arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 721 records · Page 40Linked to original sources

arXiv2019

Long-wavelength fluctuations and static correlations in quasi-2D colloidal suspensions

Dimensionality strongly affects thermal fluctuations and critical dynamics of equilibrium systems. These influences persist in amorphous systems going through the nonequilibrium glass transition. Here, we experimentally study the glass transition of quasi-2D suspensions of spherical and ellipsoidal particles under different degrees of circular confinement. We show that the strength of the long-wavelength fluctuations increases logarithmically with system sizes and displays the signature of the Mermin-Wagner fluctuations. Moreover, using confinement as a tool, we also measure static structural correlations and extract a growing static correlation length in 2D supercooled liquids. Finally, we explore the influence of the Mermin-Wagner fluctuations on the translational and orientational relaxations of 2D ellipsoidal suspensions, which leads to a new interpretation of the two-step glass transition and the orientational glass phase of anisotropic particles. Our study reveals the importance of long-wavelength fluctuations in 2D supercooled liquids and provides new insights into the role of dimensionality in the glass transition.

cond-mat.soft↗

arXiv2019

Learning Semantic Vector Representations of Source Code via a Siamese Neural Network

The abundance of open-source code, coupled with the success of recent advances in deep learning for natural language processing, has given rise to a promising new application of machine learning to source code. In this work, we explore the use of a Siamese recurrent neural network model on Python source code to create vectors which capture the semantics of code. We evaluate the quality of embeddings by identifying which problem from a programming competition the code solves. Our model significantly outperforms a bag-of-tokens embedding, providing promising results for improving code embeddings that can be used in future software engineering tasks.

cs.LG↗

arXiv2019

Estimation of Cross-Sectional Dependence in Large Panels

Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross{sectional dependence) together often results in less efficient dimension reduction and worse forecasting. This paper describes cross-sectional dependence among a large number of objects (time series) via a factor model and parameterizes its extent in terms of strength of factor loadings. A new joint estimation method, benefiting from unique feature of dimension reduction for high dimensional time series, is proposed for the parameter representing the extent and some other parameters involved in the estimation procedure. Moreover, a joint asymptotic distribution for a pair of estimators is established. Simulations illustrate the effectiveness of the proposed estimation method in the finite sample performance. Applications in cross-country macro-variables and stock returns from S&P 500 are studied.

econ.EM↗

arXiv2019

The FFBS Estimation of High Dimensional Panel Data Factor Stochastic Volatility Models

In this paper, We propose a new style panel data factor stochastic volatility model with observable factors and unobservable factors based on the multivariate stochastic volatility model, which is mainly composed of three parts, such as the mean equation, volatility equation and factor volatility evolution. The stochastic volatility equation is a 1-step forward prediction process with high dimensional parameters to be estimated. Using the Markov Chain Monte Carlo Simulation (MCMC) method, the Forward Filtering Backward Sampling (FFBS) algorithm of the stochastic volatility equation is mainly used to estimate the new model by Kalman Filter Recursive Algorithm (KFRA). The results of numeric simulation and latent factor estimation show that the algorithm possesses robustness and consistency for parameter estimation. This paper makes a comparative analysis of the observable and unobservable factors of internet finance and traditional financial listed companies in the Chinese stock market using the new model and its estimation method. The results show that the influence of observable factors is similar to the two types of listed companies, but the influence of unobservable factors is obviously different.

stat.ME↗

arXiv2019

Parallaxes for star forming regions in the inner Perseus spiral arm

We report trigonometric parallax and proper motion measurements of 6.7-GHz CH3OH and 22-GHz H2O masers in eight high-mass star-forming regions (HMSFRs) based on VLBA observations as part of the BeSSeL Survey. The distances of these HMSFRs combined with their Galactic coordinates, radial velocities, and proper motions, allow us to assign them to a segment of the Perseus arm with ~< 70 deg. These HMSFRs are clustered in Galactic longitude from ~30 deg to ~50, neighboring a dirth of such sources between longitudes ~50 deg to ~90 deg.

astro-ph.GA↗

arXiv2019

Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction

The recommender system is an important form of intelligent application, which assists users to alleviate from information redundancy. Among the metrics used to evaluate a recommender system, the metric of conversion has become more and more important. The majority of existing recommender systems perform poorly on the metric of conversion due to its extremely sparse feedback signal. To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i.e., high-level agent and low-level agent. The high-level agent catches long-term sparse conversion signals, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and interacts with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel deep hierarchical reinforcement learning algorithm via multi-goals abstraction (HRL-MG). Our proposed algorithm contains three characteristics: 1) the high-level agent generates multiple goals to guide the low-level agent in different stages, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciate benefit assignment function is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.

cs.LG↗

arXiv2019

A Matrix-in-matrix Neural Network for Image Super Resolution

In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers. However, their application is quite limited since they require high computing power. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR net work named matrixed channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Conclusions can be drawn from our extensive benchmark experiments that the proposed models achieve better performance with much fewer multiply-adds and parameters. Our models will be made publicly available.

cs.CV↗

arXiv2019

Comparison of Gaia DR2 Parallaxes of Stars with VLBI Astrometry

We compare the parallaxes of stars from VLBI astrometry in the literature to those in the Gaia DR2 catalog. Our full sample contains young stellar objects, evolved AGB stars, pulsars and other radio stars. Excluding AGB stars, which show significant discrepancies between Gaia and VLBI parallaxes, and stars in binary systems, we obtain an average, systematic, parallax offset of $-75 \pm 29~μ$as for Gaia DR2, consistent with their estimate of a parallax zero-point between $-100$ and 0 $μ$as.

astro-ph.SR↗

arXiv2019

Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR), and summarises the status quo of applying artificial intelligence (AI) techniques. We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum. Then, we transform the relationship model into the perception-planning-action model for exploring what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting human-robot teaching-learning process and AI-assisted educational techniques, illustrating the design guidelines and future research perspectives in intelligent tutoring robots.

cs.AI↗

arXiv2019

A Catalogue of OB Stars from LAMOST Spectroscopic Survey

We present 22\,901 OB spectra of 16\,032 stars identified from LAMOST DR5 dataset. A larger sample of OB candidates are firstly selected from the distributions in the spectral line indices space. Then all 22\,901 OB spectra are identified by manual inspection. Based on a sub-sample validation, we find that the completeness of the OB spectra reaches about $89\pm22$\% for the stars with spectral type earlier than B7, while around $57\pm16$\% B8--B9 stars are identified. The smaller completeness for late B stars is lead to the difficulty to discriminate them from A0--A1 type stars. The sub-classes of the OB samples are determined using the software package MKCLASS. With a careful validation using 646 sub-samples, we find that MKCLASS can give fairly reliable sub-types and luminosity class for most of the OB stars. The uncertainty of the spectral sub-type is around 1 sub-type and the uncertainty of the luminosity class is around 1 level. However, about 40\% of the OB stars are failed to be assigned to any class by MKCLASS and a few spectra are significantly misclassified by MKCLASS. This is likely because that the template spectra of MKCLASS are selected from nearby stars in the solar neighborhood, while the OB stars in this work are mostly located in the outer disk and may have lower metallicity. The rotation of the OB stars may also be responsible for the mis-classifications. Moreover, we find that the spectral and luminosity classes of the OB stars located in the Galactic latitude larger than 20$^\circ$ are substantially different with those located in latitude smaller than 20$^\circ$, which may either due to the observational selection effect or hint a different origin of the high Galactic latitude OB stars.

astro-ph.SR↗

arXiv2019

Taylor expansion based fast Multipole Methods for 3-D Helmholtz equations in Layered Media

In this paper, we develop fast multipole methods for 3D Helmholtz kernel in layered media. Two algorithms based on different forms of Taylor expansion of layered media Green's function are developed. A key component of the first algorithm is an efficient algorithm based on discrete complex image approximation and recurrence formula for the calculation of the layered media Green's function and its derivatives, which are given in terms of Sommerfeld integrals. The second algorithm uses symmetric derivatives in the Taylor expansion to reduce the size of precomputed tables for the derivatives of layered media Green's function. Numerical tests in layered media have validated the accuracy and O(N) complexity of the proposed algorithms.

math.NA↗

arXiv2019

The Distances to Molecular Clouds at High Galactic Latitudes based on GAIA DR2

We report the distances of molecular clouds at high Galactic latitudes (|b|>10$^\circ$) derived from parallax and G band extinction (A$_{\rm G}$) measurements in the second Gaia data release, Gaia DR2. Aided by Bayesian analyses, we determined distances by identifying the breakpoint in the extinction A$_{\rm G}$ towards molecular clouds and using the extinction A$_{\rm G}$ of Gaia stars around molecular clouds to confirm the breakpoint. We use nearby star-forming regions, such as Orion, Taurus, Cepheus, and Perseus, whose distances are well-known to examine the reliability of our method. By comparing with previous results, we found that the molecular cloud distances derived from this method are reliable. The systematic error in the distances is approximately 5%. In total, 52 molecular clouds have their distances well determined, most of which are at high Galactic latitudes, and we provide reliable distances for 13 molecular clouds for the first time.

astro-ph.GA↗

arXiv2019

Pairwise Teacher-Student Network for Semi-Supervised Hashing

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed. Pairwise similarity is easily obtained and widely used for retrieval, and most supervised hashing algorithms are carefully designed for the pairwise supervisions. As labeling all data pairs is difficult, semi-supervised hashing is proposed which aims at learning efficient codes with limited labeled pairs and abundant unlabeled ones. Existing methods build graphs to capture the structure of dataset, but they are not working well for complex data as the graph is built based on the data representations and determining the representations of complex data is difficult. In this paper, we propose a novel teacher-student semi-supervised hashing framework in which the student is trained with the pairwise information produced by the teacher network. The network follows the smoothness assumption, which achieves consistent distances for similar data pairs so that the retrieval results are similar for neighborhood queries. Experiments on large-scale datasets show that the proposed method reaches impressive gain over the supervised baselines and is superior to state-of-the-art semi-supervised hashing methods.

cs.CV↗

arXiv2019

Joint Cluster Unary Loss for Efficient Cross-Modal Hashing

With the rapid growth of various types of multimodal data, cross-modal deep hashing has received broad attention for solving cross-modal retrieval problems efficiently. Most cross-modal hashing methods follow the traditional supervised hashing framework in which the $O(n^2)$ data pairs and $O(n^3)$ data triplets are generated for training, but the training procedure is less efficient because the complexity is high for large-scale dataset. To address these issues, we propose a novel and efficient cross-modal hashing algorithm in which the unary loss is introduced. First of all, We introduce the Cross-Modal Unary Loss (CMUL) with $O(n)$ complexity to bridge the traditional triplet loss and classification-based unary loss. A more accurate bound of the triplet loss for structured multilabel data is also proposed in CMUL. Second, we propose the novel Joint Cluster Cross-Modal Hashing (JCCH) algorithm for efficient hash learning, in which the CMUL is involved. The resultant hashcodes form several clusters in which the hashcodes in the same cluster share similar semantic information, and the heterogeneity gap on different modalities is diminished by sharing the clusters. The proposed algorithm is able to be applied to various types of data, and experiments on large-scale datasets show that the proposed method is superior over or comparable with state-of-the-art cross-modal hashing methods, and training with the proposed method is more efficient than others.

cs.IR↗

arXiv2019

Semantic Cluster Unary Loss for Efficient Deep Hashing

Hashing method maps similar data to binary hashcodes with smaller hamming distance, which has received a broad attention due to its low storage cost and fast retrieval speed. With the rapid development of deep learning, deep hashing methods have achieved promising results in efficient information retrieval. Most of the existing deep hashing methods adopt pairwise or triplet losses to deal with similarities underlying the data, but the training is difficult and less efficient because $O(n^2)$ data pairs and $O(n^3)$ triplets are involved. To address these issues, we propose a novel deep hashing algorithm with unary loss which can be trained very efficiently. We first of all introduce a Unary Upper Bound of the traditional triplet loss, thus reducing the complexity to $O(n)$ and bridging the classification-based unary loss and the triplet loss. Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by introducing a modified Unary Upper Bound loss, named Semantic Cluster Unary Loss (SCUL). The resultant hashcodes form several compact clusters, which means hashcodes in the same cluster have similar semantic information. We also demonstrate that the proposed SCDH is easy to be extended to semi-supervised settings by incorporating the state-of-the-art semi-supervised learning algorithms. Experiments on large-scale datasets show that the proposed method is superior to state-of-the-art hashing algorithms.

cs.CV↗

arXiv2019

A novel integral equation for scattering by locally rough surfaces and application to the inverse problem: the Neumann case

This paper is concerned with direct and inverse scattering by a locally perturbed infinite plane (called a locally rough surface in this paper) on which a Neumann boundary condition is imposed. A novel integral equation formulation is proposed for the direct scattering problem which is defined on a bounded curve (consisting of a bounded part of the infinite plane containing the local perturbation and the lower part of a circle) with two corners and some closed smooth artificial curve. It is a nontrivial extension of our previous work on direct and inverse scattering by a locally rough surface from the Dirichlet boundary condition to the Neumann boundary condition [{\em SIAM J. Appl. Math.}, 73 (2013), pp. 1811-1829]. In this paper, we make us of the recursively compressed inverse preconditioning (RCIP) method developed by Helsing to solve the integral equation which is efficient and capable of dealing with large wave numbers. For the inverse problem, it is proved that the locally rough surface is uniquely determined from a knowledge of the far-field pattern corresponding to incident plane waves. Further, based on the novel integral equation formulation, a Newton iteration method is developed to reconstruct the locally rough surface from a knowledge of multiple frequency far-field data. Numerical examples are also provided to illustrate that the reconstruction algorithm is stable and accurate even for the case of multiple-scale profiles.

math.NA↗

arXiv2019

Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search

Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinforced learning methods (RL) have been dedicated to solve neural architecture search problems. However, these combinations usually concentrate on a single objective such as the error rate of image classification. They also fail to harness the very benefits from both sides. In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by leveraging good virtues from both EA and RL. In particular, we incorporate a variant of multi-objective genetic algorithm NSGA-II, in which the search space is composed of various cells so that crossovers and mutations can be performed at the cell level. Moreover, reinforced control is mixed with a natural mutating process to regulate arbitrary mutation, maintaining a delicate balance between exploration and exploitation. Therefore, not only does our method prevent the searched models from degrading during the evolution process, but it also makes better use of learned knowledge. Our experiments conducted in Super-resolution domain (SR) deliver rivalling models compared to some state-of-the-art methods with fewer FLOPS.

cs.NE↗

arXiv2019

Grain boundary effect on nanoindentation: A multiscale discrete dislocation dynamics model

Nanoindentation is a convenient method to investigate the mechanical properties of materials on small scales by utilizing low loads and small indentation depths. However, the effect of grain boundaries (GB) on the nanoindentation response remains unclear and needs to be studied by investigating in detail the interactions between dislocations and GBs during nanoindentation. In the present work, we employ a three-dimensional multiscale modeling framework, which couples three-dimensional discrete dislocation dynamics (DDD) with the Finite Element method (FEM) to investigate GB effects on the nanoindentation behavior of an aluminum bicrystal. The interaction between dislocations and GB is physically modeled in terms of a penetrable GB, where piled-up dislocations can penetrate through the GB and dislocation debris at GBs can emit full dislocations into grains. In the simulation, we confirmed two experimentally observed phenomena, namely, pop-in events and the dependence of indentation hardness on the distance from GB. Two pop-in events were observed, of which the initial pop-in event is correlated with the activation and multiplication of dislocations, while the GB pop-in event results from dislocation transmission through the GB. By changing the distance between the indenter and GB, the simulation shows that the indentation hardness increases with decreasing GB-indenter distance. A quantitative model has been formulated which relates the dependency of indentation hardness on indentation depth and on GB-indenter distance to the back stress created by piled-up geometrically necessary dislocations in the plastic zone and to the additional constraint imposed by the GB on the plastic zone size.

cond-mat.mtrl-sci↗