arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 559 records · Page 31Linked to original sources

arXiv2022

Solutions of the Schrödinger equation for anisotropic dipole-dipole interaction plus isotropic van der Waals interaction

By generalizing Bo Gao's approach [Phys. Rev. A 58, 1728 (1998)] for solving the Schrödinger equation for an isotropic van der Waals (vdW) potential to the systems with a multi-scale anisotropic long-range interaction, we derive the solutions for the Schrödinger equation for an anisotropic dipole-dipole interaction plus an isotropic attractive vdW potential, i.e., ${C_d(1-3\cos^2θ)}/{r^3}-{C_6}/{r^6}$, which is projected to the subspace with angular momentum $l\leq l_{\rm cut}$, with $l_{\rm cut}$ being an arbitrary angular-momentum cutoff. Here $θ$ is the polar angle of the coordinate $\boldsymbol{r}$ and $r=|\boldsymbol{r}|$. The asymptotic behaviors of these solutions for $r\rightarrow 0$ and $r\rightarrow \infty$ are obtained. These results can be used in the research of collisions and chemical reactions between ultra-cold polar molecules in a static electric field. Our approach to derive the solutions can be applied to the systems with a general long-range potential $\sum_{λ= 2}^{λ_{\rm max}} {V_λ(θ,φ)}/{r^λ}$, with $φ$ being the azimuthal angle of $\boldsymbol{r}$, and thus can be used in various problems on molecule-molecule interaction.

cond-mat.quant-gas↗

arXiv2022

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available.

cs.CV↗

arXiv2022

Look\&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement

Active speaker detection and speech enhancement have become two increasingly attractive topics in audio-visual scenario understanding. According to their respective characteristics, the scheme of independently designed architecture has been widely used in correspondence to each single task. This may lead to the representation learned by the model being task-specific, and inevitably result in the lack of generalization ability of the feature based on multi-modal modeling. More recent studies have shown that establishing cross-modal relationship between auditory and visual stream is a promising solution for the challenge of audio-visual multi-task learning. Therefore, as a motivation to bridge the multi-modal associations in audio-visual tasks, a unified framework is proposed to achieve target speaker detection and speech enhancement with joint learning of audio-visual modeling in this study.

cs.SD↗

arXiv2022

A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage can not be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

cs.NE↗

arXiv2022

A new post-hoc flat field measurement method for the Solar X-ray and Extreme Ultraviolet Imager onboard the Fengyun-3E satellite

The extreme ultraviolet (EUV) observations are widely used in solar activity research and space weather forecasting since they can observe both the solar eruptions and the source regions of the solar wind. Flat field processing is indispensable to remove the instrumental non-uniformity of a solar EUV imager in producing high-quality scientific data from original observed data. Fengyun-3E (FY-3E) is a meteorological satellite operated in Sun-synchronous orbit, and the routine EUV imaging data from the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) onboard FY-3E has the characteristics of concentric rotation. Taking advantage of the concentric rotation, we propose a post-hoc flat field measurement method for its EUV 195 channel in this paper. This method removes small-scale and time-varying component of the coronal activities by taking the median value for each pixel along the time axis of a concentric rotation data cube, and then derives large-scale and invariable component of the quiet coronal radiation, and finally generates a flat field image. Analysis shows that our method is able to measure the instrumental spot-like non-uniformity possibly caused by contamination on the detector, which mostly disappears after the in-orbit self-cleaning process. It can also measure the quasi-periodic grid-like non-uniformity, possibly from the obscuration of the support mesh on the rear filter. After flat field correction, these instrumental non-uniformities from the original data are effectively removed. X-EUVI 195 data after dark and flat field corrections are consistent with the 193 channel data from SDO/AIA, verifying the suitability of the method. Our method is not only suitable for FY-3E/X-EUVI but also a candidate method for the flat field measurement of future solar EUV telescopes.

astro-ph.SR↗

arXiv2022

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a promising synthesis model for high-dimensional DT from a small number of training data. In this paper, we propose a novel DT synthesis method, which makes full use of similarity prior knowledge to address this issue. Our method bases on the proposed kernel similarity embedding, which not only can mitigate the high-dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationship. Specifically, we first raise two hypotheses that are essential for DT model to generate new frames using similarity correlation. Then, we integrate kernel learning and extreme learning machine into a unified synthesis model to learn kernel similarity embedding for representing DT. Extensive experiments on DT videos collected from the internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding can effectively exhibit the discriminative representation for DT. Accordingly, our method is capable of preserving the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art methods. The code and more synthesis videos are available at our project page https://shiming-chen.github.io/Similarity-page/Similarit.html.

cs.CV↗

arXiv2022

Measurement of carbon finance level and exploration of its influencing factors

Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming. Considering the complexity and urgency of carbon market, it is necessary to construct an effective evaluation index system. This paper selected carbon finance index as a composite indicator. Taking Beijing, Shanghai, and Guangdong as examples, we adopted the classic method of multiple criteria decision analysis (MCDA) to analyze the composite indicator. Potential impact factors were screened extensively and calculated through normalization, weighting by coefficient of variation and different aggregation methods. Under the measurement of Shannon-Spearman Measure, the method with the least loss of information was used to obtain the carbon finance index (CFI) of the pilot areas. Through panel model analysis, we found that company size, the number of patents per 10,000 people and the proportion of new energy generation were the factors with significant influence. Based on the research, corresponding suggestions were put forward for different market entities. Hopefully, this research will contribute to the steady development of the national carbon market.

econ.GN↗

arXiv2022

Resilience in Industrial Internet of Things Systems: A Communication Perspective

Industrial Internet of Things is an ultra-large-scale system that is much more sophisticated and fragile than conventional industrial platforms. The effective management of such a system relies heavily on the resilience of the network, especially the communication part. Imperative as resilient communication is, there is not enough attention from literature and a standardized framework is still missing. In awareness of these, this paper intends to provide a systematic overview of resilience in IIoT with a communication perspective, aiming to answer the questions of why we need it, what it is, how to enhance it, and where it can be applied. Specifically, we emphasize the urgency of resilience studies via examining existing literature and analyzing malfunction data from a real satellite communication system. Resilience-related concepts and metrics, together with standardization efforts are then summarized and discussed, presenting a basic framework for analyzing the resilience of the system before, during, and after disruptive events. On the basis of the framework, key resilience concerns associated with the design, deployment, and operation of IIoT are briefly described to shed light on the methods for resilience enhancement. Promising resilient applications in different IIoT sectors are also introduced to highlight the opportunities and challenges in practical implementations.

eess.SY↗

arXiv2022

Text/Speech-Driven Full-Body Animation

Due to the increasing demand in films and games, synthesizing 3D avatar animation has attracted much attention recently. In this work, we present a production-ready text/speech-driven full-body animation synthesis system. Given the text and corresponding speech, our system synthesizes face and body animations simultaneously, which are then skinned and rendered to obtain a video stream output. We adopt a learning-based approach for synthesizing facial animation and a graph-based approach to animate the body, which generates high-quality avatar animation efficiently and robustly. Our results demonstrate the generated avatar animations are realistic, diverse and highly text/speech-correlated.

cs.GR↗

arXiv2022

Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength

Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems. Most of the current financial applications using RL algorithms are based on model-free method, which still faces stability and adaptivity challenges. As lots of cutting-edge model-based reinforcement learning (MBRL) algorithms mature in applications such as video games or robotics, we design a new approach that leverages resistance and support (RS) level as regularization terms for action in MBRL, to improve the algorithm's efficiency and stability. From the experiment results, we can see RS level, as a market timing technique, enhances the performance of pure MBRL models in terms of various measurements and obtains better profit gain with less riskiness. Besides, our proposed method even resists big drop (less maximum drawdown) during COVID-19 pandemic period when the financial market got unpredictable crisis. Explanations on why control of resistance and support level can boost MBRL is also investigated through numerical experiments, such as loss of actor-critic network and prediction error of the transition dynamical model. It shows that RS indicators indeed help the MBRL algorithms to converge faster at early stage and obtain smaller critic loss as training episodes increase.

q-fin.MF↗

arXiv2022

AI-aided multiscale modeling of physiologically-significant blood clots

We have developed an AI-aided multiple time stepping (AI-MTS) algorithm and multiscale modeling framework (AI-MSM) and implemented them on the Summit-like supercomputer, AIMOS. AI-MSM is the first of its kind to integrate multi-physics, including intra-platelet, inter-platelet, and fluid-platelet interactions, into one system. It has simulated a record-setting multiscale blood clotting model of 102 million particles, of which 70 flowing and 180 aggregating platelets, under dissipative particle dynamics to coarse-grained molecular dynamics. By adaptively adjusting timestep sizes to match the characteristic time scales of the underlying dynamics, AI-MTS optimally balances speeds and accuracies of the simulations.

physics.med-ph↗

arXiv2022

Phase Optimization for Massive IRS-aided Two-way Relay Network

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We mainly focus on the phase optimization of IRS to improve the system rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable rate enhancement, which is called Max-RPS-EVD. To further achieve a higher rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the case of random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20\% rate gain over random phase. Additionally, it is also proved the optimal rate can be achieved when TW-DFR and IRS are located in the middle of S and D.

cs.IT↗

arXiv2022

A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment

In the two-component Majorana dark matter model, one dark matter particle can scatter off the target nuclei, and turn into a slightly heavier component. In the framework of a simplified model with a vector boson mediator, both the tree-level and loop-level processes contribute to the signal in direct detection experiment. In this paper, we report the search results for such dark matter from PandaX-II experiment, using total data of the full 100.7 tonne$\cdot$day exposure. No significant excess is observed, so strong constraints on the combined parameter space of mediator mass and dark matter mass are derived. With the complementary search results from collider experiments, a large range of parameter space can be excluded.

hep-ex↗

arXiv2022

Chromium-Doped Bismuth Antimony Telluride for Future Quantum Hall Resistance Standards

Since 2017, epitaxial graphene has been the base material for the US national standard for resistance. A future avenue of research within electrical metrology is to remove the need for strong magnetic fields, as is currently the case for devices exhibiting the quantum Hall effect. The quantum Hall effect is just one of many research endeavours that revolve around recent quantum physical phenomena like composite fermions, charge density waves, and topological properties [1-2]. New materials, like magnetically doped topological insulators (MTIs), offer access to the quantum anomalous Hall effect, which in its ideal form, could become a future resistance standard needing only a small permanent magnet to activate a quantized resistance value [3-5]. Furthermore, these devices could operate at zero-field for measurements, making the dissemination of the ohm more economical and portable. Here we present results on precision measurements of the h/e2 quantized plateau of Cr-Doped (BixSb1-x)2Te3 and give them context by comparing them to modern graphene-based resistance standards. Ultimately, MTI-based devices could be combined in a single system with magnetic-field-averse Josephson voltage standards to obtain an alternative quantum current standard.

cond-mat.mes-hall↗

arXiv2022

Exchange-biased quantum anomalous Hall effect

The quantum anomalous Hall (QAH) effect is characterized by a dissipationless chiral edge state with a quantized Hall resistance at zero magnetic field. Manipulating the QAH state is of great importance in both the understanding of topological quantum physics and the implementation of dissipationless electronics. Here, we realized the QAH effect in the magnetic topological insulator Cr-doped (Bi,Sb)2Te3 (CBST) grown on an uncompensated antiferromagnetic insulator Al-doped Cr2O3. Through polarized neutron reflectometry (PNR), we find a strong exchange coupling between CBST and Al-Cr2O3 surface spins fixing interfacial magnetic moments perpendicular to the film plane. The interfacial coupling results in an exchange-biased QAH effect. We further demonstrate that the magnitude and sign of the exchange bias can be effectively controlled using a field training process to set the magnetization of the Al-Cr2O3 layer. Our work demonstrates the use of the exchange bias effect to effectively manipulate the QAH state, opening new possibilities in QAH-based spintronics.

cond-mat.mes-hall↗

arXiv2022

Hardness Results for Weaver's Discrepancy Problem

Marcus, Spielman and Srivastava (Annals of Mathematics 2014) solved the Kadison--Singer Problem by proving a strong form of Weaver's conjecture: they showed that for all $α> 0$ and all lists of vectors of norm at most $\sqrtα$ whose outer products sum to the identity, there exists a signed sum of those outer products with operator norm at most $\sqrt{8 α} + 2 α.$ We prove that it is NP-hard to distinguish such a list of vectors for which there is a signed sum that equals the zero matrix from those in which every signed sum has operator norm at least $κ\sqrtα$, for some absolute constant $κ> 0.$ Thus, it is NP-hard to construct a signing that is a constant factor better than that guaranteed to exist. For $α= 1/4$, we prove that it is NP-hard to distinguish whether there is a signed sum that equals the zero matrix from the case in which every signed sum has operator norm at least $1/4$.

cs.CC↗

arXiv2022

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver with $K$ subarrays and $N$ antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. First, a DOA estimator of maximizing received power (Max-RP) is proposed to find the maximum value of $K$-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low-computational-complexities. In particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB, and the proposed Max-RP and Max-RP-QI are still some performance losses $2dB\thicksim4dB$ compared to the CRLB.

eess.SP↗

arXiv2022

Low Radioactive Material Screening and Background Control for the PandaX-4T Experiment

PandaX-4T is a ton-scale dark matter direct detection experiment using a dual-phase TPC technique at the China Jinping Underground Laboratory. Various ultra-low background technologies have been developed and applied to material screening for PandaX-4T, including HPGe gamma spectroscopy, ICP-MS, NAA, radon emanation measurement system, krypton assay station, and alpha detection system. Low background materials were selected to assemble the detector. Surface treatment procedures were investigated to further suppress radioactive background. Combining measured results and Monte Carlo simulation, the total material background rates of PandaX-4T in the energy region of 1-25 keV$\rm{}_{ee}$ are estimated to be (9.9 $\pm$ 1.9) $\times \ 10^{-3}$ mDRU for electron recoil and (2.8 $\pm$ 0.6) $\times \ 10^{-4}$ mDRU for nuclear recoil. In addition, $^{nat}$Kr in the detector is estimated to be <8 ppt.

physics.ins-det↗