arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 1,297 records · Page 72Linked to original sources

arXiv2018

Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems

This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release human-annotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges.

cs.CL↗

arXiv2018

Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach

We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data taken on August 26, 2017, where 21 bursts have been previously detected. Our technique combines neural network detection with dedispersion verification. For the current application we demonstrate its advantage over a traditional brute-force dedis- persion algorithm in terms of higher sensitivity, lower false positive rates, and faster computational speed. Together with the 21 previously reported pulses, this observa- tion marks the highest number of FRB 121102 pulses from a single observation, total- ing 93 pulses in five hours, including 45 pulses within the first 30 minutes. The number of data points reveal trends in pulse fluence, pulse detection rate, and pulse frequency structure. We introduce a new periodicity search technique, based on the Rayleigh test, to analyze the time of arrivals, with which we exclude with 99% confidence pe- riodicity in time of arrivals with periods larger than 5.1 times the model-dependent time-stamp uncertainty. In particular, we rule out constant periods >10 ms in the barycentric arrival times, though intrinsic periodicity in the time of emission remains plausible.

astro-ph.HE↗

arXiv2018

The Spin of the Supermassive Black Hole in MCG-05-23-16

We present the results of a multi-epoch and multi-instrument study of the supermassive black hole at the center of the galaxy MCG-05-23-16 aiming at the determination of its spin. We have analyzed high quality X-ray data of MCG-05-23-16 from XMM-Newton, Suzaku, and NuSTAR obtained over a period of about 10~years. We have built a double-reflection spectral model that well describes the observed spectrum based on prior results suggesting that the iron K$α$ line includes both a broad component from the disk's reflection spectrum and a narrow component due to fluorescence and scattering off material by more distant matter. Our measurement of the black hole spin parameter is $a_* = 0.856\pm0.006$ (99\% confidence level).

astro-ph.HE↗

arXiv2018

Negative Photoconductance in van der Waals Heterostructures-Based Floating Gate Phototransistor

Van der Waals (vdW) heterostructures made of two-dimensional materials have been demonstrated to be versatile architectures for optoelectronic applications due to strong light-matter interactions. However, most of light-controlled phenomena and applications in the vdW heterostructures rely on positive photoconductance (PPC). Negative photoconductance (NPC) has not yet been reported in vdW heterostructures. Here we report the observation of the NPC in ReS2/h-BN/MoS2 vdW heterostructures-based floating gate phototransistor. The fabricated devices exhibit excellent performance of nonvolatile memory without light illumination. More interestingly, we observe a gate-tunable transition between the PPC and the NPC under the light illumination. The observed NPC phenomenon can be attributed to the charge transfer between floating gate and conduction channel. Furthermore, we show that the control of NPC through light intensity is promising in realization of light-tunable multi-bit memory devices. Our results may enable potential applications in multifunctional memories and optoelectronic devices.

cond-mat.mes-hall↗

arXiv2018

Sharp estimate of global Coulomb gauge

Let $A$ be a $W^{1,2}$-connection on a principle $\text{SU}(2)$-bundle $P$ over a compact $4$-manifold $M$ whose curvature $F_A$ satisfies $\|F_A\|_{L^2(M)}\le Λ$. Our main result is the existence of a global section $σ: M\to P$ with finite singularities on $M$ such that the connection form $σ^*A$ satisfies the Coulomb equation $d^*(σ^*A)=0$ and admits a sharp estimate $\|σ^*A\|_{\mathcal{L}^{4,\infty}(M)}\le C(M,Λ)$. Here $\mathcal{L}^{4,\infty}$ is a new function space we introduce in this paper that satisfies $L^4(M)\subsetneq \mathcal{L}^{4,\infty}(M)\subsetneq L^{4-ε}(M)$ for all $ε>0$.

math.DG↗

arXiv2018

Addressable electron spin resonance using donors and donor molecules in silicon

Phosphorus donor impurities in silicon are a promising candidate for solid-state quantum computing due to their exceptionally long coherence times and high fidelities. However, individual addressability of exchange coupled donor qubits with separations ~15nm is challenging. Here we show that by using atomic-precision lithography we can place a single P donor next to a 2P molecule 16(+/-1)nm apart and use their distinctive hyperfine coupling strengths to address qubits at vastly different resonance frequencies. In particular the single donor yields two hyperfine peaks separated by 97(+/-2.5)MHz, in contrast to the donor molecule which exhibits three peaks separated by 262(+/-10)MHz. Atomistic tight-binding simulations confirm the large hyperfine interaction strength in the 2P molecule with an inter-donor separation of ~0.7nm, consistent with lithographic STM images of the 2P site during device fabrication. We discuss the viability of using donor molecules for built-in addressability of electron spin qubits in silicon.

cond-mat.mes-hall↗

arXiv2018

Nonparametric Topic Modeling with Neural Inference

This work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as trainable parameters and the document-specific topic proportions are obtained by a stick-breaking construction. The inference of iTM-VAE is modeled by neural networks such that it can be computed in a simple feed-forward manner. We also describe how to introduce a hyper-prior into iTM-VAE so as to model the uncertainty of the prior parameter. Actually, the hyper-prior technique is quite general and we show that it can be applied to other AEVB based models to alleviate the {\it collapse-to-prior} problem elegantly. Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner. HiTM-VAE is even more flexible and can generate topic distributions with better variability. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-art baselines significantly. The advantages of the hyper-prior technique and the hierarchical model construction are also confirmed by experiments.

cs.CL↗

arXiv2018

Multi-epoch analysis of the X-ray spectrum of the active galactic nucleus in NGC 5506

We present a multi-epoch X-ray spectroscopy analysis of the nearby narrow-line Seyfert I galaxy NGC 5506. For the first time, spectra taken by Chandra, XMM-Newton, Suzaku, and NuSTAR - covering the 2000-2014 time span - are analyzed simultaneously, using state-of-the-art models to describe reprocessing of the primary continuum by optical thick matter in the AGN environment. The main goal of our study is determining the spin of the supermassive black hole (SMBH). The nuclear X-ray spectrum is photoelectrically absorbed by matter with column density $\simeq 3 \times 10^{22}$ cm$^{-2}$. A soft excess is present at energies lower than the photoelectric cut-off. Both photo-ionized and collisionally ionized components are required to fit it. This component is constant over the time-scales probed by our data. The spectrum at energies higher than 2 keV is variable. We propose that its evolution could be driven by flux-dependent changes in the geometry of the innermost regions of the accretion disk. The black hole spin in NGC 5506 is constrained to be 0.93$\pm _{ 0.04 }^{0.04}$ at 90% confidence level for one interesting parameter.

astro-ph.HE↗

arXiv2018

Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

In this paper, we analyze the convergence of the alternating direction method of multipliers (ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, $ϕ(x_0,\ldots,x_p,y)$, subject to coupled linear equality constraints. Our ADMM updates each of the primal variables $x_0,\ldots,x_p,y$, followed by updating the dual variable. We separate the variable $y$ from $x_i$'s as it has a special role in our analysis. The developed convergence guarantee covers a variety of nonconvex functions such as piecewise linear functions, $\ell_q$ quasi-norm, Schatten-$q$ quasi-norm ($0<q<1$), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD) penalty. It also allows nonconvex constraints such as compact manifolds (e.g., spherical, Stiefel, and Grassman manifolds) and linear complementarity constraints. Also, the $x_0$-block can be almost any lower semi-continuous function. By applying our analysis, we show, for the first time, that several ADMM algorithms applied to solve nonconvex models in statistical learning, optimization on manifold, and matrix decomposition are guaranteed to converge. Our results provide sufficient conditions for ADMM to converge on (convex or nonconvex) monotropic programs with three or more blocks, as they are special cases of our model. ADMM has been regarded as a variant to the augmented Lagrangian method (ALM). We present a simple example to illustrate how ADMM converges but ALM diverges with bounded penalty parameter $β$. Indicated by this example and other analysis in this paper, ADMM might be a better choice than ALM for some nonconvex \emph{nonsmooth} problems, because ADMM is not only easier to implement, it is also more likely to converge for the concerned scenarios.

math.OC↗

arXiv2018

A Deeply-Recursive Convolutional Network for Crowd Counting

The estimation of crowd count in images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. Recently, the convolutional neural network (CNN) based approaches have been shown to be more effective in crowd counting than traditional methods that use handcrafted features. However, the existing CNN-based methods still suffer from large number of parameters and large storage space, which require high storage and computing resources and thus limit the real-world application. Consequently, we propose a deeply-recursive network (DR-ResNet) based on ResNet blocks for crowd counting. The recursive structure makes the network deeper while keeping the number of parameters unchanged, which enhances network capability to capture statistical regularities in the context of the crowd. Besides, we generate a new dataset from the video-monitoring data of Beijing bus station. Experimental results have demonstrated that proposed method outperforms most state-of-the-art methods with far less number of parameters.

cs.CV↗

arXiv2018

Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State

Opioid related deaths are increasing dramatically in recent years, and opioid epidemic is worsening in the United States. Combating opioid epidemic becomes a high priority for both the U.S. government and local governments such as New York State. Analyzing patient level opioid related hospital visits provides a data driven approach to discover both spatial and temporal patterns and identity potential causes of opioid related deaths, which provides essential knowledge for governments on decision making. In this paper, we analyzed opioid poisoning related hospital visits using New York State SPARCS data, which provides diagnoses of patients in hospital visits. We identified all patients with primary diagnosis as opioid poisoning from 2010-2014 for our main studies, and from 2003-2014 for temporal trend studies. We performed demographical based studies, and summarized the historical trends of opioid poisoning. We used frequent item mining to find co-occurrences of diagnoses for possible causes of poisoning or effects from poisoning. We provided zip code level spatial analysis to detect local spatial clusters, and studied potential correlations between opioid poisoning and demographic and social-economic factors.

q-bio.PE↗

arXiv2018

A Counter-Forensic Method for CNN-Based Camera Model Identification

An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed. Due to the linear nature of CNNs and the high-dimensionality of images, neural networks are vulnerable to attacks with adversarial examples. These examples are imperceptibly different from correctly classified images but are misclassified with high confidence by CNNs. In this paper, we describe a counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector. Our method can use both the Fast Gradient Sign Method (FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these adversarial images and does not require direct access to the CNN. Our results show that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to our proposed method.

cs.CV↗

arXiv2018

iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.

cs.NI↗

arXiv2018

Steklov Spectral Geometry for Extrinsic Shape Analysis

We propose using the Dirichlet-to-Neumann operator as an extrinsic alternative to the Laplacian for spectral geometry processing and shape analysis. Intrinsic approaches, usually based on the Laplace-Beltrami operator, cannot capture the spatial embedding of a shape up to rigid motion, and many previous extrinsic methods lack theoretical justification. Instead, we consider the Steklov eigenvalue problem, computing the spectrum of the Dirichlet-to-Neumann operator of a surface bounding a volume. A remarkable property of this operator is that it completely encodes volumetric geometry. We use the boundary element method (BEM) to discretize the operator, accelerated by hierarchical numerical schemes and preconditioning; this pipeline allows us to solve eigenvalue and linear problems on large-scale meshes despite the density of the Dirichlet-to-Neumann discretization. We further demonstrate that our operators naturally fit into existing frameworks for geometry processing, making a shift from intrinsic to extrinsic geometry as simple as substituting the Laplace-Beltrami operator with the Dirichlet-to-Neumann operator.

cs.GR↗

arXiv2018

Automated Big Traffic Analytics for Cyber Security

Network traffic analytics technology is a cornerstone for cyber security systems. We demonstrate its use through three popular and contemporary cyber security applications in intrusion detection, malware analysis and botnet detection. However, automated traffic analytics faces the challenges raised by big traffic data. In terms of big data's three characteristics --- volume, variety and velocity, we review three state of the art techniques to mitigate the key challenges including real-time traffic classification, unknown traffic classification, and efficiency of classifiers. The new techniques using statistical features, unknown discovery and correlation analytics show promising potentials to deal with big traffic data. Readers are encouraged to devote to improving the performance and practicability of automatic traffic analytic in cyber security.

cs.CR↗

arXiv2018

Mott transition in the $π$-flux SU($4$) Hubbard model on a square lattice

We employ the projector quantum Monte Carlo simulations to study the ground-state properties of the square-lattice SU(4) Hubbard model with a $π$ flux per plaquette. In the weak coupling regime, its ground state is in the gapless Dirac semi-metal phase. With increasing repulsive interaction, we show that, a Mott transition occurs from the semimetal to the valence bond solid, accompanied by the $Z_4$ discrete symmetry breaking. Our simulations demonstrate the existence of a second-order phase transition, which confirms the Ginzburg-Landau analysis. The phase transition point and the critical exponent $η$ are also estimated. To account for the effect of a $π$ flux on the ordering in the strong coupling regime, we analytically derive by the perturbation theory the ring-exchange term which describes the leading-order difference between the $π$-flux and zero-flux SU(4) Hubbard models.

cond-mat.quant-gas↗

arXiv2018

Quantitative stratification of stationary connections

Let $A$ be a connection of a principal bundle $P$ over a Riemannian manifold $M$, such that its curvature $F_A\in L_{\text{loc}}^2(M)$ satisfies the stationarity equation. It is a consequence of the stationarity that $θ_A(x,r)=e^{cr^2}r^{4-n}\int_{B_r(x)}|F_A|^2$ is monotonically increasing in $r$, for some $c$ depending only on the local geometry of $M$. We are interested in the singular set defined by $S(A)=\{x: \lim_{r\to 0}θ_A(x,r)\neq 0\}$, and its stratification $S^k(A)=\{x: \text{no tangent measure at $x$ is $(k+1)$-symmetric}\}$. We then introduce and study the quantitative stratification $S^k_ε(A)$. Roughly speaking, $S^k_ε(A)$ consists of points at which no tangent measure of $A$ is $ε$-close to being $(k+1)$-symmetric. In the main Theorem, we show that $S^k_ε$ is $k$-rectifiable and satisfies the Minkowski volume estimate $\text{Vol}(B_r(S^k_ε)\cap B_1)\le Cr^{n-k}$. Lastly, we apply the main theorems to the stationary Yang-Mills connections to obtain a rectifiability theorem that extends some previously known results by G. Tian.

math.DG↗

arXiv2018

Ultra-Fast Ferrimagnetic All Spin Logic Device

All spin logic device (ASLD) blazes an alternative path for realizing ultra-low power computing in the Post-Moore era. However, initial device structure relying on ferromagnetic input/output and spin transfer torque (STT) driven magnetization switching degrades its performance and even hinders its realization. In this paper, we propose an ASLD based on rare-earth (RE)-transition-metal (TM) ferromagnetic alloy that can achieve an ultra-high frequency up to terahertz. The spin orbit torque (SOT) induced fast precession near the spin angular momentum compensated point is investigated through the macrospin model. Combining the non-local spin current diffusing from the input to the output, a deterministic picosecond switching can be realized without any external magnetic field. Our results show that ASLD has the potential to exceed the performance of mainstream computing.

physics.app-ph↗