arXiv subjects

Wei Yang

Publications and source records attributed to Wei Yang.

At least 271 records · Page 15Linked to original sources

arXiv2024

GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians

Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry, and sophisticated interaction with light. This paper presents GaussianHair, a novel explicit hair representation. It enables comprehensive modeling of hair geometry and appearance from images, fostering innovative illumination effects and dynamic animation capabilities. At the heart of GaussianHair is the novel concept of representing each hair strand as a sequence of connected cylindrical 3D Gaussian primitives. This approach not only retains the hair's geometric structure and appearance but also allows for efficient rasterization onto a 2D image plane, facilitating differentiable volumetric rendering. We further enhance this model with the "GaussianHair Scattering Model", adept at recreating the slender structure of hair strands and accurately capturing their local diffuse color in uniform lighting. Through extensive experiments, we substantiate that GaussianHair achieves breakthroughs in both geometric and appearance fidelity, transcending the limitations encountered in state-of-the-art methods for hair reconstruction. Beyond representation, GaussianHair extends to support editing, relighting, and dynamic rendering of hair, offering seamless integration with conventional CG pipeline workflows. Complementing these advancements, we have compiled an extensive dataset of real human hair, each with meticulously detailed strand geometry, to propel further research in this field.

cs.GR↗

arXiv2024

WEFix: Intelligent Automatic Generation of Explicit Waits for Efficient Web End-to-End Flaky Tests

Web end-to-end (e2e) testing evaluates the workflow of a web application. It simulates real-world user scenarios to ensure the application flows behave as expected. However, web e2e tests are notorious for being flaky, i.e., the tests can produce inconsistent results despite no changes to the code. One common type of flakiness is caused by nondeterministic execution orders between the test code and the client-side code under test. In particular, UI-based flakiness emerges as a notably prevalent and challenging issue to fix because the test code has limited knowledge about the client-side code execution. In this paper, we propose WEFix, a technique that can automatically generate fix code for UI-based flakiness in web e2e testing. The core of our approach is to leverage browser UI changes to predict the client-side code execution and generate proper wait oracles. We evaluate the effectiveness and efficiency of WEFix against 122 web e2e flaky tests from seven popular real-world projects. Our results show that WEFix dramatically reduces the overhead (from 3.7$\times$ to 1.25$\times$) while achieving a high correctness (98%).

cs.SE↗

arXiv2024

Sliding ferroelectric memories and synapses

Ferroelectric materials with switchable electric polarization hold great promise for a plethora of emergent applications, such as post-Moore's law nanoelectronics, beyond-Boltzmann transistors, non-volatile memories, and above-bandgap photovoltaic devices. Recent advances have uncovered an exotic sliding ferroelectric mechanism, which endows to design atomically thin ferroelectrics from non-ferroelectric parent monolayers. Although notable progress has been witnessed in understanding its fundamental properties, functional devices based on sliding ferroelectrics, the key touchstone toward applications, remain elusive. Here, we demonstrate the rewritable, non-volatile memory devices at room-temperature utilizing a two-dimensional (2D) sliding ferroelectric semiconductor of rhombohedral-stacked bilayer molybdenum disulfide. The 2D sliding ferroelectric memories (SFeMs) show superior performances with a large memory window of >8V, a high conductance ratio of above 106, a long retention time of >10 years, and a programming endurance greater than 104 cycles. Remarkably, flexible SFeMs are achieved with state-of-the-art performances competitive to their rigid counterparts and maintain their performances post bending over 103 cycles. Furthermore, synapse-specific Hebbian forms of plasticity and image recognition with a high accuracy of 97.81% are demonstrated based on flexible SFeMs. Our work demonstrates the sliding ferroelectric memories and synaptic plasticity on both rigid and flexible substrates, highlighting the great potential of sliding ferroelectrics for emerging technological applications in brain-inspired in-memory computing, edge intelligence and energy-efficient wearable electronics.

cond-mat.mes-hall↗

arXiv2024

PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models

In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Benchmarking LCGMs necessitates the creation of a set of diverse programming problems, and each problem comprises the prompt (including the task description), canonical solution, and test inputs. The existing methods for constructing such a problem set can be categorized into two main types: manual methods and perturbation-based methods. However, manual methods demand high effort and lack scalability, while also risking data integrity due to LCGMs' potentially contaminated data collection, and perturbation-based approaches mainly generate semantically homogeneous problems with the same canonical solutions and introduce typos that can be easily auto-corrected by IDE, making them ineffective and unrealistic. In this work, we propose the idea of programming problem merging (PPM) and provide two implementation of this idea, we utilize our tool on two widely-used datasets and compare it against nine baseline methods using eight code generation models. The results demonstrate the effectiveness of our tool in generating more challenging, diverse, and natural programming problems, comparing to the baselines.

cs.SE↗

arXiv2024

Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain

Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may dominate the collaboration and exhibit unintended or detrimental behaviors, leading to poor experiences for their human partners. In other words, most game AI agents are modeled in a "self-centered" manner. In this paper, we propose a "human-centered" modeling scheme for collaborative agents that aims to enhance the experience of humans. Specifically, we model the experience of humans as the goals they expect to achieve during the task. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e.g., winning games). To achieve this, we propose the Reinforcement Learning from Human Gain (RLHG) approach. The RLHG approach introduces a "baseline", which corresponds to the extent to which humans primitively achieve their goals, and encourages agents to learn behaviors that can effectively enhance humans in achieving their goals better. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings, by conducting real-world human-agent tests. Both objective performance and subjective preference results show that the RLHG agent provides participants better gaming experience.

cs.HC↗

arXiv2024

Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification

Few-shot hyperspectral image classification aims to identify the classes of each pixel in the images by only marking few of these pixels. And in order to obtain the spatial-spectral joint features of each pixel, the fixed-size patches centering around each pixel are often used for classification. However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify. These boundary patchs are mixed with multi-class spectral information. Inspired by this, we propose to augment the prototype network with TransMix for few-shot hyperspectrial image classification(APNT). While taking the prototype network as the backbone, it adopts the transformer as feature extractor to learn the pixel-to-pixel relation and pay different attentions to different pixels. At the same time, instead of directly using the patches which are cut from the hyperspectral images for training, it randomly mixs up two patches to imitate the boundary patches and uses the synthetic patches to train the model, with the aim to enlarge the number of hard training samples and enhance their diversity. And by following the data agumentation technique TransMix, the attention returned by the transformer is also used to mix up the labels of two patches to generate better labels for synthetic patches. Compared with existing methods, the proposed method has demonstrated sate of the art performance and better robustness for few-shot hyperspectral image classification in our experiments.

cs.CV↗

arXiv2024

Experimental investigation and scale analysis on melting of salty ice in a 3D-printed cavity filled with porous media

While significant interests have been devoted to the double diffusive phase change heat transfer of binary solutions, the understanding on the melting heat transfer of salty ice in porous media is still imcomplete. This work aims to explore the melting heat transfer characteristics of salty ice in a square cavity filled with homogeneous porous media subjected to lateral heating. In order to facilitate the visualization of melting dynamics in a uniform porous media, a three-dimensional (3D) printed transparent cavity filled with porous matrix was manufactured, featuring an open upper end. Aqueous solutions of sodium chloride at concentration both higher and lower than the eutectic concentration were investigated. Lateral heating experiments with constant heat flux were conducted in a subzero temperature environment. The effect of heating power, initial concentrations and different sizes of porous matrix was investigated. Distinct phenomena were observed based on the concentration of the aqueous solution. Specifically, the concentration lower than the eutectic concentration results in an upward bending melting interface. Whereas the concentration surpassing the eutectic concentration resulted in a downward bending melting interface. In the presence of salt, a melting convex appears at the bottom of the melting interface and manifests during the whole melting process. Notably, smaller matrix enables faster melting rate. Furthermore, three distinct stages were revealed in our experiments, characterized as conduction, mixture of conduction and ultimately convection stage. The correlations for Nu and the average propagation rate of mushy/liquid interface were established by order of magnitude analysis and fit well with the experiments.

physics.app-ph↗

arXiv2024

Interplay of Landau quantization and interminivalley scatterings in a weakly coupled moiré superlattice

Double layer quantum systems are promising platforms for realizing novel quantum phases. Here, we report a study of quantum oscillations (QOs) in a weakly coupled double layer system, composed of a large angle twisted double bilayer graphene (TDBG). We observe two different QOs at low temperature, one with a periodicity in carrier density (n), i.e. Shubnikov de Haas oscillation (SdHO) due to Landau quantization, and the other one in displacement field (D), resulting a grid pattern. We quantify the interlayer coupling strength by measuring the interlayer capacitance from the grid pattern with a capacitance model, revealing an electron hole asymmetry. At high temperature when SdHO are thermal smeared, we observe resistance peaks when LLs from two minivalleys in the moiré Brillion zone are aligned, regardless of carrier density; eventually, it results in a two fold increase of oscillating frequency in D, serving as a smoking gun evidence of the magneto intersubband oscillations (MISO) in a double layer system. The temperature dependence of MISO suggests electron-electron interaction between two minivalleys play a crucial rule in the scattering, and the scattering times obtained from MISO thermal damping are found to be correlated with the interlayer coupling strength. Our study reveals an intriguing interplay among Landau quantization, moiré band structure, and scatterings.

cond-mat.mes-hall↗

arXiv2024

Progressive Text-to-Image Diffusion with Soft Latent Direction

In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges. This paper introduces an innovative progressive synthesis and editing operation that systematically incorporates entities into the target image, ensuring their adherence to spatial and relational constraints at each sequential step. Our key insight stems from the observation that while a pre-trained text-to-image diffusion model adeptly handles one or two entities, it often falters when dealing with a greater number. To address this limitation, we propose harnessing the capabilities of a Large Language Model (LLM) to decompose intricate and protracted text descriptions into coherent directives adhering to stringent formats. To facilitate the execution of directives involving distinct semantic operations-namely insertion, editing, and erasing-we formulate the Stimulus, Response, and Fusion (SRF) framework. Within this framework, latent regions are gently stimulated in alignment with each operation, followed by the fusion of the responsive latent components to achieve cohesive entity manipulation. Our proposed framework yields notable advancements in object synthesis, particularly when confronted with intricate and lengthy textual inputs. Consequently, it establishes a new benchmark for text-to-image generation tasks, further elevating the field's performance standards.

cs.CV↗

arXiv2024

Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study

Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code distribution shifts, leading to inconsistent outputs. While probabilistic methods are known to mitigate such impact through uncertainty calibration and estimation, their efficacy in the language domain remains underexplored compared to their application in image-based tasks. In this work, we first introduce a large-scale benchmark dataset, incorporating three realistic patterns of code distribution shifts at varying intensities. Then we thoroughly investigate state-of-the-art probabilistic methods applied to CodeLlama using these shifted code snippets. We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision. However, our study further reveals varied performance dynamics across different criteria (e.g., calibration error vs misclassification detection) and trade-off between efficacy and efficiency, highlighting necessary methodological selection tailored to specific contexts.

cs.SE↗

arXiv2023

Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of "Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods.

cs.CV↗

arXiv2023

Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification

Occluded person re-identification (ReID) is a challenging problem due to contamination from occluders. Existing approaches address the issue with prior knowledge cues, such as human body key points and semantic segmentations, which easily fail in the presence of heavy occlusion and other humans as occluders. In this paper, we propose a feature pruning and consolidation (FPC) framework to circumvent explicit human structure parsing. The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder. Specifically, the sparse encoder drops less important image tokens, mostly related to background noise and occluders, solely based on correlation within the class token attention. Subsequently, the matching stage relies on the preserved tokens produced by the sparse encoder to identify k-nearest neighbors in the gallery by measuring the image and patch-level combined similarity. Finally, we use the feature consolidation module to compensate pruned features using identified neighbors for recovering essential information while disregarding disturbance from noise and occlusion. Experimental results demonstrate the effectiveness of our proposed framework on occluded, partial, and holistic Re-ID datasets. In particular, our method outperforms state-of-the-art results by at least 8.6\% mAP and 6.0\% Rank-1 accuracy on the challenging Occluded-Duke dataset.

cs.CV↗

arXiv2023

Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model

Text-guided motion synthesis aims to generate 3D human motion that not only precisely reflects the textual description but reveals the motion details as much as possible. Pioneering methods explore the diffusion model for text-to-motion synthesis and obtain significant superiority. However, these methods conduct diffusion processes either on the raw data distribution or the low-dimensional latent space, which typically suffer from the problem of modality inconsistency or detail-scarce. To tackle this problem, we propose a novel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, to collaboratively exploit low-dimensional and high-dimensional diffusion models for high quality detailed motion synthesis. Specifically, the basic diffusion model in low-dimensional latent space provides the intermediate denoising result that to be consistent with the textual description, while the advanced diffusion model in high-dimensional latent space focuses on the following detail-enhancing denoising process. Besides, we introduce a multi-denoiser framework for the advanced diffusion model to ease the learning of high-dimensional model and fully explore the generative potential of the diffusion model. Quantitative and qualitative experiment results on two text-to-motion benchmarks (HumanML3D and KIT-ML) demonstrate that B2A-HDM can outperform existing state-of-the-art methods in terms of fidelity, modality consistency, and diversity.

cs.CV↗

arXiv2023

Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach

The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space. Nevertheless, there are two major issues that limit the performance of latent-based diffusion. First, the compression of latent space usually causes reconstruction distortion. Second, huge computational cost constrains the parameter scale of the diffusion model. To counteract these issues, we first propose a frequency compensation module that enhances the frequency components from latent space to pixel space. The reconstruction distortion (especially for high-frequency information) can be significantly decreased. Then, we propose to use Sample-Space Mixture of Experts (SS-MoE) to achieve more powerful latent-based SR, which steadily improves the capacity of the model without a significant increase in inference costs. These carefully crafted designs contribute to performance improvements in largely explored 4x blind super-resolution benchmarks and extend to large magnification factors, i.e., 8x image SR benchmarks. The code is available at https://github.com/amandaluof/moe_sr.

cs.CV↗

arXiv2023

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment's run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory's needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

astro-ph.IM↗

arXiv2023

A Scale-out Decentralized Blockchain Ledger System for Web3.0

The development of underlying technologies in blockchain mostly revolves around a difficult problem: how to enhance the performance of the system and reduce various costs of nodes (such as communication, storage and verification) without compromising the system's security and decentralization. Various layer-1 and layer-2 protocols have provided excellent solutions for this challenge. However, they cannot yet be considered as a ``silver bullet". This paper proposes EZchain -- a novel decentralized ``scale-out" ledger system designed for web3.0, aiming to enable blockchain technology to truly support ledger applications in large-scale fully decentralized networks. Without compromising security and decentralization, EZchain successfully accomplishes the following milestones: 1) Scalability: The theoretical throughput of EZchain can be infinitely expanded, nearly unaffected by bandwidth and other resource constraints. 2) Consumer-Grade Hardware Compatibility: EZchain is designed to be compatible with consumer-grade hardware, supporting storage, computation, and verification requirements. 3) Efficient Transaction Confirmation: EZchain strives to maintain transaction confirmation delays within one minute. Our prototype experiment demonstrates that under typical daily bandwidth network conditions, EZchain's performance in all aspects approaches that of the accounts in centralized payment systems. This provides a solid infrastructure for realizing mobile payments in web3.0.

cs.CR↗

arXiv2023

Room-temperature correlated states in twisted bilayer MoS$_2$

Moiré superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic correlations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report the transport evidences of room-temperature correlated electronic states and layer-hybridized SU(4) Hubbard model simulator in AB-stacked MoS$_2$ homo-bilayer moiré superlattices. Correlated insulating states at moiré band filling factors v = 1, 2, 3 are unambiguously established in twisted bilayer MoS$_2$. Remarkably, the correlated electronic states can persist up to a record-high critical temperature of over 285 K. The realization of room-temperature correlated states in twisted bilayer MoS$_2$ can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moiré superlattice effects on electronic correlations. Furthermore, extreme large non-linear Hall responses up to room-temperature are uncovered near correlated insulating states, demonstrating the quantum geometry of moiré flat conduction band.

cond-mat.mtrl-sci↗

arXiv2023

Fine-grained Appearance Transfer with Diffusion Models

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains complex, particularly in terms of retaining detailed structural elements and ensuring information fidelity. This paper proposes an innovative framework designed to surmount these challenges by integrating various aspects of semantic matching, appearance transfer, and latent deviation. A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes. This is identified as a crucial element for the precise and natural transfer of fine-grained details. Our framework exploits this space to accomplish semantic alignment between source and target images, facilitating mask-wise appearance transfer for improved feature acquisition. A significant advancement of our method is the seamless integration of these features into the latent space, enabling more nuanced latent deviations without necessitating extensive model retraining or fine-tuning. The effectiveness of our approach is demonstrated through extensive experiments, which showcase its ability to adeptly handle fine-grained appearance transfers across a wide range of categories and domains. We provide our code at https://github.com/babahui/Fine-grained-Appearance-Transfer

cs.CV↗