arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 1,045 records · Page 58Linked to original sources

arXiv2019

Frequency response of time-delay interferometry for space-based gravitational wave antennas

Space-based gravitational wave detectors cannot keep rigid structures and precise arm length equality, so the precise equality of detector arms which is required in a ground-based interferometer to cancel the overwhelming laser noise is impossible. The time-delay interferometry method is applied to unequal arm lengths to cancel the laser frequency noise. We give analytical formulas of the averaged response functions for tensor, vector, breathing and longitudinal polarizations in different TDI combinations, and obtain their asymptotic behaviors. At low frequencies, $f\ll f_*$, the averaged response functions of all TDI combinations increase as $f^2$ for all six polarizations. The one exception is the averaged response functions of $ζ$ for all six polarizations increase as $f^4$ in the equilateral-triangle case. At high frequencies, $f\gg f_*$, the averaged response functions of all TDI combinations for the tensor and breathing modes fall off as $1/f^2$, the averaged response functions of all TDI combinations for the vector mode fall off as $\ln(f)/f^2$ , and the averaged response functions of all TDI combinations for the longitudinal mode fall as $1/f$. We also give LISA and TianQin sensitivity curves in different TDI combinations for tensor, vector, breathing and longitudinal polarizations.

gr-qc↗

arXiv2019

Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction

Mixed Integer Programming (MIP) is one of the most widely used modeling techniques for combinatorial optimization problems. In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients. This offers the opportunity for machine learning methods to explore the correlations between model structures and the resulting solution values. To address this issue, we propose to represent an MIP instance using a tripartite graph, based on which a Graph Convolutional Network (GCN) is constructed to predict solution values for binary variables. The predicted solutions are used to generate a local branching type cut which can be either treated as a global (invalid) inequality in the formulation resulting in a heuristic approach to solve the MIP, or as a root branching rule resulting in an exact approach. Computational evaluations on 8 distinct types of MIP problems show that the proposed framework improves the primal solution finding performance significantly on a state-of-the-art open-source MIP solver.

cs.AI↗

arXiv2019

Phase diagrams of the disordered Bose-Hubbard model with cavity-mediated long-range and nearest-neighbor interactions

Recent experiments with ultracold atoms in an optical lattice have realized cavity-mediated long-range interaction and observed the emergence of a supersolid phase and a density wave phase in addition to Mott insulator and superfluid phases. Here we consider theoretically the effect of uncorrelated disorder on the phase diagram of this system and study the two-dimensional Bose-Hubbard model with cavity-mediated long-range interactions and uncorrelated diagonal disorder. We also study the phase diagram of the extended Bose-Hubbard model with nearest-neighbor interactions in the presence of uncorrelated diagonal disorder. The extended Bose-Hubbard model with nearest-neighbor interactions has been realized in the experiment using dipolar interaction recently. With the help of quantum Monte Carlo simulations using the worm algorithm, we determine the phase diagram of those two models. We compare the phase diagrams of cavity-mediated long-range interactions with nearest-neighbor interactions. We show that two kinds of Bose glass phases exist: one with and one without density wave order. We also find that weak disorder enhances the supersolid phase.

cond-mat.dis-nn↗

arXiv2019

Engineering Deep Representations for Modeling Aesthetic Perception

Many aesthetic models in computer vision suffer from two shortcomings: 1) the low descriptiveness and interpretability of those hand-crafted aesthetic criteria (i.e., nonindicative of region-level aesthetics), and 2) the difficulty of engineering aesthetic features adaptively and automatically toward different image sets. To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting. More specifically, using a bag-ofwords (BoW) representation of the frequent Flickr image tags, a sparsity-constrained subspace algorithm discovers a compact set of textual attributes (e.g., landscape and sunset) for each image. Then, a weakly-supervised learning algorithm projects the textual attributes at image-level to the highly-responsive image patches at pixel-level. These patches indicate where humans look at appealing regions with respect to each textual attribute, which are employed to learn the visual attributes. Psychological and anatomical studies have shown that humans perceive visual concepts hierarchically. Hence, we normalize these patches and feed them into a five-layer convolutional neural network (CNN) to mimick the hierarchy of human perceiving the visual attributes. We apply the learned deep features on image retargeting, aesthetics ranking, and retrieval. Both subjective and objective experimental results thoroughly demonstrate the competitiveness of our approach.

cs.CV↗

arXiv2019

Boundedness of differential transforms for one-sided fractional Poisson-type operator sequence

In this paper, we analyze the convergence speed of a series related with $\mathcal{P}_τ^αf$ by discussing the behavior of the family of operators \begin{equation*} T_N^αf(t) = \sum_{j=N_1}^{N_2} v_j(\mathcal{P}_{a_{j+1}}^αf(t)-\mathcal{P}_{a_j}^αf(t)),\quad ~N=(N_1,N_2)\in \mathbb{Z}^2\quad \hbox{with} \quad N_1<N_2, \end{equation*} where $\{v_j\}_{j\in \mathbb Z}$ is a bounded number sequence, and $\{a_j\}_{j\in \mathbb{Z}}$ is a $ρ$-lacunary sequence of positive numbers, that is, $1<ρ\leq a_{j+1}/a_j, \text{for all}\ j\in \mathbb{Z}.$ We shall show the boundedness of the maximal operator \begin{equation*}T^*f(t)=\sup_N |T_N^αf(t)|, \quad t\in\mathbb{R}, \end{equation*} in the one-sided weighted Lebesgue spaces $L^p(\mathbb{R},ω)(ω\in A_p^-$), $1< p < \infty$. As a consequence we infer the existence of the limit, in norm and almost everywhere, of the family $T_N^αf$ for functions in $L^p(\mathbb{R},ω)$. Results for $L^1(\mathbb{R},ω)(ω\in A_1^-)$, $L^\infty(\mathbb{R})$ and $BMO(\mathbb{R})$ are also obtained. It is also shown that the local size of $T^*f$, for functions $f$ having local support, is the same with the order of a singular integral. Moreover, if $\{v_j\}_{j\in \mathbb Z}\in \ell^p(\mathbb Z)$, we get an intermediate size between the local size of singular integrals and Hardy-Littlewood maximal operator.

math.CA↗

arXiv2019

Interlaced Sparse Self-Attention for Semantic Segmentation

In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main idea is that we factorize the dense affinity matrix as the product of two sparse affinity matrices. There are two successive attention modules each estimating a sparse affinity matrix. The first attention module is used to estimate the affinities within a subset of positions that have long spatial interval distances and the second attention module is used to estimate the affinities within a subset of positions that have short spatial interval distances. These two attention modules are designed so that each position is able to receive the information from all the other positions. In contrast to the original self-attention module, our approach decreases the computation and memory complexity substantially especially when processing high-resolution feature maps. We empirically verify the effectiveness of our approach on six challenging semantic segmentation benchmarks.

cs.CV↗

arXiv2019

Orientation-aware Semantic Segmentation on Icosahedron Spheres

We address semantic segmentation on omnidirectional images, to leverage a holistic understanding of the surrounding scene for applications like autonomous driving systems. For the spherical domain, several methods recently adopt an icosahedron mesh, but systems are typically rotation invariant or require significant memory and parameters, thus enabling execution only at very low resolutions. In our work, we propose an orientation-aware CNN framework for the icosahedron mesh. Our representation allows for fast network operations, as our design simplifies to standard network operations of classical CNNs, but under consideration of north-aligned kernel convolutions for features on the sphere. We implement our representation and demonstrate its memory efficiency up-to a level-8 resolution mesh (equivalent to 640 x 1024 equirectangular images). Finally, since our kernels operate on the tangent of the sphere, standard feature weights, pretrained on perspective data, can be directly transferred with only small need for weight refinement. In our evaluation our orientation-aware CNN becomes a new state of the art for the recent 2D3DS dataset, and our Omni-SYNTHIA version of SYNTHIA. Rotation invariant classification and segmentation tasks are additionally presented for comparison to prior art.

cs.CV↗

arXiv2019

Distributed Power Control with Partial Channel State Information: Performance Characterization and Design

One of the goals of this paper is to contribute to finding distributed power control strategies which exploit efficiently the information available about the global channel state; it may be local or noisy. A suited way of measuring the global efficiency of a distributed power control scheme is to use the long-term utility region. First, we provide the utility region characterization for general utility functions when the channel state obeys an independent block fading law and the observation structure is memoryless. Second, the corresponding theorem is exploited to construct an iterative algorithm which provides one-shot power control strategies. The performance of the proposed algorithm is assessed for energy-efficient and spectrally efficient communications and shown to perform much better than state-of-the-art techniques, with the additional advantage of being applicable even in the presence of arbitrary observation structures such as those corresponding to noisy channel gain estimates.

cs.IT↗

arXiv2019

Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment

We propose a novel multi-scale template matching method which is robust against both scaling and rotation in unconstrained environments. The key component behind is a similarity measure referred to as scalable diversity similarity (SDS). Specifically, SDS exploits bidirectional diversity of the nearest neighbor (NN) matches between two sets of points. To address the scale-robustness of the similarity measure, local appearance and rank information are jointly used for the NN search. Furthermore, by introducing penalty term on the scale change, and polar radius term into the similarity measure, SDS is shown to be a well-performing similarity measure against overall size and rotation changes, as well as non-rigid geometric deformations, background clutter, and occlusions. The properties of SDS are statistically justified, and experiments on both synthetic and real-world data show that SDS can significantly outperform state-of-the-art methods.

cs.CV↗

arXiv2019

Coupling of surface chemistry and electric double layer at TiO$_2$ electrochemical interfaces

Surfaces of metal oxides at working conditions are usually electrified due to the acid-base chemistry. The charged interface compensated with counterions forms the so-called electric double layer. The coupling of surface chemistry and electric double layer is considered to be crucial but poorly understood because of lacking the information at the atomistic scale. Here, we used the latest development in density functional theory based finite-field molecular dynamics simulation to investigate pH-dependence of the Helmholtz capacitance at electrified rutile TiO$_2$ (110)-NaCl electrolyte interfaces. It is found that, due to competing forces from surface adsorption and from electric double layer, water molecules have a stronger structural fluctuation at high pH and this leads to a much larger capacitance. It is also seen that, interfacial proton transfers at low pH increase significantly the capacitance value. These findings elucidate the microscopic origin for the same trend observed in titration experiments.

cond-mat.soft↗

arXiv2019

ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition

Despite the recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adaptive Dimension Adjustment Tucker decomposition (ADA-Tucker). With learnable core tensors and transformation matrices, ADA-Tucker performs Tucker decomposition of arbitrary-order tensors. Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed. Therefore, the high flexibility in decomposition choice distinguishes ADA-Tucker from all previous low-rank models. To compress more, we further extend the model to Shared Core ADA-Tucker (SCADA-Tucker) by defining a shared core tensor for all layers. Our methods require no overhead of recording indices of non-zero elements. Without loss of accuracy, our methods reduce the storage of LeNet-5 and LeNet-300 by ratios of 691 times and 233 times, respectively, significantly outperforming state of the art. The effectiveness of our methods is also evaluated on other three benchmarks (CIFAR-10, SVHN, ILSVRC12) and modern newly deep networks (ResNet, Wide-ResNet).

cs.CV↗

arXiv2019

Characterization of temperatures associated to Schrodinger operators with initial data in BMO spaces

Let L be a Schrödinger operator of the form L=-Δ+V acting on L^2(\mathbb R^n) where the nonnegative potential V belongs to the reverse Hölder class B_q for some q>= n. Let BMO denote the BMO space associated to the Schrödinger operator L. In this article we will show that a function f in BMO_L is the trace of the solution of u_t+L u=0, u(x,0)= f(x), where u satisfies a Carleson-type condition. Conversely, this Carleson condition characterizes all the L-carolic functions whose traces belong to the space BMO_L. This result extends the analogous characterization founded by Fabes and Neri for the classical BMO space of John and Nirenberg.

math.AP↗

arXiv2019

Boundedness of variation operators associated with the heat semigroup generated by high order Schrödinger type operators

In this paper, we derive the $L^p$-boundedness of the variation operators associated with the heat semigroup which is generated by the high order Schrödinger type operator $(-Δ)^2+V^2$. Further more, we prove the boundedness of the variation operators on Morrey spaces. In the proof of the main results, we always make use of the variation inequalities associated with the heat semigroup generated by the biharmonic operator $(-Δ)^2.$

math.CA↗

arXiv2019

A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The approach is based on two principles: (a) if the source randomness of the network is a continuous distribution (the "semi-discrete" setting), then the Wasserstein distance is realized by a deterministic optimal transport mapping; (b) given an optimal transport mapping between a generator network and a target distribution, the Wasserstein distance may be decreased via a regression between the generated data and the mapped target points. The procedure here therefore alternates these two steps, forming an optimal transport and regressing against it, gradually adjusting the generator network towards the target distribution. Mathematically, this approach is shown to minimize the Wasserstein distance to both the empirical target distribution, and also its underlying population counterpart. Empirically, good performance is demonstrated on the training and testing sets of the MNIST and Thin-8 data. The paper closes with a discussion of the unsuitability of the Wasserstein distance for certain tasks, as has been identified in prior work [Arora et al., 2017, Huang et al., 2017].

cs.LG↗

arXiv2019

Fragmentation of shells: An analogy with the crack formation in tree bark

How does a shell explode into a series of fragments upon impact? The well accepted explanation is Mott's theory, which considers the fragmentation of shells as a random process controlled by defects. However, Mott's theory is inadequate due to its assumption of energy conversion, and it is incapable of explaining the lack of change in saturation fragment length with the increase in expansion velocity. In this paper, we present a theory to explain the physical mechanism for fragmentation of shells and propose a highly efficient model for predicting the number of necks after fragmentation. We recognize that the fragmentation problem in shells is analogous to the cracking behavior of tree bark, and closed form solutions is obtained to describe the relationship between the expansion velocity and the number of necks with consideration of the strain rate dependent strength of the shell material. The theoretical results show excellent correlation with the experimental results.

cond-mat.soft↗

arXiv2019

Carbon-Chain Molecules in Molecular Outflows and Lupus I Region--New Producing Region and New Forming Mechanism

Using the new equipment of the Shanghai Tian Ma Radio Telescope, we have searched for carbon-chain molecules (CCMs) towards five outflow sources and six Lupus I starless dust cores, including one region known to be characterized by warm carbon-chain chemistry (WCCC), Lupus I-1 (IRAS 15398-3359), and one TMC-1 like cloud, Lupus I-6 (Lupus-1A). Lines of HC3N J=2-1, HC5N J=6-5, HC7N J=14-13, 15-14, 16-15 and C3S J=3-2 were detected in all the targets except in the outflow source L1660 and the starless dust core Lupus I-3/4. The column densities of nitrogen-bearing species range from 10$^{12}$ to 10$^{14}$ cm$^{-2}$ and those of C$_3$S are about 10$^{12}$ cm$^{-2}$. Two outflow sources, I20582+7724 and L1221, could be identified as new carbon-chain--producing regions. Four of the Lupus I dust cores are newly identified as early quiescent and dark carbon-chain--producing regions similar to Lup I-6, which together with the WCCC source, Lup I-1, indicate that carbon-chain-producing regions are popular in Lupus I which can be regard as a Taurus like molecular cloud complex in our Galaxy. The column densities of C3S are larger than those of HC7N in the three outflow sources I20582, L1221 and L1251A. Shocked carbon-chain chemistry (SCCC) is proposed to explain the abnormal high abundances of C3S compared with those of nitrogen-bearing CCMs. Gas-grain chemical models support the idea that shocks can fuel the environment of those sources with enough $S^+$ thus driving the generation of S-bearing CCMs.

astro-ph.SR↗

arXiv2019

A particular carbon-chain-producing region: L1489 starless core

We detected carbon-chain molecules (CCMs) HC2n+1N (n=1-3) and C3S in Ku band as well as {high-energy excitation lines including C4H N=9-8, J=17/2-15/2, 19/2-17/2, and CH3CCH J=5-4, K=2 in the 3 mm band toward a starless core called the eastern molecular core (EMC) of L1489 IRS. Maps of all the observed lines were also obtained. Comparisons with a number of early starless cores and WCCC source L1527 show that the column densities of C4H and CH3CCH are close to those of L1527, and the CH3CCH column densities of the EMC and L1527 are slightly higher than those of TMC-1. The EMC and L1527 have similar C3S column densities, but they are much lower than those of all the starless cores, with only 6.5% and 10% of the TMC-1 value, respectively. The emissions of the N-bearing species of the EMC and L1527 are at the medium level of the starless cores. These comparisons show that the CCM emissions in the EMC are similar to those of L1527, though L1527 contains a protostar. Although dark and quiescent, the EMC is warmer and at a later evolutionary stage than classical carbon-chain--producing regions in the cold, dark, quiescent early phase. The PACS, SPIRE, and SCUBA maps evidently show that the L1489 IRS seems to be the heating source of the EMC. Although it is located at the margins of the EMC, its bolometric luminosity and bolometric temperature are relatively high. Above all, the EMC is a rather particular carbon-chain-producing region and is quite significant for CCM science.

astro-ph.SR↗

arXiv2019

Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destination can maximize a utility function which represents the task to be executed only knowing the quantized information. For example, for utility functions under the form $u\left(\boldsymbol{x};\ \boldsymbol{g}\right)$, $\boldsymbol{x}$ might represent a decision in terms of using some radio resources and $\boldsymbol{g}$ the system state which is only observed through its quantized version $Q(\boldsymbol{g})$. Both in the case where the utility function is known and the case where it is only observed through its realizations, we provide solutions to determine such a quantizer. We show how this approach applies to energy-efficient power allocation. In particular, it is seen that quantizing the state very roughly is perfectly suited to sum-rate-type function maximization, whereas energy-efficiency metrics are more sensitive to imperfections.

cs.LG↗