arXiv subjects

Bin Wang

Publications and source records attributed to Bin Wang.

At least 595 records · Page 33Linked to original sources

arXiv2022

Risk Concentration and the Mean-Expected Shortfall Criterion

Expected Shortfall (ES, also known as CVaR) is the most important coherent risk measure in finance, insurance, risk management, and engineering. Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES. In particular, the axiom of no reward for concentration (NRC) is arguably quite strong, which imposes an additive form of the risk measure on portfolios with a certain dependence structure. We move away from the axiom of NRC by introducing the notion of concentration aversion, which does not impose any specific form of the risk measure. It turns out that risk measures with concentration aversion are functions of ES and the expectation. Together with the other three standard axioms of monotonicity, translation invariance and lower semicontinuity, concentration aversion uniquely characterizes the family of ES. In addition, we establish an axiomatic foundation for the problem of mean-ES portfolio selection and new explicit formulas for convex and consistent risk measures. Finally, we provide an economic justification for concentration aversion via a few axioms on the attitude of a regulator towards dependence structures.

q-fin.MF↗

arXiv2022

The BINGO Project V: Further steps in Component Separation and Bispectrum Analysis

Observing the neutral hydrogen distribution across the Universe via redshifted 21cm line intensity mapping constitutes a powerful probe for cosmology. However, the redshifted 21cm signal is obscured by the foreground emission from our Galaxy and other extragalactic foregrounds. This paper addresses the capabilities of the BINGO survey to separate such signals. Specifically, this paper looks in detail at the different residuals left over by foreground components, shows that a noise-corrected spectrum is unbiased, and shows that we understand the remaining systematic residuals by analyzing nonzero contributions to the three-point function. We use the generalized needlet internal linear combination, which we apply to sky simulations of the BINGO experiment for each redshift bin of the survey. We present our recovery of the redshifted 21cm signal from sky simulations of the BINGO experiment, including foreground components. We test the recovery of the 21cm signal through the angular power spectrum at different redshifts, as well as the recovery of its non-Gaussian distribution through a bispectrum analysis. We find that non-Gaussianities from the original foreground maps can be removed down to, at least, the noise limit of the BINGO survey with such techniques. Our component separation methodology allows us to subtract the foreground contamination in the BINGO channels down to levels below the cosmological signal and the noise, and to reconstruct the 21cm power spectrum for different redshift bins without significant loss at multipoles $20 \lesssim \ell \lesssim 500$. Our bispectrum analysis yields strong tests of the level of the residual foreground contamination in the recovered 21cm signal, thereby allowing us to both optimize and validate our component separation analysis. (Abridged)

astro-ph.CO↗

arXiv2022

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress. To address the problems, we propose a novel model \textbf{MISC}, which firstly infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling. Our code and data could be found in \url{https://github.com/morecry/MISC}.

cs.CL↗

arXiv2022

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Previous CDR approaches mostly achieve the goal by following the Embedding and Mapping (EMCDR) idea which attempts to learn a mapping function to transfer the pre-trained user representations (embeddings) from the source domain into the target domain. However, they pre-train the user/item representations independently for each domain, ignoring to consider both domain interactions simultaneously. Therefore, the biased pre-trained representations inevitably involve the domain-specific information which may lead to negative impact to transfer information across domains. In this work, we consider a key point of the CDR task: what information needs to be shared across domains? To achieve the above idea, this paper utilizes the information bottleneck (IB) principle, and proposes a novel approach termed as CDRIB to enforce the representations encoding the domain-shared information. To derive the unbiased representations, we devise two IB regularizers to model the cross-domain/in-domain user-item interactions simultaneously and thereby CDRIB could consider both domain interactions jointly for de-biasing.

cs.IR↗

arXiv2022

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the object pose by minimizing shape re-projection constraints in all views, which greatly improves the accuracy compared with the single-view approach, and is even more accurate than the depth-based method. Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes. The annotation error is guaranteed to be less than 2mm, according to both theoretical analysis and validation experiments. We re-evaluate the state-of-the-art 3D object tracking methods with our dataset, reporting their performance ranking in real scenes. Our BCOT benchmark and code can be found at https://ar3dv.github.io/BCOT-Benchmark/.

cs.CV↗

arXiv2022

Just Rank: Rethinking Evaluation with Word and Sentence Similarities

Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to overfit to such evaluations, negatively impacting embedding models' development. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks. Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments. Finally, the practical evaluation toolkit is released for future benchmarking purposes.

cs.CL↗

arXiv2022

The BINGO Project III: Optical design and optimisation of the focal plane

The BINGO telescope was designed to measure the fluctuations of the 21-cm radiation arising from the hyperfine transition of neutral hydrogen and aims to measure the Baryon Acoustic Oscillations (BAO) from such fluctuations, therefore serving as a pathfinder to future deeper intensity mapping surveys. The requirements for the Phase 1 of the projects consider a large reflector system (two 40 m-class dishes in a crossed-Dragone configuration), illuminating a focal plane with 28 horns to measure the sky with two circular polarisations in a drift scan mode to produce measurements of the radiation in intensity as well as the circular polarisation. In this paper we present the optical design for the instrument. We describe the intensity and polarisation properties of the beams and the optical arrangement of the horns in the focal plane to produce a homogeneous and well-sampled map after the end of Phase 1. Our analysis provides an optimal model for the location of the horns in the focal plane, producing a homogeneous and Nyquist sampled map after the nominal survey time. We arrive at an optimal configuration for the optical system, including the focal plane positioning and the beam behavior of the instrument. We present an estimate of the expected side lobes both for intensity and polarisation, as well as the effect of band averaging on the final side lobes. The cross polarisation leakage values for the final configuration allow us to conclude that the optical arrangement meets the requirements of the project. We conclude that the chosen optical design meets the requirements for the project in terms of polarisation purity, area coverage as well as homogeneity of coverage so that BINGO can perform a successful BAO experiment. We further conclude that the requirements on the placement and r.m.s. error on the mirrors are also achievable so that a successful experiment can be conducted.(Abridged)

astro-ph.IM↗

arXiv2022

Nonlinear Hall effect induced by internal Coulomb interaction and phase relaxation process in a four-terminal system with time-reversal symmetry

We numerically investigate the second-order nonlinear Hall transport properties of a four-terminal system with time-reversal symmetry and broken inversion symmetry. Within the nonequilibrium Green's function formalism, the second-order nonlinear conductances are derived, where the internal Coulomb potential in response to external voltages is explicitly included to guarantee the gauge invariance. For the system with single mirror symmetry Mx, nonlinear Hall properties are only observable in the y direction and contributed solely from the second-order nonlinear effect. From the symmetry point of view, the observed nonlinear Hall transport phenomena have one-to-one correspondence with the Berry curvature dipole induced nonlinear Hall effect semiclassically obtained for the same Hamiltonian. In addition to the nonlinear Hall effect originated from symmetries of the system, it is found that the internal Coulomb potential has the same symmetry of the four-terminal system, which gives rise to an extra nonlinear Hall response. Moreover, the phase relaxation mechanism modeled by virtual probes leads to the dephasing-induced nonlinear Hall effect.

cond-mat.mes-hall↗

arXiv2022

Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Recently, mainstream approaches perform this task through adversarial learning, yet still suffer from two limitations. First, they mainly align marginal distribution by unsupervised cross-domain feature matching, and ignore each feature's categorical and positional information that can be exploited for conditional alignment; Second, they treat all classes as equally important for transferring cross-domain knowledge and ignore that different classes usually have different transferability. In this paper, we propose a joint adaptive detection framework (JADF) to address the above challenges. First, an end-to-end joint adversarial adaptation framework for object detection is proposed, which aligns both marginal and conditional distributions between domains without introducing any extra hyperparameter. Next, to consider the transferability of each object class, a metric for class-wise transferability assessment is proposed, which is incorporated into the JADF objective for domain adaptation. Further, an extended study from unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation (UFDA) is conducted, where only a few unlabeled training images are available in unlabeled target domain. Extensive experiments validate that JADF is effective in both the UDA and UFDA settings, achieving significant performance gains over existing state-of-the-art cross-domain detection methods.

cs.CV↗

arXiv2022

Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation

Although domain adaptation has been extensively studied in natural image-based segmentation task, the research on cross-domain segmentation for very high resolution (VHR) remote sensing images (RSIs) still remains underexplored. The VHR RSIs-based cross-domain segmentation mainly faces two critical challenges: 1) Large area land covers with many diverse object categories bring severe local patch-level data distribution deviations, thus yielding different adaptation difficulties for different local patches; 2) Different VHR sensor types or dynamically changing modes cause the VHR images to go through intensive data distribution differences even for the same geographical location, resulting in different global feature-level domain gap. To address these challenges, we propose a curriculum-style local-to-global cross-domain adaptation framework for the segmentation of VHR RSIs. The proposed curriculum-style adaptation performs the adaptation process in an easy-to-hard way according to the adaptation difficulties that can be obtained using an entropy-based score for each patch of the target domain, and thus well aligns the local patches in a domain image. The proposed local-to-global adaptation performs the feature alignment process from the locally semantic to globally structural feature discrepancies, and consists of a semantic-level domain classifier and an entropy-level domain classifier that can reduce the above cross-domain feature discrepancies. Extensive experiments have been conducted in various cross-domain scenarios, including geographic location variations and imaging mode variations, and the experimental results demonstrate that the proposed method can significantly boost the domain adaptability of segmentation networks for VHR RSIs. Our code is available at: https://github.com/BOBrown/CCDA_LGFA.

cs.CV↗

arXiv2022

Learning Modified Indicator Functions for Surface Reconstruction

Surface reconstruction is a fundamental problem in 3D graphics. In this paper, we propose a learning-based approach for implicit surface reconstruction from raw point clouds without normals. Our method is inspired by Gauss Lemma in potential energy theory, which gives an explicit integral formula for the indicator functions. We design a novel deep neural network to perform surface integral and learn the modified indicator functions from un-oriented and noisy point clouds. We concatenate features with different scales for accurate point-wise contributions to the integral. Moreover, we propose a novel Surface Element Feature Extractor to learn local shape properties. Experiments show that our method generates smooth surfaces with high normal consistency from point clouds with different noise scales and achieves state-of-the-art reconstruction performance compared with current data-driven and non-data-driven approaches.

cs.CV↗

arXiv2022

Quasinormal modes in two-photon autocorrelation and the geometric-optics approximation

In this work, we study the black hole light echoes in terms of the two-photon autocorrelation and explore their connection with the quasinormal modes. It is shown that the above time-domain phenomenon can be analyzed by utilizing the well-known frequency-domain relations between the quasinormal modes and characteristic parameters of null geodesics. We found that the time-domain correlator, obtained by the inverse Fourier transform, naturally acquires the echo feature, which can be attributed to a collective effect of the asymptotic poles through a weighted summation of the squared modulus of the relevant Green's functions. Specifically, the contour integral leads to a summation taking over both the overtone index and angular momentum. Moreover, the dominant contributions to the light echoes are from those in the eikonal limit, consistent with the existing findings using the geometric-optics arguments. For the Schwarzschild black holes, we demonstrate the results numerically by considering a transient spherical light source. Also, for the Kerr spacetimes, we point out a potential difference between the resulting light echoes using the geometric-optics approach and those obtained by the black hole perturbation theory. Possible astrophysical implications of the present study are addressed.

gr-qc↗

arXiv2022

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

This paper describes our submission to ICASSP 2022 Multi-channel Multi-party Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA) estimation are used to improve the accuracy of speaker diarization. Multi-channel combination and overlap detection are applied to reduce the missed speaker error. A modified DOVER-Lap is also proposed to fuse the results of different systems. We achieve the final DER of 5.79% on the Eval set and 7.23% on the Test set. For Track 2, we develop our system using the Conformer model in a joint CTC-attention architecture. Serialized output training is adopted to multi-speaker overlapped speech recognition. We propose a neural front-end module to model multi-channel audio and train the model end-to-end. Various data augmentation methods are utilized to mitigate over-fitting in the multi-channel multi-speaker E2E system. Transformer language model fusion is developed to achieve better performance. The final CER is 19.2% on the Eval set and 20.8% on the Test set.

cs.SD↗

arXiv2022

$(2+1)$-Dimensional Black Holes in $f(R,ϕ)$ Gravity

We consider a $f(R)$ gravity theory in $(2+1)$-dimensions with a self-interacting scalar field non-minimally coupled to gravity. Without specifying the form of the $f(R)$ function, solving the field equations we find that the Ricci scalar receives a non-linear correction term which breaks the conformal invariance and leads to a massless black hole solution. When the non-linear term decouples, we get a well known hairy black hole solution with the scalar field conformally coupled to gravity. We also find that the entropy of our black hole may be higher than the corresponding conformal black hole which indicates that our solution may be thermodynamically preferred.

gr-qc↗

arXiv2022

Entangling metropolitan-distance separated quantum memories

Quantum internet gives the promise of getting all quantum resources connected, and it will enable applications far beyond a localized scenario. A prototype is a network of quantum memories that are entangled and well separated. Previous realizations are limited in the distance. In this paper, we report the establishment of remote entanglement between two atomic quantum memories physically separated by 12.5 km directly in a metropolitan area. We create atom-photon entanglement in one node and send the photon to a second node for storage. We harness low-loss transmission through a field-deployed fiber of 20.5 km by making use of frequency down-conversion and up-conversion. The final memory-memory entanglement is verified to have a fidelity of 90% via retrieving to photons. Our experiment paves the way to study quantum network applications in a practical scenario.

quant-ph↗

arXiv2022

MOORe: Model-based Offline-to-Online Reinforcement Learning

With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy matters in safe real-world deployment. Besides, fast adaptation of the policy plays a vital role in practical online performance improvement. To tackle these challenges, we propose a simple yet efficient algorithm, Model-based Offline-to-Online Reinforcement learning (MOORe), which employs a prioritized sampling scheme that can dynamically adjust the offline and online data for smooth and efficient online adaptation of the policy. We provide a theoretical foundation for our algorithms design. Experiment results on the D4RL benchmark show that our algorithm smoothly transfers from offline to online stages while enabling sample-efficient online adaption, and also significantly outperforms existing methods.

cs.LG↗

arXiv2022

Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach

In order to improve the penetration of renewable energy resources for distribution networks, a joint planning model of distributed generations (DGs) and energy storage is proposed for an active distribution network by using a bi-level programming approach in this paper. In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices. To solve this model, an improved binary particle swarm optimization (IBPSO) algorithm based on chaos optimization is developed, and the optimal joint planning is achieved through alternating iterations between the two levels. The simulation results on the PG & E 69-bus distribution system demonstrate that the presented approach manages to reduce the planning deviation caused by the uncertainties of DG outputs and remarkably improve the voltage profile and operational economy of distribution systems.

eess.SY↗

arXiv2022

Forecasts on Interacting Dark Energy from 21-cm Angular Power Spectrum with BINGO and SKA observations

Neutral hydrogen (HI) intensity mapping is a promising technique to probe the large-scale structure of the Universe, improving our understanding on the late-time accelerated expansion. In this work, we first scrutinize how an alternative cosmology, interacting dark energy (IDE), can affect the 21-cm angular power spectrum relative to the concordance $Λ$CDM model. We re-derive the 21-cm brightness temperature fluctuation in the context of such interaction and uncover an extra new contribution. Then we estimate the noise level of three upcoming HI intensity mapping surveys, BINGO, SKA1-MID Band$\,$1 and Band$\,$2, respectively, and employ a Fisher matrix approach to forecast their constraints on the IDE model. We find that while $\textit{Planck}\,$ 2018 maintains its dominion over early-Universe parameter constraints, BINGO and SKA1-MID Band$\,$2 put complementary bounding to the latest CMB measurements on dark energy equation of state $w$, the interacting strength $λ_i$ and the reduced Hubble constant $h$, and SKA1-MID Band$\,$1 even outperforms $\textit{Planck}\,$ 2018 in these late-Universe parameter constraints. The expected minimum uncertainties are given by SKA1-MID Band$\,$1+$\textit{Planck}\,$: $\sim 0.34\%$ on $w$, $\sim 0.22\%$ on $h$, $\sim 0.64\%$ on HI bias $b_{\rm HI}$, and an absolute uncertainty of about $3\times10^{-4}$ ($7\times10^{-4}$) on $λ_{1}$ ($λ_{2}$). Moreover, we quantify the effects from systematics of the redshift bin number, redshift-space distortions, foreground residuals and uncertainties on the measured HI fraction, $Ω_{\mathrm{HI}}(z)$. Our results indicate a bright prospect for HI intensity mapping surveys in constraining IDE, whether on their own or further by synergies with other measurements.

astro-ph.CO↗