arXiv subjects

Tao Chen

Publications and source records attributed to Tao Chen.

At least 217 records · Page 12Linked to original sources

arXiv2025

Collective magnetism of atomic momentum states

Organization and ordering from interactions in many-body systems underlies our understanding of phases of classical and quantum matter. Magnetism has played a particularly foundational role in the study of many-body phases. Here, we explore the collective magnetism that emerges from two laser-coupled momentum modes of a scalar bosonic quantum gas. We employ adiabatic state preparation and explore the collective magnetization response to an applied bias potential, finding that the relative increase of interactions leads to an enhanced and muted response for the ground state and excited state, respectively. We further find evidence for significant $Z_2$ symmetry breaking of the sample magnetization for the ground state, consistent with the expected beyond-mean-field behavior. These results suggest that the nonlinear interactions of scalar Bose condensates could provide a simple, direct path towards the squeezing of momentum states for quantum sensing.

cond-mat.quant-gas↗

arXiv2025

Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, Network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. In the final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

cs.NI↗

arXiv2025

Multi-Level Decoupled Relational Distillation for Heterogeneous Architectures

Heterogeneous distillation is an effective way to transfer knowledge from cross-architecture teacher models to student models. However, existing heterogeneous distillation methods do not take full advantage of the dark knowledge hidden in the teacher's output, limiting their performance.To this end, we propose a novel framework named Multi-Level Decoupled Relational Knowledge Distillation (MLDR-KD) to unleash the potential of relational distillation in heterogeneous distillation. Concretely, we first introduce Decoupled Finegrained Relation Alignment (DFRA) in both logit and feature levels to balance the trade-off between distilled dark knowledge and the confidence in the correct category of the heterogeneous teacher model. Then, Multi-Scale Dynamic Fusion (MSDF) module is applied to dynamically fuse the projected logits of multiscale features at different stages in student model, further improving performance of our method in feature level. We verify our method on four architectures (CNNs, Transformers, MLPs and Mambas), two datasets (CIFAR-100 and Tiny-ImageNet). Compared with the best available method, our MLDR-KD improves student model performance with gains of up to 4.86% on CIFAR-100 and 2.78% on Tiny-ImageNet datasets respectively, showing robustness and generality in heterogeneous distillation. Code will be released soon.

cs.CV↗

arXiv2025

The Same Only Different: On Information Modality for Configuration Performance Analysis

Configuration in software systems helps to ensure efficient operation and meet diverse user needs. Yet, some, if not all, configuration options have profound implications for the system's performance. Configuration performance analysis, wherein the key is to understand (or infer) the configuration options' relations and their impacts on performance, is crucial. Two major modalities exist that serve as the source information in the analysis: either the manual or source code. However, it remains unclear what roles they play in configuration performance analysis. Much work that relies on manuals claims their benefits of information richness and naturalness; while work that trusts the source code more prefers the structural information provided therein and criticizes the timeliness of manuals. To fill such a gap, in this paper, we conduct an extensive empirical study over 10 systems, covering 1,694 options, 106,798 words in the manual, and 22,859,552 lines-of-code for investigating the usefulness of manual and code in two important tasks of configuration performance analysis, namely performance-sensitive options identification and the associated dependencies extraction. We reveal several new findings and insights, such as it is beneficial to fuse the manual and code modalities for both tasks; the current automated tools that rely on a single modality are far from being practically useful and generally remain incomparable to human analysis. All those pave the way for further advancing configuration performance analysis.

cs.SE↗

arXiv2025

EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy

3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view similarity, and use robust pointmap priors to obtain high-quality point clouds and camera poses for 3D scene initialization. After obtaining a reliable scene structure, we propose a novel densification approach that adaptively splits Gaussian primitives based on the average shape of neighboring Gaussian ellipsoids, utilizing KNN scheme. In this way, the proposed method tackles the limitation on initialization and optimization, leading to an efficient and accurate 3DGS modeling. Extensive experiments demonstrate that EasySplat outperforms the current state-of-the-art (SOTA) in handling novel view synthesis.

cs.CV↗

arXiv2025

Tensor network Monte Carlo simulations for the two-dimensional random-bond Ising model

Disordered lattice spin systems are crucial in both theoretical and applied physics. However, understanding their properties poses significant challenges for Monte Carlo simulations. In this work, we investigate the two-dimensional random-bond Ising model using the recently proposed Tensor Network Monte Carlo (TNMC) method. This method generates biased samples from conditional probabilities computed via tensor network contractions and corrects the bias using the Metropolis scheme. Consequently, the proposals provided by tensor networks function as block updates for Monte Carlo simulations. Through extensive numerical experiments, we demonstrate that TNMC simulations can be performed on lattices as large as $1024\times 1024$ spins with moderate computational resources, a substantial increase from the previous maximum size of $64\times 64$ in MCMC. Notably, we observe an almost complete absence of critical slowing down, enabling the efficient collection of unbiased samples and averaging over a large number of random realizations of bond disorders. We successfully pinpoint the multi-critical point along the Nishimori line with significant precision and accurately determined the bulk and surface critical exponents. Our findings suggest that TNMC is a highly efficient algorithm for exploring disordered and frustrated systems in two dimensions.

cond-mat.stat-mech↗

arXiv2025

Three-Dimensional Sparse Random Mode Decomposition for Mode Disentangling with Crossover Instantaneous Frequencies

Sparse random mode decomposition (SRMD) is a novel algorithm that constructs a random time-frequency feature space to sparsely approximate spectrograms, effectively separating modes. However, it fails to distinguish adjacent or overlapped frequency components, especially, those with crossover instantaneous frequencies. To address this limitation, an enhanced version, termed three-dimensional SRMD (3D-SRMD), is proposed in this letter. In 3D-SRMD, the random features are lifted from a two-dimensional space to a three-dimensional (3D) space by introducing one extra chirp rate axis. This enhancement effectively disentangles the frequency components overlapped in the low dimension. Additionally, a novel random feature generation strategy is designed to improve the separation accuracy of 3D-SRMD by combining the 3D ridge detection method. Finally, numerical experiments on both simulated and real-world signals demonstrate the effectiveness of our method.

eess.SP↗

arXiv2025

Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential

Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa. This study presents a model trained using density functional theory data for full concentration CuxW100-x compounds. Through this model, we systematically investigate the structural and mechanical properties of W-Cu alloys and have the following findings. First, the bulk modulus (B) and Young's modulus (E) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the CuxW100-x compounds as the Cu concentration rises. Second, a higher Cu content results in higher critical strain and lower critical stress for these compounds. A brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Third, tensile loading tests in the W-Cu gradient structure reveal that Cu-poor region serves as a barrier, hindering shear band propagation while promoting new shear band formation in the Cu-rich region. The above results from the DP model are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems and contribute to the advancement of methodologies for materials simulation.

cond-mat.mtrl-sci↗

arXiv2025

Decoupling Thermal Properties in Multilayer Systems for Advanced Thermoreflectance Techniques

Thermoreflectance techniques, including time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), and the square-pulsed source (SPS) method, are powerful tools for characterizing the thermal properties of bulk and thin-film materials. However, accurately interpreting their signals remains challenging due to intricate interdependencies among experimental variables. This study introduces a systematic framework based on singular value decomposition (SVD) to decouple these interdependent parameters and enhance the reliability of thermal property extraction. By applying SVD to the sensitivity matrix, we identify key parameter combinations and establish essential dimensionless numbers that govern thermoreflectance signals. The framework is applied to a GaN/Si heterostructure, where the performance of TDTR, FDTR, and SPS is evaluated and compared. The results demonstrate a high degree of consistency across all three techniques. Notably, with the intricate relationships of parameters unraveled, TDTR, FDTR, and SPS demonstrate significant potential to simultaneously and accurately extract five to seven key thermal properties, including thermal conductivity, heat capacity, and interfacial thermal conductance of the GaN/Si multilayer system. This framework not only improves the precision of thermoreflectance measurements but also lays a foundation for advanced thermal metrology in research and industrial applications.

physics.app-ph↗

arXiv2025

GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training

Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images and texts, along with the lack of automated verification in the problem-solving process. Besides, current geometric specialists are limited by their task-specific designs, making them less effective for broader geometric problems. To this end, we present GeoX, a multi-modal large model focusing on geometric understanding and reasoning tasks. Given the significant differences between geometric diagram-symbol and natural image-text, we introduce unimodal pre-training to develop a diagram encoder and symbol decoder, enhancing the understanding of geometric images and corpora. Furthermore, we introduce geometry-language alignment, an effective pre-training paradigm that bridges the modality gap between unimodal geometric experts. We propose a Generator-And-Sampler Transformer (GS-Former) to generate discriminative queries and eliminate uninformative representations from unevenly distributed geometric signals. Finally, GeoX benefits from visual instruction tuning, empowering it to take geometric images and questions as input and generate verifiable solutions. Experiments show that GeoX outperforms both generalists and geometric specialists on publicly recognized benchmarks, such as GeoQA, UniGeo, Geometry3K, and PGPS9k.

cs.CV↗

arXiv2025

Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment

Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and class imbalance, where certain disease conditions are naturally underrepresented. We present Imbalance-Aware Domain Adaptation (IADA), a novel framework that simultaneously tackles both challenges through three key components: (1) adaptive feature learning with class-specific attention mechanisms, (2) balanced domain alignment with dynamic weighting, and (3) adaptive threshold optimization. Our theoretical analysis establishes convergence guarantees and complexity bounds. Through extensive experiments on embryo development assessment across four imaging modalities, IADA demonstrates significant improvements over existing methods, achieving up to 25.19\% higher accuracy while maintaining balanced performance across classes. In challenging scenarios with low-quality imaging systems, IADA shows robust generalization with AUC improvements of up to 12.56\%. These results demonstrate IADA's potential for developing reliable and equitable medical imaging systems for diverse clinical settings. The code is made public available at \url{https://github.com/yinghemedical/imbalance-aware_domain_adaptation}

cs.CV↗

arXiv2025

Gradient descent in materia through homodyne gradient extraction

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use in gradient descent. However, the implementation of deep learning in digital computers is intrinsically energy hungry, with energy consumption becoming prohibitively high for many applications. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits and integrated photonic tensor cores to unconventional, material-based computing system. The learning process in these material systems, realized, e.g., by artificial evolution, equilibrium propagation or surrogate modelling, is a complicated and time-consuming process. Here, we demonstrate a simple yet efficient and accurate gradient extraction method, based on the principle of homodyne detection, for performing gradient descent on a loss function directly in a physical system without the need of an analytical description. By perturbing the parameters that need to be optimized using sinusoidal waveforms with distinct frequencies, we effectively obtain the gradient information in a highly robust and scalable manner. We illustrate the method in dopant network processing units, but argue that it is applicable in a wide range of physical systems. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems.

cs.NE↗

arXiv2025

Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation

Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One way to encourage LLMs to better use personalized context for generating outputs that better align with the user's expectations is to instruct them to reason over the user's past preferences, background knowledge, or writing style. To achieve this, we propose Reasoning-Enhanced Self-Training for Personalized Text Generation (REST-PG), a framework that trains LLMs to reason over personal data during response generation. REST-PG first generates reasoning paths to train the LLM's reasoning abilities and then employs Expectation-Maximization Reinforced Self-Training to iteratively train the LLM based on its own high-reward outputs. We evaluate REST-PG on the LongLaMP benchmark, consisting of four diverse personalized long-form text generation tasks. Our experiments demonstrate that REST-PG achieves significant improvements over state-of-the-art baselines, with an average relative performance gain of 14.5% on the benchmark.

cs.CL↗

arXiv2025

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning

To ease the expensive measurements during configuration tuning, it is natural to build a surrogate model as the replacement of the system, and thereby the configuration performance can be cheaply evaluated. Yet, a stereotype therein is that the higher the model accuracy, the better the tuning result would be. This "accuracy is all" belief drives our research community to build more and more accurate models and criticize a tuner for the inaccuracy of the model used. However, this practice raises some previously unaddressed questions, e.g., Do those somewhat small accuracy improvements reported in existing work really matter much to the tuners? What role does model accuracy play in the impact of tuning quality? To answer those related questions, we conduct one of the largest-scale empirical studies to date-running over the period of 13 months 24*7-that covers 10 models, 17 tuners, and 29 systems from the existing works while under four different commonly used metrics, leading to 13,612 cases of investigation. Surprisingly, our key findings reveal that the accuracy can lie: there are a considerable number of cases where higher accuracy actually leads to no improvement in the tuning outcomes (up to 58% cases under certain setting), or even worse, it can degrade the tuning quality (up to 24% cases under certain setting). We also discover that the chosen models in most proposed tuners are sub-optimal and that the required % of accuracy change to significantly improve tuning quality varies according to the range of model accuracy. Deriving from the fitness landscape analysis, we provide in-depth discussions of the rationale behind, offering several lessons learned as well as insights for future opportunities. Most importantly, this work poses a clear message to the community: we should take one step back from the natural "accuracy is all" belief for model-based configuration tuning.

cs.SE↗

arXiv2025

Distilled Lifelong Self-Adaptation for Configurable Systems

Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This unfortunately remains unaddressed in existing approaches as they either overlook the available past knowledge or rely on static exploitation of past knowledge without reasoning the usefulness of information when planning for self-adaptation. In this paper, we tackle this challenging problem by proposing DLiSA, a framework that self-adapts configurable systems. DLiSA comes with two properties: firstly, it supports lifelong planning, and thereby the planning process runs continuously throughout the lifetime of the system, allowing dynamic exploitation of the accumulated knowledge for rapid adaptation. Secondly, the planning for a newly emerged workload is boosted via distilled knowledge seeding, in which the knowledge is dynamically purified such that only useful past configurations are seeded when necessary, mitigating misleading information. Extensive experiments suggest that the proposed DLiSA significantly outperforms state-of-the-art approaches, demonstrating a performance improvement of up to 229% and a resource acceleration of up to 2.22x on generating promising adaptation configurations. All data and sources can be found at our repository: https://github.com/ideas-labo/dlisa.

cs.SE↗

arXiv2024

Coordinating O&M and Logistical Resources to Enhance Post-Disaster Resilience of Interdependent Power and Natural Gas Distribution Systems

Electric power and natural gas systems are becoming increasingly interdependent, driven by the growth of natural gas-fired generation and the electrification of the gas industry. Recent energy crises have underscored the urgent need for enhanced resilience in these interdependent systems. In response to this challenge, this paper focuses on the interdependent electric power and natural gas distribution systems and proposes a comprehensive strategy for enhancing their resilience, which involves optimally coordinating various resources encompassing emergency operation and maintenance (O&M) and logistical support, to efficiently restore damaged services and infrastructure. Specifically, generators, including those with fuel switching capability, are dispatched to provide emergency power, with the logistical process of fuel supply being particularly managed by coordinated scheduling of mobile fuel tankers. Additionally, integrated demand responses for power and gas are dispatched to alleviate operational stresses using a zonewise approach that groups customers with intertwined energy supplies into individual, dispatchable units. Furthermore, the strategy incorporates the repair of damaged facilities, with particular consideration given to the varying repair efficiencies dependent on the dispatched repair units, aiming to closely simulate actual repair scenarios. The strategy is encapsulated in a mixed-integer second-order cone programming model, with case studies conducted to validate its effectiveness.

eess.SY↗

arXiv2024

All-in-One: Transferring Vision Foundation Models into Stereo Matching

As a fundamental vision task, stereo matching has made remarkable progress. While recent iterative optimization-based methods have achieved promising performance, their feature extraction capabilities still have room for improvement. Inspired by the ability of vision foundation models (VFMs) to extract general representations, in this work, we propose AIO-Stereo which can flexibly select and transfer knowledge from multiple heterogeneous VFMs to a single stereo matching model. To better reconcile features between heterogeneous VFMs and the stereo matching model and fully exploit prior knowledge from VFMs, we proposed a dual-level feature utilization mechanism that aligns heterogeneous features and transfers multi-level knowledge. Based on the mechanism, a dual-level selective knowledge transfer module is designed to selectively transfer knowledge and integrate the advantages of multiple VFMs. Experimental results show that AIO-Stereo achieves start-of-the-art performance on multiple datasets and ranks $1^{st}$ on the Middlebury dataset and outperforms all the published work on the ETH3D benchmark.

cs.CV↗

arXiv2024

COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models

As key elements within the central dogma, DNA, RNA, and proteins play crucial roles in maintaining life by guaranteeing accurate genetic expression and implementation. Although research on these molecules has profoundly impacted fields like medicine, agriculture, and industry, the diversity of machine learning approaches-from traditional statistical methods to deep learning models and large language models-poses challenges for researchers in choosing the most suitable models for specific tasks, especially for cross-omics and multi-omics tasks due to the lack of comprehensive benchmarks. To address this, we introduce the first comprehensive multi-omics benchmark COMET (Benchmark for Biological COmprehensive Multi-omics Evaluation Tasks and Language Models), designed to evaluate models across single-omics, cross-omics, and multi-omics tasks. First, we curate and develop a diverse collection of downstream tasks and datasets covering key structural and functional aspects in DNA, RNA, and proteins, including tasks that span multiple omics levels. Then, we evaluate existing foundational language models for DNA, RNA, and proteins, as well as the newly proposed multi-omics method, offering valuable insights into their performance in integrating and analyzing data from different biological modalities. This benchmark aims to define critical issues in multi-omics research and guide future directions, ultimately promoting advancements in understanding biological processes through integrated and different omics data analysis.

q-bio.BM↗