arXiv subjects

Hang Zhang

Publications and source records attributed to Hang Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2023

Electrical manipulation of a hole `spin'-orbit qubit in nanowire quantum dot: the nontrivial magnetic field effects

Strong `spin'-orbit coupled one-dimensional hole gas is achievable in a Ge nanowire in the presence of a strong magnetic field. The strong magnetic field lifts the two-fold degeneracy in the hole subband dispersions, so that the effective low-energy subband dispersion exhibits strong `spin'-orbit coupling. Here, we study the electrical `spin' manipulation in a Ge nanowire quantum dot for both the lowest and second lowest hole subband dispersions. Using a finite square well to model the quantum dot confining potential, we calculate exactly the level splitting of the `spin'-orbit qubit and the Rabi frequency in the electric-dipole `spin' resonance. The `spin'-orbit coupling modulated longitudinal $g$-factor $g_{\rm so}$ is not only non-vanishing but also magnetic field dependent. Moreover, the `spin'-orbit couplings of the lowest and second lowest subband dispersions have opposite magnetic dependences, so that the results for these two subband dispersions are totally different.

cond-mat.mes-hall↗

arXiv2023

Spatially Covariant Lesion Segmentation

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks. In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation. SCP relaxes the spatial invariance constraint imposed by convolutional operations and optimizes an underlying implicit function that maps image coordinates to network weights, the parameters of which are obtained along with the backbone network training and later used for generating network weights to capture spatially covariant contextual information. We demonstrate the effectiveness and efficiency of the proposed SCP using two lesion segmentation tasks from different imaging modalities: white matter hyperintensity segmentation in magnetic resonance imaging and liver tumor segmentation in contrast-enhanced abdominal computerized tomography. The network using SCP has achieved 23.8%, 64.9% and 74.7% reduction in GPU memory usage, FLOPs, and network size with similar or better accuracy for lesion segmentation.

eess.IV↗

arXiv2022

A Density Evolution framework for Preferential Recovery of Covariance and Causal Graphs from Compressed Measurements

In this paper, we propose a general framework for designing sensing matrix $\boldsymbol{A} \in \mathbb{R}^{d\times p}$, for estimation of sparse covariance matrix from compressed measurements of the form $\boldsymbol{y} = \boldsymbol{A}\boldsymbol{x} + \boldsymbol{n}$, where $\boldsymbol{y}, \boldsymbol{n} \in \mathbb{R}^d$, and $\boldsymbol{x} \in \mathbb{R}^p$. By viewing covariance recovery as inference over factor graphs via message passing algorithm, ideas from coding theory, such as \textit{Density Evolution} (DE), are leveraged to construct a framework for the design of the sensing matrix. The proposed framework can handle both (1) regular sensing, i.e., equal importance is given to all entries of the covariance, and (2) preferential sensing, i.e., higher importance is given to a part of the covariance matrix. Through experiments, we show that the sensing matrix designed via density evolution can match the state-of-the-art for covariance recovery in the regular sensing paradigm and attain improved performance in the preferential sensing regime. Additionally, we study the feasibility of causal graph structure recovery using the estimated covariance matrix obtained from the compressed measurements.

cs.IT↗

arXiv2022

LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO.git.

eess.IV↗

arXiv2022

Adversarial Retriever-Ranker for dense text retrieval

Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, their model training highly relies on a negative sampling technique to build up the negative documents in their contrastive losses. To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. The two models are jointly optimized according to a minimax adversarial objective: the retriever learns to retrieve negative documents to cheat the ranker, while the ranker learns to rank a collection of candidates including both the ground-truth and the retrieved ones, as well as providing progressive direct feedback to the dual-encoder retriever. Through this adversarial game, the retriever gradually produces harder negative documents to train a better ranker, whereas the cross-encoder ranker provides progressive feedback to improve retriever. We evaluate AR2 on three benchmarks. Experimental results show that AR2 consistently and significantly outperforms existing dense retriever methods and achieves new state-of-the-art results on all of them. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%). Code and models are available at https://github.com/microsoft/AR2.

cs.CL↗

arXiv2022

CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search

In this paper, we propose the CodeRetriever model, which learns the function-level code semantic representations through large-scale code-text contrastive pre-training. We adopt two contrastive learning schemes in CodeRetriever: unimodal contrastive learning and bimodal contrastive learning. For unimodal contrastive learning, we design an unsupervised learning approach to build semantic-related code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build code-text pairs. Both contrastive objectives can fully leverage large-scale code corpus for pre-training. Extensive experimental results show that CodeRetriever achieves new state-of-the-art with significant improvement over existing code pre-trained models, on eleven domain/language-specific code search tasks with six programming languages in different code granularity (function-level, snippet-level and statement-level). These results demonstrate the effectiveness and robustness of CodeRetriever.

cs.CL↗

arXiv2022

Poolingformer: Long Document Modeling with Pooling Attention

In this paper, we introduce a two-level attention schema, Poolingformer, for long document modeling. Its first level uses a smaller sliding window pattern to aggregate information from neighbors. Its second level employs a larger window to increase receptive fields with pooling attention to reduce both computational cost and memory consumption. We first evaluate Poolingformer on two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA. Experimental results show that Poolingformer sits atop three official leaderboards measured by F1, outperforming previous state-of-the-art models by 1.9 points (79.8 vs. 77.9) on NQ long answer, 1.9 points (79.5 vs. 77.6) on TyDi QA passage answer, and 1.6 points (67.6 vs. 66.0) on TyDi QA minimal answer. We further evaluate Poolingformer on a long sequence summarization task. Experimental results on the arXiv benchmark continue to demonstrate its superior performance.

cs.CL↗

arXiv2022

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning

Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relational reasoning and compositional generalization capabilities. Previous work focuses on retrieving prototype sentences for the provided concepts to assist generation. They first use a sparse retriever to retrieve candidate sentences, then re-rank the candidates with a ranker. However, the candidates returned by their ranker may not be the most relevant sentences, since the ranker treats all candidates equally without considering their relevance to the reference sentences of the given concepts. Another problem is that re-ranking is very expensive, but only using retrievers will seriously degrade the performance of their generation models. To solve these problems, we propose the metric distillation rule to distill knowledge from the metric (e.g., BLEU) to the ranker. We further transfer the critical knowledge summarized by the distilled ranker to the retriever. In this way, the relevance scores of candidate sentences predicted by the ranker and retriever will be more consistent with their quality measured by the metric. Experimental results on the CommonGen benchmark verify the effectiveness of our proposed method: (1) Our generation model with the distilled ranker achieves a new state-of-the-art result. (2) Our generation model with the distilled retriever even surpasses the previous SOTA.

cs.CL↗

arXiv2022

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis

Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose SentiWSP, a novel Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM's knowledge about sentiment words. The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence. Extensive experimental results show that SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks. We have made our code and model publicly available at https://github.com/XMUDM/SentiWSP.

cs.CL↗

arXiv2022

Efficient Backward Reachability Using the Minkowski Difference of Constrained Zonotopes

Backward reachability analysis is essential to synthesizing controllers that ensure the correctness of closed-loop systems. This paper is concerned with developing scalable algorithms that under-approximate the backward reachable sets, for discrete-time uncertain linear and nonlinear systems. Our algorithm sequentially linearizes the dynamics, and uses constrained zonotopes for set representation and computation. The main technical ingredient of our algorithm is an efficient way to under-approximate the Minkowski difference between a constrained zonotopic minuend and a zonotopic subtrahend, which consists of all possible values of the uncertainties and the linearization error. This Minkowski difference needs to be represented as a constrained zonotope to enable subsequent computation, but, as we show, it is impossible to find a polynomial-sized representation for it in polynomial time. Our algorithm finds a polynomial-sized under-approximation in polynomial time. We further analyze the conservatism of this under-approximation technique, and show that it is exact under some conditions. Based on the developed Minkowski difference technique, we detail two backward reachable set computation algorithms to control the linearization error and incorporate nonconvex state constraints. Several examples illustrate the effectiveness of our algorithms.

eess.SY↗

arXiv2022

Structure Learning in Graphical Models from Indirect Observations

This paper considers learning of the graphical structure of a $p$-dimensional random vector $X \in R^p$ using both parametric and non-parametric methods. Unlike the previous works which observe $x$ directly, we consider the indirect observation scenario in which samples $y$ are collected via a sensing matrix $A \in R^{d\times p}$, and corrupted with some additive noise $w$, i.e, $Y = AX + W$. For the parametric method, we assume $X$ to be Gaussian, i.e., $x\in R^p\sim N(μ, Σ)$ and $Σ\in R^{p\times p}$. For the first time, we show that the correct graphical structure can be correctly recovered under the indefinite sensing system ($d < p$) using insufficient samples ($n < p$). In particular, we show that for the exact recovery, we require dimension $d = Ω(p^{0.8})$ and sample number $n = Ω(p^{0.8}\log^3 p)$. For the nonparametric method, we assume a nonparanormal distribution for $X$ rather than Gaussian. Under mild conditions, we show that our graph-structure estimator can obtain the correct structure. We derive the minimum sample number $n$ and dimension $d$ as $n\gtrsim (deg)^4 \log^4 n$ and $d \gtrsim p + (deg\cdot\log(d-p))^{β/4}$, respectively, where deg is the maximum Markov blanket in the graphical model and $β> 0$ is some fixed positive constant. Additionally, we obtain a non-asymptotic uniform bound on the estimation error of the CDF of $X$ from indirect observations with inexact knowledge of the noise distribution. To the best of our knowledge, this bound is derived for the first time and may serve as an independent interest. Numerical experiments on both real-world and synthetic data are provided confirm the theoretical results.

stat.ML↗

arXiv2022

A General Compressive Sensing Construct using Density Evolution

This paper proposes a general framework to design a sparse sensing matrix $\ensuremath{\mathbf{A}}\in \mathbb{R}^{m\times n}$, in a linear measurement system $\ensuremath{\mathbf{y}} = \ensuremath{\mathbf{Ax}}^{\natural} + \ensuremath{\mathbf{w}}$, where $\ensuremath{\mathbf{y}} \in \mathbb{R}^m$, $\ensuremath{\mathbf{x}}^{\natural}\in \RR^n$, and $\ensuremath{\mathbf{w}}$ denote the measurements, the signal with certain structures, and the measurement noise, respectively. By viewing the signal reconstruction from the measurements as a message passing algorithm over a graphical model, we leverage tools from coding theory in the design of low density parity check codes, namely the density evolution, and provide a framework for the design of matrix $\ensuremath{\mathbf{A}}$. Particularly, compared to the previous methods, our proposed framework enjoys the following desirable properties: ($i$) Universality: the design supports both regular sensing and preferential sensing, and incorporates them in a single framework; ($ii$) Flexibility: the framework can easily adapt the design of $\bA$ to a signal $\ensuremath{\mathbf{x}}^{\natural}$ with different underlying structures. As an illustration, we consider the $\ell_1$ regularizer, which correspond to Lasso, for both the regular sensing and preferential sensing scheme. Noteworthy, our framework can reproduce the classical result of Lasso, i.e., $m\geq c_0 k\log(n/k)$ (the regular sensing) with regular design after proper distribution approximation, where $c_0 > 0$ is some fixed constant. We also provide numerical experiments to confirm the analytical results and demonstrate the superiority of our framework whenever a preferential treatment of a sub-block of vector $\bx^{\natural}$ is required.

eess.SP↗

arXiv2022

Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference

When directly coupled with fluctuating energy sources such as wind and photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen (P2H) system is required to operate flexibly by dynamically adjusting its hydrogen production rate. The flex-ibility characteristics, e.g., loading range and ramping rate, of an AEL system are significantly influenced by some parameters re-lated to the dynamic processes of the AEL system. These parame-ters are usually difficult to measure directly and may even change with time. To accurately evaluate the flexibility of an AEL system in online operation, this paper presents a Bayesian Inference-based Markov Chain Monte Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint probability distribu-tions of the estimated parameters are obtained as a byproduct, which provides valuable physical insight into the AEL systems. Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation method.

eess.SY↗

arXiv2022

Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment

Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this study, we collect a large image data set to re-label them as a multi-label image classification problem so as to identify smoke and flame simultaneously. In order to solve the generalization ability of the detection model on account of the movable fluid objects with uncertain shapes like fire and smoke, and their not compactible natures as well as the complex backgrounds with high variations, we propose a data augment method by random image stitch to deploy resizing, deforming, position variation, and background altering so as to enlarge the view of the learner. Moreover, we propose a self-learning data augment method by using the class activation map to extract the highly trustable region as new data source of positive examples to further enhance the data augment. By the mutual reinforcement between the data augment and the detection model that are performed iteratively, both modules make progress in an evolutionary manner. Experiments show that the proposed method can effectively improve the generalization performance of the model for concurrent smoke and fire detection.

cs.CV↗

arXiv2022

Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems

Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by weight decomposition of objectives. This paper proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems. The Pareto front is then built accordingly. Compared with other learning-based methods, our method can greatly shorten the training time of multiple submodels. Due to the rapid and excellent adaptability of the meta-model, more submodels can be derived so as to increase the quality and diversity of the found solutions. The computational experiments on multiobjective traveling salesman problems and multiobjective vehicle routing problem with time windows demonstrate the superiority of our method over most of learning-based and iteration-based approaches.

cs.AI↗

arXiv2022

Photo-Induced Ultrafast Symmetry Switch in SnSe

Layered tin selenide (SnSe) has recently emerged as a high-performance thermoelectric material with the current record for the figure of merit (ZT) observed in the high-temperature Cmcm phase. So far, access of the Cmcm phase has been mainly obtained via thermal equilibrium methods based on sample heating or application of external pressure, thus restricting the current understanding only to ground-state conditions. Here, we investigate the ultrafast carrier and phononic dynamics in SnSe. Our results demonstrate that optical excitations can transiently switch the point-group symmetry of the crystal from Pnma to Cmcm at room temperature in a few hundreds of femtoseconds with an ultralow threshold for the excitation carrier density. This non-equilibrium Cmcm phase is found to be driven by the displacive excitation of coherent Ag phonons and, given the absence of low-energy thermal phonons, exists in SnSe with the status of 'cold lattice with hot carriers'. Our findings provide important insight for understanding non-equilibrium thermoelectric properties of SnSe.

cond-mat.mtrl-sci↗

arXiv2021

SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel

Fuzzing has become one of the most effective bug finding approach for software. In recent years, 24*7 continuous fuzzing platforms have emerged to test critical pieces of software, e.g., Linux kernel. Though capable of discovering many bugs and providing reproducers (e.g., proof-of-concepts), a major problem is that they neglect a critical function that should have been built-in, i.e., evaluation of a bug's security impact. It is well-known that the lack of understanding of security impact can lead to delayed bug fixes as well as patch propagation. In this paper, we develop SyzScope, a system that can automatically uncover new "high-risk" impacts given a bug with seemingly "low-risk" impacts. From analyzing over a thousand low-risk bugs on syzbot, SyzScope successfully determined that 183 low-risk bugs (more than 15%) in fact contain high-risk impacts, e.g., control flow hijack and arbitrary memory write, some of which still do not have patches available yet.

cs.CR↗

arXiv2021

GLGE: A New General Language Generation Evaluation Benchmark

Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models. In this paper, we present the General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. For each task, we continue to design three subtasks in terms of task difficulty (GLGE-Easy, GLGE-Medium, and GLGE-Hard). This introduces 24 subtasks to comprehensively compare model performance. To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet (The source code and dataset are publicly available at https://github.com/microsoft/glge).

cs.CL↗