Search arXiv⌕ Search

arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 1,009 records · Page 56Linked to original sources
arXiv2020

Regularity property of some operators on Lipschitz spaces and homogeneous Lipschitz spaces related to biharmonic operator

In this paper, we consider the characterizations of the Lipschitz spaces and homogeneous Lipschitz spaces associated to the biharmonic operator $Δ^2.$ With this characterizations, we prove the boundedness of the Bessel potentials, fractional integrals, fractional powers, Riesz transforms and multipliers of Laplace transforms type associated to $Δ^2$ on Lipschitz spaces and homogeneous Lipschitz spaces. The proofs of these results need the language of semigroups in an essential way.

math.CA↗
arXiv2020

paper2repo: GitHub Repository Recommendation for Academic Papers

GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order to attract more people to follow their work. Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic. The key challenge is to identify the similarity between an input paper and its related repositories across the two platforms, $\textit{without the benefit of human labeling}$. Towards that end, paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn and map the embeddings of papers and repositories into the same space, where proximity offers the basis for recommendation. To make our method more practical in real life systems, labels used for model training are computed automatically from features of user actions on GitHub. In machine learning, such automatic labeling is often called {\em distant supervision\/}. To the authors' knowledge, this is the first distant-supervised cross-platform (paper to repository) matching system. We evaluate the performance of paper2repo on real-world data sets collected from GitHub and Microsoft Academic. Results demonstrate that it outperforms other state of the art recommendation methods.

cs.SI↗
arXiv2020

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

Deep learning methods have revolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities. Therefore, it is of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. The main focus of this review is the combination of vision and natural language modalities, which has become an important topic in both the computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications. Regarding multimodal representation learning, we review the key concepts of embedding, which unify multimodal signals into a single vector space and thereby enable cross-modality signal processing. We also review the properties of many types of embeddings that are constructed and learned for general downstream tasks. Regarding multimodal fusion, this review focuses on special architectures for the integration of representations of unimodal signals for a particular task. Regarding applications, selected areas of a broad interest in the current literature are covered, including image-to-text caption generation, text-to-image generation, and visual question answering. We believe that this review will facilitate future studies in the emerging field of multimodal intelligence for related communities.

cs.AI↗
arXiv2020

SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of crossover in real-coded genetic algorithm (RCGA), in this paper we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 4 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of accuracy.

cs.NE↗
arXiv2020

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4\% mAP on COCO minival set with 27M parameters. Our implementation is available at https://github.com/ggjy/HitDet.pytorch.

cs.CV↗
arXiv2020

Robust Weyl semimetallic phase in face-centered orthogonal C6 with helical carbon chains

The exploration of topological phases in carbon allotropes offers a fascinating avenue to realize topological devices based on carbon materials. Here, using first-principles calculations, we propose a novel metastable carbon allotrope, which possesses exotically helical carbon chains bridged by quadrangle-rings. This unique structure with sp2-sp3 bonding networks crystallizes in a noncentrosymmetric face-centered orthogonal (fco) lattice with six atoms in a unit cell, thus named fco-C6. The considerable stability of fco-C6 is confirmed by phonon spectra, elastic constants, and ab initio molecular dynamics simulations. More importantly, fco-C6 exhibits extraordinary electronic properties with the minimum number of Weyl points in a time-reversal preserved Weyl system. The symmetry arguments reveal that the Weyl points are guaranteed to lie along the high-symmetry pathes and thus well separated in momentum space, exhibiting the robustness of topologically protected features. We investigate the topological surface states of fco-C6 projected on a semi-infinite (010) surface. There are only nontrivial Fermi arcs across the Fermi surface, which facilitates their measurements in experiments and further applications in carbon allotropes.

cond-mat.mtrl-sci↗
arXiv2020

Constraints of general screened modified gravities from comprehensive analysis of binary pulsars

Testing gravity by binary pulsars nowadays becomes a key issue. Screened modified gravity is a kind of scalar-tensor theory with screening mechanism in order to satisfy the tight Solar System tests. In this paper, we investigate how the screening mechanism affects the orbital dynamics of binary pulsars, and calculate in detail the five post-Keplerian (PK) parameters in this theory. These parameters differ from those of general relativity (GR), and the differences are quantified by the scalar charges, which lead to the dipole radiation in this theory. We combine the observables of PK parameters for the ten binary pulsars, respectively, to place the constraints on the scalar charges and possible deviations from GR. The dipole radiation in the neutron star (NS) - white dwarf (WD) binaries leads to more stringent constraints on deviations from GR. The most constraining systems for the scalar charges of NS and WD are PSR~B1913$+$16 and PSR~J1738$+$0333, respectively. The results of all tests exclude significant strong-field deviations and show good agreement with GR.

gr-qc↗
arXiv2020

Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square

We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely "combined Neyman-Pearson chi-square" ($χ^2_{\mathrm{CNP}}$). Through analytical derivations and toy model simulations, we show that $χ^2_\mathrm{CNP}$ leads to a significantly smaller bias on the best-fit model parameters compared to those using either Neyman's or Pearson's chi-square. When the computational cost of using the Poisson-likelihood chi-square is high, $χ^2_\mathrm{CNP}$ provides a good alternative given its natural connection to the covariance matrix formalism.

physics.data-an↗
arXiv2020

Discriminative Topic Mining via Category-Name Guided Text Embedding

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users' particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification.

cs.CL↗
arXiv2020

G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data

In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of the WA data to boost the multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that the WA data annotated with the same weak label has a high probability of being assigned to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lie on/near the latent model are likely to connect together and further form a subset/cluster for effective proposals generation. With the proposals generated, the $α$-expansion is adopted for labeling, and our method in return updates the proposals. This works in an iterative way. Extensive experiments validate our method and show that the proposed method produces noticeably better results than state-of-the-art techniques in most cases.

cs.CV↗
arXiv2020

Experimental creation of Multi-Photon High-Dimensional Layered Quantum States

Quantum entanglement is one of the most important resources in quantum information. In recent years, the research of quantum entanglement mainly focused on the increase in the number of entangled qubits or the high-dimensional entanglement of two particles. Compared with qubit states, multipartite high-dimensional entangled states have beneficial properties and are powerful for constructing quantum networks. However, there are few studies on multipartite high-dimensional quantum entanglement due to the difficulty of creating such states. In this paper, we experimentally prepared a multipartite high-dimensional state $|Ψ_{442}\rangle=\frac{1}{2}(|000\rangle+|110\rangle+|221\rangle+|331\rangle)$ by using the path mode of photons. We obtain the fidelity $F=0.854\pm0.007$ of the quantum state, which proves a real multipartite high-dimensional entangled state. Finally, we use this quantum state to demonstrate a layered quantum network in principle. Our work highlights another route towards complex quantum networks.

quant-ph↗
arXiv2020

Evolution of Ethereum: A Temporal Graph Perspective

Ethereum is one of the most popular blockchain systems that supports more than half a million transactions every day and fosters miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this paper, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytics platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract and user-contract graphs, are constructed according to trading relationship and are segmented with an appropriate time window. We observe a strong correlation between the size of user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions is validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, "the rich is already very rich".

cs.SI↗
arXiv2020

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello high-dimensional neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs, provides analytical stress tensor calculations and interfaces to both the Atomic Simulation Environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials.

physics.comp-ph↗
arXiv2020

Optical capillary-based interferometric sensor for detection of gas refractive index

In this paper, we report a capillary-based M-Z interferometer that could be used for precise detection of variations in refractive indices of gaseous samples. This sensing mechanism is quite straightforward. Cladding and core modes of a capillary are simultaneously excited by coupling coherent laser beams to the capillary cladding and core, respectively. Interferogram would be generated as the light transmitted from the core interferes with the light transmitted from the cladding. Variations in refractive index of the air filling the core lead to variations in phase difference between the core and cladding modes, thus shifting the interference fringes. Using a photodiode together with a narrow slit, we could analyze the fringe shifts. The resolution of the sensor was found to be 1*10-8 RIU, that is comparable to the highest resolution obtained by other interferometric sensors reported in previous literatures. Finally, we also analyze the temperature cross sensitivity of the sensor. The advantages of our sensor include very low cost, high sensitivity, straightforward sensing mechanism, and ease of fabrication.

physics.app-ph↗
arXiv2020

Fragility of quantum correlations and coherence in a multipartite photonic system

Certain quantum states are well-known to be particularly fragile in the presence of decoherence, as illustrated by Schrodinger's famous gedanken cat experiment. It has been better appreciated more recently that quantum states can be characterized in a hierarchy of quantum quantities such entanglement, quantum correlations, and quantum coherence. It has been conjectured that each of these quantities have various degrees of fragility in the presence of decoherence. Here we experimentally confirm this conjecture by preparing tripartite photonic states and subjecting them to controlled amounts of dephasing. When the dephasing is applied to all the qubits, we find that the entanglement is the most fragile quantity, followed by the quantum coherence, then mutual information. This is in agreement with the widely held expectation that multipartite quantum correlations are a highly fragile manifestation of quantumness. We also perform dephasing on one out of the three qubits on star and $ W \bar{W} $ states. Here the distribution of the correlations and coherence in the state becomes more important in relation to the dephasing location.

quant-ph↗
arXiv2019

Electromechanics of the liquid water vapour interface

Two collective properties distinguishing the thin liquid water vapour interface from the bulk liquid are the anisotropy of the pressure tensor giving rise to surface tension and the orientational alignment of the molecules leading to a finite dipolar surface potential. Both properties can be regarded as capillary phenomena and are likely to be coupled. We have investigated this coupling by determining the response of the tangential component of the surface tension to the application of an electric field normal to the surface using finite field molecular dynamics simulations. We find an upside down parabola with a maximum shifted away from zero field. Comparing the molecular dynamics results to an elementary electromechanical continuum model we relate the zero field derivative of the tangential part of the surface tension to the electrostatic potential generated by the spontaneous dipole alignment. The calculations show that these quantities have similar values but are not and in fact need not be identical. The electromechanical model also allows us to convert the absolute curvature of the quadratic field dependence to an effective dielectric constant of the water interface which is found to be much lower compared to the bulk value as expected.

cond-mat.soft↗
arXiv2019

Attribute-Aware Attention Model for Fine-grained Representation Learning

How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model contains two attention models: attribute-guided attention module uses attribute information to help select category features in different regions, at the same time, category-guided attention module selects local features of different attributes with the help of category cues. Through this attribute-category reciprocal process, local and global features benefit from each other. Finally, the resulting feature contains more intrinsic information for image recognition instead of the noisy and irrelevant features. Extensive experiments conducted on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the effectiveness of our $A^3M$. Code is available at https://github.com/iamhankai/attribute-aware-attention.

cs.CV↗
arXiv2019

Chemical properties of two dense cores in a Planck Galactic Cold Clump G168.72-15.48

To deepen our understanding of the chemical properties of the Planck Galactic Cold Clump (PGCC) G168.72-15.48, we performed observations of nine molecular species, namely, \ce{c-C3H}, \ce{H2CO}, \ce{HC5N}, \ce{HC7N}, \ce{SO}, \ce{CCH}, \ce{N2H+}, \ce{CH3OH}, and \ce{CH3CCH}, toward two dense cores in PGCC G168.72-15.48 using the Tianma Radio Telescope and Purple Mountain Observatory Telescope. We detected \ce{c-C3H}, \ce{H2CO}, \ce{HC5N}, \ce{N2H+}, \ce{CCH}, and \ce{CH3OH} in both G168-H1 and G168-H2 cores, whereas \ce{HC7N} and \ce{CH3CCH} were detected only in G168-H1 and SO was detected only in G168-H2. Mapping observations reveal that the \ce{CCH}, \ce{N2H+}, \ce{CH3OH}, and \ce{CH3CCH} emissions are well coupled with the dust emission in G168-H1. Additionally, \ce{N2H+} exhibits an exceptionally weak emission in the denser and more evolved G168-H2 core, which may be attributed to the \ce{N2H+} depletion. We suggest that the \ce{N2H+} depletion in G168-H2 is dominated by \ce{N2} depletion, rather than the destruction by CO. The local thermodynamic equilibrium calculations indicate that the carbon-chain molecules of \ce{CCH}, \ce{HC5N}, \ce{HC7N}, and \ce{CH3CCH} are more abundant in the younger G168-H1 core. We found that starless core G168-H1 may have the properties of cold dark clouds based on its abundances of carbon-chain molecules. While, the prestellar core G168-H2 exhibits lower carbon-chain molecular abundances than the general cold dark clouds. With our gas-grain astrochemical model calculations, we attribute the observed chemical differences between G168-H1 and G168-H2 to their different gas densities and different evolutionary stages.

astro-ph.GA↗