arXiv subjects

Bin Wang

Publications and source records attributed to Bin Wang.

At least 739 records · Page 41Linked to original sources

arXiv2020

Anapole mediated giant photothermal nonlinearity in nanostructured silicon

Featured with a plethora of electric and magnetic Mie resonances, high index dielectric nanostructures offer a versatile platform to concentrate light-matter interactions at the nanoscale. By integrating unique features of far-field scattering control and near-field concentration from radiationless anapole states, here, we demonstrate a giant photothermal nonlinearity in single subwavelength-sized silicon nanodisks. The nanoscale energy concentration and consequent near-field enhancements mediated by the anapole mode yield a reversible nonlinear scattering with a large modulation depth and a broad dynamic range, unveiling a record-high nonlinear index change up to 0.5 at mild incident light intensities on the order of MW/cm2. The observed photothermal nonlinearity showcases three orders of magnitude enhancement compared with that of unstructured bulk silicon, as well as nearly one order of magnitude higher than that through the radiative electric dipolar mode. Such nonlinear scattering can empower distinctive point spread functions in confocal reflectance imaging, offering the potential for far-field localization of nanostructured Si with an accuracy approaching 40 nm. Our findings shed new light on active silicon photonics based on optical anapoles.

physics.optics↗

arXiv2020

Full analytical formulas for frequency response of space-based gravitational wave detectors

The discovery of gravitational waves, which are ripples of space-time itself, opened a new window to test general relativity, because it predicts that there are only plus and cross polarizations for gravitational waves. For alternative theories of gravity, there may be up to six polarizations. The measurement of the polarization is one of the major scientific goals for future gravitational wave detectors. To evaluate the capability of the detector, we need to use the frequency dependent response functions averaged over the source direction and polarization angle. We derive the full analytical formulas of the averaged response functions for all six possible polarizations and present their asymptotic behaviors based on these analytical formulas. Compared with the numerical simulation, the full analytical formulas are more efficient and valid for any equal-arm interferometric gravitational wave detector without optical cavities in the arms and for a time-delay-interferometry Michelson combination.

gr-qc↗

arXiv2020

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter

With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The openness and convenience of social media platforms provide a free expression for people but also cause new social problems. The widespread of false rumors on social media can bring about the panic of the public and damage personal reputation, which makes rumor automatic detection technology become particularly necessary. The majority of existing methods for rumor detection focus on mining effective features from text contents, user profiles, and patterns of propagation. Nevertheless, these methods do not take full advantage of global semantic relations of the text contents, which characterize the semantic commonality of rumors as a key factor for detecting rumors. In this paper, we construct a tweet-word-user heterogeneous graph based on the text contents and the source tweet propagations of rumors. A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection. Experiments on real-world Twitter data demonstrate the superiority of the proposed approach, which also has a comparable ability to detect rumors at a very early stage.

cs.SI↗

arXiv2020

Modeling Discourse Structure for Document-level Neural Machine Translation

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.

cs.CL↗

arXiv2020

Reconfiguration of District Heating Network for Operational Flexibility Enhancement in Power System Unit Commitment

Massive adoptions of combined heat and power (CHP) units necessitate the coordinated operation of power system and district heating system (DHS). Exploiting the reconfigurable property of district heating networks (DHNs) provides a cost-effective solution to enhance the flexibility of the power system by redistributing heat loads in DHS. In this paper, a unit commitment considering combined electricity and reconfigurable heating network (UC-CERHN) is proposed to coordinate the day-ahead scheduling of power system and DHS. The DHS is formulated as a nonlinear and mixed-integer model with considering the reconfigurable DHN. Also, an auxiliary energy flow variable is introduced in the formed DHS model to make the commitment problem tractable, where the computational burdens are significantly reduced. Extensive case studies are presented to validate the effectiveness of the approximated model and illustrate the potential benefits of the proposed method with respect to congestion management and wind power accommodation. (Corresponding author:Hongbin Sun)

eess.SY↗

arXiv2020

SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models

Sentence embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks. Yet, it is an open problem to generate a high quality sentence representation from BERT-based word models. It was shown in previous study that different layers of BERT capture different linguistic properties. This allows us to fusion information across layers to find better sentence representation. In this work, we study the layer-wise pattern of the word representation of deep contextualized models. Then, we propose a new sentence embedding method by dissecting BERT-based word models through geometric analysis of the space spanned by the word representation. It is called the SBERT-WK method. No further training is required in SBERT-WK. We evaluate SBERT-WK on semantic textual similarity and downstream supervised tasks. Furthermore, ten sentence-level probing tasks are presented for detailed linguistic analysis. Experiments show that SBERT-WK achieves the state-of-the-art performance. Our codes are publicly available.

cs.CL↗

arXiv2020

MeshODE: A Robust and Scalable Framework for Mesh Deformation

We present MeshODE, a scalable and robust framework for pairwise CAD model deformation without prespecified correspondences. Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm. We address two challenges in this problem, namely the design of a powerful deformation function and obtaining a feature-preserving CAD deformation. While traditional deformation directly optimizes for the coordinates of the mesh vertices or the vertices of a control cage, we introduce a deep bijective mapping that utilizes a flow model parameterized as a neural network. Our function has the capacity to handle complex deformations, produces deformations that are guaranteed free of self-intersections, and requires low rigidity constraining for geometry preservation, which leads to a better fitting quality compared with existing methods. It additionally enables continuous deformation between two arbitrary shapes without supervision for intermediate shapes. Furthermore, we propose a robust preprocessing pipeline for raw CAD meshes using feature-aware subdivision and a uniform graph template representation to address artifacts in raw CAD models including self-intersections, irregular triangles, topologically disconnected components, non-manifold edges, and nonuniformly distributed vertices. This facilitates a fast deformation optimization process that preserves global and local details. Our code is publicly available.

cs.GR↗

arXiv2020

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary skill selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

cs.LG↗

arXiv2020

A Modal-Space Method for Online Power System Steady-State Stability Monitoring

This paper proposes a novel approach to estimate the steady-state angle stability limit (SSASL) by using the nonlinear power system dynamic model in the modal space. Through two linear changes of coordinates and a simplification introduced by the steady-state condition, the nonlinear power system dynamic model is transformed into a number of single-machine-like power systems whose power-angle curves can be derived and used for estimating the SSASL. The proposed approach estimates the SSASL of angles at all machines and all buses without the need for manually specifying the scenario, i.e. setting sink and source areas, and also without the need for solving multiple nonlinear power flows. Case studies on 9-bus and 39-bus power systems demonstrate that the proposed approach is always able to capture the aperiodic instability in an online environment, showing promising performance in the online monitoring of the steady-state angle stability over the traditional power flow-based analysis.

eess.SY↗

arXiv2020

Error estimates of some splitting schemes for charged-particle dynamics under strong magnetic field

In this work, we consider the error estimates of some splitting schemes for the charged-particle dynamics under a strong magnetic field. We first propose a novel energy-preserving splitting scheme with computational cost per step independent from the strength of the magnetic field. Then under the maximal ordering scaling case, we establish for the scheme and in fact for a class of Lie-Trotter type splitting schemes, a uniform (in the strength of the magnetic field) and optimal error bound in the position and in the velocity parallel to the magnetic field. For the general strong magnetic field case, the modulated Fourier expansions of the exact and the numerical solutions are constructed to obtain a convergence result. Numerical experiments are presented to illustrate the error and energy behaviour of the splitting schemes.

math.NA↗

arXiv2020

The parameter-free Finger-Of-God model and its application to 21cm intensity mapping

Using the galaxy catalog built from ELUCID N-body simulation and the semi-analytical galaxy formation model, we have built a mock HI intensity mapping map. We have implemented the Finger-of-God (FoG) effect in the map by considering the galaxy HI gas velocity dispersion. By comparing the HI power spectrum in the redshift space with the measurement from IllustrisTNG simulation, we have found that such FoG effect can explain the discrepancy between current mock map built from N-body simulation and Illustris TNG simulation. Then we built a parameter-free FoG model and a shot-noise model to calculate the HI power spectrum. We found that our model can accurately fit both the monopole and quadrupole moments of the HI matter power spectrum. Our method of building the mock HI intensity map and the parameter-free FoG model will be widely useful for the up-coming 21cm intensity mapping experiments, such as CHIME, Tianlai, BINGO, FAST and SKA. It is also crucial for us to study the non-linear effects in 21cm intensity mapping.

astro-ph.CO↗

arXiv2020

Attention-based Transducer for Online Speech Recognition

Recent studies reveal the potential of recurrent neural network transducer (RNN-T) for end-to-end (E2E) speech recognition. Among some most popular E2E systems including RNN-T, Attention Encoder-Decoder (AED), and Connectionist Temporal Classification (CTC), RNN-T has some clear advantages given that it supports streaming recognition and does not have frame-independency assumption. Although significant progresses have been made for RNN-T research, it is still facing performance challenges in terms of training speed and accuracy. We propose attention-based transducer with modification over RNN-T in two aspects. First, we introduce chunk-wise attention in the joint network. Second, self-attention is introduced in the encoder. Our proposed model outperforms RNN-T for both training speed and accuracy. For training, we achieves over 1.7x speedup. With 500 hours LAIX non-native English training data, attention-based transducer yields ~10.6% WER reduction over baseline RNN-T. Trained with full set of over 10K hours data, our final system achieves ~5.5% WER reduction over that trained with the best Kaldi TDNN-f recipe. After 8-bit weight quantization without WER degradation, RTF and latency drop to 0.34~0.36 and 268~409 milliseconds respectively on a single CPU core of a production server.

eess.AS↗

arXiv2020

Black Hole Scalarization in Gauss-Bonnet Extended Starobinsky Gravity

We propose a class of higher-derivative gravities that can be viewed as the Gauss-Bonnet extension of the Starobinsky model. The theory admits the Minkowski spacetime vacuum whose linear spectrum consists of the graviton and a massive scalar mode. In addition to the usual Schwarzschild black hole, we use numerical analysis to establish that in some suitable mass range, new black holes carrying the massive scalar hair can emerge. The new black hole serves as a "wall" separating the naked spacetime singularity and wormholes in the parameter space of the scalar hair. Our numerical results also indicate that although the new hairy black hole and the Schwarzschild have different spacetime geometry, their entropy and temperature are same for the same mass.

gr-qc↗

arXiv2020

InsteaDMatic: Towards cross-platform automated continuous rotation electron diffraction

A DigitalMicrograph script InsteaDMatic has been developed to facilitate rapid automated continuous rotation electron diffraction (cRED) data acquisition. The script coordinates microscope functions, such as stage rotation, camera functions relevant for data collection, and stores the experiment metadata. The script is compatible with any microscope that can be controlled by DigitalMicrograph and has been tested on both JEOL and Thermo Fisher Scientific microscopes. A proof-of-concept has been performed through employing InsteaDMatic for data collection and structure determination of a ZSM-5 zeolite. The influence of illumination settings and electron dose rate on the quality of diffraction data, unit cell determination and structure solution has been investigated in order to optimize the data acquisition procedure.

cond-mat.mtrl-sci↗

arXiv2020

Forecasting Interacting Vacuum-Energy Models using Gravitational Waves

The physics of the dark sector has remained one of the controversial areas of modern cosmology at present and hence it naturally attracts massive attention to the scientific community. With the developments of the astronomical data, the physics of the dark sector is becoming much more transparent than it was some twenty years back. The detection of gravitational waves (GWs) has now opened a cluster of possibilities in the cosmological regime. Being motivated by the detection of GWs and its possible impact on the physics of dark matter and dark energy, in this work we focus on the interacting dark energy models. Assuming the simplest possibility in which the vacuum energy with equation-of-state $w_x =-1$ is allowed to interact with the pressureless dark matter, we have extracted the constraints of the cosmological parameters. We find that the addition of the GWs data to the CMB measurements significantly improves up to a factor 4 of the parameters space, and up to a factor 2 for the full combination of current cosmological datasets, namely CMB+BAO+Pantheon+RSD+R16+CC+WL. The most affected parameters by the inclusion of the GWs are $Ω_ch^2$, $θ_{MC}$, $ξ$, and the derived parameters $Ω_{m0}$, $σ_8$ and $H_0$.

astro-ph.CO↗

arXiv2020

Design-controlled Synthesis of IrO$_2$ sub-monolayers on Au Nanodendrites: Marrying Plasmonic and Electrocatalytic Properties

We develop herein plasmonic-catalytic Au-IrO$_2$ nanostructures with a morphology optimized for efficient light harvesting and catalytic surface area; the nanoparticles have a dendritic morphology, with closely spaced Au branches all partially covered by an ultrathin (1 nm) IrO$_2$ shell. This nanoparticle architecture optimizes optical features due to the interactions of closely spaced plasmonic branches forming electromagnetic hot spots, and the ultra-thin IrO$_2$ layer maximizes efficient use of this expensive catalyst. This concept was evaluated towards the enhancement of the electrocatalytic performances towards the oxygen evolution reaction (OER) as a model transformation. The OER can play a central role in meeting future energy demands but the performance of conventional electrocatalysts in this reaction is limited by the sluggish OER kinetics. We demonstrate an improvement of the OER performance for one of the most active OER catalysts, IrO$_2$, by harvesting plasmonic effects from visible light illumination in multimetallic nanoparticles. We find that the OER activity for the Au-IrO$_2$ nanodendrites can be improved under LSPR excitation, matching best properties reported in the literature. Our simulations and electrocatalytic data demonstrate that the enhancement in OER activities can be attributed to an electronic interaction between Au and IrO$_2$ and to the activation of Ir-O bonds by LSPR excited hot holes, leading to a change in the reaction mechanism (rate-determinant step) under visible light illumination.

physics.app-ph↗

arXiv2020

Unified Multi-Criteria Chinese Word Segmentation with BERT

Multi-Criteria Chinese Word Segmentation (MCCWS) aims at finding word boundaries in a Chinese sentence composed of continuous characters while multiple segmentation criteria exist. The unified framework has been widely used in MCCWS and shows its effectiveness. Besides, the pre-trained BERT language model has been also introduced into the MCCWS task in a multi-task learning framework. In this paper, we combine the superiority of the unified framework and pretrained language model, and propose a unified MCCWS model based on BERT. Moreover, we augment the unified BERT-based MCCWS model with the bigram features and an auxiliary criterion classification task. Experiments on eight datasets with diverse criteria demonstrate that our methods could achieve new state-of-the-art results for MCCWS.

cs.CL↗

arXiv2020

Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach

This paper introduces a machine learning-based approach to synthetically creating realistic phasor measurement unit (PMU) data streams of multiple transient types. In contrast to the existing literature of transient simulation-based data generation methods, we propose a generative adversarial network (GAN) based approach to learning directly from the historical data and simultaneously reproduce multiple PMU data streams. The synthetic PMU data streams reflect meaningful dynamic characteristics which observe first principles such as Kirchhoff's laws. The efficacy of this approach is demonstrated by numerical studies on the IEEE 39-bus system. We validate the fidelity and flexibility of the synthetic data via statistical resemblance and modal analysis approaches. Finally we illustrate a practical application scenario for the usage of the synthetic PMU data, i.e. leverage the synthetic data to improve the performance of the event classification algorithms.

eess.SP↗