arXiv subjects

Yue Chen

Publications and source records attributed to Yue Chen.

At least 91 records · Page 5Linked to original sources

arXiv2025

A Weighted Predict-and-Optimize Framework for Power System Operation Considering Varying Impacts of Uncertainty

Prediction deviations of different uncertainties have varying impacts on downstream decision-making. Improving the prediction accuracy of critical uncertainties with significant impacts on decision-making quality yields better optimization results. Motivated by this observation, this paper proposes a novel weighted predict-and-optimize (WPO) framework for decision-making under multiple uncertainties. Specifically, we incorporate an uncertainty-aware weighting mechanism into the predictive model to capture the relative impact of each uncertainty on specific optimization tasks, and introduce a problem-driven prediction loss (PDPL) to quantify the suboptimality of the weighted predictions relative to perfect predictions in downstream optimization. By optimizing the uncertainty weights to minimize the PDPL, the proposed WPO framework enables adaptive assessment of uncertainty impacts and joint learning of prediction and optimization. Furthermore, to facilitate weight optimization, we develop a surrogate model that establishes a direct mapping between the uncertainty weights and the PDPL, where enhanced graph convolutional networks and multi-task learning are adopted for efficient surrogate model construction and training. Numerical experiments on the modified IEEE 33-bus and 123-bus systems demonstrate that the proposed WPO framework outperforms the traditional predict-then-optimize paradigm, reducing the PDPL by an average of 55% within acceptable computational time.

eess.SY↗

arXiv2025

DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy

Garment manipulation is a critical challenge due to the diversity in garment categories, geometries, and deformations. Despite this, humans can effortlessly handle garments, thanks to the dexterity of our hands. However, existing research in the field has struggled to replicate this level of dexterity, primarily hindered by the lack of realistic simulations of dexterous garment manipulation. Therefore, we propose DexGarmentLab, the first environment specifically designed for dexterous (especially bimanual) garment manipulation, which features large-scale high-quality 3D assets for 15 task scenarios, and refines simulation techniques tailored for garment modeling to reduce the sim-to-real gap. Previous data collection typically relies on teleoperation or training expert reinforcement learning (RL) policies, which are labor-intensive and inefficient. In this paper, we leverage garment structural correspondence to automatically generate a dataset with diverse trajectories using only a single expert demonstration, significantly reducing manual intervention. However, even extensive demonstrations cannot cover the infinite states of garments, which necessitates the exploration of new algorithms. To improve generalization across diverse garment shapes and deformations, we propose a Hierarchical gArment-manipuLation pOlicy (HALO). It first identifies transferable affordance points to accurately locate the manipulation area, then generates generalizable trajectories to complete the task. Through extensive experiments and detailed analysis of our method and baseline, we demonstrate that HALO consistently outperforms existing methods, successfully generalizing to previously unseen instances even with significant variations in shape and deformation where others fail. Our project page is available at: https://wayrise.github.io/DexGarmentLab/.

cs.RO↗

arXiv2025

Integrated photonic platform with high-speed entanglement generation and witnessing

High-speed generation and efficient entanglement detection on a photonic chip are essential for quantum information applications but hard to achieve due to common photonic chips' material properties and limited component performance. In this work, we experimentally demonstrate entanglement witness on a silicon photonic chip, with multi-rail single-photon entanglement generation based on decoy-state techniques. The detection is based on balanced homodyne detectors on the same photonic chip with a bandwidth of up to 12.5 GHz, which allows room-temperature operation. A loss-equivalent analysis method compensates for optical losses and system noises. Experimental results quantify an entangled state fidelity of 92% in quantum state tomography and a Clauser-Horne-Shimony-Holt (CHSH) violation lower bound of 2.59. These results establish a viable path toward fully integrated, high-bandwidth, room-temperature quantum photonic systems, with potential applications in on-chip quantum optics and quantum random number generation.

quant-ph↗

arXiv2025

Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation

Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex contact-rich nature of the tasks. Model-based optimal control methods offer flexibility to define precise motion but are limited by high computational complexity and accurate contact sensing. On the other hand, reinforcement learning (RL) handles high-dimensional spaces with strong robustness but suffers from inefficient learning, unnatural motion, and sim-to-real gaps. To address these challenges, we introduce Opt2Skill, an end-to-end pipeline that combines model-based trajectory optimization with RL to achieve robust whole-body loco-manipulation. Opt2Skill generates dynamic feasible and contact-consistent reference motions for the Digit humanoid robot using differential dynamic programming (DDP) and trains RL policies to track these optimal trajectories. Our results demonstrate that Opt2Skill outperforms baselines that rely on human demonstrations and inverse kinematics-based references, both in motion tracking and task success rates. Furthermore, we show that incorporating trajectories with torque information improves contact force tracking in contact-involved tasks, such as wiping a table. We have successfully transferred our approach to real-world applications.

cs.RO↗

arXiv2025

Easi3R: Estimating Disentangled Motion from DUSt3R Without Training

Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct supervision on large-scale 3D datasets. In contrast, the limited scale and diversity of available 4D datasets present a major bottleneck for training a highly generalizable 4D model. This constraint has driven conventional 4D methods to fine-tune 3D models on scalable dynamic video data with additional geometric priors such as optical flow and depths. In this work, we take an opposite path and introduce Easi3R, a simple yet efficient training-free method for 4D reconstruction. Our approach applies attention adaptation during inference, eliminating the need for from-scratch pre-training or network fine-tuning. We find that the attention layers in DUSt3R inherently encode rich information about camera and object motion. By carefully disentangling these attention maps, we achieve accurate dynamic region segmentation, camera pose estimation, and 4D dense point map reconstruction. Extensive experiments on real-world dynamic videos demonstrate that our lightweight attention adaptation significantly outperforms previous state-of-the-art methods that are trained or finetuned on extensive dynamic datasets. Our code is publicly available for research purpose at https://easi3r.github.io/

cs.CV↗

arXiv2025

Collaborative Compression for Large-Scale MoE Deployment on Edge

The Mixture of Experts (MoE) architecture is an important method for scaling Large Language Models (LLMs). It increases model capacity while keeping computation cost low. However, the ultra-large MoE models still have hundreds of billions of parameters, requiring massive memory/storage and leading to difficulties for deployment on resource-constrained edge platforms. Pruning or quantization alone can hardly address the issue, because of the super-aggressive compression ratio with significantly degraded accuracy and output quality. To facilitate the deployment of ultra-large MoEs on edge platforms, we propose a collaborative compression framework by combining expert pruning, mixed-precision quantization, and activation optimization. It can effectively reduce the storage footprint of the ultra-large MoE DeepSeek-V3 from 1.3TB to 103GB, while preserving high output quality with better accuracy than traditional uniform low-bit quantization methods. To the best of our knowledge, we are the first to deploy a compressed model from the ultra-large DeepSeek-V3 on the platform with a strict 128GB total memory limit. Our comprehensive experiments on multiple benchmarks under various memory constraints demonstrate the effectiveness of our method with smaller model sizes and higher accuracy than uniform low-bit quantization methods.

cs.AI↗

arXiv2025

Predict-and-Optimize Robust Unit Commitment with Statistical Guarantees via Weight Combination

The growing uncertainty from renewable power and electricity demand brings significant challenges to unit commitment (UC). While various advanced forecasting and optimization methods have been developed to predict better and address this uncertainty, most previous studies treat forecasting and optimization as separate tasks. This separation can lead to suboptimal results due to misalignment between the objectives of the two tasks. To overcome this challenge, we propose a robust UC framework that integrates forecasting and optimization processes while ensuring statistical guarantees. In the forecasting stage, we combine multiple predictions derived from diverse data sources and methodologies for an improved prediction, aiming to optimize the UC performance. In the optimization stage, the combined prediction is used to construct an uncertainty set with statistical guarantees, based on which the robust UC model is formulated. The optimal robust UC solution provides feedback to refine the weight used for combining multiple predictions. To solve the proposed integrated forecasting-optimization framework efficiently and effectively, we develop a neural network-based surrogate model for acceleration and introduce a reshaping method for the uncertainty set based on the optimization result to reduce conservativeness. Case studies on modified IEEE 30-bus and 118-bus systems demonstrate the advantages of the proposed approach.

math.OC↗

arXiv2025

ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation

Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models (LLMs) for code translation. However, existing LLMs only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code, which results in unguaranteed code executability and unreliable automated code translation. To address this issue, we propose ExeCoder, an LLM specifically designed for code translation, aimed at utilizing executability representations such as functional semantics, syntax structures, and variable dependencies to enhance the capabilities of LLMs in code translation. To evaluate the effectiveness of ExeCoder, we manually enhanced the widely used benchmark TransCoder-test, resulting in a benchmark called TransCoder-test-X that serves LLMs. Evaluation of TransCoder-test-X indicates that ExeCoder achieves state-of-the-art performance in code translation, surpassing existing open-source code LLMs by over 10.88% to 38.78% and over 27.44% to 42.97% on two metrics, and even outperforms the renowned closed-source LLM GPT-4o. Code is available at https://aka.ms/execoder

cs.SE↗

arXiv2025

OLMA: One Loss for More Accurate Time Series Forecasting

Time series forecasting faces two important but often overlooked challenges. Firstly, the inherent random noise in the time series labels sets a theoretical lower bound for the forecasting error, which is positively correlated with the entropy of the labels. Secondly, neural networks exhibit a frequency bias when modeling the state-space of time series, that is, the model performs well in learning certain frequency bands but poorly in others, thus restricting the overall forecasting performance. To address the first challenge, we prove a theorem that there exists a unitary transformation that can reduce the marginal entropy of multiple correlated Gaussian processes, thereby providing guidance for reducing the lower bound of forecasting error. Furthermore, experiments confirm that Discrete Fourier Transform (DFT) can reduce the entropy in the majority of scenarios. Correspondingly, to alleviate the frequency bias, we jointly introduce supervision in the frequency domain along the temporal dimension through DFT and Discrete Wavelet Transform (DWT). This supervision-side strategy is highly general and can be seamlessly integrated into any supervised learning method. Moreover, we propose a novel loss function named OLMA, which utilizes the frequency domain transformation across both channel and temporal dimensions to enhance forecasting. Finally, the experimental results on multiple datasets demonstrate the effectiveness of OLMA in addressing the above two challenges and the resulting improvement in forecasting accuracy. The results also indicate that the perspectives of entropy and frequency bias provide a new and feasible research direction for time series forecasting. The code is available at: https://github.com/Yuyun1011/OLMA-One-Loss-for-More-Accurate-Time-Series-Forecasting.

cs.LG↗

arXiv2025

Who Uses Whose Telescopes? Analyzing the Knowledge Geography and Research Dominance of Global Astronomical Facilities

Large-scale research infrastructures (LSRIs) are central to contemporary science policy, combining massive capital investments with international access regimes. Yet whether open access to these infrastructures translates into more equitable scientific authority remains contested. Astronomy provides a critical case: world-leading observatories are globally shared but embedded in specific national contexts. We compile a novel country--year dataset (1955--2025) linking the location of astronomical facilities with publication usage and authorship roles. This enables us to distinguish between hosting, using, and leading in telescope-based research. Our analysis reveals: (i) usage and impact are heavily concentrated in a small number of facility hubs; (ii) scientific leadership is even more unequal than access or usage (Gini coefficient 0.91 for first/corresponding authorship versus 0.85 for facilities and usage); (iii) hosting and leadership often decouple--countries such as Chile and South Africa mediate large publication volumes without commensurate gains in leading roles; and (iv) global leadership has shifted from U.S. dominance to a multi-hub system centered in the United States, Western Europe, China, Japan, and Australia. These findings challenge the assumption that international access alone democratizes science. We argue that converting participation into leadership requires domestic PI programs, investments in instrumentation and data pipelines, and governance models that distribute credit more equitably. The study highlights how the governance of LSRIs shapes global scientific hierarchies and offers design principles for infrastructures that seek not only to share data but also to broaden scientific authority.

astro-ph.IM↗

arXiv2025

Exact Solutions and Quantum Defect Theory for van der Waals Potentials in Ultracold Molecular Systems

In this paper, we have provided exact two-body solutions to the 2D and 3D Schrödinger equations with isotropic van der Waals potentials of the form $\pm 1/r^6$. Based on these solutions, we developed an analytical quantum defect theory (QDT) applicable to both quasi-2D and 3D geometries, and applied it to study the scattering properties and bound-state spectra of ultracold polar molecules confined in these geometries. Interestingly, we find that in the attractive (repulsive) van der Waals potential case, the short-range interaction can be effectively modeled by an infinite square barrier (finite square well), which leads to narrow and dense (broad and sparse) resonance structures in the quantum defect parameter. In the quasi-2D attractive case, shape resonances can appear in an ordered fashion across different partial waves, characterized by sharp phase jumps as the scattering energy is varied. Furthermore, the low-energy analytical expansions derived from QDT show excellent agreement with the exact numerical results, validating the accuracy and usefulness of our analytical approach in describing two-body physics governed by long-range van der Waals interactions.

cond-mat.quant-gas↗

arXiv2025

Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging

Deep learning-based reconstruction of positron emission tomography(PET) data has gained increasing attention in recent years. While these methods achieve fast reconstruction,concerns remain regarding quantitative accuracy and the presence of artifacts,stemming from limited model interpretability,data driven dependence, and overfitting risks.These challenges have hindered clinical adoption.To address them,we propose a conditional diffusion model with posterior physical correction (PET-DPC) for PET image reconstruction. An innovative normalization procedure generates the input Geometric TOF Probabilistic Image (GTP-image),while physical information is incorporated during the diffusion sampling process to perform posterior scatter,attenuation,and random corrections. The model was trained and validated on 300 brain and 50 whole-body PET datasets,a physical phantom,and 20 simulated brain datasets. PET-DPC produced reconstructions closely aligned with fully corrected OSEM images,outperforming end-to-end deep learning models in quantitative metrics and,in some cases, surpassing traditional iterative methods. The model also generalized well to out-of-distribution(OOD) data. Compared to iterative methods,PET-DPC reduced reconstruction time by 50% for brain scans and 85% for whole-body scans. Ablation studies confirmed the critical role of posterior correction in implementing scatter and attenuation corrections,enhancing reconstruction accuracy. Experiments with physical phantoms further demonstrated PET-DPC's ability to preserve background uniformity and accurately reproduce tumor-to-background intensity ratios. Overall,these results highlight PET-DPC as a promising approach for rapid, quantitatively accurate PET reconstruction,with strong potential to improve clinical imaging workflows.

physics.med-ph↗

arXiv2025

Multistage Robust Optimization for Time-Decoupled Power Flexibility Aggregation with Energy Storage

To mitigate global climate change, distributed energy resources (DERs), such as distributed generators, flexible loads, and energy storage systems (ESSs), have witnessed rapid growth in power distribution systems. When properly managed, these DERs can provide significant flexibility to power systems, enhancing both reliability and economic efficiency. Due to their relatively small scale, DERs are typically managed by the distribution system operator (DSO), who interacts with the transmission system operator (TSO) on their behalf. Specifically, the DSO aggregates the power flexibility of the DERs under its control, representing it as a feasible variation range of aggregate active power at the substation level. This flexibility range is submitted to the TSO, who determines a setpoint within that range. The DSO then disaggregates the setpoint to dispatch DERs. This paper focuses on the DSO's power flexibility aggregation problem. First, we propose a novel multistage robust optimization model with decision-dependent uncertainty for power flexibility aggregation. Distinct from the traditional two-stage models, our multistage framework captures the sequential decision-making of the TSO and DSO and is more general (e.g., can accommodate non-ideal ESSs). Then, we develop multiple solution methods, including exact, inner, and outer approximation approaches under different assumptions, and compare their performance in terms of applicability, optimality, and computational efficiency. Furthermore, we design greedy algorithms for DSO's real-time disaggregation. We prove that the rectangular method yields greater total aggregate flexibility compared to the existing approach. Case studies demonstrate the effectiveness of the proposed aggregation and disaggregation methods, validating their practical applicability.

math.OC↗

arXiv2025

P/D-Device: Disaggregated Large Language Model between Cloud and Devices

Serving disaggregated large language models has been widely adopted in industrial practice for enhanced performance. However, too many tokens generated in decoding phase, i.e., occupying the resources for a long time, essentially hamper the cloud from achieving a higher throughput. Meanwhile, due to limited on-device resources, the time to first token (TTFT), i.e., the latency of prefill phase, increases dramatically with the growth on prompt length. In order to concur with such a bottleneck on resources, i.e., long occupation in cloud and limited on-device computing capacity, we propose to separate large language model between cloud and devices. That is, the cloud helps a portion of the content for each device, only in its prefill phase. Specifically, after receiving the first token from the cloud, decoupling with its own prefill, the device responds to the user immediately for a lower TTFT. Then, the following tokens from cloud are presented via a speed controller for smoothed TPOT (the time per output token), until the device catches up with the progress. On-device prefill is then amortized using received tokens while the resource usage in cloud is controlled. Moreover, during cloud prefill, the prompt can be refined, using those intermediate data already generated, to further speed up on-device inference. We implement such a scheme P/D-Device, and confirm its superiority over other alternatives. We further propose an algorithm to decide the best settings. Real-trace experiments show that TTFT decreases at least 60%, maximum TPOT is about tens of milliseconds, and cloud throughput increases by up to 15x.

cs.DC↗

arXiv2025

Carbon-Aware Quantification of Real-Time Aggregate Power Flexibility of Electric Vehicles

Electric vehicles (EVs) can be aggregated to offer flexibility to power systems. However, the rapid growth in EV adoption leads to increased grid-level carbon emissions due to higher EV charging demand, challenging grid decarbonization efforts. Quantifying and managing the EV flexibility while controlling carbon emissions is crucial. This paper introduces a methodology for carbon-aware quantification of real-time aggregate EV power flexibility based on the Lyapunov optimization technique. We construct a novel queue system including EV charging queues, delay-aware virtual queues, and carbon-aware virtual queues. Based on the evolution of these queues, we define the Lyapunov drift and minimize the drift-plus-penalty term to get the real-time EV flexibility interval, which is reported to the system operator for flexibility provision. To enhance EV flexibility, we integrate dispatch signals from the system operator into the queue updates through a two-stage disaggregation process. The proposed approach is prediction-free and adaptable to various uncertainties, including EV arrivals and grid carbon intensity. Additionally, the maximum charging delay of EV charging tasks is theoretically bounded by a constant, and carbon emissions are effectively controlled. The numerical results demonstrate the effectiveness of the proposed online method and highlight its advantages over several benchmarks through comparisons.

math.OC↗

arXiv2025

WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework

Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing methods focus on augmenting datasets through rephrasing or difficulty progression but overlook the specific failure modes of LLMs. This results in synthetic questions that the model can already solve, providing minimal performance gains. To address this, we propose WarriorMath, a defect-aware framework for mathematical problem solving that integrates both targeted data synthesis and progressive training. In the synthesis stage, we employ multiple expert LLMs in a collaborative process to generate, critique, and refine problems. Questions that base LLMs fail to solve are identified and iteratively improved through expert-level feedback, producing high-quality, defect-aware training data. In the training stage, we introduce a progressive learning framework that iteratively fine-tunes the model using increasingly challenging data tailored to its weaknesses. Experiments on six mathematical benchmarks show that WarriorMath outperforms strong baselines by 12.57% on average, setting a new state-of-the-art. Our results demonstrate the effectiveness of a defect-aware, multi-expert framework for improving mathematical ability.

cs.CL↗

arXiv2025

SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users

Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.

cs.CL↗

arXiv2025

Field-induced spin continuum in twin-free Na$_3$Co$_2$SbO$_6$ revealed by magneto-THz spectroscopy

The honeycomb magnet Na$_3$Co$_2$SbO$_6$ recently emerged as a promising candidate for realizing Kitaev quantum spin liquid with relatively low levels of structural disorder. While the precise spin Hamiltonian remains controversial, the potential existence of a quantum spin liquid or other novel quantum magnetic phases continues to stimulate investigation. Here, we study the temperature and magnetic field-dependent spin excitations of Na$_3$Co$_2$SbO$_6$ on a twin-free single crystal using magneto-terahertz (THz) spectroscopy, focusing on magnetic anisotropy and field-induced unusual phases. We observe a low-energy continuum excitation above $T_N$ and a 0.5 THz (2 meV) spin wave excitation in magnetic order under zero field. Upon applying an in-plane magnetic field, the spin waves transform to a magnetic continuum over an intermediate field range, above which the system enters a spin-polarized state. Crucially, the spin excitation spectra reveal striking anisotropy between the $\textbf{a}$ and $\textbf{b}$ crystallographic axes, demanding description by a strongly anisotropic spin model. These findings establish Na$_3$Co$_2$SbO$_6$ as a model system for investigating field-tunable quantum magnetism and potential spin liquid behavior in highly anisotropic systems.

cond-mat.str-el↗