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Yi Liu

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cond-mat.supr-con Machine Learning cond-mat.mtrl-sci cond-mat.str-el Computer Vision math.GT Artificial Intelligence cond-mat.mes-hall Cryptography and Security physics.optics Information Theory math.IT math.GR Methodology Networking and Internet Architecture astro-ph.HE Computation and Language physics.ins-det Software Engineering Distributed, Parallel, and Cluster Computing eess.IV eess.SP Social and Information Networks eess.AS Emerging Technologies hep-ex hep-ph Neural and Evolutionary Computing physics.comp-ph Sound Applications astro-ph astro-ph.CO astro-ph.GA astro-ph.IM astro-ph.SR Computation Computational Engineering, Finance, and Science cond-mat.soft Information Retrieval math.DS math.NA math.OC Molecular Networks nucl-ex Numerical Analysis physics.app-ph physics.atom-ph physics.flu-dyn physics.med-ph physics.plasm-ph physics.soc-ph Populations and Evolution Programming Languages quant-ph Quantitative Methods Robotics

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preprint2026arXiv

MappingEvolve: LLM-Driven Code Evolution for Technology Mapping

Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential for core algorithm enhancement remains untapped. We introduce MappingEvolve, an open-source framework that pioneers the use of LLMs to directly evolve technology mapping code. Our method abstracts the mapping process into distinct optimization operators and employs a hierarchical agent-based architecture, comprising a Planner, Evolver, and Evaluator, to guide the evolutionary search. This structured approach enables strategic and effective code modifications. Experiments show our method significantly outperforms direct evolution and strong baselines, achieving 10.04\% area reduction versus ABC and 7.93\% versus mockturtle, with 46.6\%--96.0\% $S_{overall}$ improvement on EPFL benchmarks, while explicitly navigating the area--delay trade-off. Our code and data are available at https://github.com/Flians/MappingEvolve.

preprint2025arXiv

CoHalLo: code hallucination localization via probing hidden layer vector

The localization of code hallucinations aims to identify specific lines of code containing hallucinations, helping developers to improve the reliability of AI-generated code more efficiently. Although recent studies have adopted several methods to detect code hallucination, most of these approaches remain limited to coarse-grained detection and lack specialized techniques for fine-grained hallucination localization. This study introduces a novel method, called CoHalLo, which achieves line-level code hallucination localization by probing the hidden-layer vectors from hallucination detection models. CoHalLo uncovers the key syntactic information driving the model's hallucination judgments and locates the hallucinating code lines accordingly. Specifically, we first fine-tune the hallucination detection model on manually annotated datasets to ensure that it learns features pertinent to code syntactic information. Subsequently, we designed a probe network that projects high-dimensional latent vectors onto a low-dimensional syntactic subspace, generating vector tuples and reconstructing the predicted abstract syntax tree (P-AST). By comparing P-AST with the original abstract syntax tree (O-AST) extracted from the input AI-generated code, we identify the key syntactic structures associated with hallucinations. This information is then used to pinpoint hallucinated code lines. To evaluate CoHalLo's performance, we manually collected a dataset of code hallucinations. The experimental results show that CoHalLo achieves a Top-1 accuracy of 0.4253, Top-3 accuracy of 0.6149, Top-5 accuracy of 0.7356, Top-10 accuracy of 0.8333, IFA of 5.73, Recall@1% Effort of 0.052721, and Effort@20% Recall of 0.155269, which outperforms the baseline methods.

preprint2025arXiv

Physical properties and first-principles calculations of an altermagnet candidate Cs$_{1-δ}$V$_2$Te$_2$O

We report the crystal growth, structure, physical properties, and first-principles calculations of a vanadium-based oxytelluride Cs$_{1-δ}$V$_2$Te$_2$O. The material possesses two-dimensional V$_2$O square nets sandwiched by tellurium layers, with local crystallographic symmetry satisfying the spin symmetry for a $d$-wave altermagnet. An antiferromagnetic transition at 293 K is unambiguously evidenced from the measurements of magnetic susceptibility and specific heat. In addition, a secondary transition at $\sim$70 K is also observed, possibly associated with a Lifshitz transition. The first-principles calculations indicate robust Néel-type collinear antiferromagnetism in the V$_2$O plane. Consequently, spin splittings show up in momentum space, in relation with the real-space mirror/rotation symmetry. Interestingly, the V-$d_{yz}/d_{xz}$ electrons, which primarily contribute the quasi-one-dimensional Fermi surface, turns out to be fully orbital- and spin-polarized, akin to the case of a half metal. Our work lays a solid foundation on the potential applications utilizing altermagnetic properties in vanadium-based oxychalcogenides.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions. As a significant aspect of LLM alignment, it is thus important to formulate such a specialized set of instructions as well as investigate the resulting behavior of LLMs. To address this vacancy, we propose a new benchmark CoDI-Eval to systematically and comprehensively evaluate LLMs' responses to instructions with various constraints. We construct a large collection of constraints-attributed instructions as a test suite focused on both generalization and coverage. Specifically, we advocate an instruction diversification process to synthesize diverse forms of constraint expression and also deliberate the candidate task taxonomy with even finer-grained sub-categories. Finally, we automate the entire evaluation process to facilitate further developments. Different from existing studies on controllable text generation, CoDI-Eval extends the scope to the prevalent instruction-following paradigm for the first time. We provide extensive evaluations of representative LLMs (e.g., ChatGPT, Vicuna) on CoDI-Eval, revealing their limitations in following instructions with specific constraints and there is still a significant gap between open-source and commercial closed-source LLMs. We believe this benchmark will facilitate research into improving the controllability of LLMs' responses to instructions. Our data and code are available at https://github.com/Xt-cyh/CoDI-Eval.

preprint2024arXiv

Digger: Detecting Copyright Content Mis-usage in Large Language Model Training

Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed makeup of these datasets is often not disclosed, leading to concerns about data security and potential misuse. This is particularly relevant when copyrighted material, still under legal protection, is used inappropriately, either intentionally or unintentionally, infringing on the rights of the authors. In this paper, we introduce a detailed framework designed to detect and assess the presence of content from potentially copyrighted books within the training datasets of LLMs. This framework also provides a confidence estimation for the likelihood of each content sample's inclusion. To validate our approach, we conduct a series of simulated experiments, the results of which affirm the framework's effectiveness in identifying and addressing instances of content misuse in LLM training processes. Furthermore, we investigate the presence of recognizable quotes from famous literary works within these datasets. The outcomes of our study have significant implications for ensuring the ethical use of copyrighted materials in the development of LLMs, highlighting the need for more transparent and responsible data management practices in this field.

preprint2024arXiv

Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning

In recent years, text-to-video retrieval methods based on CLIP have experienced rapid development. The primary direction of evolution is to exploit the much wider gamut of visual and textual cues to achieve alignment. Concretely, those methods with impressive performance often design a heavy fusion block for sentence (words)-video (frames) interaction, regardless of the prohibitive computation complexity. Nevertheless, these approaches are not optimal in terms of feature utilization and retrieval efficiency. To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase. To better leverage the multi-granularity features, we devise a two-stage retrieval architecture in the retrieval phase. This solution ingeniously balances the coarse and fine granularity of retrieval content. Moreover, it also strikes a harmonious equilibrium between retrieval effectiveness and efficiency. Specifically, in training phase, we design a parameter-free text-gated interaction block (TIB) for fine-grained video representation learning and embed an extra Pearson Constraint to optimize cross-modal representation learning. In retrieval phase, we use coarse-grained video representations for fast recall of top-k candidates, which are then reranked by fine-grained video representations. Extensive experiments on four benchmarks demonstrate the efficiency and effectiveness. Notably, our method achieves comparable performance with the current state-of-the-art methods while being nearly 50 times faster.

preprint2023arXiv

Quantum phase transitions in two-dimensional superconductors: a review on recent experimental progress

Superconductor-insulator/metal transition (SIT/SMT) as a paradigm of quantum phase transition has been a research highlight over the last three decades. Benefit from recent developments in the fabrication and measurements of 2D superconducting films and nanodevices, unprecedented quantum phenomena have been revealed in the quantum phase transitions of 2D superconductors. In this review, we introduce the recent progress on quantum phase transitions in 2D superconductors, focusing on the quantum Griffiths singularity (QGS) and anomalous metal state. Characterized by a divergent critical exponent when approaching zero temperature, QGS of SMT is discovered in ultrathin crystalline Ga films and subsequently detected in various 2D superconductors. The universality of QGS indicates the profound influence of quenched disorder on quantum phase transitions. Besides, in a 2D superconducting system, whether a metallic ground state can exist is a long-sought mystery. Recently, the charge-2e quantum oscillations are observed in nanopatterned superconducting films, indicating the bosonic nature of the anomalous metal state and ending the debate on whether bosons can exist as a metal. The evidences of the anomalous metal states have also been reported in crystalline epitaxial thin films and exfoliated nanoflakes, as well as granular composite films. High quality filters are used in these works to exclude the influence of external high frequency noises in ultralow temperature measurements. The observations of QGS and metallic ground states in 2D superconductors not only reveal the prominent role of quantum fluctuations and dissipations but also provide new perspective to explore quantum phase transitions in superconducting systems.

preprint2022arXiv

A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing

Amazon Customer Service provides real-time support for millions of customer contacts every year. While bot-resolver helps automate some traffic, we still see high demand for human agents, also called subject matter experts (SMEs). Customers outreach with questions in different domains (return policy, device troubleshooting, etc.). Depending on their training, not all SMEs are eligible to handle all contacts. Routing contacts to eligible SMEs turns out to be a non-trivial problem because SMEs' domain eligibility is subject to training quality and can change over time. To optimally recommend SMEs while simultaneously learning the true eligibility status, we propose to formulate the routing problem with a nonparametric contextual bandit algorithm (K-Boot) plus an eligibility control (EC) algorithm. K-Boot models reward with a kernel smoother on similar past samples selected by $k$-NN, and Bootstrap Thompson Sampling for exploration. EC filters arms (SMEs) by the initially system-claimed eligibility and dynamically validates the reliability of this information. The proposed K-Boot is a general bandit algorithm, and EC is applicable to other bandits. Our simulation studies show that K-Boot performs on par with state-of-the-art Bandit models, and EC boosts K-Boot performance when stochastic eligibility signal exists.

preprint2022arXiv

A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling

Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains. In such situations, a vanilla ASR system usually fails to perform well on every domain. This paper proposes a multi-domain ASR framework for Tencent Map, a navigation app used on smart phones and in-vehicle infotainment systems. The proposed framework consists of three core parts: a basic ASR module to generate n-best lists of a speech query, a text classification module to determine which domain the speech query belongs to, and a reranking module to rescore n-best lists using domain-specific language models. In addition, an instance sampling based method to training neural network language models (NNLMs) is proposed to address the data imbalance problem in multi-domain ASR. In experiments, the proposed framework was evaluated on navigation domain and music domain, since navigating and playing music are two main features of Tencent Map. Compared to a general ASR system, the proposed framework achieves a relative 13% $\sim$ 22% character error rate reduction on several test sets collected from Tencent Map and our in-car voice assistant.

preprint2022arXiv

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation. Our federated learning system first departs from prior works by supporting lightweight encryption and aggregation, and resilience against drop-out clients with no impact on their participation in future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency optimization in the ciphertext domain and the support of security against an actively adversarial cloud server, which we also fully explore in this paper and provide effective and efficient mechanisms. Extensive experiments over several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system achieves accuracy comparable to the plaintext baseline, with practical performance.

preprint2022arXiv

An adaptive model checking test for functional linear model

Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the reliability of results. Limited tests in this area do not have tractable null distributions or asymptotic analysis under alternatives. Also, the functional predictor is usually assumed to be fully observed, which is impractical. To address these problems, we propose an adaptive model checking test for FLM. It combines regular moment-based and conditional moment-based tests, and achieves model adaptivity via the dimension of a residual-based subspace. The advantages of our test are manifold. First, it has a tractable chi-squared null distribution and higher powers under the alternatives than its components. Second, asymptotic properties under different underlying models are developed, including the unvisited local alternatives. Third, the test statistic is constructed upon finite grid points, which incorporates the discrete nature of collected data. We develop the desirable relationship between sample size and number of grid points to maintain the asymptotic properties. Besides, we provide a data-driven approach to estimate the dimension leading to model adaptivity, which is promising in sufficient dimension reduction. We conduct comprehensive numerical experiments to demonstrate the advantages the test inherits from its two simple components.

preprint2022arXiv

Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality

Embedding knowledge graphs into low-dimensional spaces is a popular method for applying approaches, such as link prediction or node classification, to these databases. This embedding process is very costly in terms of both computational time and space. Part of the reason for this is the optimisation of hyperparameters, which involves repeatedly sampling, by random, guided, or brute-force selection, from a large hyperparameter space and testing the resulting embeddings for their quality. However, not all hyperparameters in this search space will be equally important. In fact, with prior knowledge of the relative importance of the hyperparameters, some could be eliminated from the search altogether without significantly impacting the overall quality of the outputted embeddings. To this end, we ran a Sobol sensitivity analysis to evaluate the effects of tuning different hyperparameters on the variance of embedding quality. This was achieved by performing thousands of embedding trials, each time measuring the quality of embeddings produced by different hyperparameter configurations. We regressed the embedding quality on those hyperparameter configurations, using this model to generate Sobol sensitivity indices for each of the hyperparameters. By evaluating the correlation between Sobol indices, we find substantial variability in the hyperparameter sensitivities between knowledge graphs, with differing dataset characteristics being the probable cause of these inconsistencies. As an additional contribution of this work we identify several relations in the UMLS knowledge graph that may cause data leakage via inverse relations, and derive and present UMLS-43, a leakage-robust variant of that graph.

preprint2022arXiv

Attacking Masked Cryptographic Implementations: Information-Theoretic Bounds

Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano's inequality) to derive upper bounds on the success rate of any possible attack in terms of the number of side-channel measurements. Equivalently, this gives lower bounds on the number of queries for a given success probability of attack. In this paper, we consider cryptographic implementations protected by (first-order) masking schemes, and derive several information-theoretic bounds on the efficiency of any (second-order) attack. The obtained bounds are generic in that they do not depend on a specific attack but only on the leakage and masking models, through the mutual information between side-channel measurements and the secret key. Numerical evaluations confirm that our bounds reflect the practical performance of optimal maximum likelihood attacks.

preprint2022arXiv

Bulk domain Meissner state in the ferromagnetic superconductor EuFe$_{2}$(As$_{0.8}$P$_{0.2}$)$_{2}$: Consequence of compromise between ferromagnetism and superconductivity

Small-angle neutron scattering (SANS) measurements are performed on the ferromagnetic superconductor EuFe$_{2}$(As$_{0.8}$P$_{0.2}$)$_{2}$ ($T\rm_{sc}=22.5\,$K) to probe the delicate interplay between ferromagnetism and superconductivity. A clear signature of large ferromagnetic domains is found below the ferromagnetic ordering temperature $T\rm_{C}$ = 18.5 K. In a small temperature interval of $\sim$ 1.5 K below $T\rm_{C}$, additional SANS signal is observed, of which the indirect Fourier transform reveals characteristic length scales in between $\sim$ 80 nm to $\sim$ 160 nm. These nanometer-scaled domain structures are identified to result from an intermediate inhomogeneous Meissner effect denoted domain Meissner state, which was recently observed on the surface of EuFe$_{2}$(As$_{0.79}$P$_{0.21}$)$_{2}$ crystals by means of magnetic force microscopy [V. S. Stolyarov $\mathit{et}$ $\mathit{al.}$, Sci. Adv. 4, 1061 (2018)], ascribing to the competition between ferromagnetism and superconductivity. Our measurements clearly render the domain Meissner state as a bulk phenomenon and provide a key solution to the mystery regarding the intriguing coexistence of strong ferromagnetism and bulk superconductivity in these compounds.

preprint2022arXiv

Commentary: analyzing binary data using MCPMod when zero counts are expected

Bretz et al (2005) proposed multiple Comparison Procedure and Modeling (MCPMod) method to design and analyze dose-finding study. Pinheiro (2014) then generalized it to various types of endpoint, including but not limited to binary endpoint, survival endpoint, count data, and longitudinal data. Pinheiro (2013) recommended to use the estimated covariance matrix from the observed data to recalculate the optimal contrast and the critical value of the test For many phase II studies it is common to have small sample sizes per arm with low placebo response rates jointly. Under such circumstances, it cannot be excluded to have a zero count observed. For example, when the placebo response rate is 10%, there is about 4% chance to observe zero responders in the placebo group, or other dose group(s), which has a similar response rate as placebo. In this manuscript, we would like to illustrate the potential problem of Pinheiro (2013) using a case study and simulations. An alternative method using Firth's logistic regression was evaluated to get a stable estimate of response for each dose group. In addition, we evaluated two options to address the issue with problematic contrast coefficients.

preprint2022arXiv

Compact and variable radio emission from an active galaxy with supersoft X-ray emission

RX J1301.9+2747 is a unique active galaxy with supersoft X-ray spectrum that lacks significant emission at energies above 2 keV. In addition, it is one of few galaxies displaying quasi-periodic X-ray eruptions that recur on a timescale of 13-20 ks. We present multi-epoch radio observations of RX J1301.9+2747 using GMRT, VLA and VLBA. The VLBA imaging at 1.6 GHz reveals a compact radio emission unresolved at a scale of <0.7 pc, with a brightness temperature of T_b>5x10^7 K. The radio emission is variable by more than a factor of 2.5 over a few days, based on the data taken from VLA monitoring campaigns. The short-term radio variability suggests that the radio emitting region has a size as small as 8x10^{-4} pc, resulting in an even higher brightness temperature of T_b ~10^{12} K. A similar limit on the source size can be obtained if the observed flux variability is not intrinsic and caused by the interstellar scintillation effect. The overall radio spectrum is steep with a time-averaged spectral index alpha=-0.78+/-0.03 between 0.89 GHz and 14 GHz. These observational properties rule out a thermal or star-formation origin of the radio emission, and appear to be consistent with the scenario of episodic jet ejections driven by magnetohydrodynamic process. Simultaneous radio and X-ray monitoring observations down to a cadence of hours are required to test whether the compact and variable radio emission is correlated with the quasi-periodic X-ray eruptions.

preprint2022arXiv

Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models

While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using image classification source datasets, e.g., ImageNet. Though image classification datasets could provide the backbone networks with rich visual features and discriminative ability, they are incapable of fully pre-training the target model (i.e., backbone+segmentation modules) in an end-to-end manner. The segmentation modules are left to random initialization in the fine-tuning process due to the lack of segmentation labels in classification datasets. In our work, we propose a method that leverages Pseudo Semantic Segmentation Labels (PSSL), to enable the end-to-end pre-training for image segmentation models based on classification datasets. PSSL was inspired by the observation that the explanation results of classification models, obtained through explanation algorithms such as CAM, SmoothGrad and LIME, would be close to the pixel clusters of visual objects. Specifically, PSSL is obtained for each image by interpreting the classification results and aggregating an ensemble of explanations queried from multiple classifiers to lower the bias caused by single models. With PSSL for every image of ImageNet, the proposed method leverages a weighted segmentation learning procedure to pre-train the segmentation network en masse. Experiment results show that, with ImageNet accompanied by PSSL as the source dataset, the proposed end-to-end pre-training strategy successfully boosts the performance of various segmentation models, i.e., PSPNet-ResNet50, DeepLabV3-ResNet50, and OCRNet-HRNetW18, on a number of segmentation tasks, such as CamVid, VOC-A, VOC-C, ADE20K, and CityScapes, with significant improvements. The source code is availabel at https://github.com/PaddlePaddle/PaddleSeg.

preprint2022arXiv

Evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins in RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$

The ground-state magnetic structures of the Eu$^{2+}$ spins in recently discovered RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$ superconductors have been investigated by neutron powder diffraction measurements. It is found that as the superconductivity gets suppressed with the increase of Ni doping, the magnetic propagation vector of the Eu sublattice diminishes, corresponding to the decrease of the rotation angle between the moments in neighboring Eu layers. The ferromagnetic Eu layers are helically modulated along the $\mathit{c}$ axis with an incommensurate magnetic propagation vector in both the ferromagnetic superconductor RbEu(Fe$_{0.95}$Ni$_{0.05}$)$_{4}$As$_{4}$ and the superconducting ferromagnet RbEu(Fe$_{0.93}$Ni$_{0.07}$)$_{4}$As$_{4}$. Such a helical structure transforms into a purely collinear ferromagnetic structure for non-superconducting RbEu(Fe$_{0.91}$Ni$_{0.09}$)$_{4}$As$_{4}$, with all the Eu$^{2+}$ spins lying along the tetragonal (1 1 0) direction. The evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins with increasing Ni doping is supported by first-principles calculations. The variation of the rotation angle between adjacent Eu$^{2+}$ layers can be well explained by considering the change of magnetic exchange couplings mediated by the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction.

preprint2022arXiv

FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity

Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for the auto-tuning approaches, and the increasing demands require increasing auto-tuning efficiency and quality. Currently, the DL compilers partition the input DL models into several subgraphs and leverage the auto-tuning to find the optimal tensor codes of these subgraphs. However, existing auto-tuning approaches usually regard subgraphs as individual ones and overlook the similarities across them, and thus fail to exploit better tensor codes under limited time budgets. We propose FamilySeer, an auto-tuning framework for DL compilers that can generate better tensor codes even with limited time budgets. FamilySeer exploits the similarities and differences among subgraphs can organize them into subgraph families, where the tuning of one subgraph can also improve other subgraphs within the same family. The cost model of each family gets more purified training samples generated by the family and becomes more accurate so that the costly measurements on real hardware can be replaced with the lightweight estimation through cost model. Our experiments show that FamilySeer can generate model codes with the same code performance more efficiently than state-of-the-art auto-tuning frameworks.

preprint2022arXiv

Finite-volume hyperbolic 3-manifolds are almost determined by their finite quotient groups

For any orientable finite-volume hyperbolic 3-manifold, this paper proves that the profinite isomorphism type of the fundamental group uniquely determines the isomorphism type of the first integral cohomology, as marked with the Thurston norm and the fibered classes; moreover, up to finite ambiguity, the profinite isomorphism type determines the isomorphism type of the fundamental group, among the class of finitely generated 3-manifold groups.

preprint2022arXiv

GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport

High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes. However, motions can be too fast -- such as when the object being transported has an open-top, is fragile, or both. One way to avoid spills or damage, is to move the arm slowly. We propose an alternative: Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT), a time-optimizing motion planner based on our prior work, that includes constraints based on accelerations at the robot end-effector. With GOMP-FIT, a robot can perform high-speed motions that avoid obstacles and use inertial forces to its advantage. In experiments transporting open-top containers with varying tilt tolerances, whereas GOMP computes sub-second motions that spill up to 90% of the contents during transport, GOMP-FIT generates motions that spill 0% of contents while being slowed by as little as 0% when there are few obstacles, 30% when there are high obstacles and 45-degree tolerances, and 50% when there 15-degree tolerances and few obstacles. Videos and more at: https://berkeleyautomation.github.io/gomp-fit/.

preprint2022arXiv

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://github.com/renli1024/SE-GNN.

preprint2022arXiv

Improved-Flow Warp Module for Remote Sensing Semantic Segmentation

Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label. In this letter, we proposed a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales for remote sensing semantic segmentation. The improved-flow warp module is applied along with the feature extraction process in the convolutional neural network. First, IFWM computes the offsets of pixels by a learnable way, which can alleviate the misalignment of the multi-scale features. Second, the offsets help with the low-resolution deep feature up-sampling process to improve the feature accordance, which boosts the accuracy of semantic segmentation. We validate our method on several remote sensing datasets, and the results prove the effectiveness of our method..

preprint2022arXiv

Interfacial Charge-transfer Excitonic Insulator in a Two-dimensional Organic-inorganic Superlattice

Excitonic insulators are long-sought-after quantum materials predicted to spontaneously open a gap by the Bose condensation of bound electron-hole pairs, namely, excitons, in their ground state. Since the theoretical conjecture, extensive efforts have been devoted to pursuing excitonic insulator platforms for exploring macroscopic quantum phenomena in real materials. Reliable evidences of excitonic character have been obtained in layered chalcogenides as promising candidates. However, owing to the interference of intrinsic lattice instabilities, it is still debatable whether those features, such as charge density wave and gap opening, are primarily driven by the excitonic effect or by the lattice transition. Herein, we develop a novel charge-transfer excitonic insulator in organic-inorganic superlattice interfaces, which serves as an ideal platform to decouple the excitonic effect from the lattice effect. In this system, we observe the narrow gap opening and the formation of a charge density wave without periodic lattice distortion, providing visualized evidence of exciton condensation occurring in thermal equilibrium. Our findings identify spontaneous interfacial charge transfer as a new strategy for developing novel excitonic insulators and investigating their correlated many-body physics.

preprint2022arXiv

KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning

In general, multiple domain cyberspace security assessments can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning methods are also based on expert-given rules, resulting in inefficiency and a low degree of intelligence. To address this challenge, we create a Knowledge Graph (KG) of multiple domain cyberspace in order to provide a standard semantic description of the multiple domain cyberspace. Following that, we proposed a user's permissions reasoning method based on reinforcement learning. All permissions in cyberspace are represented as nodes, and an agent is trained to find all permissions that user can have according to user's initial permissions and cyberspace KG. We set 10 reward setting rules based on the features of cyberspace KG in the reinforcement learning of reward information setting, so that the agent can better locate user's all permissions and avoid blindly finding user's permissions. The results of the experiments showed that the proposed method can successfully reason about user's permissions and increase the intelligence level of the user's permissions reasoning method. At the same time, the F1 value of the proposed method is 6% greater than that of the Translating Embedding (TransE) method.

preprint2022arXiv

Mapping classes are almost determined by their finite quotient actions

Given any connected compact orientable surface, a pair of mapping classes are said to be procongruently conjugate if they induce a conjugate pair of outer automophisms on the profinite completion of the fundamental group of the surface. For example, this occurs if they induce conjugate outer automorphisms on every characteristic finite quotient of the fundamental group. In this paper, it is shown that every procongruent conjugacy class of mapping classes, as a subset of the surface mapping class group, is the disjoint union of at most finitely many conjugacy classes of mapping classes. For any pseudo-Anosov mapping class of a connected closed orientable surface, several topological features are confirmed to depend only on the procongurent conjugacy class of the mapping class, including: the stretching factor, the topological type of the prong singularities, the transverse orientability of the invariant foliations, and the isomorphism type of the symplectic Floer homology.

preprint2022arXiv

Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow

For marketing, we sometimes need to recommend content for multiple pages in sequence. Different from general sequential decision making process, the use cases have a simpler flow where customers per seeing recommended content on each page can only return feedback as moving forward in the process or dropping from it until a termination state. We refer to this type of problems as sequential decision making in linear--flow. We propose to formulate the problem as an MDP with Bandits where Bandits are employed to model the transition probability matrix. At recommendation time, we use Thompson sampling (TS) to sample the transition probabilities and allocate the best series of actions with analytical solution through exact dynamic programming. The way that we formulate the problem allows us to leverage TS's efficiency in balancing exploration and exploitation and Bandit's convenience in modeling actions' incompatibility. In the simulation study, we observe the proposed MDP with Bandits algorithm outperforms Q-learning with $ε$-greedy and decreasing $ε$, independent Bandits, and interaction Bandits. We also find the proposed algorithm's performance is the most robust to changes in the across-page interdependence strength.

preprint2022arXiv

Measuring neutrino dynamics in NMSSM with a right-handed sneutrino LSP at the ILC

We study the possibility of measuring neutrino Yukawa couplings in the Next-to-Minimal Supersymmetric Standard Model with right-handed neutrinos (NMSSMr) when the lightest right-handed sneutrino is the Dark Matter (DM) candidate, by exploiting a `dijet + dilepton + Missing Transverse Energy' signature. We show that, contrary to the miminal realisation of Supersymmetry (SUSY), the MSSM, wherein the DM candidate is typically a much heavier (fermionic) neutralino state, this extended model of SUSY offers one with a much lighter (bosonic) state as DM that can then be produced at the next generation of $e^+e^-$ colliders with energies up to 500 GeV or so. The ensuing signal, energing from chargino pair production and subsequent decay, is extremely pure so it also affords one with the possibility of extracting the Yukawa parameters of the (s)neutrino sector. Altogether, our results serve the purpose of motivating searches for light DM signals at such machines, where the DM candidate can have a mass around the electroweak scale.

preprint2022arXiv

Morest: Model-based RESTful API Testing with Execution Feedback

RESTful APIs are arguably the most popular endpoints for accessing Web services. Blackbox testing is one of the emerging techniques for ensuring the reliability of RESTful APIs. The major challenge in testing RESTful APIs is the need for correct sequences of API operation calls for in-depth testing. To build meaningful operation call sequences, researchers have proposed techniques to learn and utilize the API dependencies based on OpenAPI specifications. However, these techniques either lack the overall awareness of how all the APIs are connected or the flexibility of adaptively fixing the learned knowledge. In this paper, we propose Morest, a model-based RESTful API testing technique that builds and maintains a dynamically updating RESTful-service Property Graph (RPG) to model the behaviors of RESTful-services and guide the call sequence generation. We empirically evaluated Morest and the results demonstrate that Morest can successfully request an average of 152.66%-232.45% more API operations, cover 26.16%-103.24% more lines of code, and detect 40.64%-215.94% more bugs than state-of-the-art techniques. In total, we applied Morest to 6 real-world projects and found 44 bugs (13 of them cannot be detected by existing approaches). Specifically, 2 of the confirmed bugs are from Bitbucket, a famous code management service with more than 6 million users.

preprint2022arXiv

Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning

The existing network attack and defense method can be regarded as game, but most of the game only involves network domain, not multiple domain cyberspace. To address this challenge, this paper proposed a multiple domain cyberspace attack and defense game model based on reinforcement learning. We define the multiple domain cyberspace include physical domain, network domain and digital domain. By establishing two agents, representing the attacker and the defender respectively, defender will select the multiple domain actions in the multiple domain cyberspace to obtain defender's optimal reward by reinforcement learning. In order to improve the defense ability of defender, a game model based on reward randomization reinforcement learning is proposed. When the defender takes the multiple domain defense action, the reward is randomly given and subject to linear distribution, so as to find the better defense policy and improve defense success rate. The experimental results show that the game model can effectively simulate the attack and defense state of multiple domain cyberspace, and the proposed method has a higher defense success rate than DDPG and DQN.

preprint2022arXiv

Multiple-Photon Resonance Enabled Quantum Interference in Emission Spectroscopy of N_2^+

Quantum interference occurs frequently in the interaction of laser radiation with materials, leading to a series of fascinating effects such as lasing without inversion, electromagnetically induced transparency, Fano resonance, etc. Such quantum interference effects are mostly enabled by single-photon resonance with transitions in the matter, regardless of how many optical frequencies are involved. Here, we demonstrate quantum interference driven by multiple photons in the emission spectroscopy of nitrogen ions that are resonantly pumped by ultrafast infrared laser pulses. In the spectral domain, Fano resonance is observed in the emission spectrum, where a laser-assisted dynamic Stark effect creates the continuum. In the time domain, the fast-evolving emission is measured, revealing the nature of free-induction decay (FID) arising from quantum radiation and molecular cooperativity. These findings clarify the mechanism of coherent emission of nitrogen ions pumped with MIR pump laser and are likely to be universal. The present work opens a route to explore the important role of quantum interference during the interaction of intense laser pulses with materials near multiple photon resonance.

preprint2022arXiv

Muon-spin relaxation study of the layered kagome superconductor CsV$_3$Sb$_5$

The ${\mathbb{Z}}_{2}$ topological metals $R$V$_3$Sb$_5$ ($R$ = K, Rb, Cs) with a layered kagome structure provide a unique opportunity to investigate the interplay between charge order, superconductivity and topology. Here, we report muon-spin relaxation/rotation ($μ$SR) measurements performed on CsV$_3$Sb$_5$ across a broad temperature range, in order to uncover the nature of the charge-density wave order and superconductivity in this material. From zero-field $μ$SR, we find that spontaneous magnetic fields appear below 50 K which is well below the charge-density wave transition ($T^* \sim 93$ K). We show that these spontaneous fields are dynamic in nature making it difficult to associate them with a hidden static order. The superconducting state of CsV$_3$Sb$_5$ is found to preserve time-reversal symmetry and the transverse-field $μ$SR results are consistent with a superconducting state that has two fully open gaps.

preprint2022arXiv

Networks with pixels embedding: a method to improve noise resistance in images classification

In the task of image classification, usually, the network is sensitive to noises. For example, an image of cat with noises might be misclassified as an ostrich. Conventionally, to overcome the problem of noises, one uses the technique of data augmentation, that is, to teach the network to distinguish noises by adding more images with noises in the training dataset. In this work, we provide a noise-resistance network in images classification by introducing a technique of pixel embedding. We test the network with pixel embedding, which is abbreviated as the network with PE, on the mnist database of handwritten digits. It shows that the network with PE outperforms the conventional network on images with noises. The technique of pixel embedding can be used in many tasks of image classification to improve noise resistance.

preprint2022arXiv

Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. In practice, these SSFL systems implement semi-supervised training by assigning a "guessed" label to the unlabeled data near the labeled data to convert the unsupervised problem into a fully supervised problem. However, the inherent properties of such semi-supervised training techniques create a new attack surface. In this paper, we discover and reveal a simple yet powerful poisoning attack against SSFL. Our attack utilizes the natural characteristic of semi-supervised learning to cause the model to be poisoned by poisoning unlabeled data. Specifically, the adversary just needs to insert a small number of maliciously crafted unlabeled samples (e.g., only 0.1\% of the dataset) to infect model performance and misclassification. Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods. To mitigate the attacks, we propose a defense, i.e., a minimax optimization-based client selection strategy, to enable the server to select the clients who hold the correct label information and high-quality updates. Our defense further employs a quality-based aggregation rule to strengthen the contributions of the selected updates. Evaluations under different attack conditions show that the proposed defense can well alleviate such unlabeled poisoning attacks. Our study unveils the vulnerability of SSFL to unlabeled poisoning attacks and provides the community with potential defense methods.

preprint2022arXiv

PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a superior trade-off between accuracy and speed compared to other methods. On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

preprint2022arXiv

PP-Matting: High-Accuracy Natural Image Matting

Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches have achieved great improvements in image matting. However, most of them require a user-supplied trimap as an auxiliary input, which limits the matting applications in the real world. Although some trimap-free approaches have been proposed, the matting quality is still unsatisfactory compared to trimap-based ones. Without the trimap guidance, the matting models suffer from foreground-background ambiguity easily, and also generate blurry details in the transition area. In this work, we propose PP-Matting, a trimap-free architecture that can achieve high-accuracy natural image matting. Our method applies a high-resolution detail branch (HRDB) that extracts fine-grained details of the foreground with keeping feature resolution unchanged. Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask. It prevents the detail prediction from local ambiguity caused by semantic context missing. In addition, we conduct extensive experiments on two well-known benchmarks: Composition-1k and Distinctions-646. The results demonstrate the superiority of PP-Matting over previous methods. Furthermore, we provide a qualitative evaluation of our method on human matting which shows its outstanding performance in the practical application. The code and pre-trained models will be available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

preprint2022arXiv

Rate-Constrained Shaping Codes for Finite-State Channels With Cost

Shaping codes are used to generate code sequences in which the symbols obey a prescribed probability distribution. They arise naturally in the context of source coding for noiseless channels with unequal symbol costs. Recently, shaping codes have been proposed to extend the lifetime of flash memory and reduce DNA synthesis time. In this paper, we study a general class of shaping codes for noiseless finite-state channels with cost and i.i.d. sources. We establish a relationship between the code rate and minimum average symbol cost. We then determine the rate that minimizes the average cost per source symbol (total cost). An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source.

preprint2022arXiv

Sub-MHz spectral dip in a resonator-free twisted gain medium

Ultra-narrow optical spectral features resulting from highly dispersive light-matter interactions are essential for a broad range of applications such as spectroscopy, slow-light, and high-precision sensing. Features approaching sub-MHz, or equivalently, Q-factors approaching ~1 billion and beyond, are challenging to obtain in solid-state systems, ultimately limited by loss. We present a novel approach to achieve tunable sub-MHz spectral features, at room temperature, without resonators. We exploit gain-enhanced polarization pulling in a twisted birefringent medium where polarization eigenmodes are frequency-dependent. Using Brillouin gain in a commercial spun fiber, we experimentally achieve a 0.72 MHz spectral dip, the narrowest backward Brillouin scattering feature ever reported. Further optimization can potentially reduce the linewidth to <0.1 MHz. Our approach is simple and broadly applicable, offering on-demand tunability and high sensitivity, with a wide range of applications such as microwave photonic filters, slow and fast light, and optical sensing.

preprint2022arXiv

Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding

Answer grounding aims to reveal the visual evidence for visual question answering (VQA), which entails highlighting relevant positions in the image when answering questions about images. Previous attempts typically tackle this problem using pretrained object detectors, but without the flexibility for objects not in the predefined vocabulary. However, these black-box methods solely concentrate on the linguistic generation, ignoring the visual interpretability. In this paper, we propose Dual Visual-Linguistic Interaction (DaVI), a novel unified end-to-end framework with the capability for both linguistic answering and visual grounding. DaVI innovatively introduces two visual-linguistic interaction mechanisms: 1) visual-based linguistic encoder that understands questions incorporated with visual features and produces linguistic-oriented evidence for further answer decoding, and 2) linguistic-based visual decoder that focuses visual features on the evidence-related regions for answer grounding. This way, our approach ranked the 1st place in the answer grounding track of 2022 VizWiz Grand Challenge.

preprint2022arXiv

The effect of $f$-$c$ hybridization on the $γ\rightarrowα$ phase transition of cerium studied by lanthanum doping

The hybridization between the localized 4$f$ level ($f$) with conduction ($c$) states in $γ$-Ce upon cooling has been previously revealed in single crystalline thin films experimentally and theoretically, whereas its influence on the $γ\rightarrowα$ phase transition was not explicitly verified, due to the fact that the phase transition happened in the bulk-layer, leaving the surface in the $γ$ phase. Here in our work, we circumvent this issue by investigating the effect of alloying addition of La on Ce, by means of crystal structure, electronic transport and ARPES measurements, together with a phenomenological periodic Anderson model and a modified Anderson impurity model. Our current researches indicate that the weakening of $f$-$c$ hybridization is the major factor in the suppression of $γ\rightarrowα$ phase transition by La doping. The consistency of our results with the effects of other rare earth and actinide alloying additions on the $γ\rightarrowα$ phase transition of Ce is also discussed. Our work demonstrates the importance of the interaction of $f$ and $c$ electrons in understanding the unconventional phase transition in Ce, which is intuitive for further researches on other rare earth and actinide metals and alloys with similar phase transition behaviors.

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.

preprint2022arXiv

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

This paper describes our submission to ICASSP 2022 Multi-channel Multi-party Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA) estimation are used to improve the accuracy of speaker diarization. Multi-channel combination and overlap detection are applied to reduce the missed speaker error. A modified DOVER-Lap is also proposed to fuse the results of different systems. We achieve the final DER of 5.79% on the Eval set and 7.23% on the Test set. For Track 2, we develop our system using the Conformer model in a joint CTC-attention architecture. Serialized output training is adopted to multi-speaker overlapped speech recognition. We propose a neural front-end module to model multi-channel audio and train the model end-to-end. Various data augmentation methods are utilized to mitigate over-fitting in the multi-channel multi-speaker E2E system. Transformer language model fusion is developed to achieve better performance. The final CER is 19.2% on the Eval set and 20.8% on the Test set.

preprint2022arXiv

Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator

Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. Inspired by Artificial Neural Networks (ANNs) quantization technology, binarized SNN (BSNN) is introduced to solve the memory problem. Due to the lack of suitable learning algorithms, BSNN is usually obtained by ANN-to-SNN conversion, whose accuracy will be limited by the trained ANNs. In this paper, we propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators, which dynamically selects the network layers to be binarized to ensure the accuracy of the network by evaluating the error caused by the binarized weights during the network learning process. Experimental results show that this method can reduce storage space by more than 20 % without losing network accuracy. At the same time, in order to accelerate the training speed of the network, the global average pooling(GAP) layer is introduced to replace the fully connected layers by the combination of convolution and pooling, so that SNNs can use a small number of time steps to obtain better recognition accuracy. In the extreme case of using only one time step, we still can achieve 92.92 %, 91.63 % ,and 63.54 % testing accuracy on three different datasets, FashionMNIST, CIFAR-10, and CIFAR-100, respectively.

preprint2022arXiv

Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block

Spiking neural networks (SNNs), as one of the brain-inspired models, has spatio-temporal information processing capability, low power feature, and high biological plausibility. The effective spatio-temporal feature makes it suitable for event streams classification. However, neuromorphic datasets, such as N-MNIST, CIFAR10-DVS, DVS128-gesture, need to aggregate individual events into frames with a new higher temporal resolution for event stream classification, which causes high training and inference latency. In this work, we proposed a spatio-temporal compression method to aggregate individual events into a few time steps of synaptic current to reduce the training and inference latency. To keep the accuracy of SNNs under high compression ratios, we also proposed a synaptic convolutional block to balance the dramatic change between adjacent time steps. And multi-threshold Leaky Integrate-and-Fire (LIF) with learnable membrane time constant is introduced to increase its information processing capability. We evaluate the proposed method for event streams classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture datasets. The experiment results show that our proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps.

preprint2022arXiv

Virtual homological eigenvalues and the Weil--Petersson translation length

For any pseudo-Anosov automorphism on an orientable closed surface, an inquality is established bounding certain growth of virtual homological eigenvalues with the Weil--Petersson translation length. The new inquality fits nicely with other known inequalities due to Kojima and McShane, and due to Lê. The new quantity to be considered is the square sum of the logarithmic radii of the homological eigenvalues (with multiplicity) outside the complex unit circle, called the homological Jensen square sum. The main theorem is as follows. For any cofinal sequence of regular finite covers of a given surface, together with lifts of a given pseudo-Anosov, the homological Jensen square sum of the lifts grows at most linearly fast compared to the covering degree, and the square root of the growth rate is at most $1/\sqrt{4π}$ times the Weil--Petersson translation length of the given pseudo-Anosov.

preprint2021arXiv

A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis

Computed tomography (CT) imaging is a promising approach to diagnosing the COVID-19. Machine learning methods can be employed to train models from labeled CT images and predict whether a case is positive or negative. However, there exists no publicly-available and large-scale CT data to train accurate models. In this work, we propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images. Our method captures semantic information from the whole lung and highlights the functionality of each lung region for better representation learning. The method is then integrated to the last stage of the proposed transfer learning framework to reuse the complex patterns learned from the same CT images. We use a base model integrating self-attention (ATTNs) and convolutional operations. Experimental results show that networks with ATTNs induce greater performance improvement through transfer learning than networks without ATTNs. This indicates attention exhibits higher transferability than convolution. Our results also show that the proposed self-supervised learning method outperforms several baseline methods.

preprint2021arXiv

A strange metal in a bosonic system

Fermi liquid theory forms the basis for our understanding of the majority of metals, which is manifested in the description of transport properties that the electrical resistivity goes as temperature squared in the limit of zero temperature. However, the observations of strange metal states in various quantum materials, notably high-temperature superconductors, bring this spectacularly successful theoretical framework into crisis. When electron scattering rate 1/τ hits its limit, kBT/{\hbar} where {\hbar} is the reduced Planck's constant, T represents absolute temperature and kB denotes Boltzmann's constant, Planckian dissipation occurs and lends strange metals a surprising link to black holes, gravity, and quantum information theory. Here, we show the characteristic signature of strange metallicity arising unprecedentedly in a bosonic system. Our nanopatterned YBa2Cu3O7-δ(YBCO) film arrays reveal T-linear resistance as well as B-linear magnetoresistance over an extended temperature and magnetic field range in a quantum critical region in the phase diagram. Moreover, the slope of the T-linear resistance α_cp appears bounded by α_cp {\approx} h/2e^2 [1/T]_c^onset where T_c^onset is the temperature at which Cooper pairs form, intimating a common scale-invariant transport mechanism corresponding to Planckian dissipation.In contrast to fermionic systems where the temperature and magnetic field dependent scattering rates combine in quadrature of {\hbar}/τ {\approx} {\sqrt} (((k_B T)^2+(μ_B B)^2)), both terms linearly combine in the present bosonic system, i.e. {\hbar}/τ {\approx} (k_B T+[γμ]_B B), where γ is a constant. By extending the reach of strange metal phenomenology to a bosonic system, our results suggest that there is a fundamental principle governing their transport which transcends particle statistics.

preprint2021arXiv

An Empirical Study on Serverless Workflow Service

Along with the wide-adoption of Serverless Computing, more and more applications are developed and deployed on cloud platforms. Major cloud providers present their serverless workflow services to orchestrate serverless functions, making it possible to perform complex applications effectively. A comprehensive instruction is necessary to help developers understand the pros and cons, and make better choices among these serverless workflow services. However, the characteristics of these serverless workflow services have not been systematically analyzed. To fill the knowledge gap, we survey four mainstream serverless workflow services, investigating their characteristics and performance. Specifically, we review their official documents and compare them in terms of seven dimensions including programming model, state management, etc. Then, we compare the performance (i.e., execution time of functions, execution time of workflows, orchestration overhead of workflows) under various experimental settings considering activity complexity and data-flow complexity of workflows, as well as function complexity of serverless functions. Finally, we discuss and verify the service effectiveness for two actual workloads. Our findings could help application developers and serverless providers to improve the development efficiency and user experience.

preprint2021arXiv

An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis.

preprint2021arXiv

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy

Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine learning approaches have been employed to automatically predict synaptic clefts from EM images. In this work, we propose a novel and augmented deep learning model, known as CleftNet, for improving synaptic cleft detection from brain EM images. We first propose two novel network components, known as the feature augmentor and the label augmentor, for augmenting features and labels to improve cleft representations. The feature augmentor can fuse global information from inputs and learn common morphological patterns in clefts, leading to augmented cleft features. In addition, it can generate outputs with varying dimensions, making it flexible to be integrated in any deep network. The proposed label augmentor augments the label of each voxel from a value to a vector, which contains both the segmentation label and boundary label. This allows the network to learn important shape information and to produce more informative cleft representations. Based on the proposed feature augmentor and label augmentor, We build the CleftNet as a U-Net like network. The effectiveness of our methods is evaluated on both online and offline tasks. Our CleftNet currently ranks \#1 on the online task of the CREMI open challenge. In addition, both quantitative and qualitative results in the offline tasks show that our method outperforms the baseline approaches significantly.

preprint2021arXiv

Coexistence of Ferroelectric-like Polarization and Dirac-like Surface State in TaNiTe5

By combining angle-resolved photoemission spectroscopy (ARPES), scanning tunneling microscopy (STM), piezoresponse force microscopy (PFM) and first-principles calculations, we have studied the low-energy band structure, atomic structure and charge polarization on the surface of a topological semimetal candidate TaNiTe5. Dirac-like surface states were observed on the (010) surface by ARPES, consistent with the first-principles calculations. On the other hand, PFM reveals a switchable ferroelectric-like polarization on the same surface. We propose that the noncentrosymmetric surface reconstruction observed by STM could be the origin of the observed ferroelectric-like state in this novel material. Our findings provide a new platform with the coexistence of ferroelectric-like surface charge distribution and novel surface states.

preprint2021arXiv

Dirac Nodal Lines and Nodal Loops in a Topological Kagome Superconductor CsV$_3$Sb$_5$

The intertwining of charge order, superconductivity and band topology has promoted the AV$_3$Sb$_5$ (A=K, Rb, Cs) family of materials to the center of attention in condensed matter physics. Underlying those mysterious macroscopic properties such as giant anomalous Hall conductivity (AHC) and chiral charge density wave is their nontrivial band topology. While there have been numerous experimental and theoretical works investigating the nontrivial band structure and especially the van Hove singularities, the exact topological phase of this family remains to be clarified. In this work, we identify CsV$_3$Sb$_5$ as a Dirac nodal line semimetal based on the observation of multiple Dirac nodal lines and loops close to the Fermi level. Combining photoemission spectroscopy and density functional theory, we identify two groups of Dirac nodal lines along $k_z$ direction and one group of Dirac nodal loops in the A-H-L plane. These nodal loops are located at the Fermi level within the instrumental resolution limit. Importantly, our first-principle analyses indicate that these nodal loops may be a crucial source of the mysterious giant AHC observed. Our results not only provide a clear picture to categorize the band structure topology of this family of materials, but also suggest the dominant role of topological nodal loops in shaping their transport behavior.

preprint2021arXiv

Experimental Evidence of Nonlinear Avalanche Dynamics of Energetic Particle Modes

Recent observations in HL-2A tokamak give new experimental evidences of energetic particle mode (EPM) avalanche. In a strong EPM burst, the mode structure propagates radially outward within two hundred Alfvén time, while the frequency of the dominant mode changes self-consistently to maximize wave-particle power exchange and mode growth. This suggests that significant energetic particle transport occurs in this avalanche phase, in agreement with theoretical framework of EPM convective amplification. A simplified relay runner model yields satisfactory interpretations of the measurements. The results can help understanding the nonlinear dynamics of energetic particle driven modes in future burning plasmas, such as ITER.

preprint2021arXiv

Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation

In this paper, a novel machine learning regression based single event transient (SET) modeling method is proposed. The proposed method can obtain a reasonable and accurate model without considering the complex physical mechanism. We got plenty of SET current data of SMIC 130nm bulk CMOS by TCAD simulation under different conditions (e.g. different LET and different drain bias voltage). A multilayer feedfordward neural network is used to build the SET pulse current model by learning the data from TCAD simulation. The proposed model is validated with the simulation results from TCAD simulation. The trained SET pulse current model is implemented as a Verilog-A current source in the Cadence Spectre circuit simulator and an inverter with five fan-outs is used to show the practicability and reasonableness of the proposed SET pulse current model for circuit-level single-event effect (SEE) simulation.

preprint2021arXiv

PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation

Image Segmentation plays an essential role in computer vision and image processing with various applications from medical diagnosis to autonomous car driving. A lot of segmentation algorithms have been proposed for addressing specific problems. In recent years, the success of deep learning techniques has tremendously influenced a wide range of computer vision areas, and the modern approaches of image segmentation based on deep learning are becoming prevalent. In this article, we introduce a high-efficient development toolkit for image segmentation, named PaddleSeg. The toolkit aims to help both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models. Currently, PaddleSeg supports around 20 popular segmentation models and more than 50 pre-trained models from real-time and high-accuracy levels. With modular components and backbone networks, users can easily build over one hundred models for different requirements. Furthermore, we provide comprehensive benchmarks and evaluations to show that these segmentation algorithms trained on our toolkit have more competitive accuracy. Also, we provide various real industrial applications and practical cases based on PaddleSeg. All codes and examples of PaddleSeg are available at https://github.com/PaddlePaddle/PaddleSeg.

preprint2021arXiv

Superconductivity modulated by structural phase transitions in pressurized vanadium-based kagome metals

The interplay of superconductivity with electronic and structural instabilities on the kagome lattice provides a fertile ground for the emergence of unusual phenomena. The vanadium-based kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Rb, Cs) exhibit superconductivity on an almost ideal kagome lattice, with the superconducting transition temperature $T_{\rm c}$ forming two domes upon pressure-tuning. The first dome arises from the competition between superconductivity and a charge-density-wave, whereas the origin for the second dome remains unclear. Herein, we show that the appearance of the second superconducting dome in KV$_3$Sb$_5$ and RbV$_3$Sb$_5$ is associated with transitions from hexagonal $P6$/$mmm$ to monoclinic $P2$/$m$ structures, evidenced by splitting of structural peaks from synchrotron powder X-ray diffraction experiments and imaginary phonon frequencies in first-principles calculations. In KV$_3$Sb$_5$, transition to an orthorhombic $Pmmm$ structure is further observed for pressure $p\gtrsim20$ GPa, and is correlated with the strong suppression of $T_{\rm c}$ in the second superconducting dome. Our findings indicate distortions of the crystal structure modulates superconductivity in $A$V$_3$Sb$_5$ under pressure, providing a platform to study the emergence of superconductivity in the presence of multiple structural instabilities.

preprint2021arXiv

Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks

Dixon MRI is widely used for body composition studies. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed on the scanner, making the data difficult to analyse. The most common artifact are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which can be wasteful and lead to unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000 participants, and thousands of swaps will occur. If those go undetected, errors will propagate into processes such as abdominal organ segmentation and dilute the results in population-based analyses. There is a clear need for a fast and robust method to accurately separate fat and water channels. In this work we propose such a method based on style transfer using a conditional generative adversarial network. We also introduce a new Dixon loss function for the generator model. Using data from the UK Biobank Dixon MRI, our model is able to predict highly accurate fat and water channels that are free from artifacts. We show that the model separates fat and water channels using either single input (in-phase) or dual input (in-phase and opposed-phase), with the latter producing improved results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.

preprint2021arXiv

The ADENIUM Telescope- A new beam telescope for the DESY II Test Beam Facility

This version removed by arXiv administrators because the submitter did not have the right to agree to the license at the time of submission

preprint2021arXiv

Variable Selection via Thompson Sampling

Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning. The algorithm has a Bayesian spirit in the sense that it selects arms based on posterior samples of reward probabilities of each arm. By forging a connection between combinatorial binary bandits and spike-and-slab variable selection, we propose a stochastic optimization approach to subset selection called Thompson Variable Selection (TVS). TVS is a framework for interpretable machine learning which does not rely on the underlying model to be linear. TVS brings together Bayesian reinforcement and machine learning in order to extend the reach of Bayesian subset selection to non-parametric models and large datasets with very many predictors and/or very many observations. Depending on the choice of a reward, TVS can be deployed in offline as well as online setups with streaming data batches. Tailoring multiplay bandits to variable selection, we provide regret bounds without necessarily assuming that the arm mean rewards be unrelated. We show a very strong empirical performance on both simulated and real data. Unlike deterministic optimization methods for spike-and-slab variable selection, the stochastic nature makes TVS less prone to local convergence and thereby more robust.

preprint2021arXiv

Variable Selection with ABC Bayesian Forests

Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric environments such as the linear model or additive variants thereof. In this work, we abandon the linear model framework, which can be quite detrimental when the covariates impact the outcome in a non-linear way, and turn to tree-based methods for variable selection. Such variable screening is traditionally done by pruning down large trees or by ranking variables based on some importance measure. Despite heavily used in practice, these ad-hoc selection rules are not yet well understood from a theoretical point of view. In this work, we devise a Bayesian tree-based probabilistic method and show that it is consistent for variable selection when the regression surface is a smooth mix of $p>n$ covariates. These results are the first model selection consistency results for Bayesian forest priors. Probabilistic assessment of variable importance is made feasible by a spike-and-slab wrapper around sum-of-trees priors. Sampling from posterior distributions over trees is inherently very difficult. As an alternative to MCMC, we propose ABC Bayesian Forests, a new ABC sampling method based on data-splitting that achieves higher ABC acceptance rate. We show that the method is robust and successful at finding variables with high marginal inclusion probabilities. Our ABC algorithm provides a new avenue towards approximating the median probability model in non-parametric setups where the marginal likelihood is intractable.

preprint2020arXiv

A Secure Federated Learning Framework for 5G Networks

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

preprint2020arXiv

Atomic line defects and zero-energy end states in monolayer Fe(Te,Se) high-temperature superconductors

Majorana zero-energy bound states (ZEBSs) have been proposed to exist at the ends of one-dimensional Rashba nanowires proximity-coupled to an s-wave superconductor in an external magnetic field induced Zeeman field. Such hybrid structures have been a central platform in the search for non-Abelian Majorana zero modes (MZMs) toward fault-tolerant topological quantum computing. Here we report the discovery of ZEBSs simultaneously appearing at each end of a one-dimensional atomic line defect in monolayer iron-based high-temperature superconductor FeTe0.5Se0.5 films grown on SrTiO3(001) substrates. The spectroscopic properties of the ZEBSs, including the temperature and tunneling barrier dependences, as well as their fusion induced by coupling on line defects of different lengths are found to be robust and consistent with those of the MZMs. These observations suggest a realization of topological Shockley defects at the ends of an atomic line defect in a two-dimensional s-wave superconductor that can host a Kramers pair of MZMs protected by time-reversal symmetry along the chain. Our findings reveal an unprecedented class of topological line defect excitations in two-dimensional superconductor FeTe0.5Se0.5 monolayer films and offer an advantageous platform for generating topological zero-energy excitations at higher operating temperatures, in a single material, and under zero external magnetic field.

preprint2020arXiv

Comparison between $Fermi$ Detected and non-$Fermi$ Detected Superluminal Sources

Active galactic nuclei (AGNs) have been attracting research attention due to their special observable properties. Specifically, a majority of AGNs are detected by Fermi-LAT missions, but not by Fermi-LAT, which raises the question of whether any differences exist between the two. To answer this issue, we compile a sample of 291 superluminal AGNs (189 FDSs and 102 non-FDSs) from available multi-wavelength radio, optical, and X-ray (or even $γ$-ray) data and Doppler factors and proper motion ($μ$) (or apparent velocity ($β_{\rm{app}}$)); calculated the apparent velocity from their proper motion, Lorentz factor ($Γ$), viewing angle ($ϕ$) and co-moving viewing angle ($ϕ_{co}$) for the sources with available Doppler factor ($δ$); and performed some statistical analyses for both types. Our study indicated that1. In terms of average values, FDSs have higher proper motions ($μ$), apparent velocities ($β_{\rm app}$), Doppler factor ($δ$), Lorentz factor ($Γ$), and smaller viewing angle ($ϕ$). Nevertheless, there is no clear difference in co-moving viewing angles ($ϕ_{\rm co}$).

preprint2020arXiv

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50\% compared to the federated learning framework that does not use a gradient compression scheme.

preprint2020arXiv

Deep Learning of High-Order Interactions for Protein Interface Prediction

Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface from hand-crafted features. Recent approaches employ deep neural networks and predict the interaction of each amino acid pair independently. However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions. Intuitively, the prediction of an amino acid pair should depend on both their features and the information of other amino acid pairs. In this work, we propose to formulate the protein interface prediction as a 2D dense prediction problem. In addition, we propose a novel deep model to incorporate the sequential information and high-order pairwise interactions to perform interface predictions. We represent proteins as graphs and employ graph neural networks to learn node features. Then we propose the sequential modeling method to incorporate the sequential information and reorder the feature matrix. Next, we incorporate high-order pairwise interactions to generate a 3D tensor containing different pairwise interactions. Finally, we employ convolutional neural networks to perform 2D dense predictions. Experimental results on multiple benchmarks demonstrate that our proposed method can consistently improve the protein interface prediction performance.

preprint2020arXiv

Exemplar-based Generative Facial Editing

Image synthesis has witnessed substantial progress due to the increasing power of generative model. This paper we propose a novel generative approach for exemplar based facial editing in the form of the region inpainting. Our method first masks the facial editing region to eliminates the pixel constraints of the original image, then exemplar based facial editing can be achieved by learning the corresponding information from the reference image to complete the masked region. In additional, we impose the attribute labels constraint to model disentangled encodings in order to avoid undesired information being transferred from the exemplar to the original image editing region. Experimental results demonstrate our method can produce diverse and personalized face editing results and provide far more user control flexibility than nearly all existing methods.

preprint2020arXiv

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI). To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by Unmanned Aerial Vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the Federated Learning Framework not only allows various organizations or institutions to collaboratively learn a well-trained global model to monitor AQI without compromising privacy, but also expands the scope of UAV swarms monitoring. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference. The GC-LSTM model utilizes the topological structure of the ground monitoring station to capture the spatio-temporal correlation of historical observation data, which helps the aerial-ground sensing system to achieve accurate AQI inference. Through extensive case studies on a real-world dataset, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw data.

preprint2020arXiv

Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank

An end-to-end image analysis pipeline is presented for the abdominal MRI protocol used in the UK Biobank on the first 38,971 participants. Emphasis is on the processing steps necessary to ensure a high-level of data quality and consistency is produced in order to prepare the datasets for downstream quantitative analysis, such as segmentation and parameter estimation. Quality control procedures have been incorporated to detect and, where possible, correct issues in the raw data. Detection of fat-water swaps in the Dixon series is performed by a deep learning model and corrected automatically. Bone joints are predicted using a hybrid atlas-based registration and deep learning model for the shoulders, hips and knees. Simultaneous estimation of proton density fat fraction and transverse relaxivity (R2*) is performed using both the magnitude and phase information for the single-slice multiecho series. Approximately 98.1% of the two-point Dixon acquisitions were successfully processed and passed quality control, with 99.98% of the high-resolution T1-weighted 3D volumes succeeding. Approximately 99.98% of the single-slice multiecho acquisitions covering the liver were successfully processed and passed quality control, with 97.6% of the single-slice multiecho acquisitions covering the pancreas succeeding. At least one fat-water swap was detected in 1.8% of participants. With respect to the bone joints, approximately 3.3% of participants were missing at least one knee joint and 0.8% were missing at least one shoulder joint. For the participants who received both single-slice multiecho acquisition protocols for the liver a systematic difference between the two protocols was identified and modeled using multiple linear regression. The findings presented here will be invaluable for scientists who seek to use image-derived phenotypes from the abdominal MRI protocol.

preprint2020arXiv

Kondo scenario of the γ-α phase transition in single crystalline Cerium thin films

The physical mechanism driving the $γ$-$α$ phase transition of face-centre-cubic (fcc) cerium (Ce) remains controversial until now. In this work, high quality single crystalline fcc-Ce thin films were grown on Graphene/6$H$-SiC(0001) substrate, and explored by XRD and ARPES measurement. XRD spectra showed a clear $γ$-$α$ phase transition at $T_{γ-α}\approx$ 50 K, which is retarded by strain effect from substrate comparing with $T_{γ-α}$ (about 140 K) of the bulk Ce metal. However, APRES spectra did not show any signature of $α$-phase emerging in the surface-layer from 300 K to 17 K, which implied that $α$-phase might form at the bulk-layer of our Ce thin films. Besides, an evident Kondo dip near Fermi energy was observed in the APRES spectrum at 80 K, indicting the formation of Kondo singlet states in $γ$-Ce. Furthermore, the DFT+DMFT calculations were performed to simulate the electronic structures and the theoretical spectral functions agreed well with the experimental ARPES spectra. In $γ$-Ce, the behavior of the self-energy's imaginary part at low frequency not only confirmed that the Kondo singlet states emerged at $T_{\rm KS} \geq 80$ K, but also implied that they became coherent states at a lower characteristic temperature ($T_{\rm coh}\sim 40$ K) due to the indirect RKKY interaction among $f$-$f$ electrons. Besides, $T_{\rm coh}$ from the theoretical simulation was close to $T_{γ-α}$ from the XRD spectra. These issues suggested that the Kondo scenario might play an important role in the $γ$-$α$ phase transition of cerium thin films.

preprint2020arXiv

Long-Range Exciton Diffusion in Two-Dimensional Assemblies of Cesium Lead Bromide Perovskite Nanocrystals

Förster Resonant Energy Transfer (FRET)-mediated exciton diffusion through artificial nanoscale building block assemblies could be used as a new optoelectronic design element to transport energy. However, so far nanocrystal (NC) systems supported only diffusion length of 30 nm, which are too small to be useful in devices. Here, we demonstrate a FRET-mediated exciton diffusion length of 200 nm with 0.5 cm2/s diffusivity through an ordered, two-dimensional assembly of cesium lead bromide perovskite nanocrystals (PNC). Exciton diffusion was directly measured via steady-state and time-resolved photoluminescence (PL) microscopy, with physical modeling providing deeper insight into the transport process. This exceptionally efficient exciton transport is facilitated by PNCs high PL quantum yield, large absorption cross-section, and high polarizability, together with minimal energetic and geometric disorder of the assembly. This FRET-mediated exciton diffusion length matches perovskites optical absorption depth, opening the possibility to design new optoelectronic device architectures with improved performances, and providing insight into the high conversion efficiencies of PNC-based optoelectronic devices.

preprint2020arXiv

Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust accordingly for model limitations.

preprint2020arXiv

On the Performance of Direct Shaping Codes

In this work, we study a recently proposed direct shaping code for flash memory. This rate-1 code is designed to reduce the wear for SLC (one bit per cell) flash by minimizing the average fraction of programmed cells when storing structured data. Then we describe an adaptation of this algorithm that provides data shaping for MLC (two bits per cell) flash memory. It makes use of a page-dependent cost model and is designed to be compatible with the standard procedure of row-by-row, page-based, wordline programming. We also give experimental results demonstrating the performance of MLC data shaping codes when applied to English and Chinese language text. We then study the potential error propagation properties of direct shaping codes when used in a noisy flash device. In particular, we model the error propagation as a biased random walk in a multidimensional space. We prove an upper bound on the error propagation probability and propose an algorithm that can numerically approach a lower bound. Finally, we study the asymptotic performance of direct shaping codes. We prove that the SLC direct shaping code is suboptimal in the sense that it can only achieve the minimum average cost for a rate-1 code under certain conditions on the source distribution.

preprint2020arXiv

PR-NN: RNN-based Detection for Coded Partial-Response Channels

In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E2PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.

preprint2020arXiv

Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a trade-off between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning and propose a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a Federated Averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a Joint Announcement Protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for traffic flow prediction by grouping the organizations into clusters before applying FedGRU algorithm. Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.

preprint2020arXiv

Rate-Constrained Shaping Codes for Structured Sources

Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor and minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.

preprint2020arXiv

Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework

The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.

preprint2020arXiv

The motion of respiratory droplets produced by coughing

Coronavirus disease 2019 (COVID-19) has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry (PIV) and particle shadow tracking velocimetry (PSTV) to measure the velocity of the airflow and droplets involved in coughing and then constructed a physical model considering the evaporation effect to predict the motion of droplets under different weather conditions. The experimental results indicate that the convection velocity of cough airflow presents the relationship $t^{-0.7}$ with time; hence, the distance from the cougher increases by $t^{0.3}$ in the range of our measurement domain. Substituting these experimental results into the physical model reveals that the small droplets (initial diameter $D \leq$ 100 $μ$m) evaporate to droplet nuclei and that the large droplets with $D \geq$ 500 $μ$m and initial velocity $u_0 \geq$ 5 m/s travel more than 2 m. Winter conditions of low temperature and high relative humidity can cause more droplets to settle to the ground, which may be a possible driver of a second pandemic wave in the autumn and winter seasons.

preprint2020arXiv

The Pade Approximant Based Network for Variational Problems

In solving the variational problem, the key is to efficiently find the target function that minimizes or maximizes the specified functional. In this paper, by using the Pade approximant, we suggest a methods for the variational problem. By comparing the method with those based on the radial basis function networks (RBF), the multilayer perception networks (MLP), and the Legendre polynomials, we show that the method searches the target function effectively and efficiently.

preprint2020arXiv

Theory of femtosecond strong field ion excitation and subsequent lasing in N$_2^+$

Delayed cavity-free forward lasing at the wavelengths of 391 and 428 nm was observed in recent experiments in air or pure nitrogen pumped with an intense femtosecond laser pulse at wavelength of 800~nm. The mechanism responsible for the lasing is highly controversial. In this article we explain the delayed emission by the presence of long-lived polarizations coupling simultaneously ground state X$^2Σ_g^+$ to states A$^2Π_u$ and B$^2Σ_u^+$ of singly ionized nitrogen molecules N$_2^+$. Ionization of neutral nitrogen molecules in a strong laser field and subsequent ion excitation are described by a system of Bloch equations providing a distribution of ions in the ground and excited states A and B at the end of the laser pulse. The delayed signal amplification at the B-X transition wavelength is described by a system of Maxwell-Bloch equations with polarization coupling maintained by a weak laser post-pulse. Two regimes of signal amplification are identified: a signal of a few ps duration at low gas pressures and a short (sub-picosecond) signal at high gas pressures. The theoretical model compares favorably with experimental results.

preprint2020arXiv

Topological Dirac states in a layered telluride TaPdTe$_5$ with quasi-one-dimensional PdTe$_2$ chains

We report the synthesis and systematic studies of a new layered ternary telluride TaPdTe5 with quasi-one-dimensional PdTe2 chains. This compound crystalizes in a layered orthorhombic structure with space group Cmcm. Analysis of its curved field-dependent Hall resistivity, using the two-band model, indicates the hole-dominated transport with a high mobility $μ_h$ = 2.38 $\times$ 10$^3$ cm$^2$ V$^{-1}$ s$^{-1}$ at low temperatures. The in-plane magnetoresistance (MR) displays significant anisotropy with field applied along the crystallographic $b$ axis. The MR with the current applied along the $c$-axis is also measured in high magnetic fields up to 51.7 T. Remarkably, it follows a power-law dependence and reaches (9.5 $\times$ 10$^3$)% at 2.1 K without any signature of saturation. The De Haas-van Alphen oscillations show a small Fermi-surface pocket with a nontrivial Berry phase. The Shubnikov-de Haas (SdH) oscillations are detected at low temperatures and under magnetic fields above 28.5 T. Two effective masses $m^*$ (0.26$m_e$ and 0.41$m_e$) are extracted from the oscillatory SdH data. Our first-principles calculations unveil a topological Dirac cone in its surface states, and, in particular, the topological index indicates that TaPdTe$_5$ is a topologically nontrivial material.

preprint2020arXiv

Type-II Ising superconductivity and anomalous metallic state in macro-size ambient-stable ultrathin crystalline films

Recent emergence of two-dimensional (2D) crystalline superconductors has provided a promising platform to investigate novel quantum physics and potential applications. To reveal essential quantum phenomena therein, ultralow temperature transport investigation on high quality ultrathin superconducting films is critically required, although it has been quite challenging experimentally. Here we report a systematic transport study on the ultrathin crystalline PdTe2 films grown by molecular beam epitaxy (MBE). Interestingly, a new type of Ising superconductivity in 2D centrosymmetric materials is revealed by the detection of large in-plane critical field more than 7 times Pauli limit. Remarkably, in perpendicular magnetic field, we provide solid evidence of anomalous metallic state characterized by the resistance saturation at low temperatures with high quality filters. The robust superconductivity with intriguing quantum phenomena in the macro-size ambient-stable ultrathin PdTe2 films remains almost the same for 20 months, showing great potentials in electronic and spintronic applications.

preprint2020arXiv

Y(4626) as a molecular state from interaction ${D}^*_s\bar{D}_{s1}(2536)-{D}_s\bar{D}_{s1}(2536)$

Recently, a new structure $Y(4626)$ was reported by the Belle Colloboration in the process $e^+e^-\to D_s^+D_{s1}(2536)^-$. In this work, we propose an assignment of the $Y(4626)$ as a ${D}^*_s\bar{D}_{s1}(2536)$ molecular state, which decays into the $D_s^+D_{s1}(2536)^-$ channel through a coupling between ${D}^*_s\bar{D}_{s1}(2536)$ and ${D}_s\bar{D}_{s1}(2536)$ channels. With the help of the heavy quark symmetry, the potential of the interaction ${D}^*_s\bar{D}_{s1}(2536)-{D}_s\bar{D}_{s1}(2536)$ is constructed within the one-boson-exchange model, and inserted into the quasipotential Bethe-Salpeter equation. The pole of obtained scattering amplitude is searched for in the complex plane, which corresponds to a molecular state from the interaction ${D}^*_s\bar{D}_{s1}(2536)-{D}_s\bar{D}_{s1}(2536)$. The results suggest that a pole is produced near the ${D}^*_s\bar{D}_{s1}(2536)$ threshold, which exhibits as a peak in the invariant mass spectrum of the ${D}_s\bar{D}_{s1}(2536)$ channel at about 4626 MeV. It obviously favors the $Y(4265)$ as a ${D}^*_s\bar{D}_{s1}(2536)$ molecular state. In the same model, other molecular states from the interaction ${D}^*_s\bar{D}_{s1}(2536)-{D}_s\bar{D}_{s1}(2536)$ are also predicted, which can be checked in future experiments.

preprint2020arXiv

Zero-energy bound states in the high-temperature superconductors at the two-dimensional limit

Majorana zero modes (MZMs) that obey the non-Abelian statistics have been intensively investigated for potential applications in topological quantum computing. The prevailing signals in tunneling experiments "fingerprinting" the existence of MZMs are the zero-energy bound states (ZEBSs). However, nearly all of the previously reported ZEBSs showing signatures of the MZMs are observed in difficult-to-fabricate heterostructures at very low temperatures and additionally require applied magnetic field. Here, by using in-situ scanning tunneling spectroscopy, we detect the ZEBSs upon the interstitial Fe adatoms deposited on two different high-temperature superconducting one-unit-cell-thick iron chalcogenides on SrTiO3(001). The spectroscopic results resemble the phenomenological characteristics of the MZMs inside the vortex cores of topological superconductors. Our experimental findings may extend the MZM explorations in connate topological superconductors towards an applicable temperature regime and down to the two-dimensional limit. While a concrete understanding of the observations is lacking, possible explanations involving novel 2D superconducting states with spin-orbit coupling, spontaneous nucleation of anomalous vortices at the magnetic sites, and noncoplanar magnetic ordering may further stimulate theoretical understandings of the scarcely captured ZEBSs in strongly correlated systems with multiband Cooper pairing.

preprint2019arXiv

A State-Failure--Network Method to Identify Critical Components in Power Systems

In order to mitigate cascading failure blackout risks in power systems, the critical components whose failures lead to high blackout risks should be identified. In this paper, such critical components are identified by the state-failure network (SF-network) formed by cascading failure chain and loss data, which can be gathered from either utilities or simulations. The failures along the chains are recombined in the SF-network, where each failure is allocated a value that can reveal the blackout risks after their occurrences. Thus, critical failures can be identified in the SF-network where the failures raise up blackout risks, and thus the critical components can be found based on their critical failure risks. The simulation results validate the effectiveness of the proposed method.

preprint2019arXiv

Angle-resolved photoemission spectroscopy study of crystal electric field in heavy fermion compound CePt2In7

The three-dimensional electronic structure and Ce 4f electrons of the heavy fermion superconductor CePt2In7 is investigated. Angle-resolved photoemission spectroscopy using variable photon energy establishes the existence of quasi-two and three dimensional Fermi surface topologies. Temperature-dependent 4d-4f on-resonance photoemission spectroscopies reveal that heavy quasiparticle bands begin to form at a temperature well above the characteristic (coherence) temperature T*. T* emergence may be closely related to crystal electric field splitting, particularly the low-lying heavy band formed by crystal electric field splitting.

preprint2019arXiv

MulGAN: Facial Attribute Editing by Exemplar

Recent studies on face attribute editing by exemplars have achieved promising results due to the increasing power of deep convolutional networks and generative adversarial networks. These methods encode attribute-related information in images into the predefined region of the latent feature space by employing a pair of images with opposite attributes as input to train model, the face attribute transfer between the input image and the exemplar can be achieved by exchanging their attribute-related latent feature region. However, they suffer from three limitations: (1) the model must be trained using a pair of images with opposite attributes as input; (2) weak capability of editing multiple attributes by exemplars; (3) poor quality of generating image. Instead of imposing opposite-attribute constraints on the input image in order to make the attribute information of images be encoded in the predefined region of the latent feature space, in this work we directly apply the attribute labels constraint to the predefined region of the latent feature space. Meanwhile, an attribute classification loss is employed to make the model learn to extract the attribute-related information of images into the predefined latent feature region of the corresponding attribute, which enables our method to transfer multiple attributes of the exemplar simultaneously. Besides, a novel model structure is designed to enhance attribute transfer capabilities by exemplars while improve the quality of the generated image. Experiments demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA dataset.

preprint2019arXiv

PPGAN: Privacy-preserving Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate high-quality synthetic data while retaining the required data available under a reasonable privacy budget.

preprint2016arXiv

$^{133}$Cs and $^{75}$As NMR investigation of the normal metallic state of the quasi-one-dimensional Cs$_2$Cr$_3$As$_3$

We report $^{133}$Cs NMR and $^{75}$As Nuclear Quadrupole Resonance (NQR) measurements on the normal metallic state above $T_c$ of a quasi-one-dimensional superconductor Cs$_2$Cr$_3$As$_3$ ($T_c < 1.6$~K). From the $^{133}$Cs NMR Knight shift $^{133}K$ measured at the Cs1 site, we show that the uniform spin susceptibility $χ_{spin}$ increases from 295~K to $\sim$ 60~K, followed by a mild suppression; $χ_{spin}$ then levels off below $\sim$10~K. In contrast, a vanishingly small magnitude of $^{133}K$ indicates that Cs2 sites contribute very little to electrical conduction and the exchange interactions between 3d electrons at Cr sites. Low frequency Cr spin dynamics, reflected on $^{75}$As $1/T_1T$ (the nuclear spin-lattice relaxation rate $1/T_1$ divided by temperature $T$), shows an analogous trend as $χ_{spin}$. Comparison with the results of $1/T_1T$ near $T_c$ with Rb$_2$Cr$_3$As$_3$ ($T_c=6.1$~K) and Rb$_2$Cr$_3$As$_3$ ($T_c=4.8$~K) establishes a systematic trend that substitution of K$^{+}$ ions with larger alkali ions progressively suppresses Cr spin fluctuations together with $T_c$.

preprint2016arXiv

A backward pass through a CNN using a generative model of its activations

Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural network (CNN) mapping an image into one of a thousand object labels is approaching human performance in this particular task. However the mapping (neural network) does not automatically lend itself to other forms of queries, for example, to detect/reconstruct object instances, to enforce top-down signal on ambiguous inputs, or to recover object instances from occlusion. One way to address these queries is a backward pass through the network that fuses top-down and bottom-up information. In this paper, we show a way of building such a backward pass by defining a generative model of the neural network's activations. Approximate inference of the model would naturally take the form of a backward pass through the CNN layers, and it addresses the aforementioned queries in a unified framework.

preprint2016arXiv

A Capture-gated Fast Neutron Detection Method

To address the problem of the shortage of neutron detectors used in radiation portal monitors (RPMs), caused by the 3He supply crisis, research on a cadmium-based capture-gated fast neutron detector is presented in this paper. The detector is composed of many 1 cm * 1 cm * 20 cm plastic scintillator cuboids covered by 0.1 mm thick film of cadmium. The detector uses cadmium to absorb thermal neutrons and produce capture gamma-rays to indicate the detection of neutrons, and uses plastic scintillator to moderate neutrons and register gamma-rays. This design removes the volume competing relationship in traditional 3He counter-based fast neutron detectors, which hinders enhancement of the neutron detection efficiency. Detection efficiency of 21.66 +- 1.22% has been achieved with a 40.4 cm * 40.4 cm * 20 cm overall detector volume. This detector can measure both neutrons and gamma-rays simultaneously. A small detector (20.2 cm * 20.2 cm * 20 cm) demonstrated a 3.3 % false alarm rate for a 252Cf source with a neutron yield of 1841 n/s from 50 cm away within 15 seconds measurement time. It also demonstrated a very low (< 0.06%) false alarm rate for a 3.21 * 105 Bq 137Cs source. This detector offers a potential single-detector replacement for both neutron and the gamma-ray detectors in RPM systems.

preprint2016arXiv

A New Ferromagnetic Superconductor: CsEuFe$_4$As$_4$

Superconductivity (SC) and ferromagnetism (FM) are in general antagonistic, which makes their coexistence very rare. Following our recent discovery of robust coexistence of SC and FM in RbEuFe$_4$As$_4$ [Y. Liu et al., arXiv: 1605.04396 (2016)], here we report another example of such a coexistence in its sister compound CsEuFe$_4$As$_4$, synthesized for the first time. The new material exhibits bulk SC at 35.2 K and Eu$^{2+}$-spin ferromagnetic ordering at 15.5 K, demonstrating that it is a new robust ferromagnetic superconductor.

preprint2016arXiv

A New ZrCuSiAs-Type Superconductor: ThFeAsN

We report the first nitrogen-containing iron-pnictide superconductor ThFeAsN, which is synthesized by a solid-state reaction in an evacuated container. The compound crystallizes in a ZrCuSiAs-type structure with the space group P4/nmm and lattice parameters a=4.0367(1) Å and c=8.5262(2) Å at 300 K. The electrical resistivity and dc magnetic susceptibility measurements indicate superconductivity at 30 K for the nominally undoped ThFeAsN.

preprint2016arXiv

DroidLink: Automated Generation of Deep Links for Android Apps

The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific "content page" of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added "linked data" between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.

preprint2016arXiv

Giant room temperature interface spin Hall and inverse spin Hall effects

The spin Hall angle (SHA) is a measure of the efficiency with which a transverse spin current is generated from a charge current by the spin-orbit coupling and disorder in the spin Hall effect (SHE). In a study of the SHE for a Pt$|$Py (Py=Ni$_{80}$Fe$_{20}$) bilayer using a first-principles scattering approach, we find a SHA that increases monotonically with temperature and is proportional to the resistivity for bulk Pt. By decomposing the room temperature SHE and inverse SHE currents into bulk and interface terms, we discover a giant interface SHA that dominates the total inverse SHE current with potentially major consequences for applications.

preprint2016arXiv

Immersing quasi-Fuchsian surfaces of odd Euler characteristic in closed hyperbolic 3-manifolds

In this paper, it is shown that every closed hyperbolic 3-manifold contains an immersed quasi-Fuchsian closed subsurface of odd Euler characteristic. The construction adopts the good pants method, and the primary new ingredient is an enhanced version of the connection principle, which allows one to connect any two frames with a path of frames in a prescribed relative homology class of the frame bundle. The existence result is applied to show that every uniform lattice of $\mathrm{PSL}(2,\mathbb{C})$ admits an exhausting nested sequence of sublattices with exponential homological torsion growth. However, the constructed sublattices are not normal in general.

preprint2016arXiv

Superconductivity and Ferromagnetism in Hole-Doped RbEuFe$_4$As$_4$

We discover a robust coexistence of superconductivity and ferromagnetism in an iron arsenide RbEuFe$_4$As$_4$. The new material crystallizes in an intergrowth structure of RbFe$_2$As$_2$ and EuFe$_2$As$_2$, such that the Eu sublattice turns out to be primitive instead of being body-centered in EuFe$_2$As$_2$. The FeAs layers, featured by asymmetric As coordinations, are hole doped due to charge homogenization. Our combined measurements of electrical transport, magnetization and heat capacity unambiguously and consistently indicate bulk superconductivity at 36.5 K in the FeAs layers and ferromagnetism at 15 K in the Eu sublattice. Interestingly, the Eu-spin ferromagnetic ordering belongs to a rare third-order transition, according to the Ehrenfest classification of phase transition. We also identify an additional anomaly at $\sim$ 5 K, which is possibly associated with the interplay between superconductivity and ferromagnetism.

preprint2016arXiv

Superconductivity in KCa$_2$Fe$_4$As$_4$F$_2$ with Separate Double Fe$_2$As$_2$ Layers

We report synthesis, crystal structure and physical properties of a quinary iron-arsenide fluoride KCa$_2$Fe$_4$As$_4$F$_2$. The new compound crystallizes in a body-centered tetragonal lattice (with space group $I4/mmm$, $a$ = 3.8684(2) Å, c = 31.007(1) Å, and $Z$ = 2), which contains double Fe$_2$As$_2$ conducting layers separated by insulating Ca$_2$F$_2$ layers. Our measurements of electrical resistivity, dc magnetic susceptibility and heat capacity demonstrate bulk superconductivity at 33 K in KCa$_2$Fe$_4$As$_4$F$_2$.

preprint2015arXiv

A characterization of virtually embedded subsurfaces in 3-manifolds

The paper introduces the spirality character of the almost fiber part for a closed essentially immersed subsurface of a closed orientable aspherical 3-manifold, which generalizes an invariant due to Rubinstein and Wang. The subsurface is virtually embedded if and only if the almost fiber part is aspiral, and in this case, the subsurface is virtually a leaf of a taut foliation. Besides other consequences, examples are exhibited that non-geometric 3-manifolds with no Seifert fibered pieces may contain essentially immersed but not virtually embedded closed subsurfaces.

preprint2015arXiv

A study on the shielding mechanisms of SOI pixel detector

In order to tackle the charge injection issue that had perplexed the counting type SOI pixel for years, two successive chips CPIXTEG3 and CPIXTEG3b were developed utilizing two shielding mechanisms, Nested-well and Double-SOI, in the LAPIS process. A TCAD simulation showed the shielding effectiveness influenced by the high sheet resistance of shielding layers. Test structures specially designed to measure the crosstalk associated to charge injection were implemented in CPIXTEG3/3b. Measurement results proved that using shielding layer is indispensable for counting type pixel and Double-SOI is superior to Nested-well in terms of shielding effectiveness and design flexibility.

preprint2015arXiv

Cluster spin-glass ground state in quasi-one-dimensional KCr$_{3}$As$_{3}$

We report structural and physical properties of a new quasi-one-dimensional Cr-based compound, KCr$_{3}$As$_{3}$, which is prepared by potassium deintercalation from the superconductive K$_{2}$Cr$_{3}$As$_{3}$. KCr$_{3}$As$_{3}$ adopts the TlFe$_{3}$Te$_{3}$-type structure with space group $P6_{3}$/$m$ (No. 176). The high-temperature magnetic susceptibility obeys the Curie-Weiss law with an effective magnetic moment of 0.68 $μ_{\mathrm{B}}$/Cr. Below 56 K the susceptibility deviates from the high-temperature Curie-Weiss behavior, coinciding with the rapid increase in resistivity, which suggests formation of spin clusters. The short-range spin correlations are also supported by the specific-heat data. The title material does not exhibit bulk superconductivity; instead, it shows a cluster spin-glass state below $\sim$ 5 K.

preprint2015arXiv

Degree of $L^2$-Alexander torsion for 3-manifolds

For an irreducible orientable compact $3$-manifold $N$ with empty or incompressible toral boundary, the full $L^2$--Alexander torsion $τ^{(2)}(N,ϕ)(t)$ associated to any real first cohomology class $ϕ$ of $N$ is represented by a function of a positive real variable $t$. The paper shows that $τ^{(2)}(N,ϕ)$ is continuous, everywhere positive, and asymptotically monomial in both ends. Moreover, the degree of $τ^{(2)}(N,ϕ)$ equals the Thurston norm of $ϕ$. The result confirms a conjecture of J.~Dubois, S.~Friedl, and W.~Lück and addresses a question of W.~Li and W.~Zhang. Associated to any admissible homomorphism $γ:π_1(N)\to G$, the $L^2$--Alexander torsion $τ^{(2)}(N,γ,ϕ)$ is shown to be continuous and everywhere positive provided that $G$ is residually finite and $(N,γ)$ is weakly acyclic. In this case, a generalized degree can be assigned to $τ^{(2)}(N,γ,ϕ)$. Moreover, the generalized degree is bounded by the Thurston norm of $ϕ$.

preprint2015arXiv

Direct Method for Calculating Temperature-Dependent Transport Properties

We show how temperature-induced disorder can be combined in a direct way with first-principles scattering theory to study diffusive transport in real materials. Excellent (good) agreement with experiment is found for the resistivity of Cu, Pd, Pt (and Fe) when lattice (and spin) disorder are calculated from first principles. For Fe, the agreement with experiment is limited by how well the magnetization (of itinerant ferromagnets) can be calculated as a function of temperature. By introducing a simple Debye-like model of spin disorder parameterized to reproduce the experimental magnetization, the temperature dependence of the average resistivity, the anisotropic magnetoresistance and the spin polarization of a Ni$_{80}$Fe$_{20}$ alloy are calculated and found to be in good agreement with existing data. Extension of the method to complex, inhomogeneous materials as well as to the calculation of other finite-temperature physical properties within the adiabatic approximation is straightforward.

preprint2015arXiv

Efficient Interlayer Relaxation and Transition of Excitons in Epitaxial and Non-epitaxial MoS2/WS2 Heterostructures

Semiconductor heterostructures provide a powerful platform for the engineering of excitons. Here we report the excitonic properties of two-dimensional (2D) heterostructures that consist of monolayer MoS2 and WS2 stacked epitaxially or non-epitaxially in the vertical direction. We find similarly efficient interlayer relaxation and transition of excitons in both the epitaxial and nonepitaxial heterostructures. This is manifested by a two orders of magnitude decrease in the photoluminescence and the appearance of an extra absorption peak at low energy region. The MoS2/WS2 heterostructures show weak interlayer coupling and can essentially act as atomicscale heterojunctions with the intrinsic bandstructures of the two monolayers largely preserved. They are particularly promising for the applications that request efficient dissociation of excitons and strong light absorption, including photovoltaics, solar fuels, photodetectors, and optical modulators. Our results also indicate that 2D heterostructures promise unprecedented capabilities to engineer excitons from the atomic level without concerns of interfacial imperfection.

preprint2015arXiv

Laser beam self-symmetrization in air in the multifilamentation regime

We show experimental and numerical evidence of spontaneous self-symmetrization of focused laser beams experiencing multi-filamentation in air. The symmetrization effect is observed as the multiple filaments generated prior to focus approach the focal volume. This phenomenon is attributed to the nonlinear interactions amongst the different parts of the beam mediated by the optical Kerr effect, which leads to a symmetric redistribution of the wave vectors even when the beam consists of a bundle of many filaments.

preprint2015arXiv

Multiband superconductivity in Ta$_4$Pd$_3$Te$_{16}$ with anisotropic gap structure

We carried out the measurements of magnetoresistance, magnetic susceptibility and specific heat on crystals of the low-dimensional transition metal telluride Ta$_4$Pd$_3$Te$_{16}$. Our results indicate that Ta$_4$Pd$_3$Te$_{16}$ is an anisotropic type-II superconductor with the extracted Ginzburg-Landau parameter $κ_{\text{GL}}=$ 84. The upper critical field $H_{c2}$($T$) shows a linear dependence at low temperature and the anisotropy of $H_{c2}$($T$) is strongly $T$-dependent, both of which indicate a multiband scenario. A detailed analysis reveals that the electronic specific heat $C_{\text{el}}$($T$) can be consistently described by a two-gap ($s$+$d$ waves) model from the base temperature $T/T_c\sim$ 0.12 up to $T_c$. Our data suggests multiband superconductivity in Ta$_4$Pd$_3$Te$_{16}$ with anisotropic gap structure.

preprint2015arXiv

Near-Wall Dynamics of Concentrated Hard-Sphere Suspensions: Comparison of Evanescent Wave DLS Experiments, Virial Approximation and Simulations

In this article we report on a study of the near-wall dynamics of suspended colloidal hard spheres over a broad range of volume fractions. We present a thorough comparison of experimental data with predictions based on a virial approximation and simulation results. We find that the virial approach describes the experimental data reasonably well up to a volume fraction of $ϕ=0.25$ which provides us with a fast and non-costly tool for the analysis and prediction of Evanescent Wave DLS data. Based on this we propose a new method to assess the near-wall self-diffusion at elevated density. Here, we qualitatively confirm earlier results [Michailidou et al., Phys. Rev. Lett., 2009, 102, 068302], which indicate that many-particle hydrodynamic interactions are diminished by the presence of the wall at increasing volume fractions as compared to bulk dynamics. Beyond this finding we show that this diminishment is different for the particle motion normal and parallel to the wall.

preprint2015arXiv

Physical properties and electronic structure of Sr$_2$Cr$_3$As$_2$O$_2$ containing CrO$_2$ and Cr$_2$As$_2$ square-planar lattices

We report the physical properties and electronic structure calculations of a layered chromium oxypnictide, Sr$_2$Cr$_3$As$_2$O$_2$, which crystallizes in a Sr$_2$Mn$_3$As$_2$O$_2$-type structure containing both CrO$_2$ planes and Cr$_2$As$_2$ layers. The newly synthesized material exhibits a metallic conduction with a dominant electron-magnon scattering. Magnetic and specific-heat measurements indicate at least two intrinsic magnetic transitions below room temperature. One is an antiferromagnetic transition at 291 K, probably associated with a spin ordering in the Cr$_2$As$_2$ layers. Another transition is broad, occurring at around 38 K, and possibly due to a short-range spin order in the CrO$_2$ planes. Our first-principles calculations indicate predominant two-dimensional antiferromagnetic exchange couplings, and suggest a KG-type (i.e. K$_2$NiF$_4$ type for CrO$_2$ planes and G type for Cr$_2$As$_2$ layers) magnetic structure, with reduced moments for both Cr sublattices. The corresponding electronic states near the Fermi energy are mostly contributed from Cr-3$d$ orbitals which weakly (modestly) hybridize with the O-2$p$ (As-4$p$) orbitals in the CrO$_2$ (Cr$_2$As$_2$) layers. The bare bandstructure density of states at the Fermi level is only $\sim$1/4 of the experimental value derived from the low-temperature specific-heat data, consistent with the remarkable electron-magnon coupling. The title compound is argued to be a possible candidate to host superconductivity.

preprint2015arXiv

Recollision induced superradiance of ionized nitrogen molecules

We propose a new mechanism to explain the origin of optical gain in the transitions between excited and ground state of the ionized nitrogen molecule following irradiation of neutral nitrogen molecules with an intense ultra short laser pulse. An efficient transfer of population to the excited state is achieved via field-induced multiple recollisions. We show that the proposed excitation mechanism must lead to a super-radiant emission, a feature that we confirm experimentally.

preprint2015arXiv

Superconductivity in quasi-one-dimensional Cs2Cr3As3 with large interchain distance

Since the discovery of high-temperature superconductivity (SC) in quasi-two-dimensional copper oxides, a few layered compounds, which bear similarities to the cuprates, have also been found to host unconventional SC. Our recent observation of SC at 6.1 K in correlated electron material K2Cr3As3 (J. K. Bao et al., arXiv: 1412.0067) represents an obviously different paradigm, primarily because of its quasi-one-dimensional (Q1D) nature. The new material is structurally featured by the (Cr3As3)2- double-walled subnano-tubes composed of face-sharing Cr6/2 (As6/2) octahedron linear chains, which are well separated by columns of K+ counterions. Later, an isostructural superconducting Rb2Cr3As3 was synthesized, thus forming a new superconducting family. Here we report the third member, Cs2Cr3As3, which possesses the largest interchain distance. SC appears below 2.2 K. Similar to the former two sister compounds, Cs2Cr3As3 exhibits a non-Fermi liquid behavior with a linear temperature dependence of resistivity in the normal state, and a high upper critical field beyond the Pauli limit as well, suggesting common unconventional SC in the Q1D Cr-based material.

preprint2015arXiv

Test of a fine pitch SOI pixel detector with laser beam

A silicon pixel detector with fine pitch size of 19x19 um, developed base on SOI (silicon on insulator) technology, was tested under the illumination of infrared laser pulses. As an alternative way to particle beam tests, the laser pulses were tuned to very short duration and small transverse profile to simulate the tracks of MIPs (minimum ionization particles) in silicon. Hit cluster sizes were measured with focused laser pulses propagating through the SOI detector perpendicular to its surface and most of the induced charge was found to be collected inside the seed pixel. For the first time, the signal amplitude as a function of the applied bias voltage was measured for this SOI detector, deepening understanding of its depletion characteristics.

preprint2015arXiv

Unconventional superconductivity in quasi-one-dimensional Rb$_2$Cr$_3$As$_3$

Following the discovery of superconductivity in quasi-one-dimensional K$_2$Cr$_3$As$_3$ containing [(Cr$_3$As$_3$)$^{2-}$]$_{\infty}$ chains [J. K. Bao et al., arXiv: 1412.0067 (2014)], we succeeded in synthesizing an analogous compound, Rb$_2$Cr$_3$As$_3$, which also crystallizes in a hexagonal lattice. The replacement of K by Rb results in an expansion of $a$ axis by 3\%, indicating a weaker interchain coupling in Rb$_2$Cr$_3$As$_3$. Bulk superconductivity emerges at 4.8 K, above which the normal-state resistivity shows a linear temperature dependence up to 35 K. The estimated upper critical field at zero temperature exceeds the Pauli paramagnetic limit by a factor of two. Furthermore, the electronic specific-heat coefficient extrapolated to zero temperature in the mixed state increases with $\sqrt{H}$, suggesting existence of nodes in the superconducting energy gap. Hence Rb$_2$Cr$_3$As$_3$ manifests itself as another example of unconventional superconductor in the Cr$_3$As$_3$-chain based system.

preprint2014arXiv

A Fan Beam Model for Radio Pulsars. I. Observational Evidence

We propose a novel beam model for radio pulsars based on the scenario that the broadband and coherent emission from secondary relativistic particles, as they move along a flux tube in a dipolar magnetic field, forms a radially extended sub-beam with unique properties. The whole radio beam may consist of several sub-beams, forming a fan-shaped pattern. When only one or a few flux tubes are active, the fan beam becomes very patchy. This model differs essentially from the conal beam models in the respects of beam structure and predictions on the relationship between pulse width and impact angle $β$ (the angle between line of sight and magnetic pole) and the relationship between emission intensity and beam angular radius. The evidence for this model comes from the observed patchy beams of precessional binary pulsars and three statistical relationships found for a sample of 64 pulsars, of which $β$ were mostly constrained by fitting polarization position angle data with the Rotation Vector Model. With appropriate assumptions, the fan beam model can reproduce the relationship between 10\% peak pulse width and $|β|$, the anticorrelation between the emission intensity and $|β|$, and the upper boundary line in the scatter plot of $|β|$ versus pulsar distance. An extremely patchy beam model with the assumption of narrowband emission from one or a few flux tubes is studied and found unlikely to be a general model. The implications of the fan beam model to the studies on radio and gamma-ray pulsar populations and radio polarization are discussed.

preprint2014arXiv

Backward stimulated radiation from filaments in Nitrogen gas and air pumped by circularly polarized 800 nm femtosecond laser pulses

We report on strong backward stimulated emission at 337 nm in Nitrogen gas pumped by circularly polarized femtosecond laser pulses at 800 nm. A distinct dependence of the backward UV spectrum on pump laser polarization and intensity is observed, pointing to the occurrence of backward amplified spontaneous emission inside filaments. We attribute the population inversion to inelastic collision between the free electrons produced by the pump laser and neutral N2 molecules. The addition of Oxygen molecules is detrimental for the gain, reducing it to near threshold at atmospheric concentration.

preprint2014arXiv

Design of A Scalable Hybrid MAC Protocol for Heterogeneous M2M Networks

A robust and resilient Medium Access Control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in a Machine-to-Machine (M2M) network. Simplex (reservation or contention based) MAC protocols are studied in most literatures which may not be able to provide a scalable solution for M2M networks with large number of heterogeneous devices. In this paper, a scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks. In this protocol, different devices with pre-set priorities (hierarchical contending probabilities) firstly contend the transmission opportunities following the convention based $p$-persistent CSMA mechanism. Only the successful devices will be assigned a time slot for transmission following the reservation based TDMA mechanism. If the devices failed in contention at previous frame, to ensure the fairness among all devices, their contending priorities will be raised by increasing their contending probabilities at the next frame. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the channel utility by finding the key design parameters: the contention duration, initial contending probability and the incremental indicator. Analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol.

preprint2014arXiv

Estimating Clonality

Challenges of assessing complexity and clonality in populations of mixed species arise in diverse areas of modern biology, including estimating diversity and clonality in microbiome populations, measuring patterns of T and B cell clonality, and determining the underlying tumor cell population structure in cancer. Here we address the problem of quantifying populations, with our analysis directed toward systems for which previously defined algorithms allow the sequence-based identification of clonal subpopulations. Data come from replicate sequencing libraries generated from a sample, potentially with very different depths. While certain properties of the underlying clonal distribution (most notably the total number of clones) are difficult to estimate accurately from data representing a small fraction of the total population, the population-level "clonality" metric that is the sum of squared probabilities of the respective species can be calculated. (This is the sum of squared entries of a high-dimensional vector $p$ of relative frequencies.) The clonality score is the probability of a clonal relationship between two randomly chosen members of the population of interest. A principal takeaway message is that knowing a functional of $p$ well may not depend on knowing $p$ itself very well. Our work has led to software, which we call {\it lymphclon}; it has been deposited in the CRAN library.

preprint2014arXiv

Externally seeded backward lasing radiation from femtosecond laser filament in nitrogen gas

Recently, S. Mitryukovskiy et al. presented experimental evidence showing that backward stimulated radiation at 337 nm can be obtained from plasma filaments in nitrogen gas pumped by circularly polarized 800 nm femtosecond pulses (Opt. Express, 22, 12750 (2014)). Here, we report that this backward stimulated radiation is enhanced by a factor of ~ 16 in the presence of a seed pulse. This enhanced stimulated radiation can be either linearly or circularly polarized, dictated by the seeding pulse, which is distinct from the non-polarized nature of the ASE without seeding pulse. We also measured the spatial profile and estimated the energy of the radiation. This seeding effect confirms unambiguously the existence of population inversion between the C3Πu and B3Πg state of nitrogen molecules inside plasma filament and provides a possible solution to control the properties of this backward stimulated radiation.

preprint2014arXiv

Gilbert damping in noncollinear ferromagnets

The precession and damping of a collinear magnetization displaced from its equilibrium are described by the Landau-Lifshitz-Gilbert equation. For a noncollinear magnetization, it is not known how the damping should be described. We use first-principles scattering theory to investigate the damping in one-dimensional transverse domain walls (DWs) of the important ferromagnetic alloy Ni$_{80}$Fe$_{20}$ and interpret the results in terms of phenomenological models. The damping is found to depend not only on the magnetization texture but also on the specific dynamic modes of Bloch and Néel DWs. Even in the highly disordered Ni$_{80}$Fe$_{20}$ alloy, the damping is found to be remarkably nonlocal.

preprint2014arXiv

Homology of curves and surfaces in closed hyperbolic 3-manifolds

Among other things, we prove the following two topologcal statements about closed hyperbolic 3-manifolds. First, every rational second homology class of a closed hyperbolic 3-manifold has a positve integral multiple represented by an oriented connected closed $π_1$-injectively immersed quasi-Fuchsian subsurface. Second, every rationally null-homologous, $π_1$-injectively immersed oriented closed 1-submanifold in a closed hyperbolic 3-manifold has an equidegree finite cover which bounds an oriented compact $π_1$-injective immersed quasi-Fuchsian subsurface. In part, we exploit techniques developed earlier by Kahn and Markovic about good pants constructions, but we only distill geometric and topological ingredients from their papers so no hard analysis is involved in this paper.

preprint2014arXiv

Interface enhancement of Gilbert damping from first-principles

The enhancement of Gilbert damping observed for Ni80Fe20 (Py) films in contact with the non-magnetic metals Cu, Pd, Ta and Pt, is quantitatively reproduced using first-principles scattering theory. The "spin-pumping" theory that qualitatively explains its dependence on the Py thickness is generalized to include a number of factors known to be important for spin transport through interfaces. Determining the parameters in this theory from first-principles shows that interface spin-flipping makes an essential contribution to the damping enhancement. Without it, a much shorter spin-flip diffusion length for Pt would be needed than the value we calculate independently.

preprint2014arXiv

Lasing of ambient air with microjoule pulse energy pumped by a multi terawatt IR femtosecond laser

We report on the lasing action of atmospheric air pumped by an 800 nm femtosecond laser pulse with peak power up to 4 TW. Lasing emission at 428 nm increases rapidly over a small range of pump laser power, followed by saturation above ~ 1.5 TW. The maximum lasing pulse energy is measured to be 2.6 uJ corresponding to an emission power in the MW range, while a maximum conversion efficiency of is measured at moderate pump pulse energy. The optical gain inside the filament plasma is estimated to be excess of 0.7/cm. The lasing emission shows a doughnut profile, reflecting the spatial distribution of the pump-generated white-light continuum that acts as a seed for the lasing. We attribute the pronounced saturation to the defocusing of the seed in the plasma amplifying region and to the saturation of the seed intensity.

preprint2014arXiv

Superconductivity in a Layered Ta4Pd3Te16 with PdTe2 Chains

Superconductivity in low-dimensional compounds has long attracted much interest. Here we report superconductivity in a low-dimensional ternary telluride Ta4Pd3Te16 in which the repeating layers contain edge-sharing octahedrally-coordinated PdTe2 chains along the crystallographic b axis. Measurements of electrical resistivity, magnetic susceptibility and specific heat on the Ta4Pd3Te16 crystals, grown via a self-flux method, consistently demonstrate bulk superconductivity at 4.6 K. Further analyses of the data indicate significant electron-electron interaction, which allows electronic Cooper pairing in the present system.

preprint2013arXiv

A Scalable Hybrid MAC Protocol for Massive M2M Networks

In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based)MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol.

preprint2013arXiv

An Efficient MAC Protocol with Selective Grouping and Cooperative Sensing in Cognitive Radio Networks

In cognitive radio networks, spectrum sensing is a crucial technique to discover spectrum opportunities for the Secondary Users (SUs). The quality of spectrum sensing is evaluated by both sensing accuracy and sensing efficiency. Here, sensing accuracy is represented by the false alarm probability and the detection probability while sensing efficiency is represented by the sensing overhead and network throughput. In this paper, we propose a group-based cooperative Medium Access Control (MAC) protocol called GC-MAC, which addresses the tradeoff between sensing accuracy and efficiency. In GC-MAC, the cooperative SUs are grouped into several teams. During a sensing period, each team senses a different channel while SUs in the same team perform the joint detection on the targeted channel. The sensing process will not stop unless an available channel is discovered. To reduce the sensing overhead, an SU-selecting algorithm is presented to selectively choose the cooperative SUs based on the channel dynamics and usage patterns. Then, an analytical model is built to study the sensing accuracy-efficiency tradeoff under two types of channel conditions: time-invariant channel and time-varying channel. An optimization problem that maximizes achievable throughput is formulated to optimize the important design parameters. Both saturation and non-saturation situations are investigated with respect to throughput and sensing overhead. Simulation results indicate that the proposed protocol is able to significantly decrease sensing overhead and increase network throughput with guaranteed sensing accuracy.

preprint2013arXiv

Distributed Linear Convolutional Space-Time Coding for Two-Relay Full-Duplex Asynchronous Cooperative Networks

In this paper, a two-relay full-duplex asynchronous cooperative network with the amplify-and-forward (AF) protocol is considered. We propose two distributed space-time coding schemes for the cases with and without cross-talks, respectively. In the first case, each relay can receive the signal sent by the other through the cross-talk link. We first study the feasibility of cross-talk cancellation in this network and show that the cross-talk interference cannot be removed well. For this reason, we design space-time codes by utilizing the cross-talk signals instead of removing them. In the other case, the self-coding is realized individually through the loop channel at each relay node and the signals from the two relay nodes form a space-time code. The achievable cooperative diversity of both cases is investigated and the conditions to achieve full cooperative diversity are presented. Simulation results verify the theoretical analysis.

preprint2013arXiv

Distributed Space-Time Coding for Full-Duplex Asynchronous Cooperative Communications

In this paper, we propose two distributed linear convolutional space-time coding (DLC-STC) schemes for full-duplex (FD) asynchronous cooperative communications. The DLC-STC Scheme 1 is for the case of the complete loop channel cancellation, which achieves the full asynchronous cooperative diversity. The DLC-STC Scheme 2 is for the case of the partial loop channel cancellation and amplifying, where some loop signals are used as the self-coding instead of treated as interference to be directly cancelled. We show this scheme can achieve full asynchronous cooperative diversity. We then evaluate the performance of the two schemes when loop channel information is not accurate and present an amplifying factor control method for the DLC-STC Scheme 2 to improve its performance with inaccurate loop channel information. Simulation results show that the DLC-STC Scheme 1 outperforms the DLC-STC Scheme 2 and the delay diversity scheme if perfect or high quality loop channel information is available at the relay, while the DLC-STC Scheme 2 achieves better performance if the loop channel information is imperfect.

preprint2013arXiv

On slope genera of knotted tori in 4-space

In this note, we investigate genera for the slopes of a knotted torus in the 4-sphere analogous to the genus of a classical knot. We compare various formulations of this notion, and use this notion to study the extendable subgroup of the mapping class group of the knotted torus.

preprint2013arXiv

Securing Cognitive Radio Networks against Primary User Emulation Attacks

Cognitive Radio (CR) is a promising technology for next-generation wireless networks in order to efficiently utilize the limited spectrum resources and satisfy the rapidly increasing demand for wireless applications and services. Security is a very important but not well addressed issue in CR networks. In this paper we focus on security problems arising from Primary User Emulation (PUE) attacks in CR networks. We present a comprehensive introduction to PUE attacks, from the attacking rationale and its impact on CR networks, to detection and defense approaches. In order to secure CR networks against PUE attacks, a two-level database-assisted detection approach is proposed to detect such attacks. Energy detection and location verification are combined for fast and reliable detection. An admission control based defense approach is proposed to mitigate the performance degradation of a CR network under a PUE attack. Illustrative results are presented to demonstrate the effectiveness of the proposed detection and defense approaches.

preprint2013arXiv

Self-seeded lasing in ionized air pumped by 800 nm femtosecond laser pulses

We report on the lasing in air and pure nitrogen gas pumped by a single 800 nm femtosecond laser pulse. Depending on gas pressure, incident laser power and beam convergence, different lasing lines are observed in the forward direction with rapid change of their relative intensities. The lines are attributed to transitions between vibrational and rotational levels of the first negative band of the singly charged nitrogen molecule-ion. We show that self-seeding plays an important role in the observed intensity changes.

preprint2013arXiv

Transport Properties of Topological Insulators Films and Nanowires

The last several years have witnessed the rapid developments in the study and understanding of topological insulators. In this review, after a brief summary of the history of topological insulators, we focus on the recent progress made in transport experiments on topological insulator films and nanowires. Some quantum phenomena, including the weak antilocalization, the Aharonov Bohm effect and the Shubnikov de Haas oscillations, observed in these nanostructures are described. In addition, the electronic transport evidence of the superconducting proximity effect as well as an anomalous resistance enhancement in topological insulator/superconductor hybrid structures is included.

preprint2013arXiv

Virtual cubulation of nonpositively curved graph manifolds

In this paper, we show that a nontrivial compact graph manifold is nonpositively curved if and only if its fundamental group virtually embeds into a right-angled Artin group. As a consequence, nonpositively curved graph manifolds have linear fundamental groups.

preprint2012arXiv

Electricity Cost Minimization for a Residential Smart Grid with Distributed Generation and Bidirectional Power Transactions

In this paper, we consider the electricity cost minimization problem in a residential network where each community is equipped with a distributed power generation source and every household in the community has a set of essential and shiftable power demands. We allow bi-directional power transactions and assume a two-tier pricing model for the buying and selling of electricity from the grid. In this situation, in order to reduce the cost of electricity we are required to make, 1) Scheduling decisions for the shiftable demands, 2) The decisions on the amount of energy purchased from the gird by the users, 3) The decisions on the amount of energy sold to the grid by the users. We formulate a global centralized optimization problem and obtain the optimal amount of electricity consumed, sold and purchased for each household, respectively by assuming the availability of all current and future values of time-varying parameters. In reality, the lack of perfect information hampers the implementation of such global centralized optimization. Hence, we propose a distributed online algorithm which only requires the current values of the timevarying supply and demand processes. We then compare and determine the tradeoff between both formulations. Simulation results show that the proposed schemes can provide effective management for household electricity usage.

preprint2012arXiv

Spin-orbit-coupling induced domain-wall resistance in diffusive ferromagnets

We investigate diffusive transport through a number of domain wall (DW) profiles of the important magnetic alloy Permalloy taking into account simultaneously noncollinearity, alloy disorder, and spin-orbit coupling fully quantum mechanically, from first principles. In addition to observing the known effects of magnetization mistracking and anisotropic magnetoresistance, we discover a not-previously identified contribution to the resistance of a DW that comes from spin-orbit-coupling-mediated spin-flip scattering in a textured diffusive ferromagnet. This adiabatic DW resistance, which should exist in all diffusive DWs, can be observed by varying the DW width in a systematic fashion in suitably designed nanowires.

preprint2011arXiv

A Jorgensen-Thurston theorem for homomorphisms

In this note, we provide a description of the structure of homomorphisms from a finitely generated group to any torsion-free (3-dimensional) Kleinian group with uniformly bounded finite covolume. This is analogous to the Jorgensen-Thurston Theorem in hyperbolic geometry.

preprint2011arXiv

First-principles calculations of magnetization relaxation in pure Fe, Co, and Ni with frozen thermal lattice disorder

The effect of the electron-phonon interaction on magnetization relaxation is studied within the framework of first-principles scattering theory for Fe, Co, and Ni by displacing atoms in the scattering region randomly with a thermal distribution. This "frozen thermal lattice disorder" approach reproduces the non-monotonic damping behaviour observed in ferromagnetic resonance measurements and yields reasonable quantitative agreement between calculated and experimental values. It can be readily applied to alloys and easily extended by determining the atomic displacements from ab initio phonon spectra.

preprint2011arXiv

Low magnetic field reversal of electric polarization in a Y-type hexaferrite

Magnetoelectric multiferroics in which ferroelectricity and magnetism coexist have attracted extensive attention because they provide great opportunities for the mutual control of electric polarization by magnetic fields and magnetization by electric fields. From a practical point view, the main challenge in this field is to find proper multiferroic materials with a high operating temperature and great magnetoelectric sensitivity. Here we report on the magnetically tunable ferroelectricity and the giant magnetoelectric sensitivity up to 250 K in a Y-type hexaferrite, BaSrCoZnFe11AlO22. Not only the magnitude but also the sign of electric polarization can be effectively controlled by applying low magnetic fields (a few hundreds of Oe) that modifies the spiral magnetic structures. The magnetically induced ferroelectricity is stabilized even in zero magnetic field. Decayless reproducible flipping of electric polarization by oscillating low magnetic fields is shown. The maximum linear magnetoelectric coefficient reaches a high value of ~ 3.0\times10^3 ps/m at 200 K.

preprint2011arXiv

Presentation length and Simon's conjecture

In this paper, we show that any knot group maps onto at most finitely many knot groups. This gives an affirmative answer to a conjecture of J. Simon. We also bound the diameter of a closed hyperbolic 3-manifold linearly in terms of the presentation length of its fundamental group, improving a result of White.

preprint2010arXiv

A kinematic study of the compact jet in quasar B3 1633+382

We present a study of the motion of compact jet components in quasar B3 1633+382. Through analyzing 14 epochs of VLBI observations of three components (B1, B2, and B3) at 22 GHz, we find two different possibilities of component classification. Thus two corresponding kinematical models can be adopted to explain the evolutionary track of components. One is a linear motion, while another is a helical model. Future observations are needed to provide new kinematical constraints for the motion of these components in this source.

preprint2010arXiv

Extending $T^p$ automorphisms over $\RR^{p+2}$ and realizing DE attractors

In this paper we consider the realization of DE attractors by self-diffeomorphisms of manifolds. For any expanding self-map $ϕ:M\to M$ of a connected, closed $p$-dimensional manifold $M$, one can always realize a $(p,q)$-type attractor derived from $ϕ$ by a compactly-supported self-diffeomorphsm of $\RR^{p+q}$, as long as $q\geq p+1$. Thus lower codimensional realizations are more interesting, related to the knotting problem below the stable range. We show that for any expanding self-map $ϕ$ of a standard smooth $p$-dimensional torus $T^p$, there is compactly-supported self-diffeomorphism of $\RR^{p+2}$ realizing an attractor derived from $ϕ$. A key ingredient of the construction is to understand automorphisms of $T^p$ which extend over $\RR^{p+2}$ as a self-diffeomorphism via the standard unknotted embedding $\imath_p:T^p\hookrightarrow\RR^{p+2}$. We show that these automorphisms form a subgroup $E_{\imath_p}$ of $\Aut(T^p)$ of index at most $2^p-1$.

preprint2010arXiv

Investigation of Magnetic Transport Properties by Wannier Interpolation

We present an efficient {\it ab initio} approach for the study of magnetic transport properties based on the Boltzmann equation with the Wannier interpolation scheme. Within the relaxation time approximation, band-resolved electric conductivity under a finite magnetic field is obtained and the historical motion of the electron wave packet in reciprocal space is determined. As a typical application of this method, we have calculated the electric conductivities of MgB$_2$ under finite magnetic fields. Multiband characters for the individual bands are revealed, and the field dependence of the conductivity tensor is studied systematically with the field orientated parallel and normal to the $c$-axis, respectively. The obtained historical motion is employed to simulate directly the cyclotron motion in the extremal orbit and determine the corresponding effective mass. Moreover, This approach is further exploited to calculate the Hall coefficient in the low-field limit, without the complicated computation for the second ${\mathbf k}$ derivative of the band.

preprint2010arXiv

Spin structures and codimension-two homeomorphism extensions

Let $\imath: M\to \RR^{p+2}$ be a smooth embedding from a connected, oriented, closed $p$-dimesional smooth manifold to $\RR^{p+2}$, then there is a spin structure $\imath^\sharp(ς^{p+2})$ on $M$ canonically induced from the embedding. If an orientation-preserving diffeomorphism $τ$ of $M$ extends over $\imath$ as an orientation-preserving topological homeomorphism of $\RR^{p+2}$, then $τ$ preserves the induced spin structure. Let $\esg_\cat(\imath)$ be the subgroup of the $\cat$-mapping class group $\mcg_\cat(M)$ consisting of elements whose representatives extend over $\RR^{p+2}$ as orientation-preserving $\cat$-homeomorphisms, where $\cat=\topo$, $\pl$ or $\diff$. The invariance of $\imath^\sharp(ς^{p+2})$ gives nontrivial lower bounds to $[\mcg_\cat(M):\esg_\cat(\imath)]$ in various special cases. We apply this to embedded surfaces in $\RR^4$ and embedded $p$-dimensional tori in $\RR^{p+2}$. In particular, in these cases the index lower bounds for $\esg_\topo(\imath)$ are achieved for unknotted embeddings.

preprint2009arXiv

Electrical Control of Magnetization in Charge-ordered Multiferroic LuFe2O4

LuFe2O4 exhibits multiferroicity due to charge order on a frustrated triangular lattice. We find that the magnetization of LuFe2O4 in the multiferroic state can be electrically controlled by applying voltage pulses. Depending on with or without magnetic fields, the magnetization can be electrically switched up or down. We have excluded thermal heating effect and attributed this electrical control of magnetization to an intrinsic magnetoelectric coupling in response to the electrical breakdown of charge ordering. Our findings open up a new route toward electrical control of magnetization.

preprint2009arXiv

Photoinduced magnetization change in multiferroic YbFe2O4

We have studied the influence of laser illumination on the magnetization in multiferroic YbFe2O4 single crystals. A photoinduced magnetization change has been confirmed in both ab plane and c axis direction. The temperature dependence of the photoinduced magnetization reduction excludes laser heating as the cause. In terms of the breakdown of charge order driven by laser illumination, the photoinduced magnetization change provides a strong evidence for the spin-charge coupling in YbFe2O4. This photomagnetic effect based on charge-order-induced multiferroicity could be used for non-thermal optical control of magnetization.

preprint2006arXiv

Radio Polarization of BL Lacertae objects

In this paper, using the database of the university of Michigan Radio Astronomy Observatory (UMRAO) at three (4.8 GHz, 8 GHZ, and 14.5 GHz) radio frequencies, we studied the polarization properties for 47 BL Lacertae objects(38 radio selected BL Lacertae objects, 7 X-ray selected BL Lacertae, and two inter-middle objects (Mkn 421 and Mkn 501), and found that (1) The polarizations at higher radio frequency is higher than those at lower frequency, (2) The variability of polarization at higher radio frequency is higher than those at lower frequency, (3) The polarization is correlated with the radio spectral index, and (4) The polarization is correlated with core-dominance parameter for those objects with known core-dominance parameters suggesting that the relativistic beaming could explain the polarization characteristic of BL Lacs.

Yi Liu

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144 published item(s)

MappingEvolve: LLM-Driven Code Evolution for Technology Mapping

CoHalLo: code hallucination localization via probing hidden layer vector

Physical properties and first-principles calculations of an altermagnet candidate Cs$_{1-δ}$V$_2$Te$_2$O

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

Digger: Detecting Copyright Content Mis-usage in Large Language Model Training

Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning

Quantum phase transitions in two-dimensional superconductors: a review on recent experimental progress

A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing

A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

An adaptive model checking test for functional linear model

Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality

Attacking Masked Cryptographic Implementations: Information-Theoretic Bounds

Bulk domain Meissner state in the ferromagnetic superconductor EuFe$_{2}$(As$_{0.8}$P$_{0.2}$)$_{2}$: Consequence of compromise between ferromagnetism and superconductivity

Commentary: analyzing binary data using MCPMod when zero counts are expected

Compact and variable radio emission from an active galaxy with supersoft X-ray emission

Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models

Evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins in RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$

FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity

Finite-volume hyperbolic 3-manifolds are almost determined by their finite quotient groups

GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

Improved-Flow Warp Module for Remote Sensing Semantic Segmentation

Interfacial Charge-transfer Excitonic Insulator in a Two-dimensional Organic-inorganic Superlattice

KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning

Mapping classes are almost determined by their finite quotient actions

Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow

Measuring neutrino dynamics in NMSSM with a right-handed sneutrino LSP at the ILC

Morest: Model-based RESTful API Testing with Execution Feedback

Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning

Multiple-Photon Resonance Enabled Quantum Interference in Emission Spectroscopy of N_2^+

Muon-spin relaxation study of the layered kagome superconductor CsV$_3$Sb$_5$

Networks with pixels embedding: a method to improve noise resistance in images classification

Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

PP-Matting: High-Accuracy Natural Image Matting

Rate-Constrained Shaping Codes for Finite-State Channels With Cost

Sub-MHz spectral dip in a resonator-free twisted gain medium

Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding

The effect of $f$-$c$ hybridization on the $γ\rightarrowα$ phase transition of cerium studied by lanthanum doping

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

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator

Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block

Virtual homological eigenvalues and the Weil--Petersson translation length

A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis

A strange metal in a bosonic system

An Empirical Study on Serverless Workflow Service

An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy

Coexistence of Ferroelectric-like Polarization and Dirac-like Surface State in TaNiTe5

Dirac Nodal Lines and Nodal Loops in a Topological Kagome Superconductor CsV$_3$Sb$_5$

Experimental Evidence of Nonlinear Avalanche Dynamics of Energetic Particle Modes

Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation

PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation

Superconductivity modulated by structural phase transitions in pressurized vanadium-based kagome metals

Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks

The ADENIUM Telescope- A new beam telescope for the DESY II Test Beam Facility

Variable Selection via Thompson Sampling

Variable Selection with ABC Bayesian Forests

A Secure Federated Learning Framework for 5G Networks

Atomic line defects and zero-energy end states in monolayer Fe(Te,Se) high-temperature superconductors

Comparison between $Fermi$ Detected and non-$Fermi$ Detected Superluminal Sources

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

Deep Learning of High-Order Interactions for Protein Interface Prediction

Exemplar-based Generative Facial Editing

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank

Kondo scenario of the γ-α phase transition in single crystalline Cerium thin films

Long-Range Exciton Diffusion in Two-Dimensional Assemblies of Cesium Lead Bromide Perovskite Nanocrystals

Machine Learning Explanations to Prevent Overtrust in Fake News Detection

On the Performance of Direct Shaping Codes

PR-NN: RNN-based Detection for Coded Partial-Response Channels