arXiv subjects

Xin Chen

Publications and source records attributed to Xin Chen.

At least 271 records · Page 15Linked to original sources

arXiv2025

Implet: A Post-hoc Subsequence Explainer for Time Series Models

Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Implet, a novel post-hoc explainer that generates accurate and concise subsequence-level explanations for time series models. Our approach identifies critical temporal segments that significantly contribute to the model's predictions, providing enhanced interpretability beyond traditional feature-attribution methods. Based on it, we propose a cohort-based (group-level) explanation framework designed to further improve the conciseness and interpretability of our explanations. We evaluate Implet on several standard time-series classification benchmarks, demonstrating its effectiveness in improving interpretability. The code is available at https://github.com/LbzSteven/implet

cs.LG↗

arXiv2025

ProME: An Integrated Computational Platform for Material Properties at Extremes and Its Application in Multicomponent Alloy Design

We have built an integrated computational platform for material properties at extreme conditions, ProME (Professional Materials at Extremes) v1.0, which enables integrated calculations for multicomponent alloys, covering high temperatures up to tens of thousands of Kelvin, high pressures up to millions of atmospheres, and high strain rates up to millions per second. A series of software packages have been developed and integrated into ProME v1.0, including ABC (AI-Based Crystal search) for crystal structure search under pressure, SAE (Similar Atomic Environment) for disordered configuration modeling, MFP$^2$ (Multiphase Fast Previewer by Mean-Field Potential) for multiphase thermodynamic properties, HTEM (High-throughput Toolkit for Elasticity Modeling) for thermo-elastic properties, TREX (TRansport at Extremes) for electrical and thermal conductivity, Hippos (High plastic phase model software) for phase-field simulation of microstructure evolution under high strain rates, and AutoCalphad for modeling and optimization of phase diagrams with variable compositions. ProME v1.0 has been applied to design the composition of the quaternary alloys Platinum-Iridium-Aluminum-Chromium (Pt-Ir-Al-Cr) for engine nozzles of aerospace attitude-orbit control, achieving high-temperature strength comparable to the currently used Pt-Ir alloys but with significantly reduced costs for raw materials. ProME offers crucial support for advancing both fundamental scientific understanding and industrial innovation in materials research and development.

cond-mat.mtrl-sci↗

arXiv2025

Alternating Methods for Large-Scale AC Optimal Power Flow with Unit Commitment

Security-constrained unit commitment with alternating current optimal power flow (SCUC-ACOPF) is a central problem in power grid operations that optimizes commitment and dispatch of generators under a physically accurate power transmission model while encouraging robustness against component failures. SCUC-ACOPF requires solving large-scale problems that involve multiple time periods and networks with thousands of buses within strict time limits. In this work, we study a detailed SCUC-ACOPF model with a rich set of features of modern power grids, including price-sensitive load, reserve products, transformer controls, and energy-limited devices. We propose a decomposition scheme and a penalty alternating direction method to find high-quality solutions to this model. Our methodology leverages spatial and temporal decomposition, separating the problem into a set of mixed-integer linear programs for each bus and a set of continuous nonlinear programs for each time period. To improve the performance of the algorithm, we introduce a variety of heuristics, including restrictions of temporal linking constraints, a second-order cone relaxation, and a contingency screening algorithm. We quantify the quality of feasible solutions through a dual bound from a convex second-order cone program. To evaluate our algorithm, we use large-scale test cases from Challenge 3 of the U.S. Department of Energy's Grid Optimization Competition that resemble real power grid data under a variety of operating conditions and decision horizons. The experiments yield feasible solutions with an average optimality gap of 1.33%, demonstrating that this approach generates near-optimal solutions within stringent time limits.

math.OC↗

arXiv2025

First Measurement of the Muon Neutrino Interaction Cross Section and Flux as a Function of Energy at the LHC with FASER

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

hep-ex↗

arXiv2025

Simulation of the Background from $^{13}$C$(α, n)^{16}$O Reaction in the JUNO Scintillator

Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ($α, n$) reactions. In organic liquid scintillator detectors, $α$ particles emitted from intrinsic contaminants such as $^{238}$U, $^{232}$Th, and $^{210}$Pb/$^{210}$Po, can be captured on $^{13}$C nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, ($α, n$) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable $^{13}$C$(α, n)^{16}$O event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors.

physics.ins-det↗

arXiv2025

Reconstruction and Performance Evaluation of FASER's Emulsion Detector at the LHC

This paper presents the reconstruction and performance evaluation of the FASER$ν$ emulsion detector, which aims to measure interactions from neutrinos produced in the forward direction of proton-proton collisions at the CERN Large Hadron Collider. The detector, composed of tungsten plates interleaved with emulsion films, records charged particles with sub-micron precision. A key challenge arises from the extremely high track density environment, reaching $\mathcal{O}(10^5)$ tracks per cm$^2$. To address this, dedicated alignment techniques and track reconstruction algorithms have been developed, building on techniques from previous experiments and introducing further optimizations. The performance of the detector is studied by evaluating the single-film efficiency, position and angular resolution, and the impact parameter distribution of reconstructed vertices. The results demonstrate that an alignment precision of 0.3 micrometers and robust track and vertex reconstruction are achieved, enabling accurate neutrino measurements in the TeV energy range.

physics.ins-det↗

arXiv2025

A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems

We study a class of sequential decision-making problems with augmented predictions, potentially provided by a machine learning algorithm. In this setting, the decision-maker receives prediction intervals for unknown parameters that become progressively refined over time, and seeks decisions that are competitive with the hindsight optimal under all possible realizations of both parameters and predictions. We propose a minimax Markov Decision Process (minimax-MDP) framework, where the system state consists of an adversarially evolving environment state and an internal state controlled by the decision-maker. We introduce a set of future-imposed conditions that characterize the feasibility of minimax-MDPs and enable the design of efficient, often closed-form, robustly competitive policies. We illustrate the framework through three applications: multi-period inventory ordering with refining demand predictions, resource allocation with uncertain utility functions, and a multi-phase extension of the minimax-MDP applied to the inventory problem with time-varying ordering costs. Our results provide a tractable and versatile approach to robust online decision-making under predictive uncertainty.

cs.LG↗

arXiv2025

HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models

Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we propose HandDiffuse12.5M, a novel dataset that consists of temporal sequences with strong two-hand interactions. HandDiffuse12.5M has the largest scale and richest interactions among the existing two-hand datasets. We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controllers. Specifically, we apply the diffusion model as the backbone and design two motion representations for different controllers. To reduce artifacts, we also propose Interaction Loss which explicitly quantifies the dynamic interaction process. Our HandDiffuse enables various applications with vivid two-hand interactions, i.e., motion in-betweening and trajectory control. Experiments show that our method outperforms the state-of-the-art techniques in motion generation and can also contribute to data augmentation for other datasets. Our dataset, corresponding codes, and pre-trained models will be disseminated to the community for future research towards two-hand interaction modeling.

cs.CV↗

arXiv2025

DNA Probe Computing System for Solving NP-Complete Problems

Efficiently solving NP-complete problems-such as protein structure prediction, cryptographic decryption, and vulnerability detection-remains a central challenge in computer science. Traditional electronic computers, constrained by the Turing machine's one-dimensional data processing and sequential operations, struggle to address these issues effectively. To overcome this bottleneck, computational models must adopt multidimensional data structures and parallel information processing mechanisms. Building on our team's proposed probe machine model (a non-Turing computational framework), this study develops a blocking probe technique that leverages DNA computing's inherent parallelism to identify all valid solutions for NP-complete problems in a single probe operation. Using the 27-vertex 3-coloring problem as a case study, we successfully retrieved all solutions through DNA molecular probe experiments. This breakthrough demonstrates the first implementation of a fully parallel computing system at the molecular level, offering a novel paradigm for tackling computational complexity. Our results indicate that the probe machine, with its parallel architecture and molecular implementation, transcends the limitations of classical models and holds promise for solving intricate real-world problems.

cs.DS↗

arXiv2025

Optimizing Sequencing Coverage Depth in DNA Storage: Insights From DNA Storage Data

DNA storage is now being considered as a new archival storage method for its durability and high information density, but still facing some challenges like high costs and low throughput. By reducing sequencing sample size for decoding digital data, minimizing DNA coverage depth helps lower both costs and system latency. Previous studies have mainly focused on minimizing coverage depth in uniform distribution channels under theoretical assumptions. In contrast, our work uses real DNA storage experimental data to extend this problem to log-normal distribution channels, a conclusion derived from our PCR and sequencing data analysis. In this framework, we investigate both noiseless and noisy channels. We first demonstrate a detailed positive correlation between MDS code rate and the expected minimum sequencing coverage depth. Moreover, we observe that the probability of successfully decoding all information in a single sequencing run decreases and then increases as code rate rises, when the sample size is optimized for complete decoding. Then we extend the lower bounds of the DNA coverage depth from uniform to log-normal noisy channels. The findings of this study provide valuable insights for the efficient execution of DNA storage experiments.

cs.IT↗

arXiv2025

High-Throughput LLM inference on Heterogeneous Clusters

Nowadays, many companies possess various types of AI accelerators, forming heterogeneous clusters. Efficiently leveraging these clusters for high-throughput large language model (LLM) inference services can significantly reduce costs and expedite task processing. However, LLM inference on heterogeneous clusters presents two main challenges. Firstly, different deployment configurations can result in vastly different performance. The number of possible configurations is large, and evaluating the effectiveness of a specific setup is complex. Thus, finding an optimal configuration is not an easy task. Secondly, LLM inference instances within a heterogeneous cluster possess varying processing capacities, leading to different processing speeds for handling inference requests. Evaluating these capacities and designing a request scheduling algorithm that fully maximizes the potential of each instance is challenging. In this paper, we propose a high-throughput inference service system on heterogeneous clusters. First, the deployment configuration is optimized by modeling the resource amount and expected throughput and using the exhaustive search method. Second, a novel mechanism is proposed to schedule requests among instances, which fully considers the different processing capabilities of various instances. Extensive experiments show that the proposed scheduler improves throughput by 122.5% and 33.6% on two heterogeneous clusters, respectively.

cs.DC↗

arXiv2025

Finding Near-Optimal Maximum Set of Disjoint $k$-Cliques in Real-World Social Networks

A $k$-clique is a dense graph, consisting of $k$ fully-connected nodes, that finds numerous applications, such as community detection and network analysis. In this paper, we study a new problem, that finds a maximum set of disjoint $k$-cliques in a given large real-world graph with a user-defined fixed number $k$, which can contribute to a good performance of teaming collaborative events in online games. However, this problem is NP-hard when $k \geq 3$, making it difficult to solve. To address that, we propose an efficient lightweight method that avoids significant overheads and achieves a $k$-approximation to the optimal, which is equipped with several optimization techniques, including the ordering method, degree estimation in the clique graph, and a lightweight implementation. Besides, to handle dynamic graphs that are widely seen in real-world social networks, we devise an efficient indexing method with careful swapping operations, leading to the efficient maintenance of a near-optimal result with frequent updates in the graph. In various experiments on several large graphs, our proposed approaches significantly outperform the competitors by up to 2 orders of magnitude in running time and 13.3\% in the number of computed disjoint $k$-cliques, which demonstrates the superiority of the proposed approaches in terms of efficiency and effectiveness.

cs.SI↗

arXiv2025

ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps

Solving jigsaw puzzles has been extensively studied. While most existing models focus on solving either small-scale puzzles or puzzles with no gap between fragments, solving large-scale puzzles with gaps presents distinctive challenges in both image understanding and combinatorial optimization. To tackle these challenges, we propose a framework of Evolutionary Reinforcement Learning with Multi-head Puzzle Perception (ERL-MPP) to derive a better set of swapping actions for solving the puzzles. Specifically, to tackle the challenges of perceiving the puzzle with gaps, a Multi-head Puzzle Perception Network (MPPN) with a shared encoder is designed, where multiple puzzlet heads comprehensively perceive the local assembly status, and a discriminator head provides a global assessment of the puzzle. To explore the large swapping action space efficiently, an Evolutionary Reinforcement Learning (EvoRL) agent is designed, where an actor recommends a set of suitable swapping actions from a large action space based on the perceived puzzle status, a critic updates the actor using the estimated rewards and the puzzle status, and an evaluator coupled with evolutionary strategies evolves the actions aligning with the historical assembly experience. The proposed ERL-MPP is comprehensively evaluated on the JPLEG-5 dataset with large gaps and the MIT dataset with large-scale puzzles. It significantly outperforms all state-of-the-art models on both datasets.

cs.CV↗

arXiv2025

Discovery of an ultrastable antiferromagnetic two-dimensional CrF3 phase with anisotropic quasi-one-dimensional mechanical, electronic, and thermal properties

We report the discovery of an ultra-stable antiferromagnetic two-dimensional (2D) CrF3 phase that is energetically more favorable than the traditionally assumed hexagonal structure. Using first-principles calculations and evolutionary structure searches, we identify a new low-energy rectangular configuration of CrF3 with remarkable anisotropic properties. Mechanically, this phase exhibits zero in-plane Poisson's ratio, a rare negative out-of-plane Poisson's ratio, and quasi-one-dimensional (quasi-1D) behavior characterized by minimal coupling between orthogonal directions. Electronically, CrF3 shows quasi-1D transport with two independent conduction bands near the Fermi level, tunable via uniaxial strain. The calculated bandgap is 3.05 eV, which can be modulated under strain, enabling control over its electronic properties. The material also displays out-of-plane antiferromagnetic ordering with a magnetic anisotropy energy of 0.098 meV per Cr atom and an estimated Neel temperature of 20 K. Additionally, we investigate the thermal conductivity of monolayer rectangular CrF3 (r-CrF3), revealing significant anisotropy in heat transport. The thermal conductivity along the y-axis is approximately 60.5 W/mK at 300 K, much higher than along the x-axis at 13.2 W/mK. The thermal anisotropic factor is 4.58, surpassing that of other 2D materials like black phosphorene, WTe2, and arsenene, highlighting r-CrF3's potential for advanced directional heat management. Consequently, the rectangular CrF3 phase is a promising candidate for applications in spintronics, strain-engineered nanoelectronics, mechanical metamaterials, and thermal management technologies.

cond-mat.mtrl-sci↗

arXiv2025

Understanding Aha Moments: from External Observations to Internal Mechanisms

Large Reasoning Models (LRMs), capable of reasoning through complex problems, have become crucial for tasks like programming, mathematics, and commonsense reasoning. However, a key challenge lies in understanding how these models acquire reasoning capabilities and exhibit "aha moments" when they reorganize their methods to allocate more thinking time to problems. In this work, we systematically study "aha moments" in LRMs, from linguistic patterns, description of uncertainty, "Reasoning Collapse" to analysis in latent space. We demonstrate that the "aha moment" is externally manifested in a more frequent use of anthropomorphic tones for self-reflection and an adaptive adjustment of uncertainty based on problem difficulty. This process helps the model complete reasoning without succumbing to "Reasoning Collapse". Internally, it corresponds to a separation between anthropomorphic characteristics and pure reasoning, with an increased anthropomorphic tone for more difficult problems. Furthermore, we find that the "aha moment" helps models solve complex problems by altering their perception of problem difficulty. As the layer of the model increases, simpler problems tend to be perceived as more complex, while more difficult problems appear simpler.

cs.CL↗

arXiv2025

Exploring Dynamic Transformer for Efficient Object Tracking

The speed-precision trade-off is a critical problem for visual object tracking which usually requires low latency and deployment on constrained resources. Existing solutions for efficient tracking mainly focus on adopting light-weight backbones or modules, which nevertheless come at the cost of a sacrifice in precision. In this paper, inspired by dynamic network routing, we propose DyTrack, a dynamic transformer framework for efficient tracking. Real-world tracking scenarios exhibit diverse levels of complexity. We argue that a simple network is sufficient for easy frames in video sequences, while more computation could be assigned to difficult ones. DyTrack automatically learns to configure proper reasoning routes for various inputs, gaining better utilization of the available computational budget. Thus, it can achieve higher performance with the same running speed. We formulate instance-specific tracking as a sequential decision problem and attach terminating branches to intermediate layers of the entire model. Especially, to fully utilize the computations, we introduce the feature recycling mechanism to reuse the outputs of predecessors. Furthermore, a target-aware self-distillation strategy is designed to enhance the discriminating capabilities of early predictions by effectively mimicking the representation pattern of the deep model. Extensive experiments on multiple benchmarks demonstrate that DyTrack achieves promising speed-precision trade-offs with only a single model. For instance, DyTrack obtains 64.9% AUC on LaSOT with a speed of 256 fps.

cs.CV↗

arXiv2025

Carbon-Aware Optimal Power Flow

To facilitate effective decarbonization of the electric power sector, this paper introduces the generic Carbon-aware Optimal Power Flow (C-OPF) method for power system decision-making that considers demand-side carbon accounting and emission management. Built upon the classic optimal power flow (OPF) model, the C-OPF method incorporates carbon emission flow equations and constraints, as well as carbon-related objectives, to jointly optimize power flow and carbon flow. In particular, this paper establishes the feasibility and solution uniqueness of the carbon emission flow equations, and proposes modeling and linearization techniques to address the issues of undetermined power flow directions and bilinear terms in the C-OPF model. Additionally, two novel carbon emission models, together with the carbon accounting schemes, for energy storage systems are developed and integrated into the C-OPF model. Numerical simulations demonstrate the characteristics and effectiveness of the C-OPF method, in comparison with OPF solutions.

math.OC↗

arXiv2025

MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets

Multi-organ segmentation holds paramount significance in many clinical tasks. In practice, compared to large fully annotated datasets, multiple small datasets are often more accessible and organs are not labelled consistently. Normally, an individual model is trained for each of these datasets, which is not an effective way of using data for model learning. It remains challenging to train a single model that can robustly learn from several partially labelled datasets due to label conflict and data imbalance problems. We propose MO-CTranS: a single model that can overcome such problems. MO-CTranS contains a CNN-based encoder and a Transformer-based decoder, which are connected in a multi-resolution manner. Task-specific tokens are introduced in the decoder to help differentiate label discrepancies. Our method was evaluated and compared to several baseline models and state-of-the-art (SOTA) solutions on abdominal MRI datasets that were acquired in different views (i.e. axial and coronal) and annotated for different organs (i.e. liver, kidney, spleen). Our method achieved better performance (most were statistically significant) than the compared methods. Github link: https://github.com/naisops/MO-CTranS.

cs.CV↗