Source author record

Zhi Wang

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

ResearcherUnclaimed source record

Catalog footprint

What is connected

83works

43topics

4close collaborators

Actions

Connect this record

Open graph Browse works

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching

3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-training compression methods still rely on many coupled hyperparameters across pruning, transformation, quantization, and entropy coding, making it difficult to control the final compressed size and fully exploit the rate-distortion trade-off. We propose MesonGS++, a size-aware post-training codec for 3D Gaussian compression. On the codec side, MesonGS++ combines joint importance-based pruning, octree geometry coding, attribute transformation, selective vector quantization for higher-degree spherical harmonics, and group-wise mixed-precision quantization with entropy coding. On the configuration side, it treats the reserve ratio and bit-width allocation as the dominant rate-distortion knobs and jointly optimizes them under a target storage budget via discrete sampling and 0--1 integer linear programming. We further propose a linear size estimator and a CUDA parallel quantization operator to accelerate the hyperparameter searching process. Extensive experiments show that MesonGS++ achieves over 34$\times$ compression while preserving rendering fidelity, outperforming state-of-the-art post-training methods and accurately meeting target size budgets. Remarkably, without any training, MesonGS++ can even surpass the PSNR of vanilla 3DGS at a 20$\times$ compression rate on the Stump scene. Our code is available at https://github.com/mmlab-sigs/mesongs_plus

preprint2025arXiv

Semiclassical theory for proximity-induced superconducting systems with spin-orbit coupling

We develop a semiclassical theory of superconducting quasiparticles for proximity-induced superconducting systems, where spin-orbit coupling plays a critical role in shaping the quasiparticle dynamics. We reveal the structure of superconducting Berry curvatures in such systems, and derived the superconducting Berry curvature induced thermal Edelstein effect and spin Nernst effect. We calculate these two thermo-spin responses with model systems where Rashba spin-orbit coupling, proximity induced superconductivity, and ferromagnetic order are coexisting.

preprint2023arXiv

Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation

Multi-agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting. First, we design a new communication protocol that exploits the ability of depthwise convolution to efficiently extract local relations and learn local communication between neighboring agents. To facilitate multi-agent coordination, we explicitly learn the effect of joint actions by taking the policies of neighboring agents as inputs. Second, we introduce the mean-field approximation into our method to reduce the scale of agent interactions. To more effectively coordinate behaviors of neighboring agents, we enhance the mean-field approximation by a supervised policy rectification network (PRN) for rectifying real-time agent interactions and by a learnable compensation term for correcting the approximation bias. The proposed method enables efficient coordination as well as outperforms several baseline approaches on the adaptive traffic signal control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).

preprint2022arXiv

A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach

Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural language sentence in an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found that current benchmark datasets may have obvious moment annotation biases, enabling several simple baselines even without training to achieve SOTA performance. In this paper, we take a closer look at existing evaluation protocols, and find both the prevailing dataset and evaluation metrics are the devils that lead to untrustworthy benchmarking. Therefore, we propose to re-organize the two widely-used datasets, making the ground-truth moment distributions different in the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile, we introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets. New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress. Furthermore, we propose a novel causality-based Multi-branch Deconfounding Debiasing (MDD) framework for unbiased moment prediction. Specifically, we design a multi-branch deconfounder to eliminate the effects caused by multiple confounders with causal intervention. In order to help the model better align the semantics between sentence queries and video moments, we enhance the representations during feature encoding. Specifically, for textual information, the query is parsed into several verb-centered phrases to obtain a more fine-grained textual feature. For visual information, the positional information has been decomposed from moment features to enhance representations of moments with diverse locations. Extensive experiments demonstrate that our proposed approach can achieve competitive results among existing SOTA approaches and outperform the base model with great gains.

preprint2022arXiv

A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In the paper, we propose a scalable lifelong RL method that dynamically expands the network capacity to accommodate new knowledge while preventing past memories from being perturbed. We use a Dirichlet process mixture to model the non-stationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space. We formulate the prior distribution of the mixture as a Chinese restaurant process (CRP) that instantiates new mixture components as needed. The update and expansion of the mixture are governed by the Bayesian non-parametric framework with an expectation maximization (EM) procedure, which dynamically adapts the model complexity without explicit task boundaries or heuristics. Moreover, we use the domain randomization technique to train robust prior parameters for the initialization of each task model in the mixture, thus the resulting model can better generalize and adapt to unseen tasks. With extensive experiments conducted on robot navigation and locomotion domains, we show that our method successfully facilitates scalable lifelong RL and outperforms relevant existing methods.

preprint2022arXiv

A Systematic Study of Android Non-SDK (Hidden) Service API Security

Android allows apps to communicate with its system services via system service helpers so that these apps can use various functions provided by the system services. Meanwhile, the system services rely on their service helpers to enforce security checks for protection. Unfortunately, the security checks in the service helpers may be bypassed via directly exploiting the non-SDK (hidden) APIs, degrading the stability and posing severe security threats such as privilege escalation, automatic function execution without users' interactions, crashes, and DoS attacks. Google has proposed various approaches to address this problem, e.g., case-by-case fixing the bugs or even proposing a blacklist to block all the non-SDK APIs. However, the developers can still figure out new ways of exploiting these hidden APIs to evade the non-SDKs restrictions. In this paper, we systematically study the vulnerabilities due to the hidden API exploitation and analyze the effectiveness of Google's countermeasures. We aim to answer if there are still vulnerable hidden APIs that can be exploited in the newest Android 12. We develop a static analysis tool called ServiceAudit to automatically mine the inconsistent security enforcement between service helper classes and the hidden service APIs. We apply ServiceAudit to Android 6~12. Our tool discovers 112 vulnerabilities in Android 6 with higher precision than existing approaches. Moreover, in Android 11 and 12, we identify more than 25 hidden APIs with inconsistent protections; however, only one of the vulnerable APIs can lead to severe security problems in Android 11, and none of them work on Android 12.

preprint2022arXiv

Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent "recognition difficulty" differences between various samples. We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level. However, enabling this adaptive inference with changeable layer-wise quantization schemes is challenging because the combination of bit-widths and layers is growing exponentially, making it extremely difficult to train a single model in such a vast searching space and use it in practice. To solve this problem, we present the Arbitrary Bit-width Network (ABN), where the bit-widths of a single deep network can change at runtime for different data samples, with a layer-wise granularity. Specifically, first we build a weight-shared layer-wise quantizable "super-network" in which each layer can be allocated with multiple bit-widths and thus quantized differently on demand. The super-network provides a considerably large number of combinations of bit-widths and layers, each of which can be used during inference without retraining or storing myriad models. Second, based on the well-trained super-network, each layer's runtime bit-width selection decision is modeled as a Markov Decision Process (MDP) and solved by an adaptive inference strategy accordingly. Experiments show that the super-network can be built without accuracy degradation, and the bit-widths allocation of each layer can be adjusted to deal with various inputs on the fly. On ImageNet classification, we achieve 1.1% top1 accuracy improvement while saving 36.2% BitOps.

preprint2022arXiv

CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing

Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) is a feasible solution for compression, its requirement on the original dataset raises privacy concerns. In addition, it is common to integrate multiple pretrained models to achieve satisfactory performance. How to compress multiple models into a tiny model is challenging, especially when the original data are unavailable. To tackle this challenge, we propose a framework termed collaborative data-free knowledge distillation via multi-level feature sharing (CDFKD-MFS), which consists of a multi-header student module, an asymmetric adversarial data-free KD module, and an attention-based aggregation module. In this framework, the student model equipped with a multi-level feature-sharing structure learns from multiple teacher models and is trained together with a generator in an asymmetric adversarial manner. When some real samples are available, the attention module adaptively aggregates predictions of the student headers, which can further improve performance. We conduct extensive experiments on three popular computer visual datasets. In particular, compared with the most competitive alternative, the accuracy of the proposed framework is 1.18\% higher on the CIFAR-100 dataset, 1.67\% higher on the Caltech-101 dataset, and 2.99\% higher on the mini-ImageNet dataset.

preprint2022arXiv

Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public. According to our measurement, for the same task, these MLaaSes from different providers have varying performance due to the proprietary datasets, models, etc. Federating different MLaaSes together allows us to improve the analytic performance further. However, naively aggregating results from different MLaaSes not only incurs significant momentary cost but also may lead to sub-optimal performance gain due to the introduction of possible false-positive results. In this paper, we propose Armol, a framework to federate the right selection of MLaaS providers to achieve the best possible analytic performance. We first design a word grouping algorithm to unify the output labels across different providers. We then present a deep combinatorial reinforcement learning based-approach to maximize the accuracy while minimizing the cost. The predictions from the selected providers are then aggregated together using carefully chosen ensemble strategies. The real-world trace-driven evaluation further demonstrates that Armol is able to achieve the same accuracy results with $67\%$ less inference cost.

preprint2022arXiv

DeepC2: AI-powered Covert Command and Control on OSNs

Command and control (C&C) is important in an attack. It transfers commands from the attacker to the malware in the compromised hosts. Currently, some attackers use online social networks (OSNs) in C&C tasks. There are two main problems in the C&C on OSNs. First, the process for the malware to find the attacker is reversible. If the malware sample is analyzed by the defender, the attacker would be exposed before publishing the commands. Second, the commands in plain or encrypted form are regarded as abnormal contents by OSNs, which would raise anomalies and trigger restrictions on the attacker. The defender can limit the attacker once it is exposed. In this work, we propose DeepC2, an AI-powered C&C on OSNs, to solve these problems. For the reversible hard-coding, the malware finds the attacker using a neural network model. The attacker's avatars are converted into a batch of feature vectors, and the defender cannot recover the avatars in advance using the model and the feature vectors. To solve the abnormal contents on OSNs, hash collision and text data augmentation are used to embed commands into normal contents. The experiment on Twitter shows that command-embedded tweets can be generated efficiently. The malware can find the attacker covertly on OSNs. Security analysis shows it is hard to recover the attacker's identifiers in advance.

preprint2022arXiv

Deepfake Forensics via An Adversarial Game

With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also known as deepfake detection) models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. We believe training with samples that are adversarially crafted to attack the classification models improves the generalization ability considerably. Considering that AI-based face manipulation often leads to high-frequency artifacts that can be easily spotted by models yet difficult to generalize, we further propose a new adversarial training method that attempts to blur out these specific artifacts, by introducing pixel-wise Gaussian blurring models. With adversarial training, the classification models are forced to learn more discriminative and generalizable features, and the effectiveness of our method can be verified by plenty of empirical evidence. Our code will be made publicly available.

preprint2022arXiv

EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

Security issues have gradually emerged with the continuous development of artificial intelligence (AI). Earlier work verified the possibility of converting neural network models into stegomalware, embedding malware into a model with limited impact on the model's performance. However, existing methods are not applicable in real-world attack scenarios and do not attract enough attention from the security community due to performance degradation and additional workload. Therefore, we propose an improved stegomalware EvilModel. By analyzing the composition of the neural network model, three new methods for embedding malware into the model are proposed: MSB reservation, fast substitution, and half substitution, which can embed malware that accounts for half of the model's volume without affecting the model's performance. We built 550 EvilModels using ten mainstream neural network models and 19 malware samples. The experiment shows that EvilModel achieved an embedding rate of 48.52\%. A quantitative algorithm is proposed to evaluate the existing embedding methods. We also design a trigger and propose a threat scenario for the targeted attack. The practicality and effectiveness of the proposed methods were demonstrated by experiments and analyses of the embedding capacity, performance impact, and detection evasion.

preprint2022arXiv

Fully Self-Supervised Learning for Semantic Segmentation

In this work, we present a fully self-supervised framework for semantic segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation, which saves efforts for the huge amount of annotation, is crucial for building customized models from end-to-end for open-world domains. This application is eagerly needed in realistic scenarios. Even though recent self-supervised semantic segmentation methods have gained great progress, these works however heavily depend on the fully-supervised pretrained model and make it impossible a fully self-supervised pipeline. To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module. In particular, we perform pixel clustering and assignments for segmentation supervision. Preventing it from clustering a mess, we proposed 1) a pyramid-global-guided (PGG) training strategy to supervise the learning with pyramid image/patch-level pseudo labels, which are generated by grouping the unsupervised features. The stable global and pyramid semantic pseudo labels can prevent the segmentation from learning too many clutter regions or degrading to one background region; 2) in addition, we proposed context-aware embedding (CAE) module to generate global feature embedding in view of its neighbors close both in space and appearance in a non-trivial way. We evaluate our method on the large-scale COCO-Stuff dataset and achieved 7.19 mIoU improvements on both things and stuff objects

preprint2022arXiv

HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation

In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers. Based on these findings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver (HEBO). HEBO performs non-linear input and output warping, admits exact marginal log-likelihood optimisation and is robust to the values of learned parameters. We demonstrate HEBO's empirical efficacy on the NeurIPS 2020 Black-Box Optimisation challenge, where HEBO placed first. Upon further analysis, we observe that HEBO significantly outperforms existing black-box optimisers on 108 machine learning hyperparameter tuning tasks comprising the Bayesmark benchmark. Our findings indicate that the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, multi-objective acquisition ensembles with Pareto front solutions improve queried configurations, and robust acquisition maximisers afford empirical advantages relative to their non-robust counterparts. We hope these findings may serve as guiding principles for practitioners of Bayesian optimisation. All code is made available at https://github.com/huawei-noah/HEBO.

preprint2022arXiv

Higgs-Leggett mechanism for the elusive 6e superconductivity observed in Kagome vanadium-based superconductors

A recent Little-Parks experiment on Kagome-structured superconductor CsV_3Sb_5 demonstrated remarkable resistance oscillations with period \phi0/3=hc/6e. Here, we perform analysis based on a theory involving three 2e superconductivity (SC) order parameters associated with the three reciprocal lattice vectors which connect M points of the hexagonal Brillouin zone. In a ring geometry we unveil that, as a series of metastable states, phase of one SC order parameter winds 2πmore or less than the other two ones around the ring, which yields local free energy minima at integer multiples of \phi0/3. Intriguingly, the ground-state degeneracy associated with a Z_2 chirality is crucial, and the Higgs-Leggett mechanism stabilizes domain walls (DW) between chiral domains. At low temperatures DW are expelled from the system resulting in free energy minima only at integer multiples of ϕ_0. Our theory explains successfully the 6e SC observed in experiments, which opens a door for approaching rich physics of Kagome superconductors.

preprint2022arXiv

Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient, and are much faster when many central processing units (CPUs) are available due to better parallelization. In this paper, we propose a systematic incremental learning method for ES in dynamic environments. The goal is to adjust previously learned policy to a new one incrementally whenever the environment changes. We incorporate an instance weighting mechanism with ES to facilitate its learning adaptation, while retaining scalability of ES. During parameter updating, higher weights are assigned to instances that contain more new knowledge, thus encouraging the search distribution to move towards new promising areas of parameter space. We propose two easy-to-implement metrics to calculate the weights: instance novelty and instance quality. Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment. The resulting algorithm, Instance Weighted Incremental Evolution Strategies (IW-IES), is verified to achieve significantly improved performance on challenging RL tasks ranging from robot navigation to locomotion. This paper thus introduces a family of scalable ES algorithms for RL domains that enables rapid learning adaptation to dynamic environments.

preprint2022arXiv

Josephson radiation patterns in underdamped topological Josephson junctions

Josephson radiation is a useful signature for detecting Majorana zero modes in topological superconductors. We study the Josephson radiation of the underdamped topological Josephson junction within the quantum resistively and capacitively shunted junction model. We show that the quantum dynamics of the Majorana two-level system induce oscillatory patterns in the Josephson emission spectra. With the Floquet method, we obtain analytical results for these oscillatory patterns and find that they are well described by Bessel functions. We perform numerical simulations to verify the analytical results and demonstrate that these Bessel radiation patterns exist for a wide range of junction parameters.

preprint2022arXiv

Online Continual Adaptation with Active Self-Training

Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm where the learner can continually adapt to changing environments with limited labels. In this paper, we propose a new online setting -- Online Active Continual Adaptation, where the learner aims to continually adapt to changing distributions using both unlabeled samples and active queries of limited labels. To this end, we propose Online Self-Adaptive Mirror Descent (OSAMD), which adopts an online teacher-student structure to enable online self-training from unlabeled data, and a margin-based criterion that decides whether to query the labels to track changing distributions. Theoretically, we show that, in the separable case, OSAMD has an $O({T}^{2/3})$ dynamic regret bound under mild assumptions, which is aligned with the $Ω(T^{2/3})$ lower bound of online learning algorithms with full labels. In the general case, we show a regret bound of $O({T}^{2/3} + α^* T)$, where $α^*$ denotes the separability of domains and is usually small. Our theoretical results show that OSAMD can fast adapt to changing environments with active queries. Empirically, we demonstrate that OSAMD achieves favorable regrets under changing environments with limited labels on both simulated and real-world data, which corroborates our theoretical findings.

preprint2022arXiv

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze an algorithm based on the idea of model transfer, and provide gap-dependent and gap-independent upper and lower bounds that characterize the intrinsic complexity of the problem.

preprint2022arXiv

SeqNet: An Efficient Neural Network for Automatic Malware Detection

Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection. In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.

preprint2022arXiv

Thompson Sampling for Robust Transfer in Multi-Task Bandits

We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple related tasks through robust transfer of knowledge. While an upper confidence bound (UCB)-based algorithm has recently been shown to achieve nearly-optimal performance guarantees in a setting where all tasks are solved concurrently, it remains unclear whether Thompson sampling (TS) algorithms, which have superior empirical performance in general, share similar theoretical properties. In this work, we present a TS-type algorithm for a more general online multi-task learning protocol, which extends the concurrent setting. We provide its frequentist analysis and prove that it is also nearly-optimal using a novel concentration inequality for multi-task data aggregation at random stopping times. Finally, we evaluate the algorithm on synthetic data and show that the TS-type algorithm enjoys superior empirical performance in comparison with the UCB-based algorithm and a baseline algorithm that performs TS for each individual task without transfer.

preprint2022arXiv

Towards a Systematic Survey for Carbon Neutral Data Centers

Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8\% of global carbon emissions by 2030. However, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.

preprint2021arXiv

EvilModel: Hiding Malware Inside of Neural Network Models

Delivering malware covertly and evasively is critical to advanced malware campaigns. In this paper, we present a new method to covertly and evasively deliver malware through a neural network model. Neural network models are poorly explainable and have a good generalization ability. By embedding malware in neurons, the malware can be delivered covertly, with minor or no impact on the performance of neural network. Meanwhile, because the structure of the neural network model remains unchanged, it can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded in a 178MB-AlexNet model within 1% accuracy loss, and no suspicion is raised by anti-virus engines in VirusTotal, which verifies the feasibility of this method. With the widespread application of artificial intelligence, utilizing neural networks for attacks becomes a forwarding trend. We hope this work can provide a reference scenario for the defense on neural network-assisted attacks.

preprint2021arXiv

Lifelong Incremental Reinforcement Learning with Online Bayesian Inference

A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. We develop and maintain a library that contains an infinite mixture of parameterized environment models, which is equivalent to clustering environment parameters in a latent space. The prior distribution over the mixture is formulated as a Chinese restaurant process (CRP), which incrementally instantiates new environment models without any external information to signal environmental changes in advance. During lifelong learning, we employ the expectation maximization (EM) algorithm with online Bayesian inference to update the mixture in a fully incremental manner. In EM, the E-step involves estimating the posterior expectation of environment-to-cluster assignments, while the M-step updates the environment parameters for future learning. This method allows for all environment models to be adapted as necessary, with new models instantiated for environmental changes and old models retrieved when previously seen environments are encountered again. Experiments demonstrate that LLIRL outperforms relevant existing methods, and enables effective incremental adaptation to various dynamic environments for lifelong learning.

preprint2021arXiv

Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experimental results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models.

preprint2021arXiv

Partial Reduction and Cosmology at Defect Brane

Partial reduction is a Randall-Sundrum reduction for only part of the AdS region between finite tension brane and zero tension brane. This is interesting in AdS/BCFT where the AdS bulk contains a defect brane. We employ partial reduction for a AdS bulk with a brane evolving as a $2d$ Friedmann-Robertson-Walker (FRW) cosmology and demonstrate the equivalence between defect extremal surface and island formula for a large subregion fine grained entropy in boundary CFT. We then move to higher dimensions and demonstrate the existence of $4d$ massless graviton on AdS$_4$ brane in partial reduction. We also propose a partial reduction for a $4d$ FRW cosmology at defect brane and obtain the Newton constant by computing boundary entropy.

preprint2021arXiv

The emergent linear Rashba spin-orbit coupling offering the fast manipulation of hole-spin qubits in germanium

The electric dipole spin resonance (EDSR) combining strong spin-orbit coupling (SOC) and electric-dipole transitions facilitates fast spin control in a scalable way, which is the critical aspect of the rapid progress made recently in germanium (Ge) hole-spin qubits. However, a puzzle is raised because centrosymmetric Ge lacks the Dresselhaus SOC, a key element in the initial proposal of the hole-based EDSR. Here, we demonstrate that the recently uncovered finite k-linear Rashba SOC of 2D holes offers fast hole spin control via EDSR with Rabi frequencies in excellent agreement with experimental results over a wide range of driving fields. We also suggest that the Rabi frequency can reach 500 MHz under a higher gate electric field or multiple GHz in a replacement by [110]oriented wells. These findings bring a deeper understanding for hole-spin qubit manipulation and offer design principles to boost the gate speed.

preprint2021arXiv

Towards Understanding and Demystifying Bitcoin Mixing Services

One reason for the popularity of Bitcoin is due to its anonymity. Although several heuristics have been used to break the anonymity, new approaches are proposed to enhance its anonymity at the same time. One of them is the mixing service. Unfortunately, mixing services have been abused to facilitate criminal activities, e.g., money laundering. As such, there is an urgent need to systematically understand Bitcoin mixing services. In this paper, we take the first step to understand state-of-the-art Bitcoin mixing services. Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. {swapping} and {obfuscating}. Based on this model, we conduct a transaction-based analysis and successfully reveal the mixing mechanisms of four representative services. Besides, we propose a method to identify mixing transactions that leverage the obfuscating mechanism. The proposed approach is able to identify over $92$\% of the mixing transactions. Based on identified transactions, we then estimate the profit of mixing services and provide a case study of tracing the money flow of stolen Bitcoins.

preprint2021arXiv

Uniting the order and disorder dynamics in photoexcited VO2

Photoinduced phase transition (PIPT) is always treated as a coherent process, but ultrafast disordering in PIPT is observed in recent experiments. Utilizing the real-time time-dependent density functional theory (rt-TDDFT) method, here, we track the motion of individual vanadium (V) ions during PIPT in VO2 and uncover that their coherent or disordered dynamics can be manipulated by tuning the laser fluence. We find that the photoexcited holes generate a force on each V-V dimer to drive their collective coherent motion, in competing with the thermal-induced vibrations. If the laser fluence is so weak that the photoexcited hole density is too low to drive the phase transition alone, the PIPT is a disordered process due to the interference of thermal phonons. We also reveal that the photoexcited holes populated by the V-V dimerized bonding states will become saturated if the laser fluence is too strong, limiting the timescale of photoinduced phase transition.

preprint2020arXiv

COVID-19 Evolves in Human Hosts

Today, we are all threatened by an unprecedented pandemic: COVID-19. How different is it from other coronaviruses? Will it be attenuated or become more virulent? Which animals may be its original host? In this study, we collected and analyzed nearly thirty thousand publicly available complete genome sequences for COVID-19 virus from 79 different countries, the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue. We found strong similarities between the current circulating COVID-19 and SARS and MERS, as well as COVID-19 in rhinolophines and pangolins. On the contrary, COVID-19 shares little similarity with the flu-causing coronaviruses and the other known viruses. Strikingly, we observed that the divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to May 2020, suggesting COVID-19 is actively evolving in human hosts. In this paper, we first propose a novel MLCS algorithm NP-MLCS1 for the big sequence analysis, which can calculate the common model for COVID-19 complete genome sequences to provide important information for vaccine and antibody development. Geographic and time-course analysis of the evolution trees of the human COVID-19 reveals possible evolutional paths among strains from 79 countries. This finding has important implications to the management of COVID-19 and the development of vaccines and medications.

preprint2020arXiv

Fermi liquid behavior and colossal magnetoresistance in layered MoOCl2

A characteristic of a Fermi liquid is the T^2 dependence of its resistivity, sometimes referred to as the Baber law. However, for most metals, this behavior is only restricted to very low temperatures, usually below 20 K. Here, we experimentally demonstrate that for the single-crystal van der Waals layered material MoOCl2, the Baber law holds in a wide temperature range up to ~120 K, indicating that the electron-electron scattering plays a dominant role in this material. Combining with the specific heat measurement, we find that the modified Kadowaki-Woods ratio of the material agrees well with many other strongly correlated metals. Furthermore, in the magneto-transport measurement, a colossal magneto-resistance is observed, which reaches ~350% at 9 T and displays no sign of saturation. With the help of first-principles calculations, we attribute this behavior to the presence of open orbits on the Fermi surface. We also suggest that the dominance of electron-electron scattering is related to an incipient charge density wave state of the material. Our results establish MoOCl2 as a strongly correlated metal and shed light on the underlying physical mechanism, which may open a new path for exploring the effects of electron-electron interaction in van der Waals layered structures.

preprint2020arXiv

Giant momentum-dependent spin splitting in centrosymmetric low Z antiferromagnets

The energy vs. crystal momentum E(k) diagram for a solid (band structure) constitutes the road map for navigating its optical, magnetic, and transport properties. By selecting crystals with specific atom types, composition and symmetries, one could design a target band structure and thus desired properties. A particularly attractive outcome would be to design energy bands that are split into spin components with a momentum-dependent splitting, as envisioned by Pekar and Rashba [Zh. Eksperim. i Teor. Fiz. 47 (1964)], enabling spintronic application. The current paper provides "design principles" for wavevector dependent spin splitting (SS) of energy bands that parallels the traditional Dresselhaus and Rashba spin-orbit coupling (SOC) - induce splitting, but originates from a fundamentally different source -- antiferromagnetism. We identify a few generic AFM prototypes with distinct SS patterns using magnetic symmetry design principles. These tools allow also the identification of specific AFM compounds with SS belonging to different prototypes. A specific compound -- centrosymmetric tetragonal MnF2 -- is used via density functional band structure calculations to quantitatively illustrate one type of AFM SS. Unlike the traditional SOC-induced effects restricted to non-centrosymmetric crystals, we show that antiferromagnetic-induced spin splitting broadens the playing field to include even centrosymmetric compounds, and gives SS comparable in magnitude to the best known ('giant') SOC effects, even without SOC, and consequently does not rely on the often-unstable high atomic number elements required for high SOC. We envision that use of the current design principles to identify an optimal antiferromagnet with spin-split energy bands would be beneficial for efficient spin-charge conversion and spin orbit torque applications without the burden of requiring compounds containing heavy elements.

preprint2020arXiv

Josephson radiation from nonlinear dynamics of Majorana zero modes

Josephson radiation is a powerful method to probe Majorana zero modes in topological superconductors. Recently, Josephson radiation with half the Josephson frequency has been experimentally observed in a HgTe-based junction, possibly from Majorana zero modes. However, this radiation vanishes above a critical voltage, sharply contradicting previous theoretical results. In this work, we theoretically obtain a radiation spectrum quantitatively in agreement with the experiment after including the nonlinear dynamics of the Majorana states into the standard resistively shunted junction model. We further predict two new structures of the radiation spectrum for future experimental verification: an interrupted emission line and a chaotic regime. We develop a fixed-point analysis to understand all these features. Our results resolve an apparent discrepancy between theory and experiments, and will inspire reexamination of structures in radiation spectra of various topological Josephson junctions.

preprint2020arXiv

Prediction of low-Z collinear and noncollinear antiferromagnetic compounds having momentum-dependent spin splitting even without spin-orbit coupling

Recent study (Yuan et. al., Phys. Rev. B 102, 014422 (2020)) revealed a SOC-independent spin splitting and spin polarization effect induced by antiferromagnetic ordering which do not necessarily require breaking of inversion symmetry or the presence of SOC, hence can exist even in centrosymmetric, low-Z light element compounds, considerably broadening the material base for spin polarization. In the present work we develop the magnetic symmetry conditions enabling such effect, dividing the 1651 magnetic space groups into 7 different spin splitting prototypes (SST-1 to SST-7). We use the 'Inverse Design' approach of first formulating the target property (here, spin splitting in low-Z compounds not restricted to low symmetry structures), then derive the enabling physical design principles to search realizable compounds that satisfy these a priori design principles. This process uncovers 422 magnetic space groups (160 centrosymmetric and 262 non-centrosymmetric) that could hold AFM-induced, SOC-independent spin splitting and spin polarization. We then search for stable compounds following such enabling symmetries. We investigate the electronic and spin structures of some selected prototype compounds by density functional theory (DFT) and find spin textures that are different than the traditional Rashba-Dresselhaus patterns. We provide the DFT results for all antiferromagnetic spin splitting prototypes (SST-1 to SST-4) and concentrate on revealing of the AFM-induced spin splitting prototype (SST-4). The symmetry design principles along with their transformation into an Inverse Design material search approach and DFT verification could open the way to their experimental examination.M). The symmetry design principles along with their transformation into an Inverse Design material search approach and DFT verification could open the way to their experimental examination.

preprint2020arXiv

ProcData: An R Package for Process Data Analysis

Process data refer to data recorded in the log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response processes of solving the items. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for processing, describing, and analyzing process data. We define an S3 class "proc" for organizing process data and extend generic methods summary and print for class "proc". Two feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for fitting and making predictions from a neural-network-based sequence model. These functions call relevant functions in package keras for constructing and training neural networks. In addition, several response process generators and a real dataset of response processes of the climate control item in the 2012 Programme for International Student Assessment are included in the package.

preprint2020arXiv

PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures. Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker's permission to access memory locations near sensitive data. In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.

preprint2020arXiv

Statistical Analysis of Multi-Relational Network Recovery

In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and asymptotic error bounds for the (penalized) maximum likelihood estimators when the size of the network tends to infinity. The basic technique is to develop a non-asymptotic error bound for the maximum likelihood estimators through large deviations analysis of random fields. We also show that these estimators are nearly optimal in terms of minimax risk.

preprint2020arXiv

Subtask Analysis of Process Data Through a Predictive Model

Response process data collected from human-computer interactive items contain rich information about respondents' behavioral patterns and cognitive processes. Their irregular formats as well as their large sizes make standard statistical tools difficult to apply. This paper develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess performance of the new methods. We use the process data from PIAAC 2012 to demonstrate how exploratory analysis of process data can be done with the new approach.

preprint2020arXiv

Symmetry-Breaking Polymorphous Descriptions for Complex Materials without Interelectronic U

Correlated materials with open-shell d- and f-ions having degenerate band edge states show a rich variety of interesting properties ranging from metal-insulator transition to unconventional superconductivity. The textbook view for the electronic structure of these materials is that mean-field approaches are inappropriate, as the interelectronic interaction U is required to open a band gap between the occupied and unoccupied degenerate states while retaining symmetry. We show that the latter scenario often defining what Mott insulators are, is in fact not needed for the 3d binary oxides MnO, FeO, CoO, and NiO. The mean-field band theory can indeed lift such degeneracies in the binaries when nontrivial unit cell representations (polymorphous networks) are allowed to break symmetries, in conjunction with a recently developed non-empirical exchange and correlation density-functional without an on-site interelectronic interaction U. We explain how density-functional theory (DFT) in the polymorphous representation achieves band gap opening in correlated materials through a separate mechanism to the Mott-Hubbard approach. We show the method predicts magnetic moments and gaps for the four binary monoxides in both the antiferromagnetic and paramagnetic phases, offering an effective alternative to symmetry-conserving approaches for studying a range of functionalities in open d- and f-shell complex materials.

preprint2020arXiv

TeleHammer: A Formal Model of Implicit Rowhammer

The rowhammer bug allows an attacker to gain privilege escalation or steal private data. A key requirement of all existing rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We refer to such rowhammer attacks as PeriHammer. The state-of-the-art software-only defenses against PeriHammer attacks is to make the exploitable hammer rows beyond the attacker's access permission. In this paper, we question the necessity of the above requirement and propose a new class of rowhammer attacks, termed as TeleHammer. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and or software. We propose a generic model to rigorously formalize the necessary conditions to initiate TeleHammer and PeriHammer, respectively. Compared to PeriHammer, TeleHammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of TeleHammer and its advantages, we have created a TeleHammer's instance, called PThammer, which leverages the address-translation feature of modern processors. We observe that a memory access from user space can induce a load of a Level-1 page-table entry (L1PTE) from memory and thus hammer the L1PTE once, although L1PTE is not accessible to us. To achieve a high enough hammering frequency, we flush relevant TLB and cache effectively and efficiently. To this end, we demonstrate PThammer on three different test machines and show that it can cross user-kernel boundary and induce the first bit flips in L1PTEs within 15 minutes of double-sided PThammering. We have exploited PThammer to defeat advanced software-only rowhammer defenses in default system setting.

preprint2020arXiv

The polymorphous nature of cubic halide perovskites

It has been long known that numerous halide and oxide perovskites can have non-ideal octahedra, showing tilting, rotation, and metal atom displacements. It has also been known that compounds that have at low temperatures a single structural motif ("monomorphous structures") could become disordered at higher temperatures, resulting in non-ideal octahedra as an entropy effect. What is shown here is that in many cubic halide perovskites and some oxides compounds a distribution of different low-symmetry octahedra ("polymorphous networks") emerge already from the minimization of the systems internal energy, i.e., they represent the intrinsic, preferred low temperature pattern of chemical bonding. Thermal disorder effects build up at elevated temperatures on top of such low temperature polymorphous networks. Compared with the monomorphous counterparts, the polymorphous networks have lower predicted total energies (enhanced stability), larger band gaps and dielectric constants now dominated by the ionic part, and agrees much more closely with the observed pair distribution functions. The nominal cubic perovskites (Pm-3m) structure deduced from X-Ray diffraction is actually a macroscopically averaged, high symmetry configuration, which should not be used to model electronic properties, given that the latter reflect a low symmetry local configuration.

preprint2020arXiv

Understanding Electronic Peculiarities in Tetragonal FeSe as Local Structural Symmetry Breaking

Traditional band theory of perfect crystalline solids often uses as input the structure deduced from diffraction experiments; when modeled by the minimal unit cell this often produces a spatially averaged model. The present study illustrates that this is not always a safe practice unless one examines if the intrinsic bonding mechanism is capable of benefiting from the formation of a distribution of lower symmetry local environments that differ from the macroscopically averaged structure. This can happen either due to positional, or due to magnetic symmetry breaking. By removing the constraint of a small crystallographic cell, the energy minimization in the density functional theory finds atomic and spin symmetry breaking, not evident in conventional diffraction experiments but being found by local probes such as pair distribution function analysis. Here we report that large atomic and electronic anomalies in bulk tetragonal FeSe emerge from the existence of distributions of local positional and magnetic moment motifs. The found symmetry-broken motifs obtained by minimization of the internal energy represent what chemical bonding in tetragonal phase prefers as an intrinsic energy lowering static distortions. This explains observations of band renormalization, predicts orbital order and enhanced nematicity, and provides unprecedented close agreement with spectral function measured by photoemission and local atomic environment revealed by pair distribution function. While the symmetry-restricted strong correlation approach has been argued previously to be the exclusive theory needed for describing the main peculiarities of FeSe, we show here that the symmetry-broken mean-field approach addresses numerous aspects of the problem, provides intuitive insight into the electronic structure, and opens the door for large-scale mean-field calculations for similar d-electron quantum materials.

preprint2019arXiv

Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance.

preprint2019arXiv

Room temperature 2D ferromagnetism in few-layered 1$T$-CrTe$_{2}$

Spin-related electronics using two dimensional (2D) van der Waals (vdW) materials as a platform are believed to hold great promise for revolutionizing the next generation spintronics. Although many emerging new phenomena have been unravelled in 2D electronic systems with spin long-range orderings, the scarcely reported room temperature magnetic vdW material has thus far hindered the related applications. Here, we show that intrinsic ferromagnetically aligned spin polarization can hold up to 316 K in a metallic phase of 1$T$-CrTe$_{2}$ in the few-layer limit. This room temperature 2D long range spin interaction may be beneficial from an itinerant enhancement. Spin transport measurements indicate an in-plane room temperature negative anisotropic magnetoresistance (AMR) in few-layered CrTe$_{2}$, but a sign change in the AMR at lower temperature, with -0.6$\%$ at 300 K and +5$\%$ at 10 K, respectively. This behavior may originate from the specific spin polarized band structure of CrTe$_{2}$. Our findings provide insights into magnetism in few-layered CrTe$_{2}$, suggesting potential for future room temperature spintronic applications of such 2D vdW magnets.

preprint2016arXiv

A Measurement Study of TCP Performance for Chunk Delivery in DASH

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as an increasingly popular paradigm for video streaming [13], in which a video is segmented into many chunks delivered to users by HTTP request/response over Transmission Control Protocol (TCP) con- nections. Therefore, it is intriguing to study the performance of strategies implemented in conventional TCPs, which are not dedicated for video streaming, e.g., whether chunks are efficiently delivered when users per- form interactions with the video players. In this paper, we conduct mea- surement studies on users chunk requesting traces in DASH from a rep- resentative video streaming provider, to investigate users behaviors in DASH, and TCP-connection-level traces from CDN servers, to investi- gate the performance of TCP for DASH. By studying how video chunks are delivered in both the slow start and congestion avoidance phases, our observations have revealed the performance characteristics of TCP for DASH as follows: (1) Request patterns in DASH have a great impact on the performance of TCP variations including cubic; (2) Strategies in conventional TCPs may cause user perceived quality degradation in DASH streaming; (3) Potential improvement to TCP strategies for better delivery in DASH can be further explored.

preprint2016arXiv

Drone Streaming with Wi-Fi Grid Aggregation for Virtual Tour

To provide a live, active and high-quality virtual touring streaming experience, we propose an unmanned drone stereoscopic streaming paradigm using a control and streaming infrastructure of a 2.4GHz Wi-Fi grid. Our system allows users to actively control the streaming captured by a drone, receive and watch the streaming using a head mount display (HMD); a Wi-Fi grid is deployed across the remote scene with multi-channel support to enable high-bitrate stream- ing broadcast from the drones. The system adopt a joint view adaptation and drone control scheme to enable fast viewer movement including both head rotation and touring. We implement the prototype on Dji M100 quadcopter and HTC Vive in a demo scene.

preprint2016arXiv

Dynamic Flow Scheduling Strategy in Multihoming Video CDNs

Multihoming for a video Content Delivery Network (CDN) allows edge peering servers to deliver video chunks through different Internet Service Providers (ISPs), to achieve an improved quality of service (QoS) for video streaming users. However, since traditional strategies for a multihoming video CDN are simply designed according to static rules, e.g., simply sending traffic via a ISP which is the same as the ISP of client, they fail to dynamically allocate resources among different ISPs over time. In this paper, we perform measurement studies to demonstrate that such static allocation mechanism is inefficient to make full utilization of multiple ISPs' resources. To address this problem, we propose a dynamic flow scheduling strategy for multihoming video CDN. The challenge is to find the control parameters that can guide the ISP selection when performing flow scheduling. Using a data-driven approach, we find factors that have a major impact on the performance improvement in the dynamic flow scheduling. We further utilize an information gain approach to generate parameter combinations that can be used to guide the flow scheduling, i.e., to determine the ISP each request should be responded by. Our evaluation results demonstrate that our design effectively performs the flow scheduling. In particular, our design yields near optimal performance in a simulation of real-world multihoming setup.

preprint2016arXiv

Improving Crowdsourced Live Streaming with Aggregated Edge Networks

Recent years have witnessed a dramatic increase of user-generated video services. In such user-generated video services, crowdsourced live streaming (e.g., Periscope, Twitch) has significantly challenged today's edge network infrastructure: today's edge networks (e.g., 4G, Wi-Fi) have limited uplink capacity support, making high-bitrate live streaming over such links fundamentally impossible. In this paper, we propose to let broadcasters (i.e., users who generate the video) upload crowdsourced video streams using aggregated network resources from multiple edge networks. There are several challenges in the proposal: First, how to design a framework that aggregates bandwidth from multiple edge networks? Second, how to make this framework transparent to today's crowdsourced live streaming services? Third, how to maximize the streaming quality for the whole system? We design a multi-objective and deployable bandwidth aggregation system BASS to address these challenges: (1) We propose an aggregation framework transparent to today's crowdsourced live streaming services, using an edge proxy box and aggregation cloud paradigm; (2) We dynamically allocate geo-distributed cloud aggregation servers to enable MPTCP (i.e., multi-path TCP), according to location and network characteristics of both broadcasters and the original streaming servers; (3) We maximize the overall performance gain for the whole system, by matching streams with the best aggregation paths.

preprint2016arXiv

Landau-Zener-Stückelberg Interferometry for Majorana Qubit

Stimulated by a very recent experiment observing successfully two superconducting states with even- and odd-number of electrons in a nanowire topological superconductor as expected from the existence of two end Majorana quasiparticles (MQs) [Albrecht \textit{et al.}, Nature \textbf{531}, 206 (2016)], we propose a way to manipulate Majorana qubit exploiting quantum tunneling effects. The prototype setup consists of two one-dimensional (1D) topological superconductors coupled by a tunneling junction which can be controlled by gate voltage. We show that, upon current injection, the time evolution of superconducting phase difference at the junction induces an oscillation in energy levels of the Majorana parity states, whereas the level-crossing is avoided by a small coupling energy of MQs in the individual 1D superconductors. This results in a Landau-Zener-Stückelberg (LZS) interference between the Majorana parity states. Adjusting the current pulse and gate voltage, one can build a LZS interferometry which provides an arbitrary manipulation of the Majorana qubit. The LZS rotation of Majorana qubit can be monitored by the microwave radiated from the junction.

preprint2016arXiv

Node-Surface and Node-Line Fermions From Nonsymmorphic Lattice Symmetries

We propose a kind of novel topological quantum state of semimetals in a quasi-one-dimensional (1D) crystals BaMX$_3$ (M = V, Nb or Ta; X = S or Se) family by using symmetry analysis and first principles calculation. We find that in BaVS$_3$ the valence and conduction bands are degenerate in the $k_z=π/c$ plane ($c$ is the lattice constant along $\hat{z}$ axis) of the Brillouin Zone (BZ). These nodal points form a node-surface and they are protected by a nonsymmorphic crystal symmetry consisting of a two-fold rotation about the $\hat{z}$ axis and a half-translation along the same $\hat{z}$ axis. The band degeneracy in the node-surface is lifted in BaTaS$_3$ by including strong spin-orbit coupling (SOC) of Ta. The node-surface is reduced into 1D node-lines along the high-symmetry paths $k_x=0$ and $k_x$ = $\pm{\sqrt{3}}k_y$ on the $k_z=π/c$ plane. These node-lines are robust against SOC and guaranteed by the symmetries of $P6_3/mmc$ space group. These node-line states are entirely different from previous proposals which are based on the accidental band touchings. We also propose a useful material design for realizing topological node-surface and node-line semimetals.

preprint2016arXiv

Online Influence Maximization in Non-Stationary Social Networks

Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.

preprint2016arXiv

Social- and Mobility-Aware Device-to-Device Content Delivery

Mobile online social network services have seen a rapid increase, in which the huge amount of user-generated social media contents propagating between users via social connections has significantly challenged the traditional content delivery paradigm: First, replicating all of the contents generated by users to edge servers that well "fit" the receivers becomes difficult due to the limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication that allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe that (1) Device-to-device replication can significantly help users download social contents from nearby neighboring peers; (2) Both social propagation and mobility patterns affect how contents should be replicated; (3) The replication strategies depend on regional characteristics ({\em e.g.}, how users move across regions). Using these measurement insights, we propose a joint \emph{propagation- and mobility-aware} content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graph, content propagation and user mobility. We formulate the replication scheduling as an optimization problem and design distributed algorithm only using historical, local and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure movement-based and popularity-based approach, our design can significantly ($2-4$ times) improve the amount of social contents successfully delivered by device-to-device replication.

preprint2016arXiv

Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization

It is believed that hippocampus functions as a memory allocator in brain, the mechanism of which remains unrevealed. In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set of activated neuroids with stable size. Valiant proposed three requirements for the hippocampal circuit to become a stable memory allocator (SMA): stability, continuity and orthogonality. The functionality of SMA in hippocampus is essential in further computation within cortex, according to Valiant's model. In this paper, we put these requirements for memorization functions into rigorous mathematical formulation and introduce the concept of capacity, based on the probability of erroneous allocation. We prove fundamental limits for the capacity and error probability of SMA, in both data-independent and data-dependent settings. We also establish an example of stable memory allocator that can be implemented via neuroidal circuits. Both theoretical bounds and simulation results show that the neural SMA functions well.

preprint2016arXiv

Towards Network-Failure-Tolerant Content Delivery for Web Content

Popularly used to distribute a variety of multimedia content items in today Internet, HTTP-based web content delivery still suffers from various content delivery failures. Hindered by the expensive deployment cost, the conventional CDN can not deploy as many edge servers as possible to successfully deliver content items to all users under these delivery failures. In this paper, we propose a joint CDN and peer-assisted web content delivery framework to address the delivery failure problem. Different from conventional peer-assisted approaches for web content delivery, which mainly focus on alleviating the CDN servers bandwidth load, we study how to use a browser-based peer-assisted scheme, namely WebRTC, to resolve content delivery failures. To this end, we carry out large-scale measurement studies on how users access and view webpages. Our measurement results demonstrate the challenges (e.g., peers stay on a webpage extremely short) that can not be directly solved by conventional P2P strategies, and some important webpage viewing patterns. Due to these unique characteristics, WebRTC peers open up new possibilities for helping the web content delivery, coming with the problem of how to utilize the dynamic resources efficiently. We formulate the peer selection that is the critical strategy in our framework, as an optimization problem, and design a heuristic algorithm based on the measurement insights to solve it. Our simulation experiments driven by the traces from Tencent QZone demonstrate the effectiveness of our design: compared with non-peer-assisted strategy and random peer selection strategy, our design significantly improves the successful relay ratio of web content items under network failures, e.g., our design improves the content download ratio up to 60% even when users located in a particular region (e.g., city) where none can connect to the regional CDN server.

preprint2016arXiv

Understanding Content Placement Strategies in Smartrouter-based Peer CDN for Video Streaming

Recent years have witnessed a new video delivery paradigm: smartrouter-based peer video content delivery network, which is enabled by smartrouters deployed at users' homes. ChinaCache (one of the largest CDN providers in China) and Youku (a video provider using smartrouters to assist video delivery) announced their cooperation in 2015, to create a new paradigm of content delivery based on householders' network resources. This new paradigm is different from the conventional peer-to-peer (P2P) approach, because millions of dedicated smartrouters are operated by the centralized video service providers in a coordinative manner. Thus it is intriguing to study the content placement strategies used in a smartrouter-based content delivery system, as well as its potential impact on the content delivery ecosystem. In this paper, we carry out measurement studies of Youku's peer video CDN, who has deployed over 300K smartrouter devices for its video delivery. In our measurement studies, 104K videos were investigated and 4TB traffic has been analyzed, over controlled smartrouter nodes and players. Our measurement insights are as follows. First, a global content replication strategy is essential for the peer CDN systems. Second, such peer CDN deployment itself can form an effective sub-system for end-to-end QoS monitoring, which can be used for fine-grained request redirection (e.g., user-level) and content replication. We also show our analysis on the performance limitations and propose potential improvements to the peer CDN systems.

preprint2016arXiv

Understanding the Smartrouter-based Peer CDN for Video Streaming

Recent years have witnessed a new video delivery paradigm: smartrouter-based video delivery network, which is enabled by smartrouters deployed at users' homes, together with the conventional video servers deployed in the datacenters. Recently, ChinaCache, a large content delivery network (CDN) provider, and Youku, a video service provider using smartrouters to assist video delivery, announced their cooperation to create a new paradigm of content delivery based on householders' network resources. This new paradigm is different from the conventional peer-to-peer (P2P) approach, because such dedicated smartrouters are inherently operated by the centralized video service providers in a coordinative manner. It is intriguing to study the strategies, performance and potential impact on the content delivery ecosystem of such peer CDN systems. In this paper, we study the Youku peer CDN, which has deployed over 300K smartrouter devices for its video streaming. In our measurement, 78K videos were investigated and 3TB traffic has been analyzed, over controlled routers and players. Our contributions are the following measurement insights. First, a global replication and caching strategy is essential for the peer CDN systems, and proactively scheduling replication and caching on a daily basis can guarantee their performance. Second, such peer CDN deployment can itself form an effective Quality of Service (QoS) monitoring sub-system, which can be used for fine-grained user request redirection. We also provide our analysis on the performance issues and potential improvements to the peer CDN systems.

preprint2015arXiv

Data-driven Approaches for Social Video Distribution

The Internet has recently witnessed the convergence of online social network services and online video services: users import videos from content sharing sites, and propagate them along the social connections by re-sharing them. Such social behaviors have dramatically reshaped how videos are disseminated, and the users are now actively engaged to be part of the social ecosystem, rather than being passively consumers. Despite the increasingly abundant bandwidth and computation resources, the ever increasing data volume of user generated video content and the boundless coverage of socialized sharing have presented unprecedented challenges. In this paper, we first presents the challenges in social-aware video delivery. Then, we present a principal framework for data-driven social video delivery approaches. Moreover, we identify the unique characteristics of social-aware video access and the social content propagation, and closely reveal the design of individual modules and their integration towards enhancing users' experience in the social network context.

preprint2015arXiv

Dispersing Instant Social Video Service Across Multiple Clouds

Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today's Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social \emph{propagation} is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.

preprint2015arXiv

Manipulating the Majorana Qubit with the Landau-Zener-Stückelberg Interference

Constructing a universal operation scheme for Majorana qubits remains a central issue for the topological quantum computation. We study the Landau-Zener-Stückelberg interference in a Majorana qubit and show that this interference can be used to achieve controllable operations. The Majorana qubit consists of an rf SQUID with a topological nanowire Josephson junction which hosts Majorana bound states. In the SQUID, a magnetic flux pulse can drive the quantum evolution of the Majorana qubit. The qubit experiences two Landau-Zener transitions when the amplitude of the pulse is tuned around the superconducting flux quanta $2e/\hbar$. The Landau-Zener-Stückelberg interference between the two transitions rotates the Majorana qubit, with the angle controlled by the time scale of the pulse. This rotation operation implements a high-speed single-qubit gate on the Majorana qubit, which is a necessary ingredient for the topological quantum computation.

preprint2015arXiv

MAP: Microblogging Assisted Profiling of TV Shows

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relationship of its microblogging users and its propagation in the social network; iii) We present some interesting analysis, based on our framework to profile real-world TV shows.

preprint2015arXiv

Proposal for a flux qubit in a dc SQUID with the $4π$ period Josephson effect

Constructing qubits which are suitable for quantum computation remains a notable challenge. Here, we propose a superconducting flux qubit in a dc SQUID structure, formed by a conventional insulator Josephson junction and a topological nanowire Josephson junction with Majorana bound states. The zero energy Majorana bound states transport $4π$ period Josephson currents in the nanowire junction. The interplay between this $4π$ period Josephson effect and the convectional $2π$ period Josephson effect in the insulator junction induces a double-well potential energy landscape in the SQUID. As a result, the two lowest energy levels of the SQUID are isolated from other levels. These two levels show contradicting circulating supercurrents, thus can be used as a flux qubit. We reveal that this flux qubit has the merits of stability to external noises, tolerance to the deviation of system parameters, and scalability to large numbers. Furthermore, we demonstrate how to couple this flux qubit with the Majorana qubit by tuning the junction parameters, and how to use this coupling to manipulate the Majorana qubit.

preprint2014arXiv

An Analysis of Research in Software Engineering: Assessment and Trends

Glass published the first report on the assessment of systems and software engineering scholars and institutions two decades ago. The ongoing, annual survey of publications in this field provides fund managers, young scholars, graduate students, etc. with useful information for different purposes. However, the studies have been questioned by some critics because of a few shortcomings of the evaluation method. It is actually very hard to reach a widely recognized consensus on such an assessment of scholars and institutions. This paper presents a module and automated method for assessment and trends analysis in software engineering compared with the prior studies. To achieve a more reasonable evaluation result, we take into consideration more high-quality publications, the rank of each publication analyzed, and the different roles of authors named on each paper in question. According to the 7638 papers published in 36 publications from 2008 to 2013, the statistics of research subjects roughly follow power laws, implying the interesting Matthew Effect. We then identify the Top 20 scholars, institutions and countries or regions in terms of a new evaluation rule based on the frequently-used one. The top-ranked scholar is Mark Harman of the University College London, UK, the top-ranked institution is the University of California, USA, and the top-ranked country is the USA. Besides, we also show two levels of trend changes based on the EI classification system and user-defined uncontrolled keywords, as well as noteworthy scholars and institutions in a specific research area. We believe that our results would provide a valuable insight for young scholars and graduate students to seek possible potential collaborators and grasp the popular research topics in software engineering.

preprint2014arXiv

Fidelity Approach in Topological Superconductors with Disorders

We apply the fidelity approach to study the topological superconductivity in a spin-orbit coupled nanowire system. The wire is modeled as a one layer lattice chain with Zeeman energy and spin-orbit coupling, which is in proximity to a multi layer superconductor. In particular, we study the effects of disorders and find that the fidelity susceptibility show multiple peaks. It is revealed that the major peak indicates the topological quantum phase transition, while other peaks signal the pining of the Majorana bound states by disorders.

preprint2014arXiv

How far does scientific community look back?

How does the published scientific literature used by scientific community? Many previous studies make analysis on the static usage data. In this research, we propose the concept of dynamic usage data. Based on the platform of realtime.springer.com, we have been monitoring and recording the dynamic usage data of Scientometrics articles round the clock. Our analysis find that papers published in recent four years have many more downloads than papers published four years ago. According to our quantitative calculation, papers down-loaded on one day have an average lifetime of 4.1 years approximately. Classic papers are still being downloaded frequently even long after their publication. Additionally, we find that social media may reboot the attention of old scientific literature in a short time.

preprint2014arXiv

International Scientific Collaboration of China: Collaborating Countries, Institutions and Individuals

Using bibliometric methods, we investigate China's international scientific collaboration from 3 levels of collaborating countries, institutions and individuals. We design a database in SQL Server, and make analysis of Chinese SCI papers based on the corresponding author field. We find that China's international scientific collaboration is focused on a handful of countries. Nearly 95% international co-authored papers are collaborated with only 20 countries, among which the USA account for more than 40% of all. Results also show that Chinese lineage in the international co-authorship is obvious, which means Chinese immigrant scientists are playing an important role in China's international scientific collaboration, especially in English-speaking countries.

preprint2014arXiv

Temperature-Dependent Thermoelectric Properties of Individual Silver Nanowires

Individual highly pure single crystalline silver nanowires (Ag NWs) were investigated with regard to the electrical conductivity $σ$, the thermal conductivity $λ$ and the Seebeck coefficient $S$ as function of the temperature $T$ between $1.4\,\mathrm{K}$ and room temperature (RT). Transmission electron microscopy was performed subsequently to the thermoelectric characterization of the Ag NWs, so that their transport properties can be correlated with the structural data. The crystal structure, surface morphology and the rare occurrence of kinks and twinning were identified. The thermoelectric properties of the Ag NWs are discussed in comparison to the bulk: $S_{\mathrm{Ag,Pt}}(T)$ was measured with respect to platinum and is in agreement with the bulk, $σ(T)$ and $λ(T)$ showed reduced values with respect to the bulk. The latter are both notably dominated by surface scattering caused by an increased surface-to-volume ratio. By lowering $T$ the electron mean free path strongly exceeds the NW's diameter of $150\,\mathrm{nm}$ so that the transition from diffusive transport to quasi ballistic one dimensional transport is observed. An important result of this work is that the Lorenz number $L(T)$ turns out to be independent of surface scattering. Instead the characteristic of $L(T)$ is determined by the material's purity. Moreover, $σ(T)$ and $L(T)$ can be described by the bulk Debye temperature of silver.

preprint2014arXiv

Viewing Majorana Bound States by Rabi Oscillations

Rabi oscillation is a pure quantum phenomenon where the system jumps forth and back between two quantum levels under stimulation of a microwave, and a resonance occurs when the energy difference is matched by the photon energy. Rabi oscillations have been observed in various quantum systems so far, and especially are used to demonstrate the quantum coherence of quantum bits. In the present work, we explore Rabi oscillation in a system accommodating the elusive Majorana bound states (MBSs) under intensive search recently. The proposed setup is constructed by a quantum dot (QD) and a superconducting quantum interference device (SQUID), where a semiconductor nanowire with spin-orbital coupling in the topological state is introduced to form Josephson junction. When the coupling between QD and the Josephson junction is tuned by an ac gate voltage, Rabi oscillation takes place among quantum states formed by QD and MBSs, which makes it possible to reveal bizarre properties of MBSs by sensing the electron occupation on QD. Especially, one should be able to observe the fractional Josephson relation $\cos(πΦ/Φ_0)$ unique to MBSs with $Φ$ the magnetic flux applied in the SQUID and $Φ_0=hc/2e$ the flux quantum. The system has been investigated in terms of the analytic Floquet theorem and numerical simulations with fine agreement.

preprint2013arXiv

Exploring Scientists' Working Timetable: A Global Survey

In our previous study (Wang et al., 2012), we analyzed scientists' working timetable of 3 countries, using realtime downloading data of scientific literatures. In this paper, we make a through analysis about global scientists' working habits. Top 30 countries/territories from Europe, Asia, Australia, North America, Latin America and Africa are selected as representatives and analyzed in detail. Regional differences for scientists' working habits exists in different countries. Besides different working cultures, social factors could affect scientists' research activities and working patterns. Nevertheless, a common conclusion is that scientists today are often working overtime. Although scientists may feel engaged and fulfilled about their hard working, working too much still warns us to reconsider the work - life balance.

preprint2013arXiv

Fidelity in topological superconductors with end Majorana fermions

Fidelity and fidelity susceptibility are introduced to investigate the topological superconductors with end Majorana fermions. A general formalism is established to calculate the fidelity and fidelity susceptibility by solving Bogoliubov-de Gennes equations. It is shown that the fidelity susceptibility manifest itself as a peak at the topological quantum phase transition point for homogeneous Kitaev wire, thus serves as a valid indicator for the topological quantum phase transition which signals the appearance of Majorana fermions. The effect of disorders is investigated within this formalism. We consider three disordered systems and observe fidelity susceptibility peak in all of them. By analyzing the susceptibility peak, we notice that the local potential disorders and the hopping disorders can shift the phase transition point, while off-diagonal disorders have no obvious influence. Our results confirm that the existence of topological quantum phase transition is robust to these disorders, while the behavior of the phase transition might be influenced by disorders.

preprint2013arXiv

Proposal for Manipulation of Majorana Fermions in Nano-Patterned Semiconductor-Superconductor Heterostructure

We investigate a heterostructure system with a spin-orbit coupled semiconductor sandwiched by an s-wave superconductor and a ferromagnetic insulator, which supports Majorana fermions (MFs) at the superconducting vortex cores. We propose a scheme of transporting and braiding the MFs, which only requires application of point-like gate voltages in a system with nano-meter patterns. By solving the time-dependent Bogoliubov-de Gennes equation numerically, we monitor the time evolutions of MF wave-functions and show that the braiding of MFs with non-Abelian statistics can be achieved by adiabatic switching within several nano seconds.

preprint2013arXiv

Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

Terahertz (THz) imaging provides cutting edge technique in biology, medical sciences and non-destructive evaluation. However, due to the long wavelength of the THz wave, the obtained resolution of THz imaging is normally a few hundred microns and is much lower than that of the traditional optical imaging. We introduce a sub-wavelength resolution THz imaging technique which uses the THz radiation generated by a femtosecond laser filament in air as the probe. This method is based on the fact that the femtosecond laser filament forms a waveguide for the THz wave in air. The diameter of the THz beam, which propagates inside the filament, varies from 20 μm to 50 μm, which is significantly smaller than the wavelength of the THz wave. Using this highly spatially confined THz beam as the probe, THz imaging with resolution as high as 20 μm (~λ/38) can be realized.

preprint2013arXiv

Terahertz Wave Guiding by Femtosecond Laser Filament in Air

Femtosecond laser filament generates strong terahertz (THz) pulse in air. In this paper, THz pulse waveform generated by femtosecond laser filament has been experimentally investigated as a function of the length of the filament. Superluminal propagation of THz pulse has been uncovered, indicating that the filament creates a THz waveguide in air. Numerical simulation has confirmed that the waveguide is formed because of the radially non-uniform refractive index distribution inside the filament. The underlying physical mechanisms and the control techniques of this type THz pulse generation method might be revisited based on our findings. It might also potentially open a new approach for long-distance propagation of THz wave in air.

preprint2013arXiv

Topological Single Electron Pumping Assisted by Majorana Fermions

Single electron pumping based on the topological property of Majorana fermions (MFs) is proposed. The setup consists of a quantum dot and four nano topological superconductors (TSs) connected by constriction junctions, with an additional vortex located in the loop of TSs. Operation is performed by gate voltages at constriction junctions. Simulations with Bogloliubov-de Gennes equation demonstrate successfully quantum protection during switching operation.

preprint2012arXiv

Exploring scientists' working timetable: Do scientists often work overtime?

A novel method is proposed to monitor and record scientists' working timetable. We record the downloads information of scientific papers real-timely from Springer round the clock, and try to explore scientists' working habits. As our observation demonstrates, many scientists are still engaged in their research after working hours every day. Many of them work far into the night, even till next morning. In addition, research work also intrudes into their weekends. Different working time patterns are revealed. In the US, overnight work is more prevalent among scientists, while Chinese scientists mostly have busy weekends with their scientific research.

preprint2012arXiv

Ratchet potential and rectification effect in Majorana fermion SQUID

Motivated by a recent experimental progress in realizing Majorana fermions (MFs) in a heterostructure of a spin-orbit coupling nanowire and superconductor (V. Mourik et al., Science.1222360), we investigate a SQUID formed by the novel superconductor-nanowire-superconductor Josephson junction which contains MFs and a conventional superconductor-insulator-superconductor junction. It is shown that the critical current of the SQUID is different for the two current directions. Since the asymmetric Josepshon current forms a ratchet potential for the dynamics of superconducting phase, a rectification effect is expected when the SQUID is driven by an ac current. These novel properties are expected to be useful for probing the elusive MFs as well as for their dynamics.

preprint2012arXiv

Tracing scientist's research trends realtimely

In this research, we propose a method to trace scientists' research trends realtimely. By monitoring the downloads of scientific articles in the journal of Scientometrics for 744 hours, namely one month, we investigate the download statistics. Then we aggregate the keywords in these downloaded research papers, and analyze the trends of article downloading and keyword downloading. Furthermore, taking both the download of keywords and articles into consideration, we design a method to detect the emerging research trends. We find that in scientometrics field, social media, new indices to quantify scientific productivity (g-index), webometrics, semantic, text mining, open access are emerging fields that scientometrics researchers are focusing on.

preprint2011arXiv

Detecting Majorana fermions by nonlocal entanglement between quantum dots

Nonlocal entanglement between two quantum dots can be generated through Majorana fermions. The two Majorana fermions at the ends of an one-dimensional topological superconductor form a nonlocal fermion level, coupling to the occupation states of two quantum dots put close to the two ends, and the entire system will come into an entangled state. After introducing a charging energy by a capacitor, entanglement of the entire system can manifest itself through the nonlocal entanglement between the two quantum dots. That is, when measuring the electron occupations of the quantum dots, the measurement result of one quantum dot will influence the measurement result of the other quantum dot. This nonlocal entanglement between the two quantum dots is a strong evidence of the nonlocal nature of the fermion level constructed by two Majorana fermions.

preprint2011arXiv

Manipulating Edge Majorana Fermions in Vortex State

A vortex in a model spinless px+ipy superconductor induces two Majorana fermions (MFs), one in the core and the other at the sample edge. In the present work, we show that edge MF can be generated, fused, transported, and braided easily by tuning gate voltages at point-like constriction junctions. Solving the time-dependent Bogoliubov-de Gennes equation, we demonstrate that the braiding of edge MFs obeys the non-Abelian statistics. The present setup is therefore a promising implementation for topological quantum computation, and has the advantage of easy manipulation and simple device structure.

preprint2011arXiv

Time-Reversal-Symmetry-Broken Superconductivity Induced by Frustrated Inter-Component Couplings

Superconductivity is associated with spontaneously broken gauge symmetry. In some exotic superconductors the time-reversal symmetry is broken as well, accompanied with internal magnetic field. A time-reversal symmetry broken (TRSB) superconductivity without internal magnetic field involved can be induced by frustrated inter-component couplings, which becomes a realistic issue recently due to the discovery of iron-pnictide superconductors. Here we derive stability condition for this novel TRSB state using the Ginzburg-Landau (GL) theory. We find that there are multiple divergent coherence lengths, and that this novel superconductivity cannot be categorized by the GL number into type I or type II. We reveal that the critical Josephson current of a constriction junction between two bulk superconductors of different chiralities is suppressed significantly from that for same chirality. This effect provides a unique way to verify experimentally this brand new superconductivity.

preprint2010arXiv

Beam optimization of RFQ and SFRFQ combined accelerator at Peking University

The Peking University Integral Split Ring Radio Frequency Quadrupole(ISR RFQ) accelerator was constructed in 1999 with a high duty factor 16.7% and repetition frequency 166Hz, and it was able to accelerate N+, O+,O-, C+ and He+ from 1.6kev/u to 65keV/u. It was later upgraded as an injector of the Separated Function RFQ (SFRFQ). The experiments indicated that the maximum accelerated O+ beam current could exceed 3.2mA with energy 1.03MeV and an energy spread (FWHM) 3.1%. Then the beam transports through a 1m-long magnetic triplet to the entrance of SFRFQ and is finally accelerated to 1.64MeV. The beam conditioning of RFQ were carefully optimized to satisfy the requirements of the SFRFQ. The combined accelerator eventually can deliver 0.53mA O+ beam with energy 1.65MeV, which has sufficiently demonstrated the feasibility of the SFRFQ structure.

preprint2010arXiv

Extinction of quasiparticle scattering interference in cuprate superconductors

The quasiparticle scattering interference phenomenon characterized by the peaks in the local density of states is studied within the kinetic energy driven superconducting mechanism in the presence of a single impurity. By calculation of the Fourier transformed ratio of the local density of states at opposite energy, it is shown that the quasiparticle scattering interference phenomenon can be described qualitatively by a single impurity in the kinetic energy driven homogeneous d-wave superconducting state. The amplitude of the peak increases with increasing energy at the low energy, and reaches a maximum at the intermediate energy, then diminishes to zero at the high energy. The theory also predicts that with increasing doping, the position of the peak along the nodal direction moves towards to the center of the Brillouin zone, while the position of the peak along the antinodal direction is shifted to large momentum region.

preprint2010arXiv

Interference and Switching of Josephson current carried by nonlocal spin-entangled electrons in a SQUID-like system with quantum dots

Josephson current of spin-entangled electrons through the two branches of a SQUID-like structure with two quantum dots exhibits a magnetic-flux response different from the conventional Josephson current. Due to their interference, the period of maximum Josephson current changes from $h/2e$ to $h/e$, which can be used for detecting the Cooper-pair splitting efficiency. The nonlocal spin entanglement provides a quantum mechanical functionale for switching on and off this novel Josephson current, and explicitly a switch is formulated by including a pilot junction. It is shown that the device can be used to measure the magnitude of split-tunneling Josephson current.

preprint2010arXiv

Reduced fidelity in Kitaev honeycomb model

We study the reduced fidelity and reduced fidelity susceptibility in the Kitaev honeycomb model. It is shown that the reduced fidelity susceptibility of two nearest site manifest itself a peak at the quantum phase transition point, although the one-site reduced fidelity susceptibility vanishes. Our results directly reveal that the reduced fidelity susceptibility can be used to characterize the quantum phase transition in the Kitaev honeycomb model, and thus suggest that the reduced fidelity susceptibility is an accurate marker of the topological phase transition when it is properly chosen, despite of its local nature.

Zhi Wang

What is connected

Connect this record

See the researcher in context

Building this map preview

83 published item(s)

MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching

Semiclassical theory for proximity-induced superconducting systems with spin-orbit coupling

Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation

A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach

A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

A Systematic Study of Android Non-SDK (Hidden) Service API Security

Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing

Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

DeepC2: AI-powered Covert Command and Control on OSNs

Deepfake Forensics via An Adversarial Game

EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

Fully Self-Supervised Learning for Semantic Segmentation

HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation

Higgs-Leggett mechanism for the elusive 6e superconductivity observed in Kagome vanadium-based superconductors

Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

Josephson radiation patterns in underdamped topological Josephson junctions

Online Continual Adaptation with Active Self-Training

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

SeqNet: An Efficient Neural Network for Automatic Malware Detection

Thompson Sampling for Robust Transfer in Multi-Task Bandits

Towards a Systematic Survey for Carbon Neutral Data Centers

EvilModel: Hiding Malware Inside of Neural Network Models

Lifelong Incremental Reinforcement Learning with Online Bayesian Inference

Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

Partial Reduction and Cosmology at Defect Brane

The emergent linear Rashba spin-orbit coupling offering the fast manipulation of hole-spin qubits in germanium

Towards Understanding and Demystifying Bitcoin Mixing Services

Uniting the order and disorder dynamics in photoexcited VO2

COVID-19 Evolves in Human Hosts

Fermi liquid behavior and colossal magnetoresistance in layered MoOCl2

Giant momentum-dependent spin splitting in centrosymmetric low Z antiferromagnets

Josephson radiation from nonlinear dynamics of Majorana zero modes

Prediction of low-Z collinear and noncollinear antiferromagnetic compounds having momentum-dependent spin splitting even without spin-orbit coupling

ProcData: An R Package for Process Data Analysis

PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Statistical Analysis of Multi-Relational Network Recovery

Subtask Analysis of Process Data Through a Predictive Model

Symmetry-Breaking Polymorphous Descriptions for Complex Materials without Interelectronic U

TeleHammer: A Formal Model of Implicit Rowhammer

The polymorphous nature of cubic halide perovskites

Understanding Electronic Peculiarities in Tetragonal FeSe as Local Structural Symmetry Breaking

Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

Room temperature 2D ferromagnetism in few-layered 1$T$-CrTe$_{2}$

A Measurement Study of TCP Performance for Chunk Delivery in DASH

Drone Streaming with Wi-Fi Grid Aggregation for Virtual Tour

Dynamic Flow Scheduling Strategy in Multihoming Video CDNs

Improving Crowdsourced Live Streaming with Aggregated Edge Networks

Landau-Zener-Stückelberg Interferometry for Majorana Qubit

Node-Surface and Node-Line Fermions From Nonsymmorphic Lattice Symmetries

Online Influence Maximization in Non-Stationary Social Networks

Social- and Mobility-Aware Device-to-Device Content Delivery

Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization

Towards Network-Failure-Tolerant Content Delivery for Web Content

Understanding Content Placement Strategies in Smartrouter-based Peer CDN for Video Streaming

Understanding the Smartrouter-based Peer CDN for Video Streaming

Data-driven Approaches for Social Video Distribution

Dispersing Instant Social Video Service Across Multiple Clouds

Manipulating the Majorana Qubit with the Landau-Zener-Stückelberg Interference

MAP: Microblogging Assisted Profiling of TV Shows

Proposal for a flux qubit in a dc SQUID with the $4π$ period Josephson effect

An Analysis of Research in Software Engineering: Assessment and Trends

Fidelity Approach in Topological Superconductors with Disorders

How far does scientific community look back?

International Scientific Collaboration of China: Collaborating Countries, Institutions and Individuals

Temperature-Dependent Thermoelectric Properties of Individual Silver Nanowires

Viewing Majorana Bound States by Rabi Oscillations

Exploring Scientists' Working Timetable: A Global Survey

Fidelity in topological superconductors with end Majorana fermions

Proposal for Manipulation of Majorana Fermions in Nano-Patterned Semiconductor-Superconductor Heterostructure

Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

Terahertz Wave Guiding by Femtosecond Laser Filament in Air

Topological Single Electron Pumping Assisted by Majorana Fermions

Exploring scientists' working timetable: Do scientists often work overtime?