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Yong Yu

Publications and source records attributed to Yong Yu.

287 records · Page 16Linked to original sources
arXiv2015

An Investigation of the Absolute Proper Motions of the SCUSS Catalog

Absolute proper motions for $\sim$ 7.7 million objects were derived based on data from the South Galactic Cap u-band Sky Survey (SCUSS) and astrometric data derived from uncompressed Digitized Sky Surveys that the Space Telescope Science Institute (STScI) created from the Palomar and UK Schmidt survey plates. We put a great deal of effort into correcting the position-, magnitude-, and color-dependent systematic errors in the derived absolute proper motions. The spectroscopically confirmed quasars were used to test the internal systematic and random error of the proper motions. The systematic errors of the overall proper motions in the SCUSS catalog are estimated as -0.08 and -0.06 mas/yr for μα cos δ and μδ, respectively. The random errors of the proper motions in the SCUSS catalog are estimated independently as 4.2 and 4.4 mas/yr for μα cos δ and μδ. There are no obvious position-, magnitude-, and color-dependent systematic errors of the SCUSS proper motions. The random error of the proper motions goes up with the magnitude from about 3 mas/yr at u < 18.0 mag to about 7 mas/yr at u = 22.0 mag. The proper motions of stars in SCUSS catalog are compared with those in the SDSS catalog, and they are highly consistent.

astro-ph.IM↗
arXiv2014

A Latent Clothing Attribute Approach for Human Pose Estimation

As a fundamental technique that concerns several vision tasks such as image parsing, action recognition and clothing retrieval, human pose estimation (HPE) has been extensively investigated in recent years. To achieve accurate and reliable estimation of the human pose, it is well-recognized that the clothing attributes are useful and should be utilized properly. Most previous approaches, however, require to manually annotate the clothing attributes and are therefore very costly. In this paper, we shall propose and explore a \emph{latent} clothing attribute approach for HPE. Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes. The inference of the latent variables are accomplished by utilizing the framework of latent structured support vector machines (LSSVM). We employ the strategy of \emph{alternating direction} to train the LSSVM model: In each iteration, one kind of variables (e.g., human pose or clothing attribute) are fixed and the others are optimized. Our extensive experiments on two real-world benchmarks show the state-of-the-art performance of our proposed approach.

cs.CV↗
arXiv2014

A Parallel and Efficient Algorithm for Learning to Match

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.

cs.LG↗
arXiv2014

Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications. Unlike previous works that usually handle the two problems separately, our approach aims to produce a jointly optimal estimation for both HPE and GAC via a unified inference procedure. To this end, we adopt a preprocessing step to detect potential human parts from each image (i.e., a set of "candidates") that allows us to have a manageable input space. In this way, the simultaneous inference of HPE and GAC is converted to a structured learning problem, where the inputs are the collections of candidate ensembles, the outputs are the joint labels of human parts and garment attributes, and the joint feature representation involves various cues such as pose-specific features, garment-specific features, and cross-task features that encode correlations between human parts and garment attributes. Furthermore, we explore the "strong edge" evidence around the potential human parts so as to derive more powerful representations for oriented human parts. Such evidences can be seamlessly integrated into our structured learning model as a kind of energy function, and the learning process could be performed by standard structured Support Vector Machines (SVM) algorithm. However, the joint structure of the two problems is a cyclic graph, which hinders efficient inference. To resolve this issue, we compute instead approximate optima by using an iterative procedure, where in each iteration the variables of one problem are fixed. In this way, satisfactory solutions can be efficiently computed by dynamic programming. Experimental results on two benchmark datasets show the state-of-the-art performance of our approach.

cs.CV↗
arXiv2013

Hot-electron refluxing enhanced relativistic transparency of overdense plasmas

A new phenomenon of enhancing the relativistic transparency of overdense plasmas by the influence of hot-electron refluxing has been found via particle-in-cell simulations. When a p-polarized laser pulse, with intensity below the self-induced-transparency (SIT) threshold, obliquely irradiates a thin overdense plasma, the initially opaque plasma would become transparent after a time interval which linearly relies on the thickness of the plasma. This phenomenon can be interpreted by the influence of hot-electron refluxing. As the laser intensity is higher than the SIT threshold, the penetration velocity of the laser in the plasma is enhanced when the refluxing is presented. Simulation data with ion motion considered is also consistent with the assumption that hot-electron refluxing enhances transparency. These results have potential applications in laser shaping.

physics.plasm-ph↗
arXiv2013

The LAMOST Survey of Background Quasars in the Vicinity of the Andromeda and Triangulum Galaxies -- II. Results from the Commissioning Observations and the Pilot Surveys

We present new quasars discovered in the vicinity of the Andromeda and Triangulum galaxies with the LAMOST during the 2010 and 2011 observational seasons. Quasar candidates are selected based on the available SDSS, KPNO 4 m telescope, XSTPS optical, and WISE near infrared photometric data. We present 509 new quasars discovered in a stripe of ~135 sq. deg from M31 to M33 along the Giant Stellar Stream in the 2011 pilot survey datasets, and also 17 new quasars discovered in an area of ~100 sq. deg that covers the central region and the southeastern halo of M31 in the 2010 commissioning datasets. These 526 new quasars have i magnitudes ranging from 15.5 to 20.0, redshifts from 0.1 to 3.2. They represent a significant increase of the number of identified quasars in the vicinity of M31 and M33. There are now 26, 62 and 139 known quasars in this region of the sky with i magnitudes brighter than 17.0, 17.5 and 18.0 respectively, of which 5, 20 and 75 are newly-discovered. These bright quasars provide an invaluable collection with which to probe the kinematics and chemistry of the ISM/IGM in the Local Group of galaxies. A total of 93 quasars are now known with locations within 2.5 deg of M31, of which 73 are newly discovered. Tens of quasars are now known to be located behind the Giant Stellar Stream, and hundreds behind the extended halo and its associated substructures of M31. The much enlarged sample of known quasars in the vicinity of M31 and M33 can potentially be utilized to construct a perfect astrometric reference frame to measure the minute PMs of M31 and M33, along with the PMs of substructures associated with the Local Group of galaxies. Those PMs are some of the most fundamental properties of the Local Group.

astro-ph.GA↗
arXiv2012

Learning the Gain Values and Discount Factors of DCG

Evaluation metrics are an essential part of a ranking system, and in the past many evaluation metrics have been proposed in information retrieval and Web search. Discounted Cumulated Gains (DCG) has emerged as one of the evaluation metrics widely adopted for evaluating the performance of ranking functions used in Web search. However, the two sets of parameters, gain values and discount factors, used in DCG are determined in a rather ad-hoc way. In this paper we first show that DCG is generally not coherent, meaning that comparing the performance of ranking functions using DCG very much depends on the particular gain values and discount factors used. We then propose a novel methodology that can learn the gain values and discount factors from user preferences over rankings. Numerical simulations illustrate the effectiveness of our proposed methods. Please contact the authors for the full version of this work.

cs.IR↗
arXiv2012

Robust Recovery of Subspace Structures by Low-Rank Representation

In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.

cs.IT↗
arXiv2011

Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.

cs.AI↗
arXiv2011

P-T phase diagram of iron arsenide superconductor NdFeAsO0.88F0.12

NdFeAsO0.88F0.12 belongs to the recently discovered family of high-TC iron-based superconductors. The influence of high pressure on transport properties of this material has been studied. Contrary to La-based compounds, we did not observe a maximum in TC under pressure. Under compression, TC drops rapidly as a linear function of pressure with the slope k = -2.8 \pm 0.1 K / GPa. The extrapolated value of TC at zero pressure is about TC (0) = 51.7 \pm 0.4 K. At pressures higher than ~18.4 GPa, the superconducting state disappears at all measured temperatures. The resistance changes slope and shows a turn-up behavior, which may be related to the Kondo effect or a weak localization of two-dimensional carriers below ~45 K that is above TC and thus competing with the superconducting phase. The behavior of the sample is completely reversible at the decompression. On the bases of our experimental data, we propose a tentative P-T phase diagram of NdFeAsO0.88F0.12.

cond-mat.str-el↗
arXiv2011

List version of ($p$,1)-total labellings

The ($p$,1)-total number $λ_p^T(G)$ of a graph $G$ is the width of the smallest range of integers that suffices to label the vertices and the edges of $G$ such that no two adjacent vertices have the same label, no two incident edges have the same label and the difference between the labels of a vertex and its incident edges is at least $p$. In this paper we consider the list version. Let $L(x)$ be a list of possible colors for all $x\in V(G)\cup E(G)$. Define $C_{p,1}^T(G)$ to be the smallest integer $k$ such that for every list assignment with $|L(x)|=k$ for all $x\in V(G)\cup E(G)$, $G$ has a ($p$,1)-total labelling $c$ such that $c(x)\in L(x)$ for all $x\in V(G)\cup E(G)$. We call $C_{p,1}^T(G)$ the ($p$,1)-total labelling choosability and $G$ is list $L$-($p$,1)-total labelable. In this paper, we present a conjecture on the upper bound of $C_{p,1}^T$. Furthermore, we study this parameter for paths and trees in Section 2. We also prove that $C_{p,1}^T(K_{1,n})\leq n+2p-1$ for star $K_{1,n}$ with $p\geq2, n\geq3$ in Section 3 and $C_{p,1}^T(G)\leq Δ+2p-1$ for outerplanar graph with $Δ\geq p+3$ in Section 4.

math.CO↗
arXiv2011

List (d,1)-total labelling of graphs embedded in surfaces

The (d,1)-total labelling of graphs was introduced by Havet and Yu. In this paper, we consider the list version of (d,1)-total labelling of graphs. Let G be a graph embedded in a surface with Euler characteristic $ε$ whose maximum degree $Δ(G)$ is sufficiently large. We prove that the (d,1)-total choosability $C_{d,1}^T(G)$ of $G$ is at most $Δ(G)+2d$.

math.CO↗
arXiv2007

Optimal transport on wireless networks

We present a study of the application of a variant of a recently introduced heuristic algorithm for the optimization of transport routes on complex networks to the problem of finding the optimal routes of communication between nodes on wireless networks. Our algorithm iteratively balances network traffic by minimizing the maximum node betweenness on the network. The variant we consider specifically accounts for the broadcast restrictions imposed by wireless communication by using a different betweenness measure. We compare the performance of our algorithm to two other known algorithms and find that our algorithm achieves the highest transport capacity both for minimum node degree geometric networks, which are directed geometric networks that model wireless communication networks, and for configuration model networks that are uncorrelated scale-free networks.

physics.soc-ph↗
arXiv2007

Transport optimization on complex networks

We present a comparative study of the application of a recently introduced heuristic algorithm to the optimization of transport on three major types of complex networks. The algorithm balances network traffic iteratively by minimizing the maximum node betweenness with as little path lengthening as possible. We show that by using this optimal routing, a network can sustain significantly higher traffic without jamming than in the case of shortest path routing. A formula is proved that allows quick computation of the average number of hops along the path and of the average travel times once the betweennesses of the nodes are computed. Using this formula, we show that routing optimization preserves the small-world character exhibited by networks under shortest path routing, and that it significantly reduces the average travel time on congested networks with only a negligible increase in the average travel time at low loads. Finally, we study the correlation between the weights of the links in the case of optimal routing and the betweennesses of the nodes connected by them.

cond-mat.dis-nn↗
arXiv2006

Optimal routing on complex networks

We present a novel heuristic algorithm for routing optimization on complex networks. Previously proposed routing optimization algorithms aim at avoiding or reducing link overload. Our algorithm balances traffic on a network by minimizing the maximum node betweenness with as little path lengthening as possible, thus being useful in cases when networks are jamming due to queuing overload. By using the resulting routing table, a network can sustain significantly higher traffic without jamming than in the case of traditional shortest path routing.

cond-mat.stat-mech↗
arXiv2006

Congestion-gradient driven transport on complex networks

We present a study of transport on complex networks with routing based on local information. Particles hop from one node of the network to another according to a set of routing rules with different degrees of congestion awareness, ranging from random diffusion to rigid congestion-gradient driven flow. Each node can be either source or destination for particles and all nodes have the same routing capacity, which are features of ad-hoc wireless networks. It is shown that the transport capacity increases when a small amount of congestion awareness is present in the routing rules, and that it then decreases as the routing rules become too rigid when the flow becomes strictly congestion-gradient driven. Therefore, an optimum value of the congestion awareness exists in the routing rules. It is also shown that, in the limit of a large number of nodes, networks using routing based on local information jam at any nonzero load. Finally, we study the correlation between congestion at node level and a betweenness centrality measure.

cond-mat.stat-mech↗