Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention

Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention. Most existing methods utilize local networks (e.g., recurrent, convolutional, and graph convolutional networks) to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which contain more details and semantics respectively, are asynchronously captured in different level of layers. Moreover, existing methods are limited to the spatio-temporal domain and ignore information in the frequency domain. To better extract synchronous detailed and semantic information from multi-domains, we propose a residual frequency attention (rFA) block to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. Besides, a soft-margin focal loss (SMFL) is proposed to optimize the learning whole process, which automatically conducts data selection and encourages intrinsic margins in classifiers. Our approach significantly outperforms other state-of-the-art methods on several large-scale datasets.Comment: 6 pages,4 figures; accepted to ICME 201

Stochastic Throughput Optimization for Two-hop Systems with Finite Relay Buffers

Optimal queueing control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider a two-hop half-duplex relaying system with random channel connectivity. The relay is equipped with a finite buffer. We focus on stochastic link selection and transmission rate control to maximize the average system throughput subject to a half-duplex constraint. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP), which is well-known to be a difficult problem. By using sample-path analysis and exploiting the specific problem structure, we first obtain an \emph{equivalent Bellman equation} with reduced state and action spaces. By using \emph{relative value iteration algorithm}, we analyze the properties of the value function of the MDP. Then, we show that the optimal policy has a threshold-based structure by characterizing the \emph{supermodularity} in the optimal control. Based on the threshold-based structure and Markov chain theory, we further simplify the original complex stochastic optimization problem to a static optimization problem over a small discrete feasible set and propose a low-complexity algorithm to solve the simplified static optimization problem by making use of its special structure. Furthermore, we obtain the closed-form optimal threshold for the symmetric case. The analytical results obtained in this paper also provide design insights for two-hop relaying systems with multiple relays equipped with finite relay buffers.Comment: 15 pages, double-column, 9 figures, 3 tables. Accepted by IEEE Transaction on Signal Processin

Stochastic Content-Centric Multicast Scheduling for Cache-Enabled Heterogeneous Cellular Networks

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by taking into account the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using \emph{relative value iteration} and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.Comment: Accepted to IEEE Trans. on Wireless Communications (June 6, 2016). Conference version appears in ACM CoNEXT 2015 Workshop on Content Caching and Delivery in Wireless Networks (CCDWN

Optimal Dynamic Multicast Scheduling for Cache-Enabled Content-Centric Wireless Networks

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not make full use of the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). It is well-known to be a difficult problem due to the curse of dimensionality, and there generally only exist numerical solutions. By using relative value iteration algorithm and the special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Then, by exploiting these structural properties of the optimal policy, we propose two low-complexity optimal algorithms. Motivated by the switch structures of the optimal policy, to further reduce the complexity, we also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to compute this policy.Comment: 17 double-column pages; Shorter version appears in ISIT 201

Fast Shape Estimation for Galaxies and Stars

Model fitting is frequently used to determine the shape of galaxies and the point spread function, for examples, in weak lensing analyses or morphology studies aiming at probing the evolution of galaxies. However, the number of parameters in the model, as well as the number of objects, are often so large as to limit the use of model fitting for future large surveys. In this article, we propose a set of algorithms to speed up the fitting process. Our approach is divided into three distinctive steps: centroiding, ellipticity measurement, and profile fitting. We demonstrate that we can derive the position and ellipticity of an object analytically in the first two steps and thus leave only a small number of parameters to be derived through model fitting. The position, ellipticity, and shape parameters can then used in constructing orthonomal basis functions such as s\'ersiclets for better galaxy image reconstruction. We assess the efficiency and accuracy of the algorithms with simulated images. We have not taken into account the deconvolution of the point spread function, which most weak lensing analyses do.Comment: 10 pages, 11 figure

Deep Neural Architecture Search with Deep Graph Bayesian Optimization

Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks

Investigation of the Puzzling Abundance Pattern in the Stars of the Fornax Dwarf Spheroidal Galaxy

Many works have found unusual characteristics of elemental abundances in nearby dwarf galaxies. This implies that there is a key factor of galactic evolution that is different from that of the Milky Way (MW). The chemical abundances of the stars in the Fornax dwarf spheroidal galaxy (Fornax dSph) provide excellent information for setting constraints on the models of the galactic chemical evolution. In this work, adopting the five-component approach, we fit the abundances of the Fornax dSph stars, including $\alpha$ elements, iron group elements and neutron-capture elements. For most sample stars, the relative contributions from the various processes to the elemental abundances are not usually in the MW proportions. We find that the contributions from massive stars to the primary $\alpha$ elements and iron group elements increase monotonously with increasing [Fe/H]. This means that the effect of the galactic wind is not strong enough to halt star formation and the contributions from massive stars to $\alpha$ elements did not halted for [Fe/H]$\lesssim$-0.5. The average contributed ratios of various processes between the dSph stars and the MW stars monotonously decrease with increasing progenitor mass. This is important evidence of a bottom-heavy initial mass function (IMF) for the Fonax dSph, compared to the MW. Considering a bottom-heavy IMF for the dSph, the observed relations of [$\alpha$/Fe] versus [Fe/H], [iron group/Fe] versus [Fe/H] and [neutron-capture/Fe] versus [Fe/H] for the dSph stars can be explained.Comment: 38 pages, 11 figures, 2 tables. Accepted for publication in Ap

On the inductive limit of direct sums of simple TAI algebras

An ATAI (or ATAF, respectively) algebra, introduced in [Jiang1] (or in [Fa] respectively) is an inductive limit $\lim\limits_{n\rightarrow\infty}(A_{n}=\bigoplus\limits_{i=1}A_{n}^{i},\phi_{nm})$, where each $A_{n}^{i}$ is a simple separable nuclear TAI (or TAF) C*-algebra with UCT property. In [Jiang1], the second author classified all ATAI algebras by an invariant consisting orderd total K-theory and tracial state spaces of cut down algebras under an extra restriction that all element in $K_{1}(A)$ are torsion. In this paper, we remove this restriction, and obtained the classification for all ATAI algebras with the Hausdorffized algebraic $K_{1}$-group as an addition to the invariant used in [Jiang1]. The theorem is proved by reducing the class to the classification theorem of $\mathcal{AHD}$ algebras with ideal property which is done in [GJL1]. Our theorem generalizes the main theorem of [Fa] and [Jiang1] (see corollary 4.3).Comment: 24 pages. arXiv admin note: text overlap with arXiv:1607.0758

Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks

In recent years, deep learning has achieved remarkable achievements in many fields, including computer vision, natural language processing, speech recognition and others. Adequate training data is the key to ensure the effectiveness of the deep models. However, obtaining valid data requires a lot of time and labor resources. Data augmentation (DA) is an effective alternative approach, which can generate new labeled data based on existing data using label-preserving transformations. Although we can benefit a lot from DA, designing appropriate DA policies requires a lot of expert experience and time consumption, and the evaluation of searching the optimal policies is costly. So we raise a new question in this paper: how to achieve automated data augmentation at as low cost as possible? We propose a method named BO-Aug for automating the process by finding the optimal DA policies using the Bayesian optimization approach. Our method can find the optimal policies at a relatively low search cost, and the searched policies based on a specific dataset are transferable across different neural network architectures or even different datasets. We validate the BO-Aug on three widely used image classification datasets, including CIFAR-10, CIFAR-100 and SVHN. Experimental results show that the proposed method can achieve state-of-the-art or near advanced classification accuracy. Code to reproduce our experiments is available at https://github.com/zhangxiaozao/BO-Aug

Estimating R-Process Yields from Abundances of the Metal-Poor Stars

The chemical abundances of metal-poor stars provide important clues to explore stellar formation history and set significant constraints on models of the r-process. In this work, we find that the abundance patterns of the light and iron group elements of the main r-process stars are very close to those of the weak r-process stars. Based on a detailed abundance comparison, we find that the weak r-process occurs in supernovae with a progenitor mass range of $\sim11-26M_{\odot}$. Using the SN yields given by Heger & Woosley and the abundances of the weak r-process stars, the weak r-process yields are derived. The SNe with a progenitor mass range of $15M_{\odot}<M<26M_{\odot}$ are the main sites of the weak r-process and their contributions are larger than 80%. Using the abundance ratios of the weak r-process and the main r-process in the solar system, the average yields of the main r-process are estimated. The observed correlations of the [neutron-capture/Eu] versus [Eu/Fe] can be explained by mixing of the two r-process abundances in various fractions.Comment: The article has been published by PASP, 2014, 126, 54