arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 541 records · Page 30Linked to original sources

arXiv2024

StoryExplorer: A Visualization Framework for Storyline Generation of Textual Narratives

In the context of the exponentially increasing volume of narrative texts such as novels and news, readers struggle to extract and consistently remember storyline from these intricate texts due to the constraints of human working memory and attention span. To tackle this issue, we propose a visualization approach StoryExplorer, which facilitates the process of knowledge externalization of narrative texts and further makes the form of mental models more coherent. Through the formative study and close collaboration with 2 domain experts, we identified key challenges for the extraction of the storyline. Guided by the distilled requirements, we then propose a set of workflow (i.e., insight finding-scripting-storytelling) to enable users to interactively generate fragments of narrative structures. We then propose a visualization system StoryExplorer which combines stroke annotation and GPT-based visual hints to quickly extract story fragments and interactively construct storyline. To evaluate the effectiveness and usefulness of StoryExplorer, we conducted 2 case studies and in-depth user interviews with 16 target users. The result shows that users can better extract the storyline by using StoryExplorer along with the proposed workflow.

cs.HC↗

arXiv2024

ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM

Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep insights. To fill this research gap, this work aims to automatically generate charts to accurately convey the underlying data and ideas to readers, which is essentially a challenging task. The challenges originate from text ambiguities, intrinsic sparsity and uncertainty of data in text documents, and subjective sentiment differences. Specifically, we propose ChartifyText, a novel fully-automated approach that leverages Large Language Models (LLMs) to convert complex data-involved texts to expressive charts. It consists of two major modules: tabular data inference and expressive chart generation. The tabular data inference module employs systematic prompt engineering to guide the LLM (e.g., GPT-4) to infer table data, where data ranges, uncertainties, missing data values and corresponding subjective sentiments are explicitly considered. The expressive chart generation module augments standard charts with intuitive visual encodings and concise texts to accurately convey the underlying data and insights. We extensively evaluate the effectiveness of ChartifyText on real-world data-involved text documents through case studies, in-depth interviews with three visualization experts, and a carefully-designed user study with 15 participants. The results demonstrate the usefulness and effectiveness of ChartifyText in helping readers efficiently and effectively make sense of data-involved texts.

cs.HC↗

arXiv2024

ITS: Implicit Thin Shell for Polygonal Meshes

In computer graphics, simplifying a polygonal mesh surface~$\mathcal{M}$ into a geometric proxy that maintains close conformity to~$\mathcal{M}$ is crucial, as it can significantly reduce computational demands in various applications. In this paper, we introduce the Implicit Thin Shell~(ITS), a concept designed to implicitly represent the sandwich-walled space surrounding~$\mathcal{M}$, defined as~$\{\textbf{x}\in\mathbb{R}^3|ε_1\leq f(\textbf{x}) \leq ε_2, ε_1< 0, ε_2>0\}$. Here, $f$ is an approximation of the signed distance function~(SDF) of~$\mathcal{M}$, and we aim to minimize the thickness~$ε_2-ε_1$. To achieve a balance between mathematical simplicity and expressive capability in~$f$, we employ a tri-variate tensor-product B-spline to represent~$f$. This representation is coupled with adaptive knot grids that adapt to the inherent shape variations of~$\mathcal{M}$, while restricting~$f$'s basis functions to the first degree. In this manner, the analytical form of~$f$ can be rapidly determined by solving a sparse linear system. Moreover, the process of identifying the extreme values of~$f$ among the infinitely many points on~$\mathcal{M}$ can be simplified to seeking extremes among a finite set of candidate points. By exhausting the candidate points, we find the extreme values~$ε_1<0$ and $ε_2>0$ that minimize the thickness. The constructed ITS is guaranteed to wrap~$\mathcal{M}$ rigorously, without any intersections between the bounding surfaces and~$\mathcal{M}$. ITS offers numerous potential applications thanks to its rigorousness, tightness, expressiveness, and computational efficiency. We demonstrate the efficacy of ITS in rapid inside-outside tests and in mesh simplification through the control of global error.

cs.GR↗

arXiv2024

Quantum Entanglement Path Selection and Qubit Allocation via Adversarial Group Neural Bandits

Quantum Data Networks (QDNs) have emerged as a promising framework in the field of information processing and transmission, harnessing the principles of quantum mechanics. QDNs utilize a quantum teleportation technique through long-distance entanglement connections, encoding data information in quantum bits (qubits). Despite being a cornerstone in various quantum applications, quantum entanglement encounters challenges in establishing connections over extended distances due to probabilistic processes influenced by factors like optical fiber losses. The creation of long-distance entanglement connections between quantum computers involves multiple entanglement links and entanglement swapping techniques through successive quantum nodes, including quantum computers and quantum repeaters, necessitating optimal path selection and qubit allocation. Current research predominantly assumes known success rates of entanglement links between neighboring quantum nodes and overlooks potential network attackers. This paper addresses the online challenge of optimal path selection and qubit allocation, aiming to learn the best strategy for achieving the highest success rate of entanglement connections between two chosen quantum computers without prior knowledge of the success rate and in the presence of a QDN attacker. The proposed approach is based on multi-armed bandits, specifically adversarial group neural bandits, which treat each path as a group and view qubit allocation as arm selection. Our contributions encompass formulating an online adversarial optimization problem, introducing the EXPNeuralUCB bandits algorithm with theoretical performance guarantees, and conducting comprehensive simulations to showcase its superiority over established advanced algorithms.

quant-ph↗

arXiv2024

Advancing Video Anomaly Detection: A Concise Review and a New Dataset

Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. Such reviews would serve as quick references to grasp current challenges, research trends, and future directions. In this paper, we present such a review, examining models and datasets from various perspectives. We emphasize the critical relationship between model and dataset, where the quality and diversity of datasets profoundly influence model performance, and dataset development adapts to the evolving needs of emerging approaches. Our review identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, we introduce a new dataset, Multi-Scenario Anomaly Detection (MSAD), comprising 14 distinct scenarios captured from various camera views. Our dataset has diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. We conduct an in-depth analysis of recent representative models using MSAD and highlight its potential in addressing the challenges of detecting anomalies across diverse and evolving surveillance scenarios. [Project website: https://msad-dataset.github.io/]

cs.CV↗

arXiv2024

EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment

Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data era in biomolecules, with their accuracy limited by the energy functions and search algorithms. Existing deep learning methods are constrained by the learning capabilities of the networks, failing to extract effective information from sparse protein structures, which limits the accuracy of protein design. To address these shortcomings, we developed an Efficient attention-based Models for Computational Protein Design using amino acid microenvironment (EMOCPD). It aims to predict the category of each amino acid in a protein by analyzing the three-dimensional atomic environment surrounding the amino acids, and optimize the protein based on the predicted high-probability potential amino acid categories. EMOCPD employs a multi-head attention mechanism to focus on important features in the sparse protein microenvironment and utilizes an inverse residual structure to optimize the network architecture. The proposed EMOCPD achieves over 80% accuracy on the training set and 68.33% and 62.32% accuracy on two independent test sets, respectively, surpassing the best comparative methods by over 10%. In protein design, the thermal stability and protein expression of the predicted mutants from EMOCPD show significant improvements compared to the wild type, effectively validating EMOCPD's potential in designing superior proteins. Furthermore, the predictions of EMOCPD are influenced positively, negatively, or have minimal impact based on the content of the 20 amino acids, categorizing amino acids as positive, negative, or neutral. Research findings indicate that EMOCPD is more suitable for designing proteins with lower contents of negative amino acids.

cs.LG↗

arXiv2024

Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains

Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world scenarios often involve heterogeneous data domains. Federated Prototype Learning (FedPL) addresses this issue, using mean feature vectors as prototypes to enhance model generalization. However, existing FedPL methods create the same number of prototypes for each client, leading to cross-domain performance gaps and disparities for clients with varied data distributions. To mitigate cross-domain feature representation variance, we introduce FedPLVM, which establishes variance-aware dual-level prototypes clustering and employs a novel $α$-sparsity prototype loss. The dual-level prototypes clustering strategy creates local clustered prototypes based on private data features, then performs global prototypes clustering to reduce communication complexity and preserve local data privacy. The $α$-sparsity prototype loss aligns samples from underrepresented domains, enhancing intra-class similarity and reducing inter-class similarity. Evaluations on Digit-5, Office-10, and DomainNet datasets demonstrate our method's superiority over existing approaches.

cs.LG↗

arXiv2024

Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity

Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function. In the hierarchical multiple kernel clustering (HMKC) algorithm, sample features are extracted layer by layer from a high-dimensional space to maximize the retention of effective information. However, information interaction between layers is often ignored. In this model, only corresponding nodes in adjacent layers exchange information; other nodes remain isolated, and if full connectivity is adopted, the diversity of the final consistency matrix is reduced. Therefore, this paper proposes a hierarchical multiple kernel K-Means (SCHMKKM) algorithm based on sparse connectivity, which controls the assignment matrix to achieve sparse connections through a sparsity rate, thereby locally fusing the features obtained by distilling information between layers. Finally, we conduct cluster analysis on multiple datasets and compare it with the fully connected hierarchical multiple kernel K-Means (FCHMKKM) algorithm in experiments. It is shown that more discriminative information fusion is beneficial for learning a better consistent partition matrix, and the fusion strategy based on sparse connection outperforms the full connection strategy.

cs.LG↗

arXiv2024

Variational Benchmarks for Quantum Many-Body Problems

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy, and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods toward a quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible.

quant-ph↗

arXiv2024

Freezing dynamics of wetting droplet under a uniform electric field

Electrofreezing is a powerful technique that employs the electric field to control and enhance the freezing process. In this work, a phase-field-based lattice Boltzmann (LB) method is developed to study the electrofreezing process of sessile droplet on a cooled substrate. The accuracy of the present LB method is first validated through performing some simulations of the three-phase Stefan problem, the droplet freezing on a cold wall, and the droplet deformation under a uniform electric field. Then it is used to investigate the effect of an electric field on the freezing of a wetting droplet on a cold substrate, and the numerical results show that the electric field has a significant influence on the freezing time of the droplet mainly through changing the morphology of the droplet. In particular, under the effect of the electric field, the freezing time is increased for the droplet with a prolate pattern, while the freezing time of the droplet with an oblate pattern is decreased. These numerical results bring some new insights on the electrofreezing and provide a valuable guidance for the precise regulation of droplet freezing.

physics.flu-dyn↗

arXiv2024

UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning and disaster assessment.Existing Transformer-based methods suffer from the constraint between accuracy and efficiency, while the recently proposed Mamba is renowned for being efficient. Therefore, to overcome the dilemma, we propose UNetMamba, a UNet-like semantic segmentation model based on Mamba. It incorporates a mamba segmentation decoder (MSD) that can efficiently decode the complex information within high-resolution images, and a local supervision module (LSM), which is train-only but can significantly enhance the perception of local contents. Extensive experiments demonstrate that UNetMamba outperforms the state-of-the-art methods with mIoU increased by 0.87% on LoveDA and 0.39% on ISPRS Vaihingen, while achieving high efficiency through the lightweight design, less memory footprint and reduced computational cost. The source code is available at https://github.com/EnzeZhu2001/UNetMamba.

cs.CV↗

arXiv2024

Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning

A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better distinguish normal samples from abnormal samples in an error-driven way. A weight variable that could measure the degree of difficulty to all samples was assigned in this method, and the variable was constrained by adopting both hard-weighting and soft-weighting strategies to ensure the rationality of the model. Cluster analysis was carried out on multiple data sets such as images and texts, and the experimental results showed the effectiveness of the proposed algorithm.

cs.LG↗

arXiv2024

Geometrically predictable micro fabricated continuum robot

Compared to the micro continuum robots that use traditional manufacturing technology, the micro fabricated continuum robots are different in terms of the application of smart materials, additive manufacturing process, and physical field control. However, the existing geometrical prediction models of the micro continuum robots still follow the model frameworks designed for their larger counterparts, which is inconsistent with the real geometrical transformation principle of micro fabricated continuum robots. In this paper, we present a universal geometrical prediction method for the geometry transformation of the micro fabricated continuum robots based on their material properties and the displacement of the stress points. By discretizing of the micro fabricated continuum structure and applying force constraints between adjacent points to simulate material properties, formulations and simulations are demonstrated to prove the feasibility and effectiveness of the proposed method. Three micro fabricated continuum robots driven through different external field forces are investigated to show two superiorities: the geometrical deformation of a micro fabricated continuum robot under external disturbances can be predicted, and a targeted geometry can be shaped by predicting the sequence and directions of external forces. This pioneer research has contributed to promote understanding and operation of micro fabricated continuum robots and their deformation both from theoretical aspect and real experimental operations.

cs.RO↗

arXiv2024

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. While previous studies have touched on gameplay with LLM agents, research on their social behaviors is lacking. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. We evaluate its performance based on game success and analyze LLM agents' social behaviors. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions. By examining collaboration and confrontation behaviors, we offer insights into this field's research and applications. Our code is publicly available at https://github.com/3DAgentWorld/LLM-Game-Agent.

cs.CL↗

arXiv2024

Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces

Heat transfer enhancement of N-Ga-Al semiconductor heterostructure interfaces is critical for the heat dissipation in GaN-based electronic devices, while the effect of the AlxGa(1-x)N transition layer component concentration and thickness on the heat transfer mechanism at the GaN-AlN interface is unclear. In this paper, using molecular dynamics simulations based on machine learning potentials, the interfacial thermal conductance (ITC) between GaN-AlxGa(1-x)N, AlN-AlxGa(1-x)N and GaN-AlxGa(1-x)N-AlN heterostructure interfaces are calculated for different transition layer thicknesses with different concentrations of Al fractions, and the reasons for the change of ITC and its heat transfer mechanism were explained by the phonon density of states and the spectral heat current. GaN-AlN heterostructure ITC at 300 K is calculated to be 557 MW/(m2K), and the ITCs of GaN-Al0.5Ga0.5N and AlN-Al0.5Ga0.5N are improved by 128% and 229% compared to GaN-AlN, whereas the ITCs of GaN-Al0.7Ga0.3N-AlN containing a 0.5 nm transition layer improved by 27.6%. This is because elemental doping enhances phonon scattering near the interface thereby promoting phonon energy redistribution, but the bulk thermal resistance of the AlxGa(1-x)N layer also increases rapidly with increasing doping ratio, and ITC is affected by a combination of these two factors. This work aims to understand the mechanism of transition layer component concentration and thickness on the heat transfer at the GaN-AlN contact interface, which provides a useful guide for better thermal design of the GaN-AlN heterostructure interface.

cond-mat.mtrl-sci↗

arXiv2024

Hot electron lifetime exceeds 300 nanoseconds in quantum dots with high quantum efficiency

Hot electrons are theoretically predicted to be long-lived in strongly confined quantum dots, which could play vital roles in quantum dot-based optoelectronics; however, existing photoexcitation transient spectroscopy investigations reveal that their lifetime is less than 1 ps in well-passivated quantum dots because of the ultrafast electron-hole Auger-assisted cooling. Therefore, they are generally considered absent in quantum dot optoelectronic devices. Here, by using our newly developed electrically excited transient absorption spectroscopy, we surprisingly observed abundant hot electrons in both II-VI and III-VI compound quantum dot light-emitting diodes at elevated bias (>4 V), of which the lifetimes reach 59 to 371 ns, lengthened by more than 5 orders of magnitude compared with the photoexcited hot electrons. These results experimentally prove the presence of a strong phonon bottleneck effect, refreshing our understanding of the role of hot electrons in quantum dot optoelectronics.

cond-mat.mes-hall↗

arXiv2024

MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs

Recent large language models (LLMs) have demonstrated versatile capabilities in long-context scenarios. Although some recent benchmarks have been developed to evaluate the long-context capabilities of LLMs, there is a lack of benchmarks evaluating the mathematical reasoning abilities of LLMs over long contexts, which is crucial for LLMs' application in real-world scenarios. In this paper, we introduce MathHay, an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs. Unlike previous benchmarks like Needle in a Haystack, which focus primarily on information retrieval within long texts, MathHay demands models with both information-seeking and complex mathematical reasoning abilities. We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing LLMs. Even the best-performing model, Gemini-1.5-Pro-002, still struggles with mathematical reasoning over long contexts, achieving only 51.26% accuracy at 128K tokens. This highlights the significant room for improvement on the MathHay benchmark.

cs.CL↗

arXiv2024

Uncertainties of the dust grain size in protoplanetary disks retrieved from millimeter continuum observations

Investigating the dust grain size and its dependence on substructures in protoplanetary disks is a crucial step in understanding the initial process of planet formation. Spectral indices derived from millimeter observations are used as a common probe for grain size. Converting observed spectral indices into grain sizes is a complex task that involves solving the radiative transfer equation, taking into account the disk structure and dust properties. In this work, we ran reference radiative transfer models with known disk properties, and generated four synthetic images at wavelengths of 0.8, 1.3, 3, and 7.8 mm, representing high-resolution continuum observations. Rings and gaps were considered in the setup. We fit the synthetic images using the analytic solution of the radiative transfer equation to investigate the circumstances under which the input grain sizes can be recovered. The results show that fitting images at only two wavelengths is not sufficient to retrieve the grain size. Fitting three images improves the retrieval of grain size, but the dust surface density is still not well recovered. When taking all of the four images into account, degeneracies between different parameters are highly reduced, and consequently the best-fit grain sizes are consistent with the reference setup at almost all radii. We find that the inclination angle has a significant impact on the fitting results. For disks with low inclinations, the analytic approach works quite well. However, when the disk is tilted above about 60 degree, neither the grain size nor the dust surface density can be constrained, as the inclination effect will smooth out all substructures in the radial intensity profile of the disk.

astro-ph.EP↗