arXiv subjects

Yue Zhang

Publications and source records attributed to Yue Zhang.

At least 703 records · Page 39Linked to original sources

arXiv2020

Dark CP Violation and Gauged Lepton/Baryon Number for Electroweak Baryogenesis

We explore the generation of the baryon asymmetry in an extension of the Standard Model where the lepton number is promoted to a $U(1)_\ell$ gauge symmetry with an associated $Z^\prime$ gauge boson. This is based on a novel electroweak baryogenesis mechanism first proposed by us in Ref. \cite{Carena:2018cjh}. Extra fermionic degrees of freedom - including a fermionic dark matter $χ$ - are introduced in the dark sector for anomaly cancellation. Lepton number is spontaneously broken at high scale and the effective theory, containing the Standard Model, the $Z^\prime$, the fermionic dark matter, and an additional complex scalar field $S$, violates CP in the dark sector. The complex scalar field couples to the Higgs portal and is essential in enabling a strong first order phase transition. Dark CP violation is diffused in front of the bubble walls and creates a chiral asymmetry for $χ$, which in turn creates a chemical potential for the Standard Model leptons. Weak sphalerons are then in charge of transforming the net lepton charge asymmetry into net baryon number. We explore the model phenomenology related to the leptophilic $Z^\prime$, the dark matter candidate, the Higgs boson and the additional scalar, as well as implications for electric dipole moments. We also discuss the case when baryon number $U(1)_B$ is promoted to a gauge symmetry, and discuss electroweak baryogenesis and its corresponding phenomenology.

hep-ph↗

arXiv2020

Voltage-Induced Inertial Domain Wall Motion in an Antiferromagnetic Nanowire

Racetrack memory based on magnetic domain walls (DWs) motion exhibits advantages of small volume and high reading speed. When compared to current-induced DW motion, voltage-induced DW motion exhibits lower dissipation. On the other hand, the DW in an antiferromagnet (AFM) moves at a high velocity with weak stray field. In this work, the AFM DW motion induced by a gradient of magnetic anisotropy energy under a voltage pulse has been investigated in theory. The dynamics equation for the DW motion was derived. The solution indicates that the DW velocity is higher than 100 m/s, and because of inertia, the DW is able to keep moving at a speed of around 100 m/s for several nano seconds after turning off the voltage in a period of pulse. The mechanism for this DW inertia is explained based on the Lagrangian route. On the other hand, a spin wave is emitted while the DW is moving, yet the DW is still able to move at an ever increasing velocity with enlarging DW width. This indicates energy loss from emission of spin wave is less than the energy gain from the effective field of the gradient of anisotropy energy.

physics.app-ph↗

arXiv2020

Peripheral-free Device Pairing by Randomly Switching Power

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a paramount capability. Existing secure pairing protocols require auxiliary equipment/peripheral (e.g., displays, speakers and sensors) to achieve authentication, which is unacceptable for low-priced devices such as smart lamps. This paper studies how to design a peripheral-free secure pairing protocol. Concretely, we design the protocol, termed SwitchPairing, via out-of-box power supplying chargers and on-board clocks, achieving security and economics at the same time. When a user wants to pair two or more devices, he/she connects the pairing devices to the same power source, and presses/releases the switch on/off button several times. Then, the press and release timing can be used to derive symmetric keys. We implement a prototype via two CC2640R2F development boards from Texas Instruments (TI) due to its prevalence. Extensive experiments and user studies are also conducted to benchmark our protocol in terms of efficiency and security.

cs.CR↗

arXiv2020

Learning Fairness-aware Relational Structures

The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years. The necessity of encoding complex relational dependencies among the features and variables for competent predictions require the development of fair, yet expressive relational models. In this work, we introduce Fair-A3SL, a fairness-aware structure learning algorithm for learning relational structures, which incorporates fairness measures while learning relational graphical model structures. Our approach is versatile in being able to encode a wide range of fairness metrics such as statistical parity difference, overestimation, equalized odds, and equal opportunity, including recently proposed relational fairness measures. While existing approaches employ the fairness measures on pre-determined model structures post prediction, Fair-A3SL directly learns the structure while optimizing for the fairness measures and hence is able to remove any structural bias in the model. We demonstrate the effectiveness of our learned model structures when compared with the state-of-the-art fairness models quantitatively and qualitatively on datasets representing three different modeling scenarios: i) a relational dataset, ii) a recidivism prediction dataset widely used in studying discrimination, and iii) a recommender systems dataset. Our results show that Fair-A3SL can learn fair, yet interpretable and expressive structures capable of making accurate predictions.

cs.LG↗

arXiv2020

Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors

The mixed membership stochastic blockmodel (MMSB) is a popular framework for community detection and network generation. It learns a low-rank mixed membership representation for each node across communities by exploiting the underlying graph structure. MMSB assumes that the membership distributions of the nodes are independently drawn from a Dirichlet distribution, which limits its capability to model highly correlated graph structures that exist in real-world networks. In this paper, we present a flexible richly structured MMSB model, \textit{Struct-MMSB}, that uses a recently developed statistical relational learning model, hinge-loss Markov random fields (HL-MRFs), as a structured prior to model complex dependencies among node attributes, multi-relational links, and their relationship with mixed-membership distributions. Our model is specified using a probabilistic programming templating language that uses weighted first-order logic rules, which enhances the model's interpretability. Further, our model is capable of learning latent characteristics in real-world networks via meaningful latent variables encoded as a complex combination of observed features and membership distributions. We present an expectation-maximization based inference algorithm that learns latent variables and parameters iteratively, a scalable stochastic variation of the inference algorithm, and a method to learn the weights of HL-MRF structured priors. We evaluate our model on six datasets across three different types of networks and corresponding modeling scenarios and demonstrate that our models are able to achieve an improvement of 15\% on average in test log-likelihood and faster convergence when compared to state-of-the-art network models.

cs.LG↗

arXiv2020

SecEL: Privacy-Preserving, Verifiable and Fault-Tolerant Edge Learning for Autonomous Vehicles

Mobile edge computing (MEC) is an emerging technology to transform the cloud-based computing services into the edge-based ones. Autonomous vehicular network (AVNET), as one of the most promising applications of MEC, can feature edge learning and communication techniques, improving the safety for autonomous vehicles (AVs). This paper focuses on the edge learning in AVNET, where AVs at the edge of the network share model parameters instead of data in a distributed manner, and an aggregator (e.g., a base station) aggregates parameters from AVs and at the end obtains a trained model. Despite promising, security issues, such as data leakage, computing integrity invasion and fault connection in existing edge learning cases are not considered fully. To the best of our knowledge, there lacks an effective scheme simultaneously covering the foregoing security issues. Therefore, we propose \textit{SecEL}, a privacy-preserving, verifiable and fault-tolerant scheme for edge learning in AVNET. First, we leverage the primitive of bivariate polynomial-based secret sharing to encrypt model parameters by one-time padding. Second, we use homomorphic authenticator based on message authentication code to support verifiable computation. Third, we mitigate the computation failure problem caused by fault connection. Last, we simulate and evaluate SecEL in terms of time cost, throughput and classification accuracy. The experiment results demonstrate the effectiveness of SecEL.

cs.CR↗

arXiv2020

Comparison Network for One-Shot Conditional Object Detection

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed. Specifically, query and target image features are extracted through a Siamese network as mapped metrics of marginal probabilities. A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability. Once trained, ComparisonNet can detect objects of both seen and unseen classes without further training, which also has the advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.

cs.CV↗

arXiv2019

Onionchain: Towards Balancing Privacy and Traceability of Blockchain-Based Applications

With the popularity of Blockchain comes grave security-related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain.

cs.CR↗

arXiv2019

X-LineNet: Detecting Aircraft in Remote Sensing Images by a pair of Intersecting Line Segments

Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection. These DCNNs based detectors mainly belong to top-down approaches, which first enumerate massive potential locations of objects with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down approaches, this paper shows that aircraft detection via bottom-up approach still performs competitively in the era of deep learning. We present a novel one-stage and anchor-free aircraft detection model in a bottom-up manner, which formulates the task as detection of two intersecting line segments inside each target and grouping of them without any rectangular region classification. This model is named as X-LineNet. With simple post-processing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the rotating bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form which has less redundancy and can better represent aircraft than that of rectangular box. Experiments show that X-LineNet outperforms state-of-the-art one-stage object detectors and is competitive compared with advanced two-stage detectors on both UCAS-AOD and NWPU VHR-10 open dataset in the field of aircraft detection.

cs.CV↗

arXiv2019

Deep Reinforcement Learning for Smart Home Energy Management

In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

eess.SY↗

arXiv2019

Leveraging Dependency Forest for Neural Medical Relation Extraction

Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investigate a method to alleviate this problem by utilizing dependency forests. Forests contain many possible decisions and therefore have higher recall but more noise compared with 1-best outputs. A graph neural network is used to represent the forests, automatically distinguishing the useful syntactic information from parsing noise. Results on two biomedical benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.

cs.CL↗

arXiv2019

Higgs Portal to Dark QED

We introduce a light dark photon $A_μ^\prime$ to the minimal Higgs portal model, by coupling the Higgs boson to "dark QED" containing fermionic dark matter, which gives rise to rich and interesting collider phenomenology. There are two prominent features in such a simple extension -- the Higgs boson could have decays into the long-lived dark photon through the "mono-$A^\prime$ channel," or into multiple collimated leptons via "darkonium," depending on the mixing parameter of the $A_μ^\prime$ with the visible photon. We initiate a study on the possibility of probing the parameter space of the model in both the energy and the lifetime frontiers at the Large Hadron Collider.

hep-ph↗

arXiv2019

Absence of superconductivity in bulk Nd$_{1-x}$Sr$_x$NiO$_2$

Recently superconductivity at 9 - 15 K was discovered in an infinite-layer nickelate (Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ films), which has received enormous attention. Since the $Ni^{1+}$ ionic state in NdNiO$_2$ may have the $3d^9$ outer-shell electronic orbit which resembles that of the cuprates, it is very curious to know whether superconductivity discovered here has similar mechanism as that in the cuprates. By using a three-step method, we successfully synthesize the bulk samples of Nd$_{1-x}$Sr$_x$NiO$_2$ (x=0, 0.2, 0.4). The X-ray diffractions reveal that all the samples contain mainly the infinite layer phase of 112 with some amount of segregated Ni. This has also been well proved by the SEM image and the EDS composition analysis. The resistive measurements on the Sr doped samples show insulating behavior without the presence of superconductivity. Temperature dependence of the magnetic moment under high magnetic fields exhibits a Curie-Weiss law feature with the paramagnetic moment of about 2$μ_B$/f.u.. By applying pressure on Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ up to about 50.2 GPa, we find that the strong insulating behavior at ambient pressure is significantly suppressed, but superconductivity has not been observed either. Since the lattice constants derived from our XRD data are very close to those of the reported superconducting films, we argue that the superconductivity in the reported film may not originate from the expected Nd$_{0.8}$Sr$_{0.2}$NiO$_2$, but arise from the interface or the stress effect.

cond-mat.supr-con↗

arXiv2019

Transition-Based Deep Input Linearization

Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.

cs.CL↗

arXiv2019

Hierarchically-Refined Label Attention Network for Sequence Labeling

CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain over strong neural encoding. For better representing label sequences, we investigate a hierarchically-refined label attention network, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. Results on POS tagging, NER and CCG supertagging show that the proposed model not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to BiLSTM-CRF.

cs.CL↗

arXiv2019

Contrastive Attention Mechanism for Abstractive Sentence Summarization

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/Abstractive-Text-Summarization

cs.CL↗

arXiv2019

DDM: A Demand-based Dynamic Mitigation for SMT Transient Channels

Different from the traditional software vulnerability, the microarchitecture side channel has three characteristics: extensive influence, potent threat, and tough defense. The main reason for the micro-architecture side channel is resource sharing. There are many reasons for resource sharing, one of which is SMT (Simultaneous Multi-Threading) technology. In this paper, we define the SMT Transient Channel, which uses the transient state of shared resources between threads to steal information. To mitigate it, we designed a security demand-based dynamic mitigation (DDM) to Mitigate the SMT transient channels. The DDM writes the processes' security requirements to the CPU register sets, and the operating system calls the HLT instruction to dynamically turn on and off the hyper-threading according to the register values to avoid the side channels caused by execution resource sharing. During the implementation of the scheme, we modified the Linux kernel and used the MSR register groups of Intel processor. The evaluation results show that DDM can effectively protect against the transient side-channel attacks such as PortsMash that rely on SMT, and the performance loss of DDM is less than 8%.

cs.CR↗

arXiv2019

Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of encoding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we conduct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.

cs.CL↗