arXiv subjects

Bin Wang

Publications and source records attributed to Bin Wang.

At least 649 records · Page 36Linked to original sources

arXiv2021

Crystallography of the martensitic transformation between Ni2In-type hexagonal and TiNiSi-type orthorhombic phases

MnMX (M = Co or Ni, X = Si or Ge) alloys, experiencing structural transformation between Ni2In-type hexagonal and TiNiSi-type orthorhombic phases, attract considerable attention due to their potential applications as room-temperature solid refrigerants. Although lots of studies have been carried out on how to tune this transformation and obtain large entropy change in a wide temperature region, the crystallography of this martensitic transformation is still unknown. The biggest obstacle for crystallography investigation is to obtain a bulk sample, in which hexagonal and orthorhombic phases coexist, because the MnMX alloys will fragment into powders after experiencing the transformation. For this reason, we carefully tune the transformation temperature to be slightly below 300 K. In that case, a bulk sample with small amounts of orthorhombic phases distributed in hexagonal matrix is obtained. Most importantly, there are no cracks between the two phases. It facilities us to investigate the microstructure using electron microscope. The obtained results indicate that the orientation relationship between hexagonal and orthorhombic structures is [4-2-23]h//[120]o & (01-10)h//(001)o and the habit plane is {-2113.26}h. WLR theory is also adopted to calculate the habit plane. The calculated result agrees well with the measured one. Our work reveals the crystallography of hexagonal-orthorhombic transformation for the first time and is helpful for understanding the transformation-associated physical effects in MnMX alloys.

cond-mat.mtrl-sci↗

arXiv2021

Passive underwater acoustic barcodes using Rayleigh wave resonance

A passive underwater acoustic marker is presented and its feasibility for underwater recognition, positioning, and navigation is proved by a numerical method and experimental results. These markers are composed of acrylic elastic objects designed by backscattering strong peaks associated with the subsonic Rayleigh wave resonance of a polymer target excited by a broadband pulse, and having a unique acoustic signature for a selected frequency band, akin to acoustic barcodes. Therefore, the backscattering response of markers can be regulated by changing the geometry of elastic objects. These acoustic barcodes naturally operate in a wider frequency band, and have a longer lifetime and lower cost, compared with active acoustic markers.

physics.app-ph↗

arXiv2021

Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition

Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence. It is important to capture the fine-grained gloss-level details, since there is no explicit alignment between sign video frames and the corresponding glosses. Among the past works, one promising way is to adopt a one-dimensional convolutional network (1D-CNN) to temporally fuse the sequential frames. However, CNNs are agnostic to similarity or dissimilarity, and thus are unable to capture local consistent semantics within temporally neighboring frames. To address the issue, we propose to adaptively fuse local features via temporal similarity for this task. Specifically, we devise a Multi-scale Local-Temporal Similarity Fusion Network (mLTSF-Net) as follows: 1) In terms of a specific video frame, we firstly select its similar neighbours with multi-scale receptive regions to accommodate different lengths of glosses. 2) To ensure temporal consistency, we then use position-aware convolution to temporally convolve each scale of selected frames. 3) To obtain a local-temporally enhanced frame-wise representation, we finally fuse the results of different scales using a content-dependent aggregator. We train our model in an end-to-end fashion, and the experimental results on RWTH-PHOENIX-Weather 2014 datasets (RWTH) demonstrate that our model achieves competitive performance compared with several state-of-the-art models.

cs.CV↗

arXiv2021

Hawking radiation received at infinity in higher dimensional Reissner-Nordström black hole spacetimes

In this work, we investigate the Hawking radiation in higher dimensional Reissner-Nordström black holes as received by an observer, resides at infinity. The frequency-dependent transmission rates, which deform the thermal radiation emitted in the vicinity of the black hole horizon, are evaluated numerically. Apart from the case of four-dimensional spacetime, the calculations are extended to higher dimensional Reissner-Nordström metrics, and the results are found to be somewhat sensitive to the spacetime dimension. In general, it is observed that the transmission coefficients practically vanishes when the frequency of the emitted particle approaches zero. It increases with increasing frequency and eventually saturates to some value. For four-dimensional spacetime, the above result is shown to be mostly independent of the metric's parameter, neither of the orbital quantum number of the particle, once the location of the event horizon, $r_h$, and the product of the charges of the black hole and the particle $qQ$ are given. For higher-dimensional cases, on the other hand, the convergence becomes more slowly. Moreover, the difference between states with different orbital quantum numbers is found to be more significant. As the magnitude of the product of charges $qQ$ becomes more significant, the transmission coefficient exceeds one. In other words, the resultant spectral flux is amplified, which results in an accelerated process of black hole evaporation. The relation between the calculated outgoing transmission coefficient with existing results on the greybody factor is discussed.

gr-qc↗

arXiv2021

Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training

This study delves into semi-supervised object detection (SSOD) to improve detector performance with additional unlabeled data. State-of-the-art SSOD performance has been achieved recently by self-training, in which training supervision consists of ground truths and pseudo-labels. In current studies, we observe that class imbalance in SSOD severely impedes the effectiveness of self-training. To address the class imbalance, we propose adaptive class-rebalancing self-training (ACRST) with a novel memory module called CropBank. ACRST adaptively rebalances the training data with foreground instances extracted from the CropBank, thereby alleviating the class imbalance. Owing to the high complexity of detection tasks, we observe that both self-training and data-rebalancing suffer from noisy pseudo-labels in SSOD. Therefore, we propose a novel two-stage filtering algorithm to generate accurate pseudo-labels. Our method achieves satisfactory improvements on MS-COCO and VOC benchmarks. When using only 1\% labeled data in MS-COCO, our method achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP improvement compared with state-of-the-art methods.

cs.CV↗

arXiv2021

Deep Structural Point Process for Learning Temporal Interaction Networks

This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and items. Previous methods tackle this problem by using different variants of recurrent neural networks to model sequential interactions, which fail to consider the structural information of temporal interaction networks and inevitably lead to sub-optimal results. To this end, we propose a novel Deep Structural Point Process termed as DSPP for learning temporal interaction networks. DSPP simultaneously incorporates the topological structure and long-range dependency structure into our intensity function to enhance model expressiveness. To be specific, by using the topological structure as a strong prior, we first design a topological fusion encoder to obtain node embeddings. An attentive shift encoder is then developed to learn the long-range dependency structure between users and items in continuous time. The proposed two modules enable our model to capture the user-item correlation and dynamic influence in temporal interaction networks. DSPP is evaluated on three real-world datasets for both tasks of item prediction and time prediction. Extensive experiments demonstrate that our model achieves consistent and significant improvements over state-of-the-art baselines.

cs.SI↗

arXiv2021

On an alternative mechanism for the black hole echoes

Gravitational wave echoes from the black holes have been suggested as a crucial observable to probe the spacetime in the vicinity of the horizon. In particular, it was speculated that the echoes are closely connected with specific characteristics of the exotic compact objects, and moreover, possibly provide an access to the quantum nature of gravity. Recently, it was shown that the discontinuity in the black hole metric substantially modifies the asymptotical behavior of quasinormal frequencies. In the present study, we proceed further and argue that a discontinuity planted into the metric furnishes an alternative mechanism for the black hole echoes. Physically, the latter may correspond to an uneven matter distribution inside the surrounding halo. To demonstrate the results, we first numerically investigate the temporal evolution of the scalar perturbations around a black hole that possesses a nonsmooth effective potential. It is shown that the phenomenon persists even though the discontinuity can be located further away from the horizon with rather insignificant strength. Besides, we show that the echoes in the present model can be derived analytically based on the modified pole structure of the associated Green function. The asymptotical properties of the quasinormal mode spectrum and the echoes are found to be closely connected, as both features can be attributed to the same origin. In particular, the period of the echoes in the time domain $T$ is shown to be related to the asymptotic spacing between successive poles along the real axis in the frequency domain $Δ(\Reω)$, by a simple relation $\lim\limits_{\Reω\to+\infty}Δ(\Reω) = 2π/T$. Moreover, we discuss possible distinguishment between different echo mechanisms. The potential astrophysical implications of the present findings are also addressed.

gr-qc↗

arXiv2021

An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification

In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential. In this paper, an inverse-based network solution is proposed by a hierarchical method for computing and store the approximate inverse of the conductance matrix in electromagnetic transient (EMT) simulations. The proposed method can also efficiently update the inverse by modifying only local sub-matrices to reflect changes in the network, e.g., loss of a line. Experiments on a series of simplified 179-bus Western Interconnection demonstrate the advantages of the proposed methods.

eess.SY↗

arXiv2021

The BINGO Project VI: HI Halo Occupation Distribution and Mock Building

BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations.) is a radio telescope designed to survey from 980 MHz to 1260 MHz, observe the neutral Hydrogen (HI) 21-cm line and detect BAO (Baryon Acoustic Oscillation) signal with Intensity Mapping technique. Here we present our method to generate mock maps of the 21-cm Intensity Mapping signal covering the BINGO frequency range and related test results. (Abridged)

astro-ph.CO↗

arXiv2021

Non-line-of-sight imaging with picosecond temporal resolution

Non-line-of-sight (NLOS) imaging enables monitoring around corners and is promising for diverse applications. The resolution of transient NLOS imaging is limited to a centimeter scale, mainly by the temporal resolution of the detectors. Here, we construct an up-conversion single-photon detector with a high temporal resolution of ~1.4 ps and a low noise count rate of 5 counts per second (cps). Notably, the detector operates at room temperature, near-infrared wavelength. Using this detector, we demonstrate high-resolution and low-noise NLOS imaging. Our system can provide a 180 μm axial resolution and a 2 mm lateral resolution, which is more than one order of magnitude better than that in previous experiments. These results open avenues for high-resolution NLOS imaging techniques in relevant applications.

physics.optics↗

arXiv2021

Unsupervised Co-part Segmentation through Assembly

Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For the training stage, we leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful object parts. More importantly, we introduce a dual procedure of part-assembly to form a closed loop with part-segmentation, enabling an effective self-supervision. We demonstrate the effectiveness of our approach with a host of extensive experiments, ranging from human bodies, hands, quadruped, and robot arms. We show that our approach can achieve meaningful and compact part segmentation, outperforming state-of-the-art approaches on diverse benchmarks.

cs.CV↗

arXiv2021

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination indispensable in cooperative MARL constrains each agent's policy. However, existing transfer methods focus exclusively on agent policy and ignores coordination knowledge. We propose a new architecture that realizes robust coordination knowledge transfer through appropriate decomposition of the overall coordination into several coordination patterns. We use a novel mixing network named level-adaptive QTransformer (LA-QTransformer) to realize agent coordination that considers credit assignment, with appropriate coordination patterns for different agents realized by a novel level-adaptive Transformer (LA-Transformer) dedicated to the transfer of coordination knowledge. In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios. Extensive experiments in StarCraft II micro-management show that LA-QTransformer together with PIT achieves superior performance compared with state-of-the-art baselines.

cs.AI↗

arXiv2021

Baryon Acoustic Oscillations from Integrated Neutral Gas Observations: an instrument to observe the 21cm hydrogen line in the redshift range 0.13 $<$ z $<$ 0.45 -- status update

BINGO (BAO from Integrated Neutral Gas Observations) is a unique radio telescope designed to map the intensity of neutral hydrogen distribution at cosmological distances, making the first detection of Baryon Acoustic Oscillations (BAO) in the frequency band 980 MHz - 1260 MHz, corresponding to a redshift range $0.127 < z < 0.449$. BAO is one of the most powerful probes of cosmological parameters and BINGO was designed to detect the BAO signal to a level that makes it possible to put new constraints on the equation of state of dark energy. The telescope will be built in Paraíba, Brazil and consists of two $\thicksim$ 40m mirrors, a feedhorn array of 28 horns, and no moving parts, working as a drift-scan instrument. It will cover a $15^{\circ}$ declination strip centered at $\sim δ=-15^{\circ}$, mapping $\sim 5400$ square degrees in the sky. The BINGO consortium is led by University of São Paulo with co-leadership at National Institute for Space Research and Campina Grande Federal University (Brazil). Telescope subsystems have already been fabricated and tested, and the dish and structure fabrication are expected to start in late 2020, as well as the road and terrain preparation.

astro-ph.CO↗

arXiv2021

Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning

Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions corresponding to the pre-order traversal of an Abstract Syntax Tree. However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions. In this paper, we first throughly analyze the context modeling difference between neural code generation models with different traversals based decodings (preorder traversal vs breadth-first traversal), and then propose to introduce a mutual learning framework to jointly train these models. Under this framework, we continuously enhance both two models via mutual distillation, which involves synchronous executions of two one-to-one knowledge transfers at each training step. More specifically, we alternately choose one model as the student and the other as its teacher, and require the student to fit the training data and the action prediction distributions of its teacher. By doing so, both models can fully absorb the knowledge from each other and thus could be improved simultaneously. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness of our approach. We release our code at https://github.com/DeepLearnXMU/CGML.

cs.AI↗

arXiv2021

Few-Shot Event Detection with Prototypical Amortized Conditional Random Field

Event detection tends to struggle when it needs to recognize novel event types with a few samples. The previous work attempts to solve this problem in the identify-then-classify manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation. In this paper, we present a novel unified model which converts the task to a few-shot tagging problem with a double-part tagging scheme. To this end, we first propose the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which approximates the transition scores between labels based on the label prototypes. Then Gaussian distribution is introduced for modeling of the transition scores to alleviate the uncertain estimation resulting from insufficient data. Experimental results show that the unified models work better than existing identify-then-classify models and our PA-CRF further achieves the best results on the benchmark dataset FewEvent. Our code and data are available at http://github.com/congxin95/PA-CRF.

cs.CL↗

arXiv2021

Power System Differential-Algebraic Equations

This document presents an introduction of two commonly used power system differential algebraic equations for studying electromechanical oscillation and transient stability. Two types of generator models are used to formulate the power system model, respectively: the second-order classical model and the fourth-order generator model. An example is provided on the IEEE 9-bus system.

eess.SY↗

arXiv2021

Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions

Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner. However, most of the current communication and localization systems are separately designed, leading to an under-utilization of radio resources and network performance degradation. In this paper, we propose an integrated communication and navigation (ICAN) framework to fully unleash the potential of ultra-dense LEO satellite networks for optimal provisioning of differentiated services. The specific benefits, feasibility analysis and challenges for ICAN enabled satellite system are explicitly discussed. In particular, a novel beam hopping based ICAN satellite system solution is devised to adaptively tune the network beam layout for dual functional communication and positioning purposes. Furthermore, a thorough experimental platform is built following the Third Generation Partnership Project (3GPP) defined non-terrestrial network simulation parameters to validate the performance gain of the ICAN satellite system

eess.SP↗

arXiv2021

Quasi-Dynamic Traffic Assignment using High Performance Computing

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is two-fold: 1) to introduce a new traffic assignment approach - a quasi-dynamic traffic assignment (QDTA) model and 2) to describe how we parallelized the QDTA algorithms to leverage High-Performance Computing (HPC) and scale to large metropolitan areas while dramatically reducing compute time. We examine and compare different scenarios, including a baseline static traffic assignment (STA) and a quasi-dynamic scenario inspired by the user-equilibrium (UET). Results are presented for the San Francisco Bay Area which accounts for 19M trips/day and an urban road network of 1M links. We utilize an iterative gradient descent method, where the step size is selected using a Quasi-Newton method with parallelized cost function evaluations and compare it to using pre-defined step sizes (MSA). Using the parallelized line search provides a 16 percent reduction in total execution time due to a reduction in the number of gradient descent iterations required for convergence. The full day QDTA comprising 96 optimization steps over 15 minute intervals runs in about 4 minutes on 1,024 cores of the NERSC Cori computer, which represents a speedup of over 36x versus serial execution. To our knowledge, this compute time is significantly lower than other traffic assignment solutions for a problem of this scale.

cs.DC↗