arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 901 records · Page 50Linked to original sources

arXiv2023

Slowing down of charged particles in the dusty plasmas with a non-thermal velocity alpha-distribution

The slowing down of a charged particle beam passing through the dusty plasma with a non-thermal velocity alpha-distribution is studied. By using the Fokker-Planck collision theory, we derive the deceleration factor and slowing down time and make the numerical analyses. We show that the non-thermal velocity alpha-distributions of the plasma components have a significant effect on the slowing down. With increase of the mean velocity, the deceleration factor increases rapidly, reaches a peak and then decreases gradually. And the entire peak of the deceleration factor moves generally to the right with the increase of the alpha-parameter. The slowing down time decreases with the increase of the non-thermal alpha-parameter, and so the slowing down time in the non-thermal dusty plasma is generally less than that in a Maxwellian one.

physics.plasm-ph↗

arXiv2023

MSG-BART: Multi-granularity Scene Graph-Enhanced Encoder-Decoder Language Model for Video-grounded Dialogue Generation

Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos. However, existing large visual-language models are not effective due to their latent features and decoder-only structure, especially with respect to spatio-temporal relationship reasoning. In this paper, we propose a novel approach named MSG-BART, which enhances the integration of video information by incorporating a multi-granularity spatio-temporal scene graph into an encoder-decoder pre-trained language model. Specifically, we integrate the global and local scene graph into the encoder and decoder, respectively, to improve both overall perception and target reasoning capability. To further improve the information selection capability, we propose a multi-pointer network to facilitate selection between text and video. Extensive experiments are conducted on three video-grounded dialogue benchmarks, which show the significant superiority of the proposed MSG-BART compared to a range of state-of-the-art approaches.

cs.CV↗

arXiv2023

OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control

In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a bottom-up design approach that allows users to easily design and experiment with various application scenarios on top of GPU-parallelized simulations. It also offers a range of benchmark tasks, presenting challenges ranging from single-drone hovering to over-actuated system tracking. In summary, we propose an open-sourced drone simulation platform, equipped with an extensive suite of tools for drone learning. It includes 4 drone models, 5 sensor modalities, 4 control modes, over 10 benchmark tasks, and a selection of widely used RL baselines. To showcase the capabilities of OmniDrones and to support future research, we also provide preliminary results on these benchmark tasks. We hope this platform will encourage further studies on applying RL to practical drone systems.

cs.RO↗

arXiv2023

Nonadiabatic dynamics near metal surfaces with periodic drivings: A generalized surface hopping in Floquet representation

With light-matter interaction extending into strong regime, as well as rapid development of laser technology, systems subjecting to a time-periodic perturbation are attracted broad attention. Floquet theorem and Floquet time-independent Hamiltonian are powerful theoretical framework to investigate the systems subjecting to time-periodic drivings. In this study, we extend the previous generalized SH algorithm near metal surface (J. Chem. Theory Comput. 2017, 13, 6, 2430-2439) to the Floquet space, and hence, we develop a generalized Floquet representation based surface hopping (FR-SH) algorithm. Here, we consider open quantum system with fast drivings. We expect that the present algorithm will be useful for understanding the chemical processes of molecules under time-periodic drivings near the metal surface.

quant-ph↗

arXiv2023

Optimizing V2V Unicast Communication Transmission with Reinforcement Learning and Vehicle Clustering

Efficient routing algorithms based on vehicular ad hoc networks (VANETs) play an important role in emerging intelligent transportation systems. This highly dynamic topology faces a number of wireless communication service challenges. In this paper, we propose a protocol based on reinforcement learning and vehicle node clustering, the protocol is called Qucts, solve vehicle-to-fixed-destination or V2V messaging problems. Improve message delivery rates with minimal hops and latency, link stability is also taken into account. The agreement is divided into three levels, first cluster the vehicles, each cluster head broadcasts its own coordinates and speed, to get more cluster members. Also when a cluster member receives another cluster head broadcast message, the cluster head generates a list of surrounding clusters, find the best cluster to the destination as the next cluster during message passing. Second, the protocol constructs a Q-value table based on the state after clustering, used to participate in the selection of messaging clusters. Finally, we introduce parameters that express the stability of the vehicle within the cluster, for communication node selection. This protocol hierarchy makes Qucts an offline and online solution. In order to distinguish unstable nodes within a cluster, Coding of each road, will have vehicles with planned routes, For example, car hailing and public bus. Compare the overlap with other planned paths vehicles in the cluster, low overlap is labeled as unstable nodes. Vehicle path overlap rate without a planned path is set to the mean value. Comparing Qucts with existing routing protocols through simulation, Our proposed Qucts scheme provides large improvements in both data delivery rate and end-to-end delay reduction.

cs.NI↗

arXiv2023

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

cs.LG↗

arXiv2023

Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP). Although convenient for research and practical applications, open-source LLMs with fewer parameters often suffer from severe hallucinations compared to their larger counterparts. This paper focuses on measuring and reducing hallucinations in BLOOM 7B, a representative of such weaker open-source LLMs that are publicly available for research and commercial applications. We introduce HaloCheck, a lightweight BlackBox knowledge-free framework designed to quantify the severity of hallucinations in LLMs. Additionally, we explore techniques like knowledge injection and teacher-student approaches to alleviate hallucinations in low-parameter LLMs. Our experiments effectively demonstrate the reduction of hallucinations in challenging domains for these LLMs.

cs.CL↗

arXiv2023

A matter wave Rarity-Tapster interferometer to demonstrate non-locality

We present an experimentally viable approach to demonstrating quantum non-locality in a matter wave system via a Rarity-Tapster interferometer using two $s$-wave scattering halos generated by colliding helium Bose-Einstein condensates. The theoretical basis for this method is discussed, and its suitability is experimentally quantified. As a proof of concept, we demonstrate an interferometric visibility of $V=0.42(9)$, corresponding to a maximum CSHS-Bell parameter of $S=1.1(1)$, for the Clauser-Horne-Shimony-Holt (CHSH) version of the Bell inequality, between atoms separated by $\sim 4$ correlation lengths. This constitutes a significant step towards a demonstration of a Bell inequality violation for motional degrees of freedom of massive particles and possible measurements of quantum effects in a gravitationally sensitive system.

quant-ph↗

arXiv2023

Periodically driven open quantum systems with vibronic interaction: Resonance effects and vibrationally mediated decoupling

Periodic driving and Floquet engineering have emerged as invaluable tools for controlling and uncovering novel phenomena in quantum systems. In this study, we adopt these methods to manipulate nonequilibrium processes within electronic-vibronic open quantum systems. Through resonance mechanisms and by focusing on the limit-cycle dynamics and quantum thermodynamic properties, we illustrate the intricate interplay between the driving field and vibronic states and its overall influence on the electronic system. Specifically, we observe an effective decoupling of the electronic system from the periodic driving at specific frequencies, a phenomenon that is mediated by the vibrational mode interaction. Additionally, we engineer the driving field to obtain a partial removal of the Franck-Condon blockade. These insights hold promise for efficient charge current control. Our results are obtained from numerically exact calculations of the hierarchical equations of motion and further analyzed by a time-periodic master equation approach.

quant-ph↗

arXiv2023

Trion states and quantum criticality of attractive SU(3) Dirac fermions

We perform the projector quantum Monte Carlo (QMC) simulation to study the trion formation and quantum phase transition in the half-filled attractive SU(3) Hubbard model on a honeycomb lattice. With increasing attractive Hubbard interaction, our simulations demonstrate a continuous quantum phase transition from the semimetal to charge density wave (CDW) at the critical coupling $U_c/t=-1.52(2)$. The critical exponents $ν=0.82(3)$ and $η=0.58(4)$ determined by the QMC simulation remarkably disagree with those of the $N=3$ chiral Ising universality class suggested by the effective Gross-Neveu-Yukawa (GNY) theory, but coincide with the $N=1$ chiral Ising universality class. In the CDW phase, we show that on-site and off-site trions coexist and the off-site trion forms a local bond state. Our work not only illustrates the formation of off-site trions in two-dimensional Hubbard model, but also raises doubts about the extent of applicability of GNY model on the attractive SU(3) Dirac fermions.

cond-mat.quant-gas↗

arXiv2023

An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models

Large language models (LLMs) have achieved significant success in interacting with human. However, recent studies have revealed that these models often suffer from hallucinations, leading to overly confident but incorrect judgments. This limits their application in the medical domain, where tasks require the utmost accuracy. This paper introduces an automated evaluation framework that assesses the practical capabilities of LLMs as virtual doctors during multi-turn consultations. Consultation tasks are designed to require LLMs to be aware of what they do not know, to inquire about missing medical information from patients, and to ultimately make diagnoses. To evaluate the performance of LLMs for these tasks, a benchmark is proposed by reformulating medical multiple-choice questions from the United States Medical Licensing Examinations (USMLE), and comprehensive evaluation metrics are developed and evaluated on three constructed test sets. A medical consultation training set is further constructed to improve the consultation ability of LLMs. The results of the experiments show that fine-tuning with the training set can alleviate hallucinations and improve LLMs' performance on the proposed benchmark. Extensive experiments and ablation studies are conducted to validate the effectiveness and robustness of the proposed framework.

cs.CL↗

arXiv2023

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation.

eess.IV↗

arXiv2023

NLTE analysis for Y I and Y II in atmospheres of F-G-K stars

The non-local thermodynamical equilibrium (NLTE) line formation of Y I and Y II is considered in 1D LTE model atmospheres of F-G-K-type stars. The model atom was constructed with the most up-to-date atomic data, including quantum cross sections and rate coefficients for transitions in inelastic collisions of Y I and Y II with hydrogen atoms. For seven reference stars, we obtained an agreement between NLTE abundances inferred from the two ionization stages, while the difference in LTE abundance (Y I - Y II) can reach up to -0.31 dex. In the atmospheres of F-G-K-type stars, for both Y I and Y II lines, the NLTE abundance corrections are positive. In solar metallicity stars, the NLTE abundance corrections for Y II lines do not exceed 0.12 dex, while in atmospheres of metal-poor stars they do not exceed 0.21 dex. For Y I lines, the NLTE abundance corrections can reach up to 0.5 dex. We determined the yttrium NLTE abundances for a sample of 65 F and G dwarfs and subgiants in the -2.62~$\leq$~[Fe/H]~$\leq$~+0.24 metallicity range, using high-resolution spectra. For stars with [Fe/H]~$\leq$~-1.5, [Y/Fe] versus [Fe/H] diagram reveals positive trend with an average value of [Y/Fe]~$\simeq$~0. For metal-poor stars, among Sr, Y, and Zr, the arrangement [Sr/Fe] < [Y/Fe] < [Zr/Fe] remains consistent. The current study is useful for the Galactic chemical evolution research. The model atom will be applied for NLTE yttrium abundance determination in very metal-poor stars studied with LAMOST and Subaru.

astro-ph.SR↗

arXiv2023

On rational functional identities involving inverses on matrix rings

Let $n\geq 3$ be an integer. Let $\mathcal{D}$ be a division ring with char$(\mathcal{D})>n$ or char$(\mathcal{D})=0$. Let $\mathcal{R}=M_m(\mathcal{D})$ be a ring of $n\times n$ matrices over $D$, $m\geq 2$. The main theorem in the paper states that the only additive maps $f$ and $g$ satisfying that $f(X)+X^ng(X^{-1})=0$ for all invertible $X\in \mathcal{R}$, are zero maps, which generalizes both a result proved by Dar and Jing and a result proved by Catalano and Merch$\acute{a}$n.

math.RA↗

arXiv2023

From orthogonal link to phase vortex in generalized dynamical Hopf insulators

In the creation of Hopf topological matters, the old paradigm is to conceive the Hopf invariant first, and then display its intuitive topology through links. Here we brush aside this effort and put forward a new recipe for unraveling the quenched two-dimensional (2D) two-band Chern insulators under a parallel quench protocol, which implies that the quench quantities with different momentum k are parallel or antiparallel to each other. We find that whether the dynamical Hopf invariant exists or not, the links in (2+1)D space always keep their standard shape even for topological initial states, and trace out the trajectories of phase vortices. The linking number is exactly equal to the difference between pre- and post-quench Chern numbers regardless of the construction of homotopy groups. We employ two concrete examples to illustrate these results, highlighting the polarity reversal at fixed points.

cond-mat.str-el↗

arXiv2023

Interpretable Outlier Summarization

Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evaluating outlier detection results and effectively turn the outliers into actionable insights, the users often expect a system to automatically produce interpretable summarizations of subgroups of outlier detection results. Unfortunately, to date no such systems exist. To fill this gap, we propose STAIR which learns a compact set of human understandable rules to summarize and explain the anomaly detection results. Rather than use the classical decision tree algorithms to produce these rules, STAIR proposes a new optimization objective to produce a small number of rules with least complexity, hence strong interpretability, to accurately summarize the detection results. The learning algorithm of STAIR produces a rule set by iteratively splitting the large rules and is optimal in maximizing this objective in each iteration. Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR. Taking data locality into consideration, it simultaneously partitions data and learns a set of localized rules for each partition. Our experimental study on many outlier benchmark datasets shows that STAIR significantly reduces the complexity of the rules required to summarize the outlier detection results, thus more amenable for humans to understand and evaluate, compared to the decision tree methods.

cs.LG↗

arXiv2023

An introduction to relative Calabi-Yau structures

These are notes taken by the second author for a series of three lectures by the first author on absolute and relative Calabi-Yau completions and Calabi-Yau structures given at the workshop of the International Conference on Representations of Algebras which was held online in November 2020. Such structures are relevant for (higher) representation theory as well as for the categorification of cluster algebras with coefficients. After a quick reminder on dg categories and their Hochschild and cyclic homologies, we present examples of absolute and relative Calabi-Yau completions (in the sense of Yeung). In many examples, these are related to higher preprojective algebras in the sense of Iyama-Oppermann. We conclude with the definition of relative (left and right) Calabi-Yau structures after Brav-Dyckerhoff.

math.RT↗

arXiv2023

Learning Optimal Strategies for Temporal Tasks in Stochastic Games

Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct controllers. In this work, we introduce a model-free reinforcement learning (RL) approach to derive controllers from given LTL specifications even when the environment is completely unknown. We model the problem as a stochastic game (SG) between the controller and the adversarial environment; we then learn optimal control strategies that maximize the probability of satisfying the LTL specifications against the worst-case environment behavior. We first construct a product game using the deterministic parity automaton (DPA) translated from the given LTL specification. By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this enables the use of model-free RL algorithms to learn an optimal controller strategy. To deal with the common scalability problems when the number of sets defining the acceptance condition of the DPA (usually referred as colors), is large, we propose a lazy color generation method where distinct rewards and discount factors are utilized only when needed, and an approximate method where the controller eventually focuses on only one color. In several case studies, we show that our approach is scalable to a wide range of LTL formulas, significantly outperforming existing methods for learning controllers from LTL specifications in SGs.

cs.AI↗