arXiv subjects

Tao Yang

Publications and source records attributed to Tao Yang.

At least 343 records · Page 19Linked to original sources

arXiv2019

Distributed Bandit Online Convex Optimization with Time-Varying Coupled Inequality Constraints

This paper considers the problem of distributed bandit online convex optimization with time-varying coupled inequality constraints. This problem can be defined as a repeated game between a group of learners and an adversary. The learners attempt to minimize a sequence of global loss functions and at the same time satisfy a sequence of coupled constraint functions. The global loss and the coupled constraint functions are the sum of local convex loss and constraint functions, respectively, which are adaptively generated by the adversary. The local loss and constraint functions are revealed in a bandit manner, i.e., only the values of loss and constraint functions at sampled points are revealed to the learners, and the revealed function values are held privately by each learner. We consider two scenarios, one- and two-point bandit feedback, and propose two corresponding distributed bandit online algorithms used by the learners. We show that sublinear expected regret and constraint violation are achieved by these two algorithms, if the accumulated variation of the comparator sequence also grows sublinearly. In particular, we show that $\mathcal{O}(T^{θ_1})$ expected static regret and $\mathcal{O}(T^{7/4-θ_1})$ constraint violation are achieved in the one-point bandit feedback setting, and $\mathcal{O}(T^{\max\{κ,1-κ\}})$ expected static regret and $\mathcal{O}(T^{1-κ/2})$ constraint violation in the two-point bandit feedback setting, where $θ_1\in(3/4,5/6]$ and $κ\in(0,1)$ are user-defined trade-off parameters. Finally, these theoretical results are illustrated by numerical simulations of a simple power grid example.

math.OC↗

arXiv2019

On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.

cs.AI↗

arXiv2019

Gaussian processes reconstruction of modified gravitational wave propagation

Recent work has shown that modified gravitational wave (GW) propagation can be a powerful probe of dark energy and modified gravity, specific to GW observations. We use the technique of Gaussian processes, that allows the reconstruction of a function from the data without assuming any parametrization, to measurements of the GW luminosity distance from simulated joint GW-GRB detections, combined with measurements of the electromagnetic luminosity distance by simulated DES data. For the GW events we consider both a second-generation LIGO/Virgo/Kagra (HVLKI) network, and a third-generation detector such as the Einstein Telescope. We find that the HVLKI network at target sensitivity, with $O(15)$ neutron star binaries with electromagnetic counterpart, could already detect deviations from GR at a level predicted by some modified gravity models, and a third-generation detector such as ET would have a remarkable discovery potential. We discuss the complementarity of the Gaussian processes technique to the $(Ξ_0,n)$ parametrization of modified GW propagation.

astro-ph.CO↗

arXiv2019

Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition

Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming. For the same action, the knowledge learned from different media types, e.g., videos and images, may be related and complementary. However, due to the domain shifts and heterogeneous feature representations between videos and images, the performance of classifiers trained on images may be dramatically degraded when directly deployed to videos. In this paper, we propose a novel method, named Deep Image-to-Video Adaptation and Fusion Networks (DIVAFN), to enhance action recognition in videos by transferring knowledge from images using video keyframes as a bridge. The DIVAFN is a unified deep learning model, which integrates domain-invariant representations learning and cross-modal feature fusion into a unified optimization framework. Specifically, we design an efficient cross-modal similarities metric to reduce the modality shift among images, keyframes and videos. Then, we adopt an autoencoder architecture, whose hidden layer is constrained to be the semantic representations of the action class names. In this way, when the autoencoder is adopted to project the learned features from different domains to the same space, more compact, informative and discriminative representations can be obtained. Finally, the concatenation of the learned semantic feature representations from these three autoencoders are used to train the classifier for action recognition in videos. Comprehensive experiments on four real-world datasets show that our method outperforms some state-of-the-art domain adaptation and action recognition methods.

cs.CV↗

arXiv2019

Controllable splitting dynamics of a doubly quantized vortex in a rotating ring-shaped condensate

We study the dynamics of a doubly quantized vortex (DQV), created by releasing a ring-shaped Bose-Einstein condensate with quantized circulation into harmonic potential traps. It is shown that a DQV can be generated and exists stably in the middle of the ring-shaped condensate with the initial circulation $s = 2$ after released into the rotationally symmetric trap potential. For an asymmetric trap with a small degree of anisotropy the DQV initially splits into two singly quantized vortices and revives again but eventually evolves into two unit vortices due to the dynamic instability. For the degree of anisotropy above a critical value, the DQV is extremely unstably and decays rapidly into two singlet vortices. The geometry-dependent lifetime of the DQV and vortex-induced excitations are also discussed intensively.

cond-mat.quant-gas↗

arXiv2019

Matching Based Two-Timescale Resource Allocation for Cooperative D2D Communication

We consider a cooperative device-to-device (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist cellular users (CUs) in exchange for the opportunities to use the licensed spectrum. To reduce the overhead, we propose a novel two-timescale resource allocation scheme, in which the pairing between CUs and D2D pairs is decided at a long timescale and time allocation factor for CU and D2D pair is determined at a short timescale. Specifically, to characterize the long-term payoff of each potential CU-D2D pair, we investigate the optimal cooperation policy to decide the time allocation factor based on the instantaneous channel state information (CSI). We prove that the optimal policy is a threshold policy. Since CUs and D2D pairs are self-interested, they are paired only when they agree to cooperate mutually. Therefore, to study the behaviors of CUs and D2D pairs, we formulate the pairing problem as a matching game, based on the long-term payoff of each possible pairing. Furthermore, unlike most previous matching model in D2D networks, we allow transfer between CUs and D2D pairs to improve the performance. Besides, we propose an algorithm, which converges to an epsilon-stable matching.

cs.IT↗

arXiv2019

Global Temporal Representation based CNNs for Infrared Action Recognition

Infrared human action recognition has many advantages, i.e., it is insensitive to illumination change, appearance variability, and shadows. Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered. In this letter, we propose a novel global temporal representation named optical-flow stacked difference image (OFSDI) and extract robust and discriminative feature from the infrared action data by considering the local, global, and spatial temporal information together. Due to the small size of the infrared action dataset, we first apply convolutional neural networks on local, spatial, and global temporal stream respectively to obtain efficient convolutional feature maps from the raw data rather than train a classifier directly. Then these convolutional feature maps are aggregated into effective descriptors named three-stream trajectory-pooled deep-convolutional descriptors by trajectory-constrained pooling. Furthermore, we improve the robustness of these features by using the locality-constrained linear coding (LLC) method. With these features, a linear support vector machine (SVM) is adopted to classify the action data in our scheme. We conduct the experiments on infrared action recognition datasets InfAR and NTU RGB+D. The experimental results show that the proposed approach outperforms the representative state-of-the-art handcrafted features and deep learning features based methods for the infrared action recognition.

cs.CV↗

arXiv2019

Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition

Recognizing human actions from varied views is challenging due to huge appearance variations in different views. The key to this problem is to learn discriminant view-invariant representations generalizing well across views. In this paper, we address this problem by learning view-invariant representations hierarchically using a novel method, referred to as Joint Sparse Representation and Distribution Adaptation (JSRDA). To obtain robust and informative feature representations, we first incorporate a sample-affinity matrix into the marginalized stacked denoising Autoencoder (mSDA) to obtain shared features, which are then combined with the private features. In order to make the feature representations of videos across views transferable, we then learn a transferable dictionary pair simultaneously from pairs of videos taken at different views to encourage each action video across views to have the same sparse representation. However, the distribution difference across views still exists because a unified subspace where the sparse representations of one action across views are the same may not exist when the view difference is large. Therefore, we propose a novel unsupervised distribution adaptation method that learns a set of projections that project the source and target views data into respective low-dimensional subspaces where the marginal and conditional distribution differences are reduced simultaneously. Therefore, the finally learned feature representation is view-invariant and robust for substantial distribution difference across views even the view difference is large. Experimental results on four multiview datasets show that our approach outperforms the state-ofthe-art approaches.

cs.CV↗

arXiv2019

Distributed Least Squares Solver for Network Linear Equations

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples.

math.OC↗

arXiv2019

Exponential Convergence for Distributed Smooth Optimization Under the Restricted Secant Inequality Condition

This paper considers the distributed smooth optimization problem in which the objective is to minimize a global cost function formed by a sum of local smooth cost functions, by using local information exchange. The standard assumption for proving exponential/linear convergence of first-order methods is the strong convexity of the cost functions, which does not hold for many practical applications. In this paper, we first show that the continuous-time distributed primal-dual gradient algorithm converges to one global minimizer exponentially under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than the strong convexity condition since it does not require convexity and the global minimizers are not necessary to be unique. We then show that the discrete-time distributed primal-dual algorithm constructed by using the Euler's approximation method converges to one global minimizer linearly under the same condition. The theoretical results are illustrated by numerical simulations.

math.OC↗

arXiv2019

The existence of a nontrivial weak solution to a double critical problem involving fractional Laplacian in ${\R}^n$ with a Hardy term

In this paper, we consider the existence of nontrivial weak solutions to a double critical problem involving fractional Laplacian with a Hardy term: \begin{equation} \label{eq0.1} (-Δ)^{s}u-γ {\frac{u}{|x|^{2s}}}= {\frac{{|u|}^{ {2^{*}_{s}}(β)-2}u}{|x|^β}}+ \big [ I_μ* F_α(\cdot,u) \big](x)f_α(x,u), \ \ u \in {\dot{H}}^s(\R^{n}) \end{equation} where $s \in(0,1)$, $0\leq α,β<2s<n$, $μ\in (0,n)$, $γ<γ_{H}$, $I_μ(x)=|x|^{-μ}$, $F_α(x,u)=\frac{ {|u(x)|}^{ {2^{\#}_μ }(α)} }{ {|x|}^{ {δ_μ (α)} } }$, $f_α(x,u)=\frac{ {|u(x)|}^{{ 2^{\#}_μ }(α)-2}u(x) }{ {|x|}^{ {δ_μ (α)} } }$, $2^{\#}_μ (α)=(1-\fracμ{2n})\cdot 2^{*}_{s} (α)$, $δ_μ (α)=(1-\fracμ{2n})α$, ${2^{*}_{s}}(α)=\frac{2(n-α)}{n-2s}$ and $γ_{H}=4^s\frac{Γ^2(\frac{n+2s}{4})} {Γ^2(\frac{n-2s}{4})}$. We show that problem (\ref{eq0.1}) admits at least a weak solution under some conditions. To prove the main result, we develop some useful tools based on a weighted Morrey space. To be precise, we discover the embeddings \begin{equation} \label{eq0.2} {\dot{H}}^s(\R^{n}) \hookrightarrow {L}^{2^*_{s}(α)}(\R^{n},|y|^{-α}) \hookrightarrow L^{p,\frac{n-2s}{2}p+pr}(\R^{n},|y|^{-pr}) \end{equation} where $s \in (0,1)$, $0<α<2s<n$, $p\in[1,2^*_{s}(α))$, $r=\fracα{ 2^*_{s}(α) }$; We also establish an improved Sobolev inequality. By using mountain pass lemma along with an improved Sobolev inequality, we obtain a nontrivial weak solution to problem (\ref{eq0.1}) in a direct way. It is worth while to point out that the improved Sobolev inequality could be applied to simplify the proof of the main results in \cite{NGSS} and \cite{RFPP}.

math.AP↗

arXiv2019

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning

We develop a distributed stochastic gradient descent algorithm for solving non-convex optimization problems under the assumption that the local objective functions are twice continuously differentiable with Lipschitz continuous gradients and Hessians. We provide sufficient conditions on step-sizes that guarantee the asymptotic mean-square convergence of the proposed algorithm. We apply the developed algorithm to a distributed supervised-learning problem, in which a set of networked agents collaboratively train their individual neural nets to recognize handwritten digits in images. Results indicate that all agents report similar performance that is also comparable to the performance of a centrally trained neural net. Numerical results also show that the proposed distributed algorithm allows the individual agents to recognize the digits even though the training data corresponding to all the digits is not locally available to each agent.

math.OC↗

arXiv2019

Multi-Grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

cs.CL↗

arXiv2019

Collective oscillations of a Bose-Einstein condensate induced by a vortex ring

We study the collective oscillations of three-dimensional Bose-Einstein condensates (BECs) excited by a vortex ring. We identify independent, integrated, and stationary modes of the center-of-mass oscillation of the condensate with respect to the vortex ring movement. We show that the oscillation amplitude {of the center-of-mass of the condensate} depends strongly on the initial radius of the vortex ring, the inter-atomic interaction, and the aspect ration of the trap, while the oscillation frequency is fixed and equal to the frequency of the harmonic trap in the direction of the ring movement. However, when applying Kelvin wave perturbations on the vortex ring, the center-of-mass oscillation of the BEC is changed nontrivially with respect to the perturbation modes, the long-scale perturbation strength as well as the wave number of the perturbations. The parity of the wave number of the Kelvin perturbations plays important role on the mode of the center-of-mass oscillation of the condensate.

cond-mat.quant-gas↗

arXiv2019

Optomechanical suspended waveguide for broadband phase modulation with frequency memory effect

Whether it is for transmitting information or for controlling intensity, light modulation is among the essential functions commonly used in complex optical systems. In integrated optics, modulation principles usually include the use of electro-optical effects or acousto-optics varying index waves. Here, we demonstrate a concept of light-modulation based on a vibrating suspended cantilever clamped on both ends, acting as an optical waveguide. In this approach, an optical phase shift is introduced by the physical elongation and by the stress induced in the vibrating cantilever. Most interesting, such a device can store optical information in the form of a phase shift, related to the frequency path-dependence of the vibration. This remarkable property stems from the intrinsic non-linear dynamical behavior of the device, resembling Duffing-like oscillators. In this proof-of-concept, the micro-device is manufactured in a single fused silica chip using femtosecond laser exposure combined with chemical etching and CO2-laser polishing to achieve optical roughness quality. The cantilever vibration results from dielectrophoresis forces arising in the silica beam as a consequence of the non-uniform electrostatic field applied to it. Noticeably, being made entirely of fused silica, the device is capable of broadband operation, from the near UV to the mid-IR range, and this, at kHz frequencies.

physics.optics↗

arXiv2019

Learning for Matching Game in Cooperative D2D Communication with Incomplete Information

This paper considers a cooperative device-to-device (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist cellular users (CUs) in exchange for the opportunities to use licensed spectrum. Based on the interaction of each D2D pair and each CU, we formulate the pairing problem between multiple cues and multiple D2D pairs as a one-to-one matching game. Unlike most existing works, we consider a realistic scenario with incomplete channel information. Thus, each CU lacks enough information to establish its preference over D2D pairs. Therefore, traditional matching algorithms are not suitable for our scenario. To this end, we convert the matching game to an equivalent non-cooperative game, and then propose a novel learning algorithm, which converges to a stable matching.

cs.IT↗

arXiv2019

Event-Triggered Control for Consensus of Multi-Agent Systems with Nonlinear Output and Directed Topologies

We propose a distributed event-triggered control law to solve the consensus problem for multi-agent systems with nonlinear output. Under the condition that the underlying digraph is strongly connected, we propose some sufficient conditions related to the nonlinear output function and initial states to guarantee that the event-triggered controller realizes consensus. Then the results are extended to the case where the underlying directed graph contains a directed spanning tree. These theoretical results are illustrated by numerical simulations.

eess.SY↗

arXiv2019

Vortex-dynamical Interpretation of Anti-phase and In-phase Flickering of Dual Buoyant Diffusion Flames

Anti-phase and in-phase flickering modes of dual buoyant diffusion flames were numerically investigated and theoretically analyzed in this study. Inspired by the flickering mechanism of a single buoyant diffusion flame, for which the deformation, stretching, or even pinch-off of the flame surface result from the formation and evolution of the toroidal vortices, we attempted to understand the anti-phase and in-phase flickering of dual buoyant diffusion flames from the perspective of vortex dynamics. The interaction between the inner-side shear layers of the two flames was identified to be responsible for the different flickering modes. Specifically, the transition between anti-phase and in-phase flickering modes can be predicted by a unified regime nomogram of the normalized flickering frequency versus a characteristic Reynolds number, which accounts for the viscous effect on vorticity diffusion between the two inner-side shear layers. Physically, the transition of the vortical structures from symmetric (in-phase) to staggered (anti-phase) in a dual-flame system can be interpreted as being similar to the mechanism causing flow transition in the wake of a bluff body and forming the Karman vortex street.

physics.flu-dyn↗