arXiv subjects

Yong Wang

Publications and source records attributed to Yong Wang.

At least 469 records · Page 26Linked to original sources

arXiv2020

MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Although convolutional neural networks (CNNs) are promoting the development of medical image semantic segmentation, the standard model still has some shortcomings. First, the feature mapping from the encoder and decoder sub-networks in the skip connection operation has a large semantic difference. Second, the remote feature dependence is not effectively modeled. Third, the global context information of different scales is ignored. In this paper, we try to eliminate semantic ambiguity in skip connection operations by adding attention gates (AGs), and use attention mechanisms to combine local features with their corresponding global dependencies, explicitly model the dependencies between channels and use multi-scale predictive fusion to utilize global information at different scales. Compared with other state-of-the-art segmentation networks, our model obtains better segmentation performance while introducing fewer parameters.

eess.IV↗

arXiv2020

A Transactive Retail Market Mechanism for Active Distribution Network Integrated with Large-scale Distributed Energy Resources

The burgeoning integration of distributed energy resources (DER) poses new challenges for the economic and safe operation of the electricity system. The current distribution-side policy is largely based on mandatory regulations and incentives, rather than the design of a competitive market mechanism to arouse DERs to freely compete in the retail market. To address this issue, we proposed a transactive retail market mechanism to attract the active participation of profit-driven DER retailers in a deregulated way. Considering the limited competitive property of the retail market, a bi-level DSO-dominated framework is constructed to simulate the virtual game between distribution system operator (DSO) and DER retailers. Specifically, the flexible interval pricing of retailers is modeled as a series of binary revenue constraints to influence the DSO's centralized economic dispatch decisions, where the time-varying distribution locational marginal price is adopted to settle energy activities at different locations over the time horizon. Due to the uncertain power flow path during the coordinated decision-making process, we proposed a general undirected second-order cone-based AC radial power flow model and provided sufficient conditions to ensure its exactness. Also, we applied a series of approximation and relaxation techniques to transform the bi-level mixed-integer quadratic framework with highly discrete induced domains into a solvable mixed-integer semidefinite programming problem. It is demonstrated in the case study that the proposed mechanism can not only improve market efficiency but also eliminate market power and the resulting market failures.

eess.SY↗

arXiv2020

Frequency Separation based Adaptive Feedforward Control for Rejecting Wideband Vibration with Application to Hard Disk Drives

In this paper, a frequency separation based adaptive feedforward control algorithm is developed with the ability to identify the plant and do compensation region by region. In this algorithm, the accelerometer signal is filtered by a series of uniformly distributed bandpass filters to generate a bunch of subband signals which are mutually exclusive in spectrum. In each subband, the corresponding subband signal acts as the feedforward signal and only the frequency response of system in that region needs to be identified, thus a pretty low order model can be expected to have efficient compensation. Starting from the first region, the feedforward control parameters are learned simultaneously with the low order plant model in the same region and then moves to the next region until all the regions are performed.

eess.SY↗

arXiv2020

Affine connections on singular warped products

In this paper, we introduce semi-symmetric metric Koszul forms and semi-symmetric non-metric Koszul forms on singular semi-Riemannian manifolds. Semi-symmetric metric Koszul forms and semi-symmetric non-metric Koszul forms and their curvature of semi-regular warped products are expressed in terms of those of the factor manifolds. We also introduce Koszul forms associated to the almost product structure on singular almost product semi-Riemannian manifolds. Koszul forms associated to the almost product structure and their curvature of semi-regular almost product warped products are expressed in terms of those of the factor manifolds. Furthermore, we generalize the results in \cite{St2} to singular multiply warped products.

math.DG↗

arXiv2020

XLBoost-Geo: An IP Geolocation System Based on Extreme Landmark Boosting

IP geolocation aims at locating the geographical position of Internet devices, which plays an essential role in many Internet applications. In this field, a long-standing challenge is how to find a large number of highly-reliable landmarks, which is the key to improve the precision of IP geolocation. To this end, many efforts have been made, while many IP geolocation methods still suffer from unacceptable error distance because of the lack of landmarks. In this paper, we propose a novel IP geolocation system, named XLBoost-Geo, which focuses on enhancing the number and the density of highly reliable landmarks. The main idea is to extract location-indicating clues from web pages and locating the web servers based on the clues. Based on the landmarks, XLBoost-Geo is able to geolocate arbitrary IPs with little error distance. Specifically, we first design an entity extracting method based on a bidirectional LSTM neural network with a self-adaptive loss function (LSTM-Ada) to extract the location-indicating clues on web pages and then generate landmarks based on the clues. Then, by measurements on network latency and topology, we estimate the closest landmark and associate the coordinate of the landmark with the location of the target IP. The results of our experiments clearly validate the effectiveness and efficiency of the extracting method, the precision, number, coverage of the landmarks, and the precision of the IP geolocation. On RIPE Atlas nodes, XLBoost-Geo achieves 2,561m median error distance, which outperforms SLG and IPIP.

cs.NI↗

arXiv2020

Lyapunov functions for nabla discrete fractional order systems

This paper focuses on the fractional difference of Lyapunov functions related to Riemann-Liouville, Caputo and Grunwald-Letnikov definitions. A new way of building Lyapunov functions is introduced and then five inequalities are derived for each definition. With the help of the developed inequalities, the sufficient conditions can be obtained to guarantee the asymptotic stability of the nabla discrete fractional order nonlinear systems. Finally, three illustrative examples are presented to demonstrate the validity and feasibility of the proposed theoretical results.

math.DS↗

arXiv2020

On the Parallel Composition for True Concurrency

For insight into the parallel composition for true concurrency, we recall the axiomatization of the parallel composition modulo truly concurrent behavioral equivalences as the sidelights of truly concurrent process algebra APTC. We prove that: (1) There is a finite sound and complete axiomatization of the parallel composition modulo pomset, step and hp-bisimulations, without any auxiliary operators. (2) There does not exist a finite sound and complete axiomatization of the parallel composition modulo hhp-bisimulation, without any auxiliary operator. (3) There is a finite sound and complete axiomatization of the parallel composition modulo pomset, step, hp-. and hhp-bisimulations, with the auxiliary left parallel composition and communication merge.

cs.LO↗

arXiv2020

On the Sparsity of Neural Machine Translation Models

Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources. In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better performance. Experiments and analyses are systematically conducted on different datasets and NMT architectures. We show that: 1) the pruned parameters can be rejuvenated to improve the baseline model by up to +0.8 BLEU points; 2) the rejuvenated parameters are reallocated to enhance the ability of modeling low-level lexical information.

cs.CL↗

arXiv2020

QLens: Visual Analytics of Multi-step Problem-solving Behaviors for Improving Question Design

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' problem-solving processes unfold step by step to infer whether students' problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from the online platforms, provides the opportunity to analyze problem-solving behaviors. However, it is still challenging to interpret, summarize, and compare the high dimensional problem-solving sequence data. In this paper, we present a visual analytics system, QLens, to help question designers inspect detailed problem-solving trajectories, compare different student groups, distill insights for design improvements. In particular, QLens models problem-solving behavior as a hybrid state transition graph and visualizes it through a novel glyph-embedded Sankey diagram, which reflects students' problem-solving logic, engagement, and encountered difficulties. We conduct three case studies and three expert interviews to demonstrate the usefulness of QLens on real-world datasets that consist of thousands of problem-solving traces.

cs.HC↗

arXiv2020

TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Group

Tax evasion is a serious economic problem for many countries, as it can undermine the government' s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they failed to support the analysis and exploration of the uprising related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data, and interviews with domain experts.

cs.HC↗

arXiv2020

TradAO: A Visual Analytics System for Trading Algorithm Optimization

With the wide applications of algorithmic trading, it has become critical for traders to build a winning trading algorithm to beat the market. However, due to the lack of efficient tools, traders mainly rely on their memory to manually compare the algorithm instances of a trading algorithm and further select the best trading algorithm instance for the real trading deployment. We work closely with industry practitioners to discover and consolidate user requirements and develop an interactive visual analytics system for trading algorithm optimization. Structured expert interviews are conducted to evaluateTradAOand a representative case study is documented for illustrating the system effectiveness. To the best of our knowledge, previous financial data visual analyses have mainly aimed to assist investment managers in investment portfolio analysis but have neglected the need of traders in developing trading algorithms for portfolio execution.TradAOis the first visual analytics system that assists users in comprehensively exploring the performances of a trading algorithm with different parameter settings.

cs.HC↗

arXiv2020

Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry

In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the middle frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of depth and pose, we can segment the occluded region explicitly. The occlusion information is used in unsupervised learning of depth, pose and optical flow, as the image reconstructed by depth-pose and optical flow will be invalid in occluded regions. A less-than-mean mask is designed to further exclude the mismatched pixels interfered with by motion or illumination change in the training of depth and pose networks. This method is also used to exclude some trivial mismatched pixels in the training of the optical flow network. Maximum normalization is proposed for depth smoothness term to restrain depth degradation in textureless regions. In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of optical flow. Our experiments in KITTI dataset demonstrate that the model based on three regions, full and explicit segmentation of the occlusion region, the rigid region, and the non-rigid region with corresponding unsupervised losses can improve performance on three tasks significantly. The source code is available at: https://github.com/guangmingw/DOPlearning.

cs.CV↗

arXiv2020

Hilbert expansion of the Boltzmann equation with specular boundary condition in half-space

Boundary effects play an important role in the study of hydrodynamic limits in the Boltzmann theory. Based on a systematic derivation and study of the viscous layer equations and the $L^2$ to $L^\infty$ framework, we establish the validity of the Hilbert expansion for the Boltzmann equation with specular reflection boundary conditions, which leads to derivations of compressible Euler equations and acoustic equations.

math.AP↗

arXiv2020

Boundary layer solution of the Boltzmann equation for specular boundary condition

In the paper, we establish the existence of steady boundary layer solution of Boltzmann equation with specular boundary condition in $L^2_{x,v}\cap L^\infty_{x,v}$ in half-space. The uniqueness, continuity and exponential decay of the solution are obtained, and such estimates are important to prove the Hilbert expansion of Boltzmann equation for half-space problem with specular boundary condition.

math.AP↗

arXiv2020

Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online question pools. In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R^2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions. The experiment results show that our approach can achieve a much higher accuracy of student performance prediction than both traditional machine learning approaches and GNN models.

cs.LG↗

arXiv2020

Surface Superconductivity in the type II Weyl Semimetal TaIrTe4

The search for unconventional superconductivity in Weyl semimetal materials is currently an exciting pursuit, since such superconducting phases could potentially be topologically nontrivial and host exotic Majorana modes. The layered material TaIrTe4 is a newly predicted time-reversal invariant type II Weyl semimetal with minimum number of Weyl points. Here, we report the discovery of surface superconductivity in Weyl semimetal TaIrTe4. Our scanning tunneling microscopy/spectroscopy (STM/S) visualizes Fermi arc surface states of TaIrTe4 that are consistent with the previous angle-resolved photoemission spectroscopy (ARPES) results. By a systematic study based on STS at ultralow temperature, we observe uniform superconducting gaps on the sample surface. The superconductivity is further confirmed by electrical transport measurements at ultralow temperature, with an onset transition temperature (Tc) up to 1.54 K being observed. The normalized upper critical field h*(T/Tc) behavior and the stability of the superconductivity against the ferromagnet indicate that the discovered superconductivity is unconventional with the p-wave pairing. The systematic STS, thickness and angular dependent transport measurements reveal that the detected superconductivity is quasi-one-dimensional (quasi-1D) and occurs in the surface states. The discovery of the surface superconductivity in TaIrTe4 provides a new novel platform to explore topological superconductivity and Majorana modes.

cond-mat.supr-con↗

arXiv2020

Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis

Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body. But PET data is always unavailable due to different reasons such as cost, radiation, or other limitations. In this paper, we propose a 3D end-to-end synthesis network, called Bidirectional Mapping Generative Adversarial Networks (BMGAN), where image contexts and latent vector are effectively used and jointly optimized for brain MR-to-PET synthesis. Concretely, a bidirectional mapping mechanism is designed to embed the semantic information of PET images into the high dimensional latent space. And the 3D DenseU-Net generator architecture and the extensive objective functions are further utilized to improve the visual quality of synthetic results. The most appealing part is that the proposed method can synthesize the perceptually realistic PET images while preserving the diverse brain structures of different subjects. Experimental results demonstrate that the performance of the proposed method outperforms other competitive cross-modality synthesis methods in terms of quantitative measures, qualitative displays, and classification evaluation.

eess.IV↗

arXiv2020

Population Distribution in the Wake of a Sphere

The fluid physics of the heat and mass transfer from an object in its wake has much importance for natural phenomena as well as for many engineering applications. Here, we report numerical results on the population density of the spatial distribution of fluid velocity, pressure, scalar concentration and scalar fluxes of a wake flow past a sphere in the steady wake regime (Reynolds number 25 to 285). We find the population density to be well described by a Lorentzian distribution. We observe this apparently universal form both in the symmetric wake regime and in the more complex three dimensional wake structure of the steady oblique regime with Reynolds number larger than 225. The population density distribution identifies the increase in dimensionless kinetic energy and scalar fluxes with the increase in Reynolds number, whereas the dimensionless scalar population density shows negligible variation with the Reynolds number.

physics.flu-dyn↗