arXiv subjects

Jun Liu

Publications and source records attributed to Jun Liu.

At least 775 records · Page 43Linked to original sources

arXiv2018

Effelsberg Monitoring of a Sample of RadioAstron Blazars: Analysis of Intra-Day Variability

We present the first results of an ongoing intra-day variability (IDV) flux density monitoring program of 107 blazars, which were selected from a sample of RadioAstron space very long baseline interferometry (VLBI) targets. The~IDV observations were performed with the Effelsberg 100-m radio telescope at 4.8\,GHz, focusing on the statistical properties of IDV in a relatively large sample of compact active galactic nuclei (AGN). We investigated the dependence of rapid ($<$3 day) variability on various source properties through a likelihood approach. We found that the IDV amplitude depends on flux density and that fainter sources vary by about a factor of 3 more than their brighter counterparts. We also found a significant difference in the variability amplitude between inverted- and flat-spectrum radio sources, with the former exhibiting stronger variations. $γ$-ray loud sources were found to vary by up to a factor 4 more than $γ$-ray quiet ones, with 4$σ$ significance. However a galactic latitude dependence was barely observed, which suggests that it is predominantly the intrinsic properties (e.g., angular size, core-dominance) of the blazars that determine how they scintillate, rather than the directional dependence in the interstellar medium (ISM). We showed that the uncertainty in the VLBI brightness temperatures obtained from the space VLBI data of the RadioAstron satellite can be as high as $\sim$70\% due to the presence of the rapid flux density variations. Our statistical results support the view that IDV at centimeter wavelengths is predominantly caused by interstellar scintillation (ISS) of the emission from the most compact, core-dominant region in an AGN.

astro-ph.GA↗

arXiv2018

Dual Spaces of Anisotropic Mixed-Norm Hardy Spaces

Let $\vec{a}:=(a_1,\ldots,a_n)\in[1,\infty)^n$, $\vec{p}:=(p_1,\ldots,p_n)\in(0,\infty)^n$ and $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ be the anisotropic mixed-norm Hardy space associated with $\vec{a}$ defined via the non-tangential grand maximal function. In this article, the authors give the dual space of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$, which was asked by Cleanthous et al. in [J. Geom. Anal. 27 (2017), 2758-2787]. More precisely, via first introducing the anisotropic mixed-norm Campanato space $\mathcal{L}_{\vec{p},\,q,\,s}^{\vec{a}}(\mathbb{R}^n)$ with $q\in[1,\infty]$ and $s\in\mathbb{Z}_+:=\{0,1,\ldots\}$, and applying the known atomic and finite atomic characterizations of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$, the authors prove that the dual space of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ is the space $\mathcal{L}_{\vec{p},\,r',\,s}^{\vec{a}}(\mathbb{R}^n)$ with $\vec{p}\in(0,1]^n$, $r\in(1,\infty]$, $1/r+1/r'=1$ and $s\in[\lfloor\fracν{a_-}(\frac{1}{p_-}-1) \rfloor,\infty)\cap\mathbb{Z}_+$, where $ν:=a_1+\cdots+a_n$, $a_-:=\min\{a_1,\ldots,a_n\}$, $p_-:=\min\{p_1,\ldots,p_n\}$ and, for any $t\in \mathbb{R}$, $\lfloor t\rfloor$ denotes the largest integer not greater than $t$. This duality result is new even for the isotropic mixed-norm Hardy spaces on $\mathbb{R}^n$.

math.CA↗

arXiv2018

Robust Abstractions for Control Synthesis: Robustness Equals Realizability for Linear-Time Properties

We define robust abstractions for synthesizing provably correct and robust controllers for (possibly infinite) uncertain transition systems. It is shown that robust abstractions are sound in the sense that they preserve robust satisfaction of linear-time properties. We then focus on discrete-time control systems modelled by nonlinear difference equations with inputs and define concrete robust abstractions for them. While most abstraction techniques in the literature for nonlinear systems focus on constructing sound abstractions, we present computational procedures for constructing both sound and approximately complete robust abstractions for general nonlinear control systems without stability assumptions. Such procedures are approximately complete in the sense that, given a concrete discrete-time control system and an arbitrarily small perturbation of this system, there exists a finite transition system that robustly abstracts the concrete system and is abstracted by the slightly perturbed system simultaneously. A direct consequence of this result is that robust control synthesis for discrete-time nonlinear systems and linear-time specifications is robustly decidable. More specifically, if there exists a robust control strategy that realizes a given linear-time specification, we can algorithmically construct a (potentially less) robust control strategy that realizes the same specification. The theoretical results are illustrated with a simple motion planning example.

eess.SY↗

arXiv2018

Atomic and Littlewood-Paley Characterizations of Anisotropic Mixed-Norm Hardy Spaces and Their Applications

Let $\vec{a}:=(a_1,\ldots,a_n)\in[1,\infty)^n$, $\vec{p}:=(p_1,\ldots,p_n)\in(0,\infty)^n$ and $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ be the anisotropic mixed-norm Hardy space associated with $\vec{a}$ defined via the non-tangential grand maximal function. In this article, via first establishing a Calderón-Zygmund decomposition and a discrete Calderón reproducing formula, the authors then characterize $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$, respectively, by means of atoms, the Lusin area function, the Littlewood-Paley $g$-function or $g_λ^\ast$-function. The obtained Littlewood-Paley $g$-function characterization of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ coincidentally confirms a conjecture proposed by Hart et al. [Trans. Amer. Math. Soc. (2017), DOI: 10.1090/tran/7312]. Applying the aforementioned Calderón-Zygmund decomposition as well as the atomic characterization of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$, the authors establish a finite atomic characterization of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$, which further induces a criterion on the boundedness of sublinear operators from $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ into a quasi-Banach space. Then, applying this criterion, the authors obtain the boundedness of anisotropic Calderón-Zygmund operators from $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ to itself [or to $L^{\vec{p}}(\mathbb{R}^n)$]. The obtained atomic characterizations of $H_{\vec{a}}^{\vec{p}}(\mathbb{R}^n)$ and boundedness of anisotropic Calderón-Zygmund operators on these Hardy-type spaces positively answer two questions mentioned by Cleanthous et al. in [J. Geom. Anal. 27 (2017), 2758-2787]. All these results are new even for the isotropic mixed-norm Hardy spaces on $\mathbb{R}^n$.

math.CA↗

arXiv2018

Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

cs.CV↗

arXiv2017

Convergence rate for a Gauss collocation method applied to constrained optimal control

A local convergence rate is established for a Gauss orthogonal collocation method applied to optimal control problems with control constraints. If the Hamiltonian possesses a strong convexity property, then the theory yields convergence for problems whose optimal state and costate possess two square integrable derivatives. The convergence theory is based on a stability result for the sup-norm change in the solution of a variational inequality relative to a 2-norm perturbation, and on a Sobolev space bound for the error in interpolation at the Gauss quadrature points and the additional point -1. The tightness of the convergence theory is examined using a numerical example.

math.NA↗

arXiv2017

Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection

With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D based human detection. Our method achieves superior performance on two publicly available datasets.

cs.CV↗

arXiv2017

Modified multi-dimensional limiting process with enhanced shock stability on unstructured grids

The basic concept of multi-dimensional limiting process (MLP) on unstructured grids is inherited and modified for improving shock stabilities and reducing numerical dissipation on smooth regions. A relaxed version of MLP condition, simply named as weak-MLP, is proposed for reducing dissipation. Moreover, a stricter condition, that is the strict-MLP condition, is proposed to enhance the numerical stability. The maximum/minimum principle is fulfilled by both the strict- and weak-MLP condition. A differentiable pressure weight function is applied for the combination of two novel conditions, and thus the modified limiter is named as MLP-pw(pressure-weighted). A series of numerical test cases show that MLP-pw limiter has improved stability and convergence, especially in hypersonic simulations. Furthermore, the limiter also shows lower dissipation in regions without significant pressure transition. Therefore, MLP-pw limiter can capture contact discontinuity and expansion accurately.

math.NA↗

arXiv2017

Elastic and electronic tuning of magnetoresistance in MoTe$_2$

Quasi-two dimensional transition metal dichalcogenides (TMD) exhibit dramatic properties that may transform electronic and photonic devices. We report on how the anomalously large magnetoresistance (MR) observed under high magnetic field in MoTe$_2$, a type II Weyl semimetal, can be reversibly controlled under tensile strain. The MR is enhanced by as much as ~ 30 % at low temperatures and high magnetic fields, when uniaxial strain is applied along the $a$-crystallographic direction and reduced by about the same amount when strain is applied along the $b$-direction. We show that the large in-plane electric anisotropy is coupled with the structural transition from the 1T' monoclinic to the Td orthorhombic Weyl phase. A shift of the Td - 1T' phase boundary is achieved by minimal tensile strain. The sensitivity of the MR to tensile strain suggests the possibility of a nontrivial spin-orbital texture of the electron and hole pockets in the vicinity of Weyl points. Our ab initio calculations indeed show a significant orbital mixing on the Fermi surface, which is modified by the tensile strains.

cond-mat.mtrl-sci↗

arXiv2017

Efficient Time Domain Decomposition Algorithms for Parabolic PDE-Constrained Optimization Problems

Optimization with time-dependent partial differential equations (PDEs) as constraints {appears} in many science and engineering applications. The associated first-order necessary optimality system consists of one forward and one backward time-dependent PDE coupled with optimality conditions. An optimization process by using the one-shot method determines the optimal control, state and adjoint state at once, with the cost of solving a large scale, fully discrete optimality system. Hence, such {a} one-shot method could easily become computationally prohibitive when the time span is long or time step is small. To overcome this difficulty, we propose several time domain decomposition algorithms for improving the {computational efficiency of the one-shot method}. In these algorithms, the optimality system is split into many small subsystems over a much smaller time interval, which are coupled by appropriate continuity matching conditions. Both one-level and two-level multiplicative and additive Schwarz algorithms are developed for iteratively solving the decomposed subsystems in parallel. In particular, the convergence of the one-level, non-overlapping algorithms is proved. The effectiveness of our proposed algorithms is demonstrated by both 1D and 2D numerical experiments, where the developed two-level algorithms {show convergence rates that are scalable} with respect to the number of subdomains.

math.NA↗

arXiv2017

On the Powerball Method for Optimization

We propose a new method to accelerate the convergence of optimization algorithms. This method simply adds a power coefficient $γ\in[0,1)$ to the gradient during optimization. We call this the Powerball method and analyze the convergence rate for the Powerball method for strongly convex functions. While theoretically the Powerball method is guaranteed to have a linear convergence rate in the same order of the gradient method, we show that empirically it significantly outperforms the gradient descent and Newton's method, especially during the initial iterations. We demonstrate that the Powerball method provides a $10$-fold speedup of the convergence of both gradient descent and L-BFGS on multiple real datasets.

eess.SY↗

arXiv2017

PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

cs.DB↗

arXiv2017

Chlorine and Bromine Isotope Fractionation of Halogenated Organic Compounds in Electron Ionization Mass Spectrometry

Revelation of chlorine and bromine isotope fractionation of halogenated organic compounds (HOCs) in electron ionization mass spectrometry (EI-MS) is crucial for compound-specific chlorine/bromine isotope analysis (CSIA-Cl/Br) using gas chromatography EI-MS (GC-EI-MS). This study systematically investigated chlorine/bromine isotope fractionation in EI-MS of HOCs including 12 organochlorines and 5 organobromines using GC-double focus magnetic-sector high resolution MS (GC-DFS-HRMS). Chlorine/bromine isotope fractionation behaviors of the HOCs in EI-MS showed varied isotope fractionation patterns and extents depending on compounds. Besides, isotope fractionation patterns and extents varied at different EI energies, demonstrating potential impacts of EI energy on the chlorine/bromine isotope fractionation. Hypotheses of inter-ion and intra-ion isotope fractionations were applied to interpreting the isotope fractionation behaviors. The inter-ion and intra-ion isotope fractionations counteractively contributed to the apparent isotope ratio for a certain dehalogenated product ion. The isotope fractionation mechanisms were tentatively elucidated on basis of the quasi-equilibrium theory. In the light of the findings of this study, isotope ratio evaluation scheme using complete molecular ions and the EI source with sufficient stable EI energies may be helpful to achieve optimal precision and accuracy of CSIA-Cl/Br data. The method and results of this study can help to predict isotope fractionation of HOCs during dehalogenation processes and further to reveal the dehalogenation pathways.

physics.chem-ph↗

arXiv2017

Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates

Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2017

Anisotropic Variable Hardy-Lorentz Spaces and Their Real Interpolation

Let $p(\cdot):\ \mathbb R^n\to(0,\infty)$ be a variable exponent function satisfying the globally log-Hölder continuous condition, $q\in(0,\infty]$ and $A$ be a general expansive matrix on $\mathbb{R}^n$. In this article, the authors first introduce the anisotropic variable Hardy-Lorentz space $H_A^{p(\cdot),q}(\mathbb R^n)$ associated with $A$, via the radial grand maximal function, and then establish its radial or non-tangential maximal function characterizations. Moreover, the authors also obtain characterizations of $H_A^{p(\cdot),q}(\mathbb R^n)$, respectively, in terms of the atom and the Lusin area function. As an application, the authors prove that the anisotropic variable Hardy-Lorentz space $H_A^{p(\cdot),q}(\mathbb R^n)$ severs as the intermediate space between the anisotropic variable Hardy space $H_A^{p(\cdot)}(\mathbb R^n)$ and the space $L^\infty(\mathbb R^n)$ via the real interpolation. This, together with a special case of the real interpolation theorem of H. Kempka and J. Vybíral on the variable Lorentz space, further implies the coincidence between $H_A^{p(\cdot),q}(\mathbb R^n)$ and the variable Lorentz space $L^{p(\cdot),q}(\mathbb R^n)$ when $\mathop\mathrm{essinf}_{x\in\mathbb{R}^n}p(x)\in (1,\infty)$.

math.CA↗

arXiv2017

TruthDiscover: Resolving Object Conflicts on Massive Linked Data

Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existing methods cannot be trivially extended to resolve conflicts in Linked Data because Linked Data has a scale-free property. In this demonstration, we present a novel system called TruthDiscover, to identify the truth in Linked Data with a scale-free property. First, TruthDiscover leverages the topological properties of the Source Belief Graph to estimate the priori beliefs of sources, which are utilized to smooth the trustworthiness of sources. Second, the Hidden Markov Random Field is utilized to model interdependencies among objects for estimating the trust values of objects accurately. TruthDiscover can visualize the process of resolving conflicts in Linked Data. Experiments results on four datasets show that TruthDiscover exhibits satisfactory accuracy when confronted with data having a scale-free property.

cs.DB↗

arXiv2017

Exploiting Source-Object Network to Resolve Object Conflicts in Linked Data

Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple different sources often provide conflicting objects for a certain predicate of the same real-world entity, so-called object conflicts problem. Currently, the object conflicts problem has not received sufficient attention in the Linked Data community. In this paper, we first formalize the object conflicts resolution problem as computing the joint distribution of variables on a heterogeneous information network called the Source-Object Network, which successfully captures the all correlations from objects and Linked Data sources. Then, we introduce a novel approach based on network effects called ObResolution(Object Resolution), to identify a true object from multiple conflicting objects. ObResolution adopts a pairwise Markov Random Field (pMRF) to model all evidences under a unified framework. Extensive experimental results on six real-world datasets show that our method exhibits higher accuracy than existing approaches and it is robust and consistent in various domains. \keywords{Linked Data, Object Conflicts, Linked Data Quality, Truth Discovery

cs.DB↗

arXiv2017

A Procedural Texture Generation Framework Based on Semantic Descriptions

Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as "regular," "lacelike," and "repetitive" and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.

cs.CV↗