arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

At least 847 records · Page 47Linked to original sources

arXiv2017

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as \textit{early rumor detection}, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model on the basis of recurrent neural networks (RNN) to learn \textit{selectively} temporal hidden representations of sequential posts for identifying rumors. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features; (2) the introduction of soft attention mechanism can effectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.

cs.CL↗

arXiv2017

Relativistic full-potential multiple scattering theory: An ab initio method and its applications

The Green function plays an essential role in the Kohn-Korringa-Rostocker (KKR) multiple scattering method. In practice, it is constructed from the regular and irregular solutions of the local Kohn-Sham equation and robust methods exist for spherical potentials. However, when applied to a non-spherical potential, numerical errors from the irregular solutions give rise to pathological behaviors of the charge density at small radius. Here we present a full-potential implementation of the relativistic KKR method to perform \textit{ab initio} self-consistent calculation by directly solving the Dirac differential equations. The pathology around the origin is completely eliminated with the help of an efficient pole-searching technique. This method is utilized to investigate the crystal structures of polonium and their bulk properties. The noble metals are also calculated, both as a test of our method and to study the relativistic effects.

cond-mat.mtrl-sci↗

arXiv2017

Finding Modes by Probabilistic Hypergraphs Shifting

In this paper, we develop a novel paradigm, namely hypergraph shift, to find robust graph modes by probabilistic voting strategy, which are semantically sound besides the self-cohesiveness requirement in forming graph modes. Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes. Specifically, we convert the problem of seeking graph modes as the problem of seeking maximizers of a novel objective function with the aim to generate good graph modes based on sifting edges in hypergraphs. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the object semantics besides the self-cohesiveness requirement. We also formally prove that our technique is always convergent. Extensive empirical studies on synthetic and real world data sets are conducted on clustering and graph matching. They demonstrate that our techniques significantly outperform the existing techniques.

cs.AI↗

arXiv2017

Label Refinement Network for Coarse-to-Fine Semantic Segmentation

We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. We define loss functions at several stages in the network to provide supervisions at different stages. Our experimental results on several standard datasets demonstrate that the proposed model provides an effective way of producing pixel-wise dense image labeling.

cs.CV↗

arXiv2017

Stochastic Patching Process

Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions. To alleviate this limitation, we propose a parsimonious partition model, named Stochastic Patching Process (SPP), to deal with multi-dimensional arrays. SPP adopts an "enclosing" strategy to attach rectangular patches to dense regions. SPP is self-consistent such that it can be extended to infinite arrays. We apply SPP to relational modeling and the experimental results validate its merit compared to the state-of-the-arts.

cs.AI↗

arXiv2017

Evolution-Preserving Dense Trajectory Descriptors

Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets. This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a novel type of video descriptor that significantly outperforms Trajectory-pooled Deep-learning Descriptors. EPT descriptors are defined based on dense trajectories, and they provide complimentary benefits to video descriptors that are not based on trajectories. In particular, we show that the combination of EPT descriptors and VideoDarwin leads to state-of-the-art performance on Hollywood2 and UCF101 datasets.

cs.CV↗

arXiv2017

Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results. In fact, dealing with the landmarks with \illshapes caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper we propose a novel framework, namely multi-query expansions, to retrieve semantically robust landmarks by two steps. Firstly, we identify the top-$k$ photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible \illshape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical \emph{collaborative filtering} methods, we propose to learn a \emph{collaborative} deep networks based semantically, nonlinear and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over \emph{collaborative} user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods.

cs.CV↗

arXiv2017

Testing reanalysis datasets in Antarctica: Trends, persistence properties and trend significance

The reanalysis datasets provide very important sources for investigating the climate dynamics and climate changes in Antarctica. In this paper, three major reanalysis data are compared with Antarctic station data over the last 35 years: the National Centers for Environmental Prediction and the National Center for Atmospheric Research reanalysis (NCEP1), NCEP-DOE Reanalysis 2 (NCEP2), and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim). In our assessment, we compare the linear trends, the fluctuations around the trends, the persistence properties and the significance level of warming trends in the reanalysis data with the observational ones. We find that NCEP1 and NCEP2 show spurious warming trends in all parts of Antarctica except the Peninsula, while ERA-Interim is quite reliable except at Amundsen-Scott. To investigate the persistence of the data sets, we consider the lag-1 autocorrelation $C(1)$ and the Hurst exponent. While $C(1)$ varies quite erratically in different stations, the Hurst exponent shows similar patterns all over Antarctica. Regarding the significance of the trends, NCEP1 and NCEP2 differ considerably from the observational datasets by strongly exaggerating the warming trends. In contrast, ERA-Interim gives reliable results at most stations except at Amundsen-Scott where it shows a significant cooling trend.

physics.ao-ph↗

arXiv2016

Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions

Learning hash functions/codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; 3) they often incur cumbersome training model caused by the neighborhood graph construction using all $N$ points in the database ($O(N)$). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. To achieve both the robustness and training efficiency, we propose an approach to effectively and efficiently learning low-rank kernelized \footnote{We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix.} hash functions shared across views. Specifically, we utilize landmark graphs to construct tractable similarity matrices in multi-views to automatically discover neighborhood structure in the data. To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data. In particular, a latent kernelized similarity matrix is recovered by rank minimization on multiple kernel-based similarity matrices. Extensive experiments on real-world multi-view datasets validate the efficacy of our method in the presence of error corruptions.

cs.LG↗

arXiv2016

PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers

Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing. This work presents a contrasting perspective: customizing numerical optimization specifically for secure settings. We propose a seemingly less-favorable optimization method that can in fact significantly accelerate privacy-preserving logistic regression. Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner. Extensive theoretical and empirical evaluations prove the competitive performance of our two secure proposals while without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x, respectively, over state-of-the-art; and even faster as data scales up. Such drastic speedup is on top of and in addition to performance improvements from existing (and future) state-of-the-art cryptography. Our work provides a new way towards efficient and practical privacy-preserving logistic regression for large-scale studies which are common for modern science.

cs.LG↗

arXiv2016

The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. The dichotomous trade-off between scalability and efficiency (SE) on the one hand and maintainability and portability (MP) on the other hand is one of the core challenges. We report on the SE/MP trade-off in the context of an ongoing systematic literature review (SLR). After characterizing the issue of the SE/MP trade-off using two examples from our own research, we (1) review the 33 identified articles that assess the trade-off, (2) summarize the proposed solutions for the trade-off, and (3) discuss the findings for SSE and future work. Overall, we see evidence for the SE/MP trade-off and first solution approaches. However, a strong empirical foundation has yet to be established; general quantitative metrics and methods supporting software developers in addressing the trade-off have to be developed. We foresee considerable future work in SSE across scientific communities.

cs.SE↗

arXiv2016

Electronic structure of the ingredient planes of cuprate superconductor Bi2Sr2CuO6+δ: a comparison study with Bi2Sr2CaCu2O8+δ

By means of low-temperature scanning tunneling microscopy, we report on the electronic structures of BiO and SrO planes of Bi2Sr2CuO6+δ (Bi-2201) superconductor prepared by argon-ion bombardment and annealing. Depending on post annealing conditions, the BiO planes exhibit either pseudogap (PG) with sharp coherence peaks and an anomalously large gap of 49 meV or van Hove singularity (VHS) near the Fermi level, while the SrO is always characteristic of a PG-like feature. This contrasts with Bi2Sr2CaCu2O8+δ (Bi-2212) superconductor where VHS occurs solely on the SrO plane. We disclose the interstitial oxygen dopants (δ in the formulas) as a primary cause for the occurrence of VHS, which are located dominantly around the BiO and SrO planes, respectively, in Bi-2201 and Bi-2212. This is supported by the contrasting structural buckling amplitude of BiO and SrO planes in the two superconductors. Our findings provide solid evidence for the irrelevance of PG to the superconductivity in the two superconductors, as well as insights into why Bi-2212 can achieve a higher superconducting transition temperature than Bi-2201, and by implication, the mechanism of cuprate superconductivity.

cond-mat.supr-con↗

arXiv2016

Phase Retrieval From the Magnitudes of Affine Linear Measurements

In this paper, we consider the phase retrieval problem in which one aims to recover a signal from the magnitudes of affine measurements. Let $\{{\mathbf a}_j\}_{j=1}^m \subset {\mathbb H}^d$ and ${\mathbf b}=(b_1, \ldots, b_m)^\top\in{\mathbb H}^m$, where ${\mathbb H}={\mathbb R}$ or ${\mathbb C}$. We say $\{{\mathbf a}_j\}_{j=1}^m$ and $\mathbf b$ are affine phase retrievable for ${\mathbb H}^d$ if any ${\mathbf x}\in{\mathbb H}^d$ can be recovered from the magnitudes of the affine measurements $\{|<{\mathbf a}_j,{\mathbf x}>+b_j|,\, 1\leq j\leq m\}$. We develop general framework for affine phase retrieval and prove necessary and sufficient conditions for $\{{\mathbf a}_j\}_{j=1}^m$ and $\mathbf b$ to be affine phase retrievable. We establish results on minimal measurements and generic measurements for affine phase retrieval as well as on sparse affine phase retrieval. In particular, we also highlight some notable differences between affine phase retrieval and the standard phase retrieval in which one aims to recover a signal $\mathbf x$ from the magnitudes of its linear measurements. In standard phase retrieval, one can only recover $\mathbf x$ up to a unimodular constant, while affine phase retrieval removes this ambiguity. We prove that unlike standard phase retrieval, the affine phase retrievable measurements $\{{\mathbf a}_j\}_{j=1}^m$ and $\mathbf b$ do not form an open set in ${\mathbb H}^{m\times d}\times {\mathbb H}^m$. Also in the complex setting, the standard phase retrieval requires $4d-O(\log_2d)$ measurements, while the affine phase retrieval only needs $m=3d$ measurements.

cs.IT↗

arXiv2016

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.

cs.LG↗

arXiv2016

TaskMe: Multi-Task Allocation in Mobile Crowd Sensing

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).

cs.HC↗

arXiv2016

Nodeless pairing in superconducting copper-oxide monolayer films on Bi2Sr2CaCu2O8+δ

The pairing mechanism of high-temperature superconductivity in cuprates remains the biggest unresolved mystery in condensed matter physics. To solve the problem, one of the most effective approaches is to investigate directly the superconducting CuO2 layers. Here, by growing CuO2 monolayer films on Bi2Sr2CaCu2O8+δ substrates, we identify two distinct and spatially separated energy gaps centered at the Fermi energy, a smaller U-like gap and a larger V-like gap on the films, and study their interactions with alien atoms by low-temperature scanning tunneling microscopy. The newly discovered U-like gap exhibits strong phase coherence and is immune to scattering by K, Cs and Ag atoms, suggesting its nature as a nodeless superconducting gap in the CuO2 layers, whereas the V-like gap agrees with the well-known pseudogap state in the underdoped regime. Our results support an s-wave superconductivity in Bi2Sr2CaCu2O8+δ, which, we propose, originates from the modulation-doping resultant two-dimensional hole liquid confined in the CuO2 layers.

cond-mat.supr-con↗

arXiv2016

Fast Phase Retrieval from Local Correlation Measurements

We develop a fast phase retrieval method which can utilize a large class of local phaseless correlation-based measurements in order to recover a given signal ${\bf x} \in \mathbb{C}^d$ (up to an unknown global phase) in near-linear $\mathcal{O} \left( d \log^4 d \right)$-time. Accompanying theoretical analysis proves that the proposed algorithm is guaranteed to deterministically recover all signals ${\bf x}$ satisfying a natural flatness (i.e., non-sparsity) condition for a particular choice of deterministic correlation-based measurements. A randomized version of these same measurements is then shown to provide nonuniform probabilistic recovery guarantees for arbitrary signals ${\bf x} \in \mathbb{C}^d$. Numerical experiments demonstrate the method's speed, accuracy, and robustness in practice -- all code is made publicly available. Finally, we conclude by developing an extension of the proposed method to the sparse phase retrieval problem; specifically, we demonstrate a sublinear-time compressive phase retrieval algorithm which is guaranteed to recover a given $s$-sparse vector ${\bf x} \in \mathbb{C}^d$ with high probability in just $\mathcal{O}(s \log^5 s \cdot \log d)$-time using only $\mathcal{O}(s \log^4 s \cdot \log d)$ magnitude measurements. In doing so we demonstrate the existence of compressive phase retrieval algorithms with near-optimal linear-in-sparsity runtime complexities.

math.NA↗

arXiv2016

Detector-decoy high-dimensional quantum key distribution

The decoy-state high-dimensional quantum key distribution provides a practical secure way to share more private information with high photon-information efficiency. In this paper, based on detector-decoy method, we propose a detector-decoy high-dimensional quantum key distribution protocol. Employing threshold detectors and a variable attenuator, we can estimate single-photon fraction of postselected events and Eves Holevo information under the Gaussian collective attack with much simpler operations in practical implementation. By numerical evaluation, we show that without varying source intensity and optimizing decoy-state intensity, our protocol could perform much better than one-decoy-state protocol and as well as the two-decoy-state protocol. Specially, when the detector efficiency is lower, the advantage of the detector-decoy method becomes more prominent.

quant-ph↗