arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

At least 649 records · Page 36Linked to original sources

arXiv2021

Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical information. However, most existing networks simply stacks the convolutional layers and hence failing to fully discover local and global feature information among layers. In this paper, we mainly explore how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers. Technically, we propose an efficient and effective CNN framework, i.e., Fast Dense Residual Network (FDRN), for text recognition. To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time. After fully learning local residual dense features, we utilize the sum operation and several f-RDBs to define a new block termed global dense block (GDB) by imitating the construction of dense blocks to learn global dense residual features adaptively in a holistic way. Finally, we use two convolution layers to construct a down-sampling block to reduce the global feature size and extract deeper features. Extensive simulations show that FDRN obtains the enhanced recognition results, compared with other related models.

cs.CV↗

arXiv2021

Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2021

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests. Given the fact that uncertainty quantifies the potential variations around prediction results, traditional learning schemes always lack uncertainty labels, and conventional uncertainty quantification approaches mostly rely upon statistical estimations with Bayesian Neural Networks or ensemble methods. However, they have never involved any spatiotemporal evolution of uncertainties under various contexts, and also have kept suffering from the poor efficiency of statistical uncertainty estimation while training models with multiple times. To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. To address the issue of lacking labels of uncertainty, we propose a hierarchical data turbulence scheme where we can actively inject controllable uncertainty for guidance, and hence provide insights to both uncertainty quantification and weak supervised learning. Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. Extensive experiments on three real-world spatiotemporal mobility sets have corroborated the superiority of our proposed model in terms of both forecasting and uncertainty quantification.

cs.LG↗

arXiv2021

Sending or not sending twin-field quantum key distribution with distinguishable decoy states

Twin-field quantum key distribution (TF-QKD) and its variants can overcome the fundamental rate-distance limit of QKD which has been demonstrated in the laboratory and field while their physical implementations with side channels remains to be further researched. We find the external modulation of different intensity states through the test, required in those TF-QKD with post-phase compensation, shows a side channel in frequency domain. Based on this, we propose a complete and undetected eavesdropping attack, named passive frequency shift attack, on sending or not-sending (SNS) TF-QKD protocol given any difference between signal and decoy states in frequency domain which can be extended to other imperfections with distinguishable decoy states. We analyze this attack by giving the formula of upper bound of real secure key rate and comparing it with lower bound of secret key rate under Alice and Bob's estimation with the consideration of actively odd-parity pairing (AOPP) method and finite key effects. The simulation results show that Eve can get full information about the secret key bits without being detected at long distance. Our results emphasize the importance of practical security at source and might provide a valuable reference for the practical implementation of TF-QKD.

quant-ph↗

arXiv2021

Mutant reduction evaluation: what is there and what is missing?

Background. Many mutation reduction strategies, which aim to reduce the number of mutants, have been proposed. Problem. It is important to measure the ability of a mutation reduction strategy to maintain test suite effectiveness evaluation. However, existing evaluation indicators are unable to measure the "order-preserving ability". Objective. We aim to propose evaluation indicators to measure the "order-preserving ability" of a mutation reduction strategy, which is important but missing in our community. Method. Given a test suite on a Software Under Test (SUT) with a set of original mutants, we leverage the test suite to generate a group of test suites that have a partial order relationship in fault detecting potential. When evaluating a reduction strategy, we first construct two partial order relationships among the generated test suites in terms of mutation score, one with the original mutants and another with the reduced mutants. Then, we measure the extent to which the two partial order relationships are consistent. The more consistent the two partial order relationships are, the stronger the Order Preservation (OP) of the mutation reduction strategy is, and the more effective the reduction strategy is. Furthermore, we propose Effort-aware Relative Order Preservation (EROP) to measure how much gain a mutation reduction strategy can provide compared with a random reduction strategy. Result. The experimental results show that OP and EROP are able to efficiently measure the "order-preserving ability" of a mutation reduction strategy. As a result, they have a better ability to distinguish various mutation reduction strategies compared with the existing evaluation indicators. Conclusion. We suggest, for the researchers, that OP and EROP should be used to measure the effectiveness of a mutant reduction strategy.

cs.SE↗

arXiv2021

Overlapping Domain Decomposition Methods for Ptychographic Imaging

In ptychography experiments, redundant scanning is usually required to guarantee the stable recovery, such that a huge amount of frames are generated, and thus it poses a great demand of parallel computing in order to solve this large-scale inverse problem. In this paper, we propose the overlapping Domain Decomposition Methods(DDMs) to solve the nonconvex optimization problem in ptychographic imaging. They decouple the problem defined on the whole domain into subproblems only defined on the subdomains with synchronizing information in the overlapping regions of these subdomains,thus leading to highly parallel algorithms with good load balance. More specifically, for the nonblind recovery (with known probe in advance), by enforcing the continuity of the overlapping regions for the image (sample), the nonlinear optimization model is established based on a novel smooth-truncated amplitude-Gaussian metric (ST-AGM). Such metric allows for fast calculation of the proximal mapping with closed form, and meanwhile provides the possibility for the convergence guarantee of the first-order nonconvex optimization algorithm due to its Lipschitz smoothness. Then the Alternating Direction Method of Multipliers (ADMM) is utilized to generate an efficient Overlapping Domain Decomposition based Ptychography algorithm(OD2P) for the two-subdomain domain decomposition (DD), where all subproblems can be computed with close-form solutions.Due to the Lipschitz continuity for the gradient of the objective function with ST-AGM, the convergence of the proposed OD2P is derived under mild conditions. Moreover, it is extended to more general case including multiple-subdomain DD and blind recovery. Numerical experiments are further conducted to show the performance of proposed algorithms, demonstrating good convergence speed and robustness to the noise.

math.NA↗

arXiv2021

NBSearch: Semantic Search and Visual Exploration of Computational Notebooks

Code search is an important and frequent activity for developers using computational notebooks (e.g., Jupyter). The flexibility of notebooks brings challenges for effective code search, where classic search interfaces for traditional software code may be limited. In this paper, we propose, NBSearch, a novel system that supports semantic code search in notebook collections and interactive visual exploration of search results. NBSearch leverages advanced machine learning models to enable natural language search queries and intuitive visualizations to present complicated intra- and inter-notebook relationships in the returned results. We developed NBSearch through an iterative participatory design process with two experts from a large software company. We evaluated the models with a series of experiments and the whole system with a controlled user study. The results indicate the feasibility of our analytical pipeline and the effectiveness of NBSearch to support code search in large notebook collections.

cs.HC↗

arXiv2021

Panel: Humans and Technology for Inclusive Privacy and Security

Computer security and user privacy are critical issues and concerns in the digital era due to both increasing users and threats to their data. Separate issues arise between generic cybersecurity guidance (i.e., protect all user data from malicious threats) and the individualistic approach of privacy (i.e., specific to users and dependent on user needs and risk perceptions). Research has shown that several security- and privacy-focused vulnerabilities are technological (e.g., software bugs (Streiff, Kenny, Das, Leeth, & Camp, 2018), insecure authentication (Das, Wang, Tingle, & Camp, 2019)), or behavioral (e.g., sharing passwords (Das, Dingman, & Camp, 2018); and compliance (Das, Dev, & Srinivasan, 2018) (Dev, Das, Rashidi, & Camp, 2019)). This panel proposal addresses a third category of sociotechnical vulnerabilities that can and sometimes do arise from non-inclusive design of security and privacy. In this panel, we will address users' needs and desires for privacy. The panel will engage in in-depth discussions about value-sensitive design while focusing on potentially vulnerable populations, such as older adults, teens, persons with disabilities, and others who are not typically emphasized in general security and privacy concerns. Human factors have a stake in and ability to facilitate improvements in these areas.

cs.CR↗

arXiv2021

Provable Near-Optimal Low-Multilinear-Rank Tensor Recovery

We consider the problem of recovering a low-multilinear-rank tensor from a small amount of linear measurements. We show that the Riemannian gradient algorithm initialized by one step of iterative hard thresholding can reconstruct an order-$d$ tensor of size $n\times\ldots\times n$ and multilinear rank $(r,\ldots,r)$ with high probability from only $O(nr^2 + r^{d+1})$ measurements, assuming $d$ is a constant. This sampling complexity is optimal in $n$, compared to existing results whose sampling complexities are all unnecessarily large in $n$. The analysis relies on the tensor restricted isometry property (TRIP) and the geometry of the manifold of all tensors with a fixed multilinear rank. High computational efficiency of our algorithm is also achieved by doing higher order singular value decomposition on intermediate small tensors of size only $2r\times \ldots\times 2r$ rather than on tensors of size $n\times \ldots\times n$ as usual.

math.NA↗

arXiv2021

Solving phase retrieval with random initial guess is nearly as good as by spectral initialization

The problem of recovering a signal $\mathbf{x}\in \mathbb{R}^n$ from a set of magnitude measurements $y_i=|\langle \mathbf{a}_i, \mathbf{x} \rangle |, \; i=1,\ldots,m$ is referred as phase retrieval, which has many applications in fields of physical sciences and engineering. In this paper we show that the smoothed amplitude flow model for phase retrieval has benign geometric structure under the optimal sampling complexity. In particular, we show that when the measurements $\mathbf{a}_i\in \mathbb{R}^n$ are Gaussian random vectors and the number of measurements $m\ge Cn$, our smoothed amplitude flow model has no spurious local minimizers with high probability, ie., the target solution $\mathbf{x}$ is the unique global minimizer (up to a global phase) and the loss function has a negative directional curvature around each saddle point. Due to this benign geometric landscape, the phase retrieval problem can be solved by the gradient descent algorithms without spectral initialization. Numerical experiments show that the gradient descent algorithm with random initialization performs well even comparing with state-of-the-art algorithms with spectral initialization in empirical success rate and convergence speed.

cs.IT↗

arXiv2021

k-core percolation on interdependent and interconnected multiplex networks

Many real-world networks are coupled together to maintain their normal functions. Here we study the robustness of multiplex networks with interdependent and interconnected links under k-core percolation, where a node fails when it connects to a threshold of less than k neighbors. By deriving the self-consistency equations, we solve the key quantities of interests such as the critical threshold and size of the giant component analytically and validate the theoretical results with numerical simulations. We find a rich phase transition phenomenon as we tune the inter-layer coupling strength. Specifically speaking, in the ER-ER multiplex networks, with the increase of coupling strength, the size of the giant component in each layer first undergoes a first-order transition and then a second-order transition and finally a first-order transition. This is due to the nature of inter-layer links with both connectivity and dependency simultaneously. The system is more robust if the dependency on the initial robust network is strong and more vulnerable if the dependency on the initial attacked network is strong. These effects are even amplified in the cascading process. When applying our model to the SF-SF multiplex networks, the type of transition changes. The system undergoes a first-order phase transition first only when the two layers' mutually coupling is very strong and a second-order transition in other conditions.

physics.soc-ph↗

arXiv2020

Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. However, 3D convolutions are computationally inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields to capture the global cues in the entire image. In this work, we propose a hybrid representation learning approach to address this challenge. The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image. In inference, the proposed approach extracts local segments of vessels using CNNs, classifies each segment based on global geometry using the point-cloud network, and finally connects all the segments that belong to the same vessel using the shortest-path algorithm. This combination results in an efficient, fully-automatic and template-free approach to centerline extraction from 3D images. We validate the proposed approach on CTA datasets and demonstrate its superior performance compared to both traditional and CNN-based baselines.

eess.IV↗

arXiv2020

Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming

Serverless clouds allocate multiple tasks (e.g., micro-services) from multiple users on a shared pool of computing resources. This enables serverless cloud providers to reduce their resource usage by transparently aggregate similar tasks of a certain context (e.g., video processing) that share the whole or part of their computation. To this end, it is crucial to know the amount of time-saving achieved by aggregating the tasks. Lack of such knowledge can lead to uninformed merging and scheduling decisions that, in turn, can cause deadline violation of either the merged tasks or other following tasks. Accordingly, in this paper, we study the problem of estimating execution-time saving resulted from merging tasks with the example in the context of video processing. To learn the execution-time saving in different forms of merging, we first establish a set of benchmarking videos and examine a wide variety of video processing tasks -- with and without merging in place. We observed that although merging can save up to 44% in the execution-time, the number of possible merging cases is intractable. Hence, in the second part, we leverage the benchmarking results and develop a method based on Gradient Boosting Decision Tree (GBDT) to estimate the time-saving for any given task merging case. Experimental results show that the method can estimate the time-saving with the error rate of 0.04, measured based on Root Mean Square Error (RMSE).

cs.DC↗

arXiv2020

Estimation of the Galaxy Quenching Rate in the Illustris Simulation

Quenching is a key topic in exploring the formation and evolution of galaxies. In this work, we study the quenching rate, i.e., the variation in the fraction of quenched galaxies per unit time, of the Illustris-1 simulation. By building the quenched fraction function $f(m,ρ, t)$ of each snapshot in the simulation, we derive an accurate form of quenching rate as $\Re_q=df(m,ρ,t)/dt$. According to the analytic expression of the quenching rate $\Re_q$, we split it into four components: mass quenching, environmental quenching, intrinsic mass quenching and intrinsic environmental quenching. The precise value and evolutions can be given via the formula of $\Re_q$. With this method, we analyze the Illustris-1 simulation. We find that quenched galaxies concentrate around $M_*\simeq10^{11}h^{-1}M_\odot$ and $δ+1\simeq10^{3.5}$ at earlier times, and that the quenching galaxy population slowly shifts to lower stellar mass and lower overdensity regions with time. We also find that mass quenching dominates the quenching process in this simulation, in agreement with some previous analytical models. Intrinsic quenching is the second most important component. Environmental quenching is very weak, because it is possible that the pre- or postprocessing of environments disguises environmental quenching as intrinsic quenching. We find that our method roughly predict the actual quenching rate. It could well predict the actual amount of galaxies quenched by intrinsic quenching. However, it overestimates the amount of mass quenching galaxies and underestimates the amount of environmental quenching. We suggest that the reason is the nonlinearity of the environmental overdensity change and mass growth of the galaxy.

astro-ph.GA↗

arXiv2020

On Pole-Zero Assignment of Vibratory Systems by Multi-Input Feedback Control

In this paper, we consider the pole-zero assignment problem for vibratory systems via multi-input feedback control. We propose a multi-step two-stage approach for solving the multi-input pole-zero assignment problem. We first reformulate the assignment problem as a multi-step single-input pole-zero assignment problem. Then, in each step, we propose a two-stage approach for solving the single-input pole-zero assignment problem. In the first stage, based on the measured receptances, we replace the selected zeros of the prescribed open-loop point receptance to the desired locations, where we need to solve a small underdetermined linear equation for finding the corresponding columns of the feedback matrices. In the second stage, by using linear matrix inequalities, the complete closed-loop poles are assigned to the prescribed subregion of the complex left-hand plane. Finally, we give some numerical examples to demonstrate the effectiveness of our method.

math.NA↗

arXiv2020

Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis

Australian water infrastructure is more than a hundred years old, thus has begun to show its age through water main failures. Our work concerns approximately half a million pipelines across major Australian cities that deliver water to houses and businesses, serving over five million customers. Failures on these buried assets cause damage to properties and water supply disruptions. We applied Machine Learning techniques to find a cost-effective solution to the pipe failure problem in these Australian cities, where on average 1500 of water main failures occur each year. To achieve this objective, we construct a detailed picture and understanding of the behaviour of the water pipe network by developing a Machine Learning model to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes and other environmental factors. Our results indicate that our system incorporating a nonparametric survival analysis technique called "Random Survival Forest" outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associated with the long-term predictions.

cs.LG↗

arXiv2020

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the evolution of various chemical phases. In this work, we introduce a data-driven approach to construct the effective Hamiltonian and study the thermodynamics of HEAs through canonical Monte Carlo simulation. The main characteristic of our method is to use pairwise interactions between atoms as features and systematically improve the representativeness of the dataset using samples from Monte Carlo simulation. We find this method produces highly robust and accurate effective Hamiltonians that give less than 0.1 mRy test error for all the three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. Using replica exchange to speed up the MC simulation, we calculated the specific heats and short-range order parameters in a wide range of temperatures. For all the studied materials, we find there are two major order-disorder transitions occurring respectively at $T_1$ and $T_2$, where $T_1$ is near room temperature but $T_2$ is much higher. We further demonstrate that the transition at $T_1$ is caused by W and Nb while the one at $T_2$ is caused by the other elements. By comparing with experiments, {\color{black} the results provide insight into the role of chemical ordering in the strength and ductility of HEAs.

cond-mat.mtrl-sci↗

arXiv2020

On Efficient and Scalable Time-Continuous Spatial Crowdsourcing -- Full Version

The proliferation of advanced mobile terminals opened up a new crowdsourcing avenue, spatial crowdsourcing, to utilize the crowd potential to perform real-world tasks. In this work, we study a new type of spatial crowdsourcing, called time-continuous spatial crowdsourcing (TCSC in short). It supports broad applications for long-term continuous spatial data acquisition, ranging from environmental monitoring to traffic surveillance in citizen science and crowdsourcing projects. However, due to limited budgets and limited availability of workers in practice, the data collected is often incomplete, incurring data deficiency problem. To tackle that, in this work, we first propose an entropy-based quality metric, which captures the joint effects of incompletion in data acquisition and the imprecision in data interpolation. Based on that, we investigate quality-aware task assignment methods for both single- and multi-task scenarios. We show the NP-hardness of the single-task case, and design polynomial-time algorithms with guaranteed approximation ratios. We study novel indexing and pruning techniques for further enhancing the performance in practice. Then, we extend the solution to multi-task scenarios and devise a parallel framework for speeding up the process of optimization. We conduct extensive experiments on both real and synthetic datasets to show the effectiveness of our proposals.

cs.DB↗