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Bin Wang

Publications and source records attributed to Bin Wang.

At least 523 records · Page 29Linked to original sources
arXiv2023

Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation

Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence. However, the general full-sentence translation test set is acquired by offline translation of the entire source sentence, which is not designed for SimulMT evaluation, making us rethink whether this will underestimate the performance of SimulMT models. In this paper, we manually annotate a monotonic test set based on the MuST-C English-Chinese test set, denoted as SiMuST-C. Our human evaluation confirms the acceptability of our annotated test set. Evaluations on three different SimulMT models verify that the underestimation problem can be alleviated on our test set. Further experiments show that finetuning on an automatically extracted monotonic training set improves SimulMT models by up to 3 BLEU points.

cs.CL↗
arXiv2023

A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples

Detection of pulmonary nodules by CT is used for screening lung cancer in early stages.omputer aided diagnosis (CAD) based on deep-learning method can identify the suspected areas of pulmonary nodules in CT images, thus improving the accuracy and efficiency of CT diagnosis. The accuracy and robustness of deep learning models. Method:In this paper, we explore (1) the data augmentation method based on the generation model and (2) the model structure improvement method based on the embedding mechanism. Two strategies have been introduced in this study: a new data augmentation method and a embedding mechanism. In the augmentation method, a 3D pixel-level statistics algorithm is proposed to generate pulmonary nodule and by combing the faked pulmonary nodule and healthy lung, we generate new pulmonary nodule samples. The embedding mechanism are designed to better understand the meaning of pixels of the pulmonary nodule samples by introducing hidden variables. Result: The result of the 3DVNET model with the augmentation method for pulmonary nodule detection shows that the proposed data augmentation method outperforms the method based on generative adversarial network (GAN) framework, training accuracy improved by 1.5%, and with embedding mechanism for pulmonary nodules classification shows that the embedding mechanism improves the accuracy and robustness for the classification of pulmonary nodules obviously, the model training accuracy is close to 1 and the model testing F1-score is 0.90.Conclusion:he proposed data augmentation method and embedding mechanism are beneficial to improve the accuracy and robustness of the model, and can be further applied in other common diagnostic imaging tasks.

eess.IV↗
arXiv2023

Practicing carpe diem in the journey of studying physics: A brief review of the scientific contribution of Ru-Keng Su

We briefly review the scientific contributions of the late Prof. Ru-Keng Su in his academic life. In the area of intermediate and high-energy nuclear physics, Su explored various topics in high-energy nuclear physics and particle physics, inclusively about the finite temperature field theory, effective models for nuclear and quark matter, soliton, and quasiparticle models, among others. In gravity and cosmology, Su's research primarily embraces black hole thermodynamics, quasinormal modes, cosmological microwave background radiation, modified theories of gravity, and AdS/CFT correspondence and its applications. Besides, many aspects of Su's distinguished impact on the Chinese academic physics community are discussed. We also summarize the biographical and academic career of Su. This article is an elaborated version of the memorial article that will be published in \href{https://www.mdpi.com/journal/symmetry}{\it symmetry}.

physics.hist-ph↗
arXiv2023

A novel class of linearly implicit energy-preserving schemes for conservative systems

We consider a kind of differential equations d/dt y(t) = R(y(t))y(t) + f(y(t)) with energy conservation. Such conservative models appear for instance in quantum physics, engineering and molecular dynamics. A new class of energy-preserving schemes is constructed by the ideas of scalar auxiliary variable (SAV) and splitting, from which the nonlinearly implicit schemes have been improved to be linearly implicit. The energy conservation and error estimates are rigorously derived. Based on these results, it is shown that the new proposed schemes have unconditionally energy stability and can be implemented with a cost of solving a linearly implicit system. Numerical experiments are done to confirm these good features of the new schemes.

math.NA↗
arXiv2023

A new hazard event classification model via deep learning and multifractal

Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and suggestions, and such hazards can be considered as hazard events (HaE). The classification research on HaE has much irreplaceable pragmatic values. In this paper, we present a novel deep learning model termed DLF through multifractal to explore HaE classification where the motivation is that HaE can be naturally regarded as a kind of time series. Specifically, first HaE is vectorized to get HaE time series by employing BERT. Then, a new multifractal analysis method termed HmF-DFA is proposed to win HaE fractal series by analyzing HaE time series. Finally, a new hierarchical gating neural network (HGNN) is designed to process HaE fractal series to accomplish the classification of HaE from three aspects: severity, possibility and risk. We take HAZOP reports of 18 processes as cases, and launch the experiments on this basis. Results demonstrate that compared with other classifiers, DLF classifier performs better under metrics of precision, recall and F1-score, especially for the severity aspect. Also, HmF-DFA and HGNN effectively promote HaE classification. Our HaE classification system can serve application incentives to experts, engineers, employees, and other enterprises. We hope our research can contribute added support to the daily practice in industrial safety.

cs.CL↗
arXiv2023

Analysis of November 21, 2021, Kaua`i Island Power System 18-20 Hz Oscillations

This letter discusses the 18-20 Hz oscillation event at 05:30 am on November 21, 2021, in Kaua`i's power system following the trip of an oil power plant. As far as the authors are aware, this is the first report of a transmission system-wide subsynchronous oscillation driven by inverter-based resources (though the system in question is relatively small). In this letter, we leverage two data-based methods-the dissipating energy flow method and the sub/super-synchronous power flow method-to locate the sources of the oscillation. Also, we build an electromagnetic transient model of the Kaua`i power system and replay the 18-20 Hz oscillation. Finally, we propose two mitigation methods and validate their effectiveness via numerical simulation.

eess.SY↗
arXiv2023

Rainbow Hamilton cycle in hypergraph system

In this paper, we develop a new rainbow Hamilton framework, which is of independent interest, settling the problem proposed by Gupta, Hamann, Müyesser, Parczyk, and Sgueglia when $k=3$, and draw the general conclusion for any $k\geq3$ as follows. A $k$-graph system $\textbf{H}=\{H_i\}_{i\in[n]}$ is a family of not necessarily distinct $k$-graphs on the same $n$-vertex set $V$, moreover, a $k$-graph $H$ on $V$ is rainbow if $E(H)\subseteq \bigcup_{i\in[n]}E(H_i)$ and $|E(H)\cap E(H_i)|\leq1$ for $i\in[n]$. We show that given $γ> 0$, sufficiently large $n$ and an $n$-vertex $k$-graph system $\textbf{H}=\{H_i\}_{i\in[n]}$ , if $δ_{k-2}(H_i)\geq(5/9+γ)\binom{n}{2}$ for $i\in[n]$ where $k\geq3$, then there exists a rainbow tight Hamilton cycle. This result implies the conclusion in a single graph, which was proved by Lang and Sanhueza-Matamala [$J. Lond. Math. Soc., 2022$], Polcyn, Reiher, Rödl and Schülke [$J. Combin. Theory \ Ser. B, 2021$] independently.

math.CO↗
arXiv2023

Magnetic-induced Spontaneous Scalarization in Dynamcial Chern-Simons Gravity

In the framework of the dynamical Chern-Simons gravity, we study the scalar field perturbations of the Reissner-Nordström-Melvin spacetime, which describes a charged black hole permeated by a uniform magnetic field. In the presence of the magnetic field, the scalar field acquires an effective mass whose square takes negative value in the half domain of the angular direction. This inevitably introduces the tachyonic instability and associated spontaneous scalarization as long as the coupling constant between the scalar field and the Chern-Simons invariant exceeds a threshold value. We study the object pictures of the time evolutions of the scalar field perturbations at the linear level, and find that the presence of the magnetic field will dramatically change the waveforms and associated ringdown modes. Nonlinear evolutions for the unstable perturbations are also performed in the decoupling limit, which demonstrate the scalar cloud as the final fate. Influences of the coupling constant and the black hole charge on the wave dynamics are also studied.

gr-qc↗
arXiv2023

Quantum Fluctuation of the Quantum Geometric Tensor and its Manifestation as Intrinsic Hall Signatures in Time-Reversal Invariant Systems

In time-reversal invariant systems, all charge Hall effects predicted so far are extrinsic effects due to the dependence on the relaxation time. We explore intrinsic Hall signatures by studying quantum noise spectrum of the Hall current in time-reversal invariant systems, and discover intrinsic thermal Hall noises in both linear and nonlinear regimes. As the band geometric characteristics, quantum geometric tensor and Berry curvature play critical roles in various Hall effects, so are their quantum fluctuations. It is found that the thermal Hall noise in linear order of the electric field is purely intrinsic, and the second-order thermal Hall noise has both intrinsic and extrinsic contributions. In particular, the intrinsic part of the second-order thermal Hall noise is a manifestation of the quantum fluctuation of quantum geometric tensor, which widely exists as long as Berry curvature is nonzero. These intrinsic thermal Hall noises provide direct measurable means to band geometric information, including Berry curvature related quantities and quantum fluctuation of quantum geometric tensor.

cond-mat.mes-hall↗
arXiv2023

Topological Hall Effect Driven by Short-Range Magnetic Orders in EuZn$_2$As$_2$

Short-range (SR) magnetic orders such as magnetic glass orders or fluctuations in a quantum system usually host exotic states or critical behaviors. As the long-range (LR) magnetic orders, SR magnetic orders can also break time-reversal symmetry and drive the non-zero Berry curvature leading to novel transport properties. In this work, we report that in EuZn$_2$As$_2$ compound, besides the LR A-type antiferromagnetic (AF) order, the SR magnetic order is observed in a wide temperature region. The magnetization measurements and electron spin resonance (ESR) measurements reveal the ferromagnetic (FM) correlations for this SR magnetic order which results in an obvious anomalous Hall effect above the AF transition. Moreover the ESR results reveal that this FM SR order coexists with LR AF order exhibiting anisotropic magnetic correlations below the AF transition. The interactions of LR and SR magnetism evolving with temperature and field can host non-zero spin charility and berry curvature leading the additional topological Hall contribution even in a centrosymmetric simple AF system. Our results indicate that EuZn$_2$As$_2$ is a fertile platform to investigate exotic magnetic and electronic states.

cond-mat.str-el↗
arXiv2023

Higher-order Oscillatory Planar Hall Effect in Topological Kagome Metal

Exploration of exotic transport behavior for quantum materials is of great interest and importance for revealing exotic orders to bring new physics. In this Letter, we report the observation of exotic prominent planar Hall effect (PHE) and planar anisotropic magnetoresistivity (PAMR) in strange kagome metal KV$_3$Sb$_5$. The PHE and PAMR, which are driven by an in-plane magnetic field and display sharp difference from other Hall effects driven by an out-of-plane magnetic field or magnetization, exhibit exotic higher-order oscillations in sharp contrast to those following empirical rule only allowing twofold symmetrical oscillations. These higher-order oscillations exhibit strong field and temperature dependence and vanish around charge density wave (CDW) transition. The unique transport properties suggest a significant interplay of the lattice, magnetic and electronic structure in KV$_3$Sb$_5$. This interplay can couple the hidden anisotropy and transport electrons leading to the novel PHE and PAMR in contrast to other materials.

cond-mat.str-el↗
arXiv2023

BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.

cs.CL↗
arXiv2023

Rotating $(2+1)$-dimensional Black Holes in Einstein-Maxwell-Dilaton Theory

We consider Einstein-Maxwell-Dilaton theory in $(2+1)$-dimensions where the coupling between the scalar field and the Maxwell invariant is the dilatonic coupling $f(ϕ) = \exp (-2αϕ)$ and obtain novel exact rotating black hole solutions. The dilatonic parameter $α$ impacts the metric function, affecting the rotating properties of the black hole, its mass and also its thermodynamics. Calculating the entropy we find that it is always positive and the dilatonic black holes may have higher entropy than the BTZ black hole. Depending on the parameters, the dilatonic BTZ-like black hole may be thermodynamically preferred than the BTZ black hole which is recovered when $α=0$.

gr-qc↗
arXiv2023

Evaluation of radiative depolarization in the future Circular Electron-Positron Collider

Polarized lepton beams are an important aspect in the design of the future 100 km-scale Circular Electron-Positron Collider (CEPC). Precision beam energy calibration using resonant depolarization, as well as longitudinally polarized colliding beams are being actively investigated. The achievable beam polarization level for various beam energies and application scenarios depends on the radiative depolarization in the collider rings. In this paper the radiative depolarization effects are evaluated for a CEPC collider ring lattice with detailed machine imperfections and corrections. Simulations with the SLIM and Monte-Carlo approaches using the Bmad/PTC codes are compared with the theory of the effects of spin diffusion for ultra-high beam energies and the validity of the theories is thereby addressed. The paper concludes with a summary and suggestions for further investigations.

physics.acc-ph↗
arXiv2023

High-fidelity ptychographic imaging of highly periodic structures enabled by vortex high harmonic beams

Ptychographic Coherent Diffractive Imaging enables diffraction-limited imaging of nanoscale structures at extreme ultraviolet and x-ray wavelengths, where high-quality image-forming optics are not available. However, its reliance on a set of diverse diffraction patterns makes it challenging to use ptychography to image highly periodic samples, limiting its application to defect inspection for electronic and photonic devices. Here, we use a vortex high harmonic light beam driven by a laser carrying orbital angular momentum to implement extreme ultraviolet ptychographic imaging of highly periodic samples with high fidelity and reliability. We also demonstrate, for the first time to our knowledge, ptychographic imaging of an isolated, near-diffraction-limited defect in an otherwise periodic sample using vortex high harmonic beams. This enhanced metrology technique can enable high-fidelity imaging and inspection of highly periodic structures for next-generation nano, energy, photonic and quantum devices.

physics.optics↗
arXiv2022

Black hole images: A Review

In recent years, unprecedented progress has been achieved regarding black holes' observation through the electromagnetic channel. The images of the supermassive black holes M87$^{*}$ and Sgr A$^{*}$ released by the Event Horizon Telescope (EHT) Collaboration provided direct visual evidence for their existence, which has stimulated further studies on various aspects of the compact celestial objects. Moreover, the information stored in these images provides a new way to understand the pertinent physical processes that occurred near the black holes, to test alternative theories of gravity, and to furnish insight into fundamental physics. In this review, we briefly summarize the recent developments on the topic. In particular, we elaborate on the features and formation mechanism of black hole shadows, the properties of black hole images illuminated by the surrounding thin accretion disk, and the corresponding polarization patterns. The potential applications of the relevant studies are also addressed.

astro-ph.HE↗
arXiv2022

Dilation-Erosion for Single-Frame Supervised Temporal Action Localization

To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available (https://github.com/LingJun123/single-frame-TAL).

cs.CV↗
arXiv2022

An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks

Convolutional neural networks (CNNs) have constantly achieved better performance over years by introducing more complex topology, and enlarging the capacity towards deeper and wider CNNs. This makes the manual design of CNNs extremely difficult, so the automated design of CNNs has come into the research spotlight, which has obtained CNNs that outperform manually-designed CNNs. However, the computational cost is still the bottleneck of automatically designing CNNs. In this paper, inspired by transfer learning, a new evolutionary computation based framework is proposed to efficiently evolve CNNs without compromising the classification accuracy. The proposed framework leverages multi-source domains, which are smaller datasets than the target domain datasets, to evolve a generalised CNN block only once. And then, a new stacking method is proposed to both widen and deepen the evolved block, and a grid search method is proposed to find optimal stacking solutions. The experimental results show the proposed method acquires good CNNs faster than 15 peer competitors within less than 40 GPU-hours. Regarding the classification accuracy, the proposed method gains its strong competitiveness against the peer competitors, which achieves the best error rates of 3.46%, 18.36% and 1.76% for the CIFAR-10, CIFAR-100 and SVHN datasets, respectively.

cs.CV↗