arXiv subjects

Wei Dai

Publications and source records attributed to Wei Dai.

At least 109 records · Page 6Linked to original sources

arXiv2025

Study of the EEC discrimination power on quark and gluon jet quenching effects in heavy-ion collisions at $\sqrt{s}=5.02$ TeV

We present a systematic investigation of flavor-dependent jet quenching using energy-energy correlators (EEC) in $\sqrt{\rm s}=5.02$ TeV Pb+Pb collisions. Employing the improved SHELL model, which incorporates collisional and radiative energy loss, as well as medium response, we quantify distinct quenching signatures for quark and gluon jets. Key findings include: (1) Pure quark jets exhibit strong EEC enhancement at large angular scales, while gluon jets show a bimodal enhancement pattern at both small and large scales; (2) Dual-shift decomposition in the EEC ratio reveals shifts toward large primarily driven by energy loss, while small-$R_{L}$~shifts extend beyond selection bias and indicate intrinsic enhancement of the gluon-initiated jets; (3) Quark jets experience global suppression of averaged energy weight $\langle\mathrm{weight}\rangle(R_{L})$, whereas gluon jets exhibit concentration toward small $R_{L}$; (4) Mechanism decomposition identifies elastic energy loss concentrating $\langle\mathrm{weight}\rangle(R_{L})$ toward small $R_{L}$, radiative loss dominating quark jet modification, and medium response amplifying large $R_{L}$ ~enhancement via soft hadrons. The observed flavor dependence in EEC modifications is dominantly driven by intrinsic jet structure differences rather than medium-induced mechanisms. We propose photon-tagged jets as quark proxies and inclusive charged-hadron jets as gluon proxies, finding they reproduce the respective flavor-specific quenching patterns. Our work establishes the EEC as a precision probe of color-charge-dependent jet-medium interactions, providing new constraints for the detailed $\hat{q}$ extraction and QGP tomography, while highlighting the critical role of pre-quenching flavor asymmetries.

nucl-th↗

arXiv2025

Compositional Attribute Imbalance in Vision Datasets

Visual attribute imbalance is a common yet underexplored issue in image classification, significantly impacting model performance and generalization. In this work, we first define the first-level and second-level attributes of images and then introduce a CLIP-based framework to construct a visual attribute dictionary, enabling automatic evaluation of image attributes. By systematically analyzing both single-attribute imbalance and compositional attribute imbalance, we reveal how the rarity of attributes affects model performance. To tackle these challenges, we propose adjusting the sampling probability of samples based on the rarity of their compositional attributes. This strategy is further integrated with various data augmentation techniques (such as CutMix, Fmix, and SaliencyMix) to enhance the model's ability to represent rare attributes. Extensive experiments on benchmark datasets demonstrate that our method effectively mitigates attribute imbalance, thereby improving the robustness and fairness of deep neural networks. Our research highlights the importance of modeling visual attribute distributions and provides a scalable solution for long-tail image classification tasks.

cs.CV↗

arXiv2025

Distributionally Robust Receive Combining

This article investigates signal estimation in wireless transmission (i.e., receive combining) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated. Particular attention is paid to handling various uncertainties such as the uncertainty of the transmit signal covariance, the uncertainty of the channel matrix, the uncertainty of the channel noise covariance, the existence of channel impulse noises, the non-ideality of the power amplifiers, and the limited sample size of pilots. To proceed, a distributionally robust receive combining framework that is insensitive to the above uncertainties is proposed, which reveals that channel estimation is not a necessary operation. For optimal linear estimation, the proposed framework includes several existing combiners as special cases such as diagonal loading and eigenvalue thresholding. For optimal nonlinear estimation, estimators are limited in reproducing kernel Hilbert spaces and neural network function spaces, and corresponding uncertainty-aware solutions (e.g., kernelized diagonal loading) are derived. In addition, we prove that the ridge and kernel ridge regression methods in machine learning are distributionally robust against diagonal perturbation in feature covariance.

eess.SP↗

arXiv2025

Full characterization of measurement-induced transitions of a superconducting qubit

Repeated quantum non-demolition measurement is a cornerstone of quantum error correction protocols. In superconducting qubits, the speed of dispersive state readout can be enhanced by increasing the power of the readout tone. However, such an increase has been found to result in additional qubit state transitions that violate the desired quantum non-demolition character of the measurement. Recently, the readout of a transmon superconducting qubit was improved by using a tone with frequency much larger than the qubit frequency. Here, we experimentally identify the mechanisms of readout-induced transitions in this regime. In the dominant mechanism, the energy of an incoming readout photon is partially absorbed by the transmon and partially returned to the transmission line as a photon with lower frequency. Other mechanisms involve the excitation of unwanted package modes, decay via material defects, and, at higher qubit frequencies, the activation of undesired resonances in the transmon spectrum. Our work provides a comprehensive characterization of superconducting qubit state transitions caused by a strong drive.

quant-ph↗

arXiv2025

CogMath: Assessing LLMs' Authentic Mathematical Ability from a Human Cognitive Perspective

Although large language models (LLMs) show promise in solving complex mathematical tasks, existing evaluation paradigms rely solely on a coarse measure of overall answer accuracy, which are insufficient for assessing their authentic capabilities. In this paper, we propose \textbf{CogMath}, which comprehensively assesses LLMs' mathematical abilities through the lens of human cognition. Specifically, inspired by psychological theories, CogMath formalizes human reasoning process into 3 stages: \emph{problem comprehension}, \emph{problem solving}, and \emph{solution summarization}. Within these stages, we investigate perspectives such as numerical calculation, knowledge, and counterfactuals, and design a total of 9 fine-grained evaluation dimensions. In each dimension, we develop an ``\emph{Inquiry}-\emph{Judge}-\emph{Reference}'' multi-agent system to generate inquiries that assess LLMs' mastery from this dimension. An LLM is considered to truly master a problem only when excelling in all inquiries from the 9 dimensions. By applying CogMath on three benchmarks, we reveal that the mathematical capabilities of 7 mainstream LLMs are overestimated by 30\%-40\%. Moreover, we locate their strengths and weaknesses across specific stages/dimensions, offering in-depth insights to further enhance their reasoning abilities.

cs.AI↗

arXiv2025

Optimizing the pump coupling for a three-wave mixing Josephson parametric amplifier

Josephson element-based parametric amplifiers (JPAs) typically require rf pump power that is several orders of magnitude stronger than the maximum signal power they can handle. The low power efficiency and strong pump leakage towards signal circuitry could be critical concerns in application. In this work, we discuss how to optimize the pump coupling scheme for a three-wave mixing JPA by employing microwave filtering techniques, with the goal of maximizing the pump power efficiency and minimize pump leakage without sacrificing other properties of interest. We implement the corresponding filter design in a SNAIL-based JPA and demonstrate more than three orders of magnitude improvement in both power efficiency and pump leakage suppression compared to a similar device with regular capacitive coupling, while maintaining state-of-the-art dynamic range and near-quantum-limited noise performance. Furthermore, we show experimentally that the filter-coupled JPA is more robust against noise input from the pump port, exhibiting no significant change in added noise performance with up to 4 K of effective noise temperature at the pump port.

quant-ph↗

arXiv2025

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Large Language Models (LLMs) have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen. Existing solutions primarily rely on privacy-enhancing technologies to mitigate such risks, facing the trade-off among efficiency, privacy, and utility. To narrow this gap, we propose Cape, a context-aware prompt perturbation mechanism based on differential privacy, to enable efficient inference with an improved privacy-utility trade-off. Concretely, we introduce a hybrid utility function that better captures the token similarity. Additionally, we propose a bucketized sampling mechanism to handle large sampling space, which might lead to long-tail phenomenons. Extensive experiments across multiple datasets, along with ablation studies, demonstrate that Cape achieves a better privacy-utility trade-off compared to prior state-of-the-art works.

cs.CR↗

arXiv2025

Valida ISA Spec, version 1.0: A zk-Optimized Instruction Set Architecture

The Valida instruction set architecture is designed for implementation in zkVMs to optimize for fast, efficient execution proving. This specification intends to guide implementors of zkVMs and compiler toolchains for Valida. It provides an unambiguous definition of the semantics of Valida programs and may be used as a starting point for formalization efforts.

cs.CR↗

arXiv2025

Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning

Data heterogeneity in federated learning, characterized by a significant misalignment between local and global distributions, leads to divergent local optimization directions and hinders global model training. Existing studies mainly focus on optimizing local updates or global aggregation, but these indirect approaches demonstrate instability when handling highly heterogeneous data distributions, especially in scenarios where label skew and domain skew coexist. To address this, we propose a geometry-guided data generation method that centers on simulating the global embedding distribution locally. We first introduce the concept of the geometric shape of an embedding distribution and then address the challenge of obtaining global geometric shapes under privacy constraints. Subsequently, we propose GGEUR, which leverages global geometric shapes to guide the generation of new samples, enabling a closer approximation to the ideal global distribution. In single-domain scenarios, we augment samples based on global geometric shapes to enhance model generalization; in multi-domain scenarios, we further employ class prototypes to simulate the global distribution across domains. Extensive experimental results demonstrate that our method significantly enhances the performance of existing approaches in handling highly heterogeneous data, including scenarios with label skew, domain skew, and their coexistence. Code published at: https://github.com/WeiDai-David/2025CVPR_GGEUR

cs.CV↗

arXiv2025

Uncertainty Awareness in Wireless Communications and Sensing

Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. Despite transformative capabilities, wireless systems often face diverse uncertainties in design and operation, such as modeling errors due to incomplete physical knowledge, statistical errors arising from data scarcity, measurement errors caused by sensor imperfections, computational errors owing to resource limitation, and unpredictability of environmental evolution. Once ignored, these uncertainties can lead to severe outcomes, e.g., performance degradation, system untrustworthiness, inefficient resource utilization, and security vulnerabilities. As such, this article reviews mature and emerging architectural, computational, and operational countermeasures, encompassing uncertainty-aware designs of signals and systems (e.g., diversity, adaptivity, modularity), as well as uncertainty-aware modeling and computational frameworks (e.g., risk-informed optimization, robust signal processing, and trustworthy machine learning). Trade-offs to employ these methods, e.g., robustness vs optimality, are also highlighted.

eess.SP↗

arXiv2025

The Maxwell-Klein-Gordon equation with scattering data

It has been shown in [Yang-Yu 2019] that general large solutions to the Cauchy problem for the Maxwell-Klein-Gordon system (MKG) in the Minkowski space $\mathbb{R}^{1+3}$ decay like linear solutions. One hence can define the associated radiation field on the future null infinity as the limit of $(r\underlineα, rϕ)$ along the out going null geodesics. In this paper, we show the existence of a global solution to the MKG system which scatters to any given sufficiently localized radiation field with arbitrarily large size and total charge. The result follows by studying the characteristic initial value problem for the MKG system with general large data by using gauge invariant vector field method. We in particular extend the small data result of He in \cite{MR4299134} to a class of general large data.

math.AP↗

arXiv2025

Characteristic initial value problem for nonlinear wave equation with singular initial data

In this paper, we study the characteristic initial value problem for a class of nonlinear wave equations with data on a conic light cone in the Minkowski space $\mathbb{R}^{1+3}$. We show the existence of local solution for a class of singular initial data in the sense that the standard energy could be infinite and the solution may blow up at the conic point. As an application, we improve our previous result on the inverse scattering problem for the Maxwell-Klein-Gordon equations with scattering data on the future null infinity.

math.AP↗

arXiv2025

CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models

Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduce Clinical Large-Scale Integrative Multimodal Benchmark (CLIMB), a comprehensive clinical benchmark unifying diverse clinical data across imaging, language, temporal, and graph modalities. CLIMB comprises 4.51 million patient samples totaling 19.01 terabytes distributed across 2D imaging, 3D video, time series, graphs, and multimodal data. Through extensive empirical evaluation, we demonstrate that multitask pretraining significantly improves performance on understudied domains, achieving up to 29% improvement in ultrasound and 23% in ECG analysis over single-task learning. Pretraining on CLIMB also effectively improves models' generalization capability to new tasks, and strong unimodal encoder performance translates well to multimodal performance when paired with task-appropriate fusion strategies. Our findings provide a foundation for new architecture designs and pretraining strategies to advance clinical AI research. Code is released at https://github.com/DDVD233/climb.

cs.LG↗

arXiv2025

NuExo: A Wearable Exoskeleton Covering all Upper Limb ROM for Outdoor Data Collection and Teleoperation of Humanoid Robots

The evolution from motion capture and teleoperation to robot skill learning has emerged as a hotspot and critical pathway for advancing embodied intelligence. However, existing systems still face a persistent gap in simultaneously achieving four objectives: accurate tracking of full upper limb movements over extended durations (Accuracy), ergonomic adaptation to human biomechanics (Comfort), versatile data collection (e.g., force data) and compatibility with humanoid robots (Versatility), and lightweight design for outdoor daily use (Convenience). We present a wearable exoskeleton system, incorporating user-friendly immersive teleoperation and multi-modal sensing collection to bridge this gap. Due to the features of a novel shoulder mechanism with synchronized linkage and timing belt transmission, this system can adapt well to compound shoulder movements and replicate 100% coverage of natural upper limb motion ranges. Weighing 5.2 kg, NuExo supports backpack-type use and can be conveniently applied in daily outdoor scenarios. Furthermore, we develop a unified intuitive teleoperation framework and a comprehensive data collection system integrating multi-modal sensing for various humanoid robots. Experiments across distinct humanoid platforms and different users validate our exoskeleton's superiority in motion range and flexibility, while confirming its stability in data collection and teleoperation accuracy in dynamic scenarios.

cs.RO↗

arXiv2025

Towards Fine-Grained Video Question Answering

In the rapidly evolving domain of video understanding, Video Question Answering (VideoQA) remains a focal point. However, existing datasets exhibit gaps in temporal and spatial granularity, which consequently limits the capabilities of existing VideoQA methods. This paper introduces the Multi-Object Multi-Actor Question Answering (MOMA-QA) dataset, which is designed to address these shortcomings by emphasizing temporal localization, spatial relationship reasoning, and entity-centric queries. With ground truth scene graphs and temporal interval annotations, MOMA-QA is ideal for developing models for fine-grained video understanding. Furthermore, we present a novel video-language model, SGVLM, which incorporates a scene graph predictor, an efficient frame retriever, and a pre-trained large language model for temporal localization and fine-grained relationship understanding. Evaluations on MOMA-QA and other public datasets demonstrate the superior performance of our model, setting new benchmarks for VideoQA.

cs.CV↗

arXiv2025

MobileViM: A Light-weight and Dimension-independent Vision Mamba for 3D Medical Image Analysis

Efficient evaluation of three-dimensional (3D) medical images is crucial for diagnostic and therapeutic practices in healthcare. Recent years have seen a substantial uptake in applying deep learning and computer vision to analyse and interpret medical images. Traditional approaches, such as convolutional neural networks (CNNs) and vision transformers (ViTs), face significant computational challenges, prompting the need for architectural advancements. Recent efforts have led to the introduction of novel architectures like the ``Mamba'' model as alternative solutions to traditional CNNs or ViTs. The Mamba model excels in the linear processing of one-dimensional data with low computational demands. However, Mamba's potential for 3D medical image analysis remains underexplored and could face significant computational challenges as the dimension increases. This manuscript presents MobileViM, a streamlined architecture for efficient segmentation of 3D medical images. In the MobileViM network, we invent a new dimension-independent mechanism and a dual-direction traversing approach to incorporate with a vision-Mamba-based framework. MobileViM also features a cross-scale bridging technique to improve efficiency and accuracy across various medical imaging modalities. With these enhancements, MobileViM achieves segmentation speeds exceeding 90 frames per second (FPS) on a single graphics processing unit (i.e., NVIDIA RTX 4090). This performance is over 24 FPS faster than the state-of-the-art deep learning models for processing 3D images with the same computational resources. In addition, experimental evaluations demonstrate that MobileViM delivers superior performance, with Dice similarity scores reaching 92.72%, 86.69%, 80.46%, and 77.43% for PENGWIN, BraTS2024, ATLAS, and Toothfairy2 datasets, respectively, which significantly surpasses existing models.

cs.CV↗

arXiv2025

Spatial-Temporal Perception with Causal Inference for Naturalistic Driving Action Recognition

Naturalistic driving action recognition is essential for vehicle cabin monitoring systems. However, the complexity of real-world backgrounds presents significant challenges for this task, and previous approaches have struggled with practical implementation due to their limited ability to observe subtle behavioral differences and effectively learn inter-frame features from video. In this paper, we propose a novel Spatial-Temporal Perception (STP) architecture that emphasizes both temporal information and spatial relationships between key objects, incorporating a causal decoder to perform behavior recognition and temporal action localization. Without requiring multimodal input, STP directly extracts temporal and spatial distance features from RGB video clips. Subsequently, these dual features are jointly encoded by maximizing the expected likelihood across all possible permutations of the factorization order. By integrating temporal and spatial features at different scales, STP can perceive subtle behavioral changes in challenging scenarios. Additionally, we introduce a causal-aware module to explore relationships between video frame features, significantly enhancing detection efficiency and performance. We validate the effectiveness of our approach using two publicly available driver distraction detection benchmarks. The results demonstrate that our framework achieves state-of-the-art performance.

cs.CV↗

arXiv2025

Spectra and azimuthal harmonic of charmed mesons in Pb+Pb collisions at LHC energies

Using the recombination model (RM) extended to the heavy flavor scenario, we investigate the production of charmed mesons, i.e., $D^0$, $D_s^+$ and $J/ψ$, in Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$ and 5.02 TeV at the CERN Large Hadron Collider (LHC) including the heavy flavor energy loss inside the hot and dense medium. Soft, semihard, and hard partons all play important roles in our model and are uniformly treated for all mesons produced. The resulting transverse momentum spectra and the second azimuthal harmonic $v_2$ for these mesons are presented and compared with the available experimental data. The good agreement provides strong support again that the quark recombination mechanism is a universal scheme of hadronization.

hep-ph↗