-Queue-First (LQF) algorithm. It is known that the LQF algorithm achieves the full capacity region, $Λ$, when the interference graph satisfies the so-called local pooling condition. For a general graph $G$, LQF achieves (i.e., stabilizes) a part of the capacity region, $σ^*(G) Λ$, where $σ^*(G)$ is the overall local pooling factor of the interference graph $G$ and $σ^*(G) \leq 1$. It has been shown later that LQF achieves a larger rate region, $Σ^*(G) Λ$, where $Σ^ (G)$ is a diagonal matrix. The contribution of this paper is to describe three new achievable rate regions, which are larger than the previously-known regions. In particular, the new regions include all the extreme points of the capacity region and are not convex in general. We also discover a counter-intuitive phenomenon in which increasing the arrival rate may sometime help to stabilize the network. This phenomenon can be well explained using the theory developed in the paper.

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preprint2011arXiv

Quantum discord and geometry for a class of two-qubit states

We study the level surfaces of quantum discord for a class of two-qubit states with parallel nonzero Bloch vectors. The dynamic behavior of quantum discord under decoherence is investigated. It is shown that a class of X states has sudden transition between classical and quantum correlations under decoherence. Our results include the ones in M. D. Lang and C. M. Caves [Phys. Rev. Lett. 105, 150501 (2010)] as a special case and show new pictures and structures of quantum discord.

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preprint2011arXiv

Solar winds along curved magnetic field lines

Both remote-sensing measurements using the interplanetary scintillation (IPS) technique and in situ measurements by the Ulysses spacecraft show a bimodal structure for the solar wind at solar minimum conditions. At present what makes the fast wind fast and the slow wind slow still remains to be answered. While a robust empirical correlation exists between the coronal expansion rate $f_c$ of the flow tubes and the speeds $v$ measured in situ, further data analysis suggests that $v$ depends on more than just $f_c$. We examine whether the non-radial shape of field lines, which naturally accompanies any non-radial expansion, could be an additional geometrical factor. We solved the transport equations incorporating the heating due to turbulent Alfvén waves for an electron-proton solar wind along curved field lines given by an analytical magnetic field model, representative of a solar minimum corona. The field line shape is found to influence substantially the solar wind parameters, reducing the asymptotic speed by up to $\sim 130$ km s$^{-1}$, or by $\sim 28%$ in relative terms, compared with the case neglecting the field line curvature. This effect was interpreted in the general framework of energy addition in the solar wind: Relative to the straight case, the field line curvature enhances the effective energy deposition to the subsonic flow, resulting in a higher proton flux and a lower terminal proton speed. Our computations suggest that the field line curvature could be a geometrical factor which, in addition to the tube expansion, substantially influences the solar wind speed. Furthermore, at solar minima although the field line curvature unlikely affects the polar fast solar wind, it does help make the wind at low latitudes slow, thereby helping better reproduce the Ulysses measurements.

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preprint2011arXiv

Standing shocks in the inner slow solar wind

The slow solar wind, or at least a significant part of it, originates from the region bordering streamer helmets, where the flow tube geometry is distinct from flow tubes rooted in the center of coronal holes. We examine whether this particular tube geometry leads to standing shocks in the inner slow wind in this region. To isolate the influence of tube geometry, a simple isothermal wind model is employed and is solved in terms of the Lambert W function. In addition to a continuous solution, the model may also admit solutions with shocks, readily constructed with a graphical approach. When allowed, the shock solutions appear in pairs, one with a shock located in the streamer stalk, the other with a shock below the cusp along the streamer border. We show that solutions with standing shocks exist in a broad area in the parameter space characterizing the wind temperature and flow tube. In particular, streamers with cusps located at a heliocentric distance $\gtrsim 3.2 R_\odot$ can readily support discontinuous slow winds with temperatures barely higher than 1 MK.

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preprint2011arXiv

Ultra-thin SWNTs Films with Tunable, Anisotropic Transport Properties

Directional transport properties at the nanoscale remain a challenge primarily due to issues associated with control over the underlying anisotropy and scalability to macroscopic scales. In this letter, we develop a facile approach based on template-guided fluidic assembly of high mobility building blocks - single walled carbon nanotubes (SWNTs) - to fabricate ultra-thin and anisotropic SWNT films. A major advancement is the complete control over the anisotropy in the assembled nanostructure, realized by three-dimensional engineering of dip-coated SWNT thin films into alternating hydrophilic and hydrophobic micro-line patterns with prescribed intra/inter-line widths and line thicknesses. Variations in the contact line profile results in an evaporation-controlled assembly mechanism that leads to the formation of an alternating, and more importantly, contiguous SWNT network. Evidently, the nanoscopic thickness modulations are direct reflections of the substrate geometry and chemistry. The nanostructured film exhibits significant anisotropy in their electrical and thermal transport properties as well as optical transparency, as revealed by characterization studies. The direct interplay between the anisotropy and the 3D micro-line patterns of the substrate combined with the wafer-level scalability of the fluidic assembly allows us to tune the transport properties for a host of nanoelectronic applications.

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preprint2011arXiv

Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions

Genetic interactions have been widely used to define functional relationships between proteins and pathways. In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions between domains of those proteins. The domain genetic interactions rarely overlap with the domain physical interactions from iPfam database and provide a complementary view about domain relationships. Moreover, we found that domains in multidomain yeast proteins contribute to their genetic interactions differently. The domain genetic interactions help more precisely define the function related to the synthetic lethal genetic interactions, and then help understand how domains contribute to different functionalities of multidomain proteins. Using the probabilities of domain genetic interactions, we were able to predict novel yeast synthetic lethal genetic interactions. Furthermore, we had also identified novel compensatory pathways from the predicted synthetic lethal genetic interactions. Our study significantly improved the understanding of yeast mulitdomain proteins, the synthetic lethal genetic interactions and the functional relationships between proteins and pathways.

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preprint2011arXiv

What geometrical factors determine the in situ solar wind speed?

At present it remains to address why the fast solar wind is fast and the slow wind is slow. Recently we have shown that the field line curvature may substantially influence the wind speed $v$, thereby offering an explanation for the Arge et al. finding that $v$ depends on more than just the flow tube expansion factor. Here we show by extensive numerical examples that the correlation between $v$ and field line curvature is valid for rather general base boundary conditions and for rather general heating functions. Furthermore, the effect of field line curvature is even more pronounced when the proton-alpha particle speed difference is examined. We suggest that any solar wind model has to take into account the field line shape for any quantitative analysis to be made.

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preprint2010arXiv

Effectively integrating information content and structural relationship to improve the GO-based similarity measure between proteins

The Gene Ontology (GO) provides a knowledge base to effectively describe proteins. However, measuring similarity between proteins based on GO remains a challenge. In this paper, we propose a new similarity measure, information coefficient similarity measure (SimIC), to effectively integrate both the information content (IC) of GO terms and the structural information of GO hierarchy to determine the similarity between proteins. Testing on yeast proteins, our results show that SimIC efficiently addresses the shallow annotation issue in GO, thus improves the correlations between GO similarities of yeast proteins and their expression similarities as well as between GO similarities of yeast proteins and their sequence similarities. Furthermore, we demonstrate that the proposed SimIC is superior in predicting yeast protein interactions. We predict 20484 yeast protein-protein interactions (PPIs) between 2462 proteins based on the high SimIC values of biological process (BP) and cellular component (CC). Examining the 214 MIPS complexes in our predicted PPIs shows that all members of 159 MIPS complexes can be found in our PPI predictions, which is more than those (120/214) found in PPIs predicted by relative specificity similarity (RSS). Integrating IC and structural information of GO hierarchy can improve the effectiveness of the semantic similarity measure of GO terms. The new SimIC can effectively correct the effect of shallow annotation, and then provide an effective way to measure similarity between proteins based on Gene Ontology.

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preprint2010arXiv

Large-Scale DNS of Gas-Solid Flow on Mole-8.5

Direct numerical simulation (DNS) for gas-solid flow is implemented on a multi-scale supercomputing system, Mole-8.5, featuring massive parallel GPU-CPU hybrid computing, for which the lattice Boltzmann method (LBM) is deployed together with the immersed moving boundary (IMB) method and discrete element method (DEM). A two-dimensional suspension with about 1,166,400 75-micron solid particles distributed in an area of 11.5cm x46cm, and a three-dimensional suspension with 129,024 solid particles in a domain of 0.384cm x1.512cm x0.384cm are fully-resolved below particle scale and distinct multi-scale heterogeneity are observed. Almost 20-fold speedup is achieved on one Nvidia C2050 GPU over one core of Intel E5520 CPU in double precision, and nearly ideal scalability is maintained when using up to 672 GPUs. The simulations demonstrate that LB-IMB-DEM modeling with parallel GPU computing may suggest a promising approach for exploring the fundamental mechanisms and constitutive laws of complex gas-solid flow, which are, so far, poorly understood in both experiments and theoretical studies.

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preprint2010arXiv

Negative refraction at deep-ultraviolet frequency in monocrystalline graphite

Negative refraction is such a prominent electromagnetic phenomenon that most researchers believe it can only occur in artificially engineered metamaterials. In this article, we report negative refraction for all incident angles for the first time in a naturally existing material. Using ellipsometry measurement of the equifrequency contour in the deep-ultraviolet frequency region (typically 254 nm), obvious negative refraction was demonstrated in monocrystalline graphite for incident angles ranging from 20o to 70o. This negative refraction is attributed to extremely strong anisotropy in the crystal structure of graphite, which gives the crystal indefinite permeability. This result not only explores a new route to identifying natural negative-index materials, but it also holds promise for the development of an ultraviolet hyperlens, which may lead to a breakthrough in nanolithography, the most critical technology necessary for the next generation of electronics.

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preprint2010arXiv

Perpendicular Ion Heating by Low-Frequency Alfven-Wave Turbulence in the Solar Wind

We consider ion heating by turbulent Alfven waves (AWs) and kinetic Alfven waves (KAWs) with perpendicular wavelengths comparable to the ion gyroradius and frequencies smaller than the ion cyclotron frequency. When the turbulence amplitude exceeds a certain threshold, an ion's orbit becomes chaotic. The ion then interacts stochastically with the time-varying electrostatic potential, and the ion's energy undergoes a random walk. Using phenomenological arguments, we derive an analytic expression for the rates at which different ion species are heated, which we test by simulating test particles interacting with a spectrum of randomly phased AWs and KAWs. We find that the stochastic heating rate depends sensitively on the quantity epsilon = dv/vperp, where vperp is the component of the ion velocity perpendicular to the background magnetic field B0, and dv (dB) is the rms amplitude of the velocity (magnetic-field) fluctuations at the gyroradius scale. In the case of thermal protons, when epsilon << eps1, where eps1 is a constant, a proton's magnetic moment is nearly conserved and stochastic heating is extremely weak. However, when epsilon > eps1, the proton heating rate exceeds the cascade power that would be present in strong balanced KAW turbulence with the same value of dv, and magnetic-moment conservation is violated. For the random-phase waves in our test-particle simulations, eps1 is approximately 0.2. For protons in low-beta plasmas, epsilon is approximately dB/B0 divided by the square root of beta, and epsilon can exceed eps1 even when dB/B0 << eps1. At comparable temperatures, alpha particles and minor ions have larger values of epsilon than protons and are heated more efficiently as a result. We discuss the implications of our results for ion heating in coronal holes and the solar wind.

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preprint2010arXiv

Topological transitions in carbon nanotube networks via nanoscale confinement

Efforts aimed at large-scale integration of nanoelectronic devices that exploit the superior electronic and mechanical properties of single-walled carbon nanotubes (SWCNTs) remain limited by the difficulties associated with manipulation and packaging of individual SWNTs. Alternative approaches based on ultra-thin carbon nanotube networks (CNNs) have enjoyed success of late with the realization of several scalable device applications. However, precise control over the network electronic transport is challenging due to i) an often uncontrollable interplay between network coverage and its topology and ii) the inherent electrical heterogeneity of the constituent SWNTs. In this letter, we use template-assisted fluidic assembly of SWCNT networks to explore the effect of geometric confinement on the network topology. Heterogeneous SWCNT networks dip-coated onto sub-micron wide ultra-thin polymer channels exhibit a topology that becomes increasingly aligned with decreasing channel width and thickness. Experimental scale coarse-grained computations of interacting SWCNTs show that the effect is a reflection of an aligned topology that is no longer dependent on the network density, which in turn emerges as a robust knob that can induce semiconductor-to-metallic transitions in the network response. Our study demonstrates the effectiveness of directed assembly on channels with varying degrees of confinement as a simple tool to tailor the conductance of the otherwise heterogeneous network, opening up the possibility of robust large-scale CNN-based devices.

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preprint2009arXiv

Spontaneous symmetry breaking and bifurcations in ground state fidelity for quantum lattice systems

Spontaneous symmetry breaking occurs in a system when its Hamiltonian possesses a certain symmetry, whereas the ground state wave functions do not preserve it. This provides such a scenario that a bifurcation, which breaks the symmetry, occurs when some control parameter crosses its critical value. It is unveiled that the ground state fidelity per lattice site exhibits such a bifurcation for quantum lattice systems undergoing quantum phase transitions. The significance of this result lies in the fact that the ground state fidelity per lattice site is \textit{universal}, in the sense that it is model-independent, in contrast to (model-dependent) order parameters. This fundamental quantity may be computed by exploiting the newly-developed tensor network algorithms on infinite-size lattices. We illustrate the scheme in terms of the quantum Ising model in a transverse magnetic field and the spin 1/2 XYX model in an external magnetic field on an infinite-size lattice in one spatial dimension.

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preprint2007arXiv

Angular momentum transport and proton-alpha differential streaming in the solar wind

The effect of solar rotation on the proton-alpha differential flow speed, $v_{αp}$, and consequently on the angular momentum transport in the solar wind, is explored. It is found that the force introduced by the azimuthal components plays an important role in the force balance in interplanetary space, bringing the radial flow speeds of the species considered closer to each other. For the fast solar wind, the model cannot account for the decrease of $v_{αp}$ observed by Helios between 0.3 and 1 AU. However, it can reproduce the profile of $v_{αp}$ measured by Ulysses beyond 2 AU, if the right value for $v_{αp}$ is imposed at that distance. In the slow solar wind, the effect of solar rotation is more pronounced if one starts with the value measured by Helios at 0.3 AU. In this case, solar rotation introduces a relative change of 10-16% in the radial flow speed of the alpha particles between 1 and 4 AU. The model calculations also show that, although alpha particles consume only a small fraction of the energy and linear momentum fluxes of protons, they cannot be neglected when considering the proton angular momentum flux ${\cal L}_p$. In most examples, it is found that ${\cal L}_p$ is determined by $v_{αp}$ for both the fast and the slow wind. In the slow solar wind, it is also found that the proton and alpha angular momentum fluxes ${\cal L}_p$ and ${\cal L}_α$ can be several times larger in magnitude than the flux carried by the magnetic stresses ${\cal L}_M$. While the sum of the angular momentum fluxes ${\cal L}_P={\cal L}_p+{\cal L}_α$ of both species is found to be smaller than the magnetic stress ${\cal L}_M$, for the fast and slow wind alike, this result is at variance with the Helios measurements.

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preprint2007arXiv

Propagation of non-WKB Alfven waves in a multicomponent solar wind with differential ion flow

The propagation of dissipationless, hydromagnetic, non-WKB, purely toroidal Alfvén waves in a realistic background three-fluid solar wind with axial symmetry and differential proton-alpha flow is investigated. The wave equations are derived from standard multi-fluid 5-moment equations. The Alfvénic point, where the combined poloidal Alfvén Mach number $M_T=1$, is found to be a singular point for the wave equation, which is then numerically solved for three representative angular frequencies $ω=10^{-3}$, $10^{-4}$ and $10^{-5}$ rad s$^{-1}$ with a fixed wave amplitude of 10 km s$^{-1}$ imposed at the coronal base (1 $R_\odot$). Between 1 $R_\odot$ and 1 AU, the numerical solutions show substantial deviation from the WKB expectations. Even for the relatively high frequency $ω=10^{-3}$ rad s$^{-1}$, a WKB-like behavior can be seen only in regions $r\gtrsim 10$ $R_\odot$. In the low-frequency case $ω=10^{-5}$ rad s$^{-1}$, the computed profiles of wave-related parameters show a spatial dependence distinct from the WKB one, the deviation being particularly pronounced in interplanetary space. In the inner corona $r\lesssim 4$ $R_\odot$, the computed ion velocity fluctuations are considerably smaller than the WKB expectations in all cases, as is the computed wave-induced acceleration exerted on protons or alpha particles. With the chosen base wave amplitude, the wave acceleration has negligible effect on the ion force balance in the corona. Hence processes other than the non-WKB wave acceleration are needed to accelerate the ions out of the gravitational potential well of the Sun. However, at large distances beyond the Alfvénic point, the low-frequency waves can play an important role in the ion dynamics, with the net effect being to equalize the speeds of the two ion species considered.

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preprint2003arXiv

Two band gap field-dependent thermal conductivity of $MgB_2$

The thermal conductivity $κ(H,T)$ of the new superconductor $MgB_2$ was studied as a function of the temperature and a magnetic field. No anomaly in the thermal conductivity $κ(H,T)$ is observed around the superconducting transition in absence or presence of magnetic fields up to 14 Tesla; upon that field the superconductivity of $MgB_2$ persisted. The thermal conductivity in zero-field shows a $T$-linear increase up to 50K. The thermal conductivity is found to increase with increasing field at high fields. We interpret the findings as if there are two subsystems of quasiparticles with different field-dependent characters in a two ($L$ and $S$)-band superconductor reacting differently with the vortex structure. The unusual enhancement of $κ(H ,T)$ at low temperature but higher than a ($H_{c2S}\simeq 3T$) critical field is interpreted as a result of the overlap of the low energy states outside the vortex cores in the $S$-band.

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