arXiv subjects

Wei Yang

Publications and source records attributed to Wei Yang.

At least 181 records · Page 10Linked to original sources

arXiv2025

Data Movement Model for the Vera C. Rubin Observatory

The sky images captured nightly by the camera on the Vera C. Rubin Observatory's telescope will be processed across facilities on three continents. Data acquisition will occur at the observatory's location on Cerro Pachón in the Andes mountains of Chile. A first copy of the raw image data set is stored at the summit and immediately transmitted via dedicated network links to the archive center within the US Data Facility at SLAC National Accelerator Laboratory in California, USA and from there to two European facilities for processing and archiving purposes. Data products resulting from periodic processing campaigns of the entire set of images collected since the beginning of the survey are made available to the scientific community in the form of data releases. In this paper we present an overall view of how we leverage the tools selected for managing the movement of data among the Rubin processing and serving facilities, including Rucio and FTS. We also present the tools we developed to integrate Rucio's data model and Rubin's Data Butler, the software abstraction layer that mediates all access to storage by pipeline tasks that implement science algorithms.

astro-ph.IM↗

arXiv2025

Nonlinear skin effect regime when a radio frequency electromagnetic field penetrates into a background plasma

Two-dimensional, electromagnetic particle-in-cell simulations are employed to study particle kinetics and power deposition in the skin layer when a Radio Frequency (RF) electromagnetic field penetrates into a background plasma. We identify a new regime at low frequency ($\sim\mathrm{MHz}$) and low pressure, where the motion of electrons can be highly nonlinear in the skin region. Through most of the RF cycle, the electrons are trapped in the effective potential formed by the vector and electrostatic potentials, with energy deposition being small and magnetic moment $μ$ no longer being an adiabatic invariant. However, for a brief period around the null of the oscillating magnetic field, the electrons get detrapped, causing a jet-like current penetrating into the bulk plasma. During these brief periods, the power deposition becomes high, exhibiting a periodic burst nature. Based on kinetic theory, we provide analytical expressions for the plasma current and energy deposition in the new regime. A criterion for transition between the newly identified low-frequency, periodic-burst regime and the usual anomalous non-local skin effect regime is proposed and verified.

physics.plasm-ph↗

arXiv2025

Cosmos World Foundation Model Platform for Physical AI

Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make Cosmos open-source and our models open-weight with permissive licenses available via https://github.com/nvidia-cosmos/cosmos-predict1.

cs.CV↗

arXiv2025

RVISmith: Fuzzing Compilers for RVV Intrinsics

Modern processors are equipped with single instruction multiple data (SIMD) instructions for fine-grained data parallelism. Compiler auto-vectorization techniques that target SIMD instructions face performance limitations due to insufficient information available at compile time, requiring programmers to manually manipulate SIMD instructions. SIMD intrinsics, a type of built-in function provided by modern compilers, enable programmers to manipulate SIMD instructions within high-level programming languages. Bugs in compilers for SIMD intrinsics can introduce potential threats to software security, producing unintended calculation results, data loss, program crashes, etc. To detect bugs in compilers for SIMD intrinsics, we propose RVISmith, a randomized fuzzer that generates well-defined C programs that include various invocation sequences of RVV (RISC-V Vector Extension) intrinsics. We design RVISmith to achieve the following objectives: (i) achieving high intrinsic coverage, (ii) improving sequence variety, and (iii) without known undefined behaviors. We implement RVISmith based on the ratified RVV intrinsic specification and evaluate our approach with three modern compilers: GCC, LLVM, and XuanTie. Experimental results show that RVISmith achieves 11.5 times higher intrinsic coverage than the state-of-the-art fuzzer for RVV intrinsics. By differential testing that compares results across different compilers, optimizations, and equivalent programs, we detect and report 13 previously unknown bugs of the three compilers under test to date. Of these bugs, 10 are confirmed and another 3 are fixed by the compiler developers.

cs.CR↗

arXiv2025

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction

Social media popularity prediction plays a crucial role in content optimization, marketing strategies, and user engagement enhancement across digital platforms. However, predicting post popularity remains challenging due to the complex interplay between visual, textual, temporal, and user behavioral factors. This paper presents HyperFusion, a hierarchical multimodal ensemble learning framework for social media popularity prediction. Our approach employs a three-tier fusion architecture that progressively integrates features across abstraction levels: visual representations from CLIP encoders, textual embeddings from transformer models, and temporal-spatial metadata with user characteristics. The framework implements a hierarchical ensemble strategy combining CatBoost, TabNet, and custom multi-layer perceptrons. To address limited labeled data, we propose a two-stage training methodology with pseudo-labeling and iterative refinement. We introduce novel cross-modal similarity measures and hierarchical clustering features that capture inter-modal dependencies. Experimental results demonstrate that HyperFusion achieves competitive performance on the SMP challenge dataset. Our team achieved third place in the SMP Challenge 2025 (Image Track). The source code is available at https://anonymous.4open.science/r/SMPDImage.

cs.MM↗

arXiv2025

MVP: Winning Solution to SMP Challenge 2025 Video Track

Social media platforms serve as central hubs for content dissemination, opinion expression, and public engagement across diverse modalities. Accurately predicting the popularity of social media videos enables valuable applications in content recommendation, trend detection, and audience engagement. In this paper, we present Multimodal Video Predictor (MVP), our winning solution to the Video Track of the SMP Challenge 2025. MVP constructs expressive post representations by integrating deep video features extracted from pretrained models with user metadata and contextual information. The framework applies systematic preprocessing techniques, including log-transformations and outlier removal, to improve model robustness. A gradient-boosted regression model is trained to capture complex patterns across modalities. Our approach ranked first in the official evaluation of the Video Track, demonstrating its effectiveness and reliability for multimodal video popularity prediction on social platforms. The source code is available at https://anonymous.4open.science/r/SMPDVideo.

cs.CV↗

arXiv2025

A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems

Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leveraging the computational potential of quantum systems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems, we employ a decomposition strategy guided by maximum energy impact (MEI), integrating QA with a steepest descent method to enhance local search efficiency. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II and the ILP-based $ε$-constraint method. Experimental results reveal that while our methods produce fewer non-dominated solutions than $ε$-constraint, they achieve significant reductions in execution time. Moreover, compared to NSGA-II, our methods deliver more non-dominated solutions with superior computational efficiency. These findings underscore the potential of QA in advancing scalable and efficient solutions for SBSE challenges.

cs.SE↗

arXiv2025

CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation

Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.

eess.IV↗

arXiv2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load-derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

cs.DC↗

arXiv2025

Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems

The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems (DDLSs) have emerged, offering input-adaptive computation to optimize runtime efficiency. While these systems succeed in reducing cost, their dynamic nature introduces subtle and underexplored security risks. In particular, input-dependent execution pathways create opportunities for adversaries to degrade efficiency, resulting in excessive latency, energy usage, and potential denial-of-service in time-sensitive deployments. This work investigates the security implications of dynamic behaviors in DDLSs and reveals how current systems expose efficiency vulnerabilities exploitable by adversarial inputs. Through a survey of existing attack strategies, we identify gaps in the coverage of emerging model architectures and limitations in current defense mechanisms. Building on these insights, we propose to examine the feasibility of efficiency attacks on modern DDLSs and develop targeted defenses to preserve robustness under adversarial conditions.

cs.LG↗

arXiv2025

CodeImprove: Program Adaptation for Deep Code Models

Leveraging deep learning (DL)-based code analysis tools to solve software engineering tasks is becoming increasingly popular. Code models often suffer performance degradation due to various reasons (e.g., code data shifts). Retraining is often required to address these issues, but frequent model updates are costly in labeling and deployment. In this paper, we explore an alternative solution: Adapting the program inputs to the code models. This can be achieved by two steps: 1) input validation that focuses on identifying whether an input is an out-of-scope input program that are beyond a model's handling capability, and 2) input adaptation that adapts out-of-scope inputs to become in-scope inputs. Validating program input is challenging, as current techniques focus on continuous inputs such as image data and fail with discrete inputs like code data, which have unique characteristics and are processed differently by deep learning models. Adapting out-of-scope programs is also challenging due to their vast search spaces. Therefore, in this paper, we propose CodeImprove, which distinguishes out-of-scope from normal inputs and converts such out-of-scope inputs back to in-scope inputs through program transformation. In particular, we propose a validity score metric to identify out-of-scope inputs and leverage genetic algorithms to apply semantic preserving program transformation to convert out-of-scope inputs to in-scope inputs. Our experimental results show CodeImprove can enhance up to 8.78% of accuracy, and 51.28% of relative improvements in three code models on two SE tasks. Additionally, our input validation is promising in detecting out-of-scope inputs (AUC score of 0.924).

cs.SE↗

arXiv2025

Efficiency Robustness of Dynamic Deep Learning Systems

Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address efficiency challenges, Dynamic Deep Learning Systems (DDLSs) adapt inference computation based on input complexity, reducing overhead. While this dynamic behavior improves efficiency, such behavior introduces new attack surfaces. In particular, efficiency adversarial attacks exploit these dynamic mechanisms to degrade system performance. This paper systematically explores efficiency robustness of DDLSs, presenting the first comprehensive taxonomy of efficiency attacks. We categorize these attacks based on three dynamic behaviors: (i) attacks on dynamic computations per inference, (ii) attacks on dynamic inference iterations, and (iii) attacks on dynamic output production for downstream tasks. Through an in-depth evaluation, we analyze adversarial strategies that target DDLSs efficiency and identify key challenges in securing these systems. In addition, we investigate existing defense mechanisms, demonstrating their limitations against increasingly popular efficiency attacks and the necessity for novel mitigation strategies to secure future adaptive DDLSs.

cs.LG↗

arXiv2025

Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model

Large Language Models (LLMs) have garnered significant attention in Recommendation Systems (RS) due to their extensive world knowledge and robust reasoning capabilities. However, a critical challenge lies in enabling LLMs to effectively comprehend and extract insights from massive user behaviors. Current approaches that directly leverage LLMs for user interest learning face limitations in handling long sequential behaviors, effectively extracting interest, and applying interest in practical scenarios. To address these issues, we propose a Hierarchical Tree Search-based User Lifelong Behavior Modeling framework (HiT-LBM). HiT-LBM integrates Chunked User Behavior Extraction (CUBE) and Hierarchical Tree Search for Interest (HTS) to capture diverse interests and interest evolution of user. CUBE divides user lifelong behaviors into multiple chunks and learns the interest and interest evolution within each chunk in a cascading manner. HTS generates candidate interests through hierarchical expansion and searches for the optimal interest with process rating model to ensure information gain for each behavior chunk. Additionally, we design Temporal-Ware Interest Fusion (TIF) to integrate interests from multiple behavior chunks, constructing a comprehensive representation of user lifelong interests. The representation can be embedded into any recommendation model to enhance performance. Extensive experiments demonstrate the effectiveness of our approach, showing that it surpasses state-of-the-art methods.

cs.IR↗

arXiv2025

Optimized View and Geometry Distillation from Multi-view Diffuser

Generating multi-view images from a single input view using image-conditioned diffusion models is a recent advancement and has shown considerable potential. However, issues such as the lack of consistency in synthesized views and over-smoothing in extracted geometry persist. Previous methods integrate multi-view consistency modules or impose additional supervisory to enhance view consistency while compromising on the flexibility of camera positioning and limiting the versatility of view synthesis. In this study, we consider the radiance field optimized during geometry extraction as a more rigid consistency prior, compared to volume and ray aggregation used in previous works. We further identify and rectify a critical bias in the traditional radiance field optimization process through score distillation from a multi-view diffuser. We introduce an Unbiased Score Distillation (USD) that utilizes unconditioned noises from a 2D diffusion model, greatly refining the radiance field fidelity. We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images. Finally, we recover faithful geometry and texture directly from the refined multi-view images. Empirical evaluations demonstrate that our optimized geometry and view distillation technique generates comparable results to the state-of-the-art models trained on extensive datasets, all while maintaining freedom in camera positioning. Please see our project page at https://youjiazhang.github.io/USD/.

cs.CV↗

arXiv2025

CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image

Recovering high-quality 3D scenes from a single RGB image is a challenging task in computer graphics. Current methods often struggle with domain-specific limitations or low-quality object generation. To address these, we propose CAST (Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel method for 3D scene reconstruction and recovery. CAST starts by extracting object-level 2D segmentation and relative depth information from the input image, followed by using a GPT-based model to analyze inter-object spatial relationships. This enables the understanding of how objects relate to each other within the scene, ensuring more coherent reconstruction. CAST then employs an occlusion-aware large-scale 3D generation model to independently generate each object's full geometry, using MAE and point cloud conditioning to mitigate the effects of occlusions and partial object information, ensuring accurate alignment with the source image's geometry and texture. To align each object with the scene, the alignment generation model computes the necessary transformations, allowing the generated meshes to be accurately placed and integrated into the scene's point cloud. Finally, CAST incorporates a physics-aware correction step that leverages a fine-grained relation graph to generate a constraint graph. This graph guides the optimization of object poses, ensuring physical consistency and spatial coherence. By utilizing Signed Distance Fields (SDF), the model effectively addresses issues such as occlusions, object penetration, and floating objects, ensuring that the generated scene accurately reflects real-world physical interactions. CAST can be leveraged in robotics, enabling efficient real-to-simulation workflows and providing realistic, scalable simulation environments for robotic systems.

cs.CV↗

arXiv2025

PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer

Shape primitive abstraction, which decomposes complex 3D shapes into simple geometric elements, plays a crucial role in human visual cognition and has broad applications in computer vision and graphics. While recent advances in 3D content generation have shown remarkable progress, existing primitive abstraction methods either rely on geometric optimization with limited semantic understanding or learn from small-scale, category-specific datasets, struggling to generalize across diverse shape categories. We present PrimitiveAnything, a novel framework that reformulates shape primitive abstraction as a primitive assembly generation task. PrimitiveAnything includes a shape-conditioned primitive transformer for auto-regressive generation and an ambiguity-free parameterization scheme to represent multiple types of primitives in a unified manner. The proposed framework directly learns the process of primitive assembly from large-scale human-crafted abstractions, enabling it to capture how humans decompose complex shapes into primitive elements. Through extensive experiments, we demonstrate that PrimitiveAnything can generate high-quality primitive assemblies that better align with human perception while maintaining geometric fidelity across diverse shape categories. It benefits various 3D applications and shows potential for enabling primitive-based user-generated content (UGC) in games. Project page: https://primitiveanything.github.io

cs.GR↗

arXiv2025

Understanding the analysis of wave function

We obtained the order $ρ$ and the type $σ$ of wave function with power-law potentials, and found that the order and the type are compatible with the condition $|σ|ρ=\sqrt{v_m}$ except to $m=-2$. At the same time, we ansatz that the wave function satisfaction relation $ψ(r)=f(r)\exp[g(r)]$, where $f(r),g(r)$ are polynomials, and the power of $g(r)$ is no more than the order $ρ$.

quant-ph↗

arXiv2025

Generalizing the exact quantization rule to multiple real turning points

We generalize the exact quantization rule to multiple turning points, which are all on the real axis and are even in number. We found that when we take wave functions of different energy levels, they are stable between two adjacent turning points and are always integer multiples of π. We verify the effectiveness of the exact quantization rule by examining double square well potential and biharmonic potential.

quant-ph↗