跳到正文
研踪PaperTrail

版本更新

查看已发布论文的最新修订，在详情页对照实际观测到的版本。

部分采集异常

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+93新增 AI 导读

arXiv 最近完成扫描：2026-11-16 01:34 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-15 02:28 — 2026-11-16 02:28（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-16 01:34最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

论文修订列表

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 6,729 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-16 01:33（北京时间）
版本更新 · v2arXiv:2506.09195

Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Networks

Haoran Peng, Ying-Jun Angela Zhang

原文摘要

This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance.

eess.SP · cs.AI · cs.MA

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.22847

Decentralized Ranking Aggregation via Gossip: Convergence and Robustness

Kerrian Le Caillec, Anna Van Elst, Igor Colin, Stephan Clémençon

原文摘要

The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical guarantees in a centralized setting, \textit{i.e.}, when all the ranking data to be aggregated can be brought together in a single computing unit. For many technologies (\textit{e.g.} peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees of convergence and resilience to potential contamination in a decentralized setting, when preference data is initially distributed across a communicating network, remains a major methodological challenge. Indeed, in recent years, the literature on decentralized computation has mainly focused on computing or optimizing statistics such as arithmetic means using gossip algorithms. The purpose of this article is precisely to study how to achieve reliable and resilient consensus on collective rankings in a decentralized setting, thereby raising new questions, robustness to corrupted nodes, and scalability through reduced communication costs in particular. The approach proposed and analyzed here relies on the robustness guarantees offered by random gossip communication, which allows autonomous agents to compute a global ranking consensus using local interactions only, without coordination or a central authority.

cs.LG · cs.AI · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08100

An Integrated Open-Source Stack for Vision-Based Autonomous Navigation of Wheeled Rovers

Iaroslav Kolomiets, Iaroslav Sheipak, Mikhail Reppo, Vsevolod Egorov, Viktor Chertopolokhov, Isaac Chairez

原文摘要

Vision-based rover autonomy requires integrating perception, localization, planning and platform-specific control under limited sensing and computing resources. We present an open-source ROS 2 stack for following directional arrows, reaching a terminal cone and returning autonomously to the start. It connects YOLOv8n detection and geometric direction, range and bearing estimation with RGB-D SLAM, wheel-inertial state estimation, kinematics-aware planning, behavior-tree mission management and STM32 actuation. Standard interfaces separate reusable autonomy from the hardware layer; a proportional path-following plugin supports low-speed rover motion. Public resources include software, configuration, a labeled dataset, trained weights and evaluation logs. On 478 held-out images, the detector achieves mAP@0.5 of 0.720 under the Ultralytics evaluator and outperforms a classical OpenCV baseline under a shared evaluation protocol. The reported perception latency is 25 ms on a Jetson Orin NX. Three missions, one indoor and two outdoor, demonstrate marker-guided navigation and autonomous return without operator motion commands or reported collisions. Field results document integration failures and corrective choices; repeated instrumented trials remain necessary to quantify physical navigation errors and reliability.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.10819

Differentially Private Verification of Distribution Properties

Elbert Du, Cynthia Dwork, Pranay Tankala, Linjun Zhang

原文摘要

A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of verifying properties of distributions with the assistance of a powerful, knowledgeable, but untrusted prover. In this work, we initiate the study of differentially private distribution property verification. After all, if we do not trust the prover to help us with verification, why should we trust it with our sensitive sample? We map a landscape of differentially private verification of properties of distributions. In the non-private case it is known that one-round private-coin protocols can have substantially lower complexity than public-coin (AM) protocols. In contrast, the possibility for improvement in differentially private interactive proofs depends on the privacy parameter regime and model. Drawing on connections between privacy and replicability and privacy amplification techniques in the literature we show: 1. There exists a reduction from any one-round $(\varepsilon,δ)$-differentially private private-coin protocol to a differentially private AM protocol for the parameter regime $\varepsilon = O(1/\sqrt{s})$ and $δ= O(1/s^{5/2})$ with approximately the same privacy and sample and communication complexities. In the local model, this is relaxed to $\varepsilon = O(1/\sqrt{\log s})$ 2. However, when the privacy guarantee is very relaxed ($\varepsilon \in Ω(\log s)$), private coins indeed reduce sample and communication complexities. We also obtain a computationally efficient Merlin-Arthur proof for privately testing whether samples are drawn from a product distribution and prove that its sample complexity is optimal up to a $polylog N$ factor by reducing uniformity testing to independence testing with Boolean attributes and appealing to known lower bounds on sample complexity for private uniformity testing.

cs.DS · cs.CC · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v6arXiv:2511.15238

A Quantitative Approach to Reach-Avoid Reachability Analysis for Discrete-Time Nonlinear Control Systems

Ihab Tabbara, Eliya Badr, Hussein Sibai

原文摘要

A reach-avoid specification requires a dynamical system to reach a target set of states while avoiding a failure set. Reachability analysis partitions the state space into parts starting from which the specification can be satisfied by following some control signal and those from which it cannot despite the best control effort. In this paper, we present a quantitative approach for reachability analysis to analyze the satisfaction of avoid and reach-avoid specifications for discrete-time nonlinear control systems. In such an approach, a value function assigns a value to each state that determines how robustly the system satisfies or violates the specification starting from it and following the optimal control signal. However, computing such a value function is difficult. Instead, our approach constructs discrete abstractions on which it performs value iteration to compute sound bounds on the desired value function. The resulting lower bound enables a sound under-approximation of the reach-avoid set (RAS) and, in the avoid case, a sound over-approximation of the backward reachable set (BRS). When the control set is finite, the upper bound additionally enables a sound over-approximation of the RAS and, in the avoid case, a sound under-approximation of the BRS. Moreover, the obtained bounds provide quantitative guarantees on safety margins and, in discounted reach-avoid problems, on safe target-reaching times. When the control set is finite, they also bound failure-reaching times. Our approach further refines the abstraction by splitting the grid cells that could not be classified as within or outside the BRS (or RAS) to improve the bounds. We evaluate our approach on five systems with up to five-dimensional state spaces.

eess.SY · cs.FL · cs.SC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37534

Computing GR(1) Assume-Guarantee Contracts

Yigit Narter, Emily Zhao, Inigo Incer

原文摘要

Assume-guarantee contracts provide a set of algebraic operations that enable the efficient compositional design and analysis of complex engineering systems. The explicit computation of these operations at component interfaces is currently supported only for some logics lacking a notion of time. This paper develops theory and algorithms to compute these operations when the contracts are expressed in the GR(1) modulo theory. The key challenge is the computation of two quantifier elimination problems for temporal formulas. We develop context-aided variable elimination procedures on GR(1)-modulo formulas, establish their soundness, characterize subproblem-wise optimal solutions within an admissible class of formulas, and use the procedures to compute contract operations, including composition and quotient. We implement the resulting framework in Pacti, automating compositional reasoning over GR(1) contracts. Finally, we demonstrate the framework on an automated emergency-braking case study, where we compute system-level contracts and expose compatibility conditions arising from the interaction of component specifications. Experiments on synthetic families show that the cost is governed by the number of eliminated variables and liveness conjuncts, and a comparison with exact automata-based elimination shows complementary scaling.

cs.LO · eess.SY

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.28618

Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs

Azuad Islam Ruhan, Md. Rejaul Korim Sadi, Rakibul Islam Rafi, Dewan Fahad Chowdhury, Nazira Bani Opi, Sherin Afrin Mim, Golam Mostofa Naeem

原文摘要

We compared the recorded behavior of competitive programmers who became inactive on Codeforces with that of active users of similar skill. From 6,927 accounts collected through the public Codeforces application programming interface in June 2026, we removed 888 accounts whose retrieved submission histories were incomplete and restricted both groups to a peak rating below 1500. This left 3,421 active accounts and 908 accounts with no submission for at least three months, labeled attrition. The differences between the groups were small. Accounts labeled attrition accepted smaller shares of their attempts, overall and on mathematics and data-structure problems (rank-biserial r from -0.16 to -0.12), and solved fewer problems per rated contest (median 5.95 versus 7.80; r = -0.13); struggle time, upsolving and recovery after rating drops did not differ after correction for multiple testing. Two pre-specified models using eight features that do not depend on the length of the observation window reached cross-validated areas under the receiver operating characteristic curve of 0.596 (logistic regression) and 0.611 (random forest), with probability estimates barely better than the base rate. Across random samples, stricter cleaning rules, other definitions of inactivity and matching on account tenure, the value ranged from 0.58 to 0.66. Because the features summarize each user's whole recorded history, these results describe differences between groups and do not show that inactivity can be predicted for individual users. The data and code are publicly archived, and one command regenerates every reported number.

cs.CY

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.27759

Holonic Graceful Transitions Across Centralized, Decentralized, Distributed, and Local Control in DER-rich Cyber-Power Distribution System

Md Fazley Rafy, Niloy Patari, Anurag K. Srivastava

原文摘要

The increasing penetration of distributed energy resources (DERs) in distribution system necessitates adaptive coordination frameworks. These frameworks must remain optimal during normal operation and resilient under cyber physical disturbances. Existing coordinated control approaches are typically deployed as static architectures with limited ability to adapt when communication degrades, local instability emerges, and operating conditions become spatially heterogeneous. This work addresses the gap by proposing a DER service-independent edge autonomous holonic adaptive coordination framework. In this framework, each DER controller executes its own control actions and transitions among centralized, distributed, decentralized, and local autonomous coordination modes without always relying on static coordination commands from the grid operator. The framework preserves coordination continuity across modes by retaining local controller states while reconfiguring only the coordination topology, information exchange pattern, and fallback action associated with the active DER service. Graceful transitions are enabled through dwell timers, rate limiting, and safety overrides to prevent dynamic instability during mode changes. Volt VAR control is used as a representative distribution automation application to validate the proposed architecture in a cyber-physical Hardware-in-the-loop (HIL) testbed. The proposed approach is evaluated under diverse cyber-physical event-based scenarios, showing region-confined adaptation under localized disturbance, reduced coordination traffic during coordination switching, and node-confined mitigation under cyber attack through edge anomaly detection and neighbor-corroborated impact estimation.

eess.SY · cs.NI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02875

AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games

Woohyeon Byeon, Seongmin Kim, Jiwon Jeon, Woojun Kim, Youngchul Sung

原文摘要

In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call gift over-reliance. To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs. Our approach introduces an adaptive refusal mechanism that allows agents to selectively reject a portion of received gifts from their policy-update signals, thereby mitigating gift over-reliance and preventing the agents from being exploited by other agents. This mechanism is gifting-method-agnostic, serving as a plug-in for existing schemes while enabling decentralized learning. We demonstrate that across diverse MMG environments and gifting schemes, our mechanism consistently reduces gift over-reliance and prevents lock-in to sacrificing policies, improving $α$-fairness as a result, without necessitating major changes to existing training pipelines.

cs.MA · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.19096

Approximating Minimum Dominating Set with Few Awake Rounds

Hongyan Ji, Shreyas Pai, Sriram V. Pemmaraju

原文摘要

We study the Minimum Dominating Set (MDS) problem in the sleeping CONGEST model (Chatterjee, Gmyr, and Pandurangan, PODC 2020), a generalization of the standard CONGEST model, in which a node may sleep in some rounds and can only compute, send messages, or receive messages when it is awake. The awake complexity of an algorithm in this model is the worst case number (over all inputs and all nodes) of rounds a node is awake for during the execution of the algorithm. While there are several $O(\log Δ)$-approximation algorithms (in expectation) for MDS that run in $O(\log^2 Δ)$ rounds, all of these have $Ω(\log^2 Δ)$ awake complexity. Whether this awake complexity can be improved is the question that drives our work. We present the first $O(\log Δ)$-approximation algorithm for MDS with $O(\log Δ)$ awake complexity. The approximation guarantee holds in expectation, and the algorithm runs in $O(\log^2Δ)$ rounds. We can reduce the awake complexity further, but at the cost of approximation: we present, for any $1<α\le\min\{Δ,\log n\}$, an algorithm in the sleeping CONGEST model that computes an $\tilde{O}(α\log_αΔ)$-approximate dominating set in expectation in $\tilde{O}(\logΔ\cdot \log_α Δ)$ rounds with $\tilde{O}(\log_α Δ)$ awake complexity. Our results depend on a generalization of the CONGEST model Set Cover algorithm of Grunau, Mitrović, Rubinfeld, and Vakilian (SODA 2020) that we develop. This generalization computes an $O(p\cdot q\cdot\log_pΔ)$-approximate dominating set in $O(\log_pΔ\cdot\log_qΔ)$ rounds for parameters $1<p,q\leΔ$. Our sleeping CONGEST algorithms apply a variety of techniques including sampling-based estimation and scheduling using virtual binary trees to the aforementioned 2-parameter Set Cover algorithm.

cs.DC

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.02327

Denoising data using convex relaxations

Charles Fefferman, Aalok Gangopadhyay, Matti Lassas, Jonathan Marty, Hariharan Narayanan

原文摘要

We study the problem of denoising observations \(Y_i=X_i+Z_i\), where the latent variables \(X_i\) are sampled from a low-dimensional manifold in \(\mathbb{R}^n\) and the noise variables \(Z_i\) are isotropic Gaussian. We propose a convex-relaxation estimator that first reduces dimension by principal component analysis and then projects the observations onto the convex hull of the projected latent manifold. We construct a statistical oracle that estimates its supporting hyperplanes from empirical Gaussian tail probabilities of the noisy sample. Under a lower-mass condition on the latent distribution, we prove finite-sample guarantees for the oracle and derive error bounds for the resulting denoiser. The analysis combines risk bounds for least-squares projection under convex constraints (due to Chatterjee) with entropy bounds for the manifold. We also verify the assumptions of the framework for a Cryo-Electron Microscopy observation model by establishing suitable covering number and Lipschitz estimates for the associated group action and imaging operators.

stat.ME · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.18218

DualHash: Stochastic Primal-Dual Algorithms for W-Type Regularized Deep Hashing

Luxuan Li, Xiao Wang, Chunfeng Cui

原文摘要

Deep hashing encodes high-dimensional image features into compact binary codes, enabling efficient similarity search in large-scale content-based image retrieval, a problem akin to semantic-preserving lossy compression under extreme bit-rate constraints. A fundamental challenge in deep hashing stems from the discrete nature of quantization in generating the codes. W-type regularizations, such as $z\mapsto\||z|-1\|_1$, are widely adopted and empirically effective at pushing network outputs toward binary values. However, existing methods often directly optimize these regularizations without convergence guarantees. While proximal gradient methods offer a promising solution, the coupling between W-type regularizers and neural network outputs results in composite forms that generally lack closed-form proximal solutions. In this paper, using Fenchel duality, we reformulate the nonconvex W-type regularization term through its convex conjugate, yielding a min-max problem with tractable proximal updates. We show that this min-max problem captures the stationarity of the original deep hashing objective up to the quantization error. Building on this problem, we present a stochastic primal-dual hashing algorithm, referred to as DualHash, with oracle complexity guarantees. We derive two algorithm instances: a momentum-based version with $O(\varepsilon^{-4})$ oracle complexity and an improved $O(\varepsilon^{-3})$ version using variance reduction, both for $\varepsilon$-KKT points of the min-max problem, which yield near-stationarity guarantees for the original objective. Experiments on three standard datasets demonstrate competitive retrieval accuracy and reduced quantization error, validating the practical value of DualHash for image retrieval problems. Our implementation of DualHash is available at https://github.com/RailLee/DualHash.

math.OC · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.17769

Prior-Aided Masked Vector Quantization CSI Feedback for FDD Massive MIMO Systems

Yi Song, Tianyu Yang, Kangda Zhi, Shuangyang Li, Giuseppe Caire

原文摘要

Downlink channel state information (CSI) feedback is a key bottleneck in frequency-division duplex (FDD) massive MIMO systems, as the user equipment (UE) must convey its estimated channel to the base station (BS) over a limited uplink (UL) budget. To improve CSI reconstruction accuracy under tight feedback constraints, we propose prior-aided masked vector quantization (PM-VQ), a learning-based separate source--channel coding (SSCC) feedback scheme conditioned on the average angle--delay power map---a compact representation of the channel second-order statistics available at both the UE and the BS. In PM-VQ, a prior-aided encoder maps the CSI to latent tokens, and a spatially-adaptive masking module (SAMM) scores and selects the most informative tokens within the feedback budget. The selected tokens are vector-quantized and fed back together with their positions, while an adaptive de-masking module (ADM) completes the latent representation at the BS before prior-conditioned decoding. To support variable-rate compression, a single model is trained over a range of selected-token counts, enabling operation across multiple feedback dimensions without retraining. We evaluate PM-VQ against three representative baselines on a Sionna-generated 3GPP TR~38.901 UMa dataset, focusing on the most challenging diffuse regime where channel energy is spread across many angle--delay coefficients. Simulation results show that PM-VQ achieves the lowest NMSE across all tested SNR levels and feedback dimensions in this regime. Moreover, the angle--delay power-map prior remains beneficial even when estimated from only a few channel realizations.

cs.IT

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.00038

BuildGraph: A Synthetic Multi-Archetype Building Knowledge Graph Dataset

Wooyoung Jung

原文摘要

Semantic querying of building knowledge graphs (KGs) underpins the integration of artificial intelligence into building operations, from natural-language access to cross-building analytics, but such KGs are rarely public owing to proprietary, security, and cost barriers. BuildGraph is a synthetic building KG dataset of 120 buildings in the Brick Schema ontology, grounded in U.S. Department of Energy prototype models and sensor-placement patterns from real buildings. It spans eight commercial building types across three ASHRAE energy-code vintages, with five realizations per archetype varying URI naming, sensor-attachment predicates, and topology. A 75-query SPARQL benchmark confirms structural completeness: BuildGraph reaches 91.5% Query Answerability Rate versus 35.8% for 59 real-world Brick files. Independently, it reproduces real buildings' sensor-type proportions on their shared vocabulary (cosine 0.86-0.94), evidence of realistic instrumentation where measurable. A downstream text-to-SPARQL experiment with Gemma 4 (26B) reaches 27.3% Row-Matching F1 (+17.5 pp over zero-shot) on 12 held-out buildings. BuildGraph gives facility managers and digital-twin developers a testbed for portable analytics and natural-language interfaces, and its dataset, generator, and benchmark are openly available at https://github.com/humanbuildingsynergy/BuildGraph.

cs.DB

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.30955

Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration

Jeffrey Jewett, William Solow, Sandhya Saisubramanian

原文摘要

Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or become computationally intractable when learning conditional and quantified effects. We present Online Hypothesis-Driven Conditional Action Model Learning (OHCAM), an online approach for learning action models with conditional and quantified effects from limited interactions with the environment. OHCAM maintains a belief over hypothesized action models and actively selects informative actions to reduce uncertainty by maximizing disagreement among competing hypotheses, while being robust to noisy observations. To enable scalability, OHCAM begins with a small set of simple action model hypotheses and expands to more complex conditions only when the current hypotheses become inconsistent with the data. Experiments on six benchmark planning domains demonstrate that OHCAM is sample efficient in learning action models that solve substantially more tasks than baselines, even with observation noise. We validate OHCAM on two tasks using a Kinova Gen3 robot, demonstrating the real-world applicability of our approach.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.20858

Track-Free Dense Structure from Motion

Pak Hong Chui, Hector Andrade-Loarca, Daniel Cremers

原文摘要

Classical Bundle Adjustment (BA) relies on precise metric initialization and known camera intrinsics, and traditional Structure-from-Motion (SfM) pipelines struggle to exploit modern dense matchers because rigid track-building heuristics break down under their inherent noise. We present Track-Free Dense Structure from Motion (TFD-SfM), which jointly optimizes camera extrinsics, focal lengths, and geometry from a strict cold start. Our central idea is to relax the rigidities of classical SfM into soft, probabilistic decisions under Negative Log-Likelihood (NLL) objectives. Instead of forcing matched pixels onto a single shared 3D point, we model each match as a 3D Gaussian whose variance is optimized jointly with the geometry. Unreliable matches take on large variance and contribute little to the loss. This smooths the optimization landscape, so the solver converges even from identity cameras and uniform depths. A kinematic relative-pose parameterization stabilizes the cold start, and a dual-hypothesis mechanism resolves the mirror-symmetry ambiguity on object-centric scenes. Across DTU, Mip-NeRF~360, ETH3D, and IMC2021, TFD-SfM is competitive with both classical and learning-based SfM baselines and leads on ETH3D, recovering accurate camera poses, intrinsics, and dense geometry from unordered, uncalibrated image collections. Project~page: https://cphjsn.github.io/tfd-sfm/

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.01759

Scheduling on identical machines with conflicts to minimize the total completion time

Nour ElHouda Tellache, Lydia Aoudia, Mourad Boudhar

原文摘要

This paper addresses the problem of scheduling jobs on identical machines with conflict constraints, where certain jobs cannot be scheduled simultaneously on different machines. We focus on the case where conflicts can be represented by a simple undirected graph, and the objective is to minimize the total completion time. We establish that the problem is strongly NP-hard even on two machines with two distinct processing times. For unit-time jobs, we prove that the problem is polynomially solvable on two machines for any conflict graph, but becomes strongly NP-hard on three machines. We further identify polynomially solvable cases for specific classes of conflict graphs. For the general problem, we propose mathematical models, lower bounds, and a genetic algorithm. We evaluate their performance through computational experiments on a wide range of instances derived from well-known benchmark instances in the literature.

cs.DM · math.OC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05197

Robust Reconstruction of Power Lines from Incomplete Mobile and Aerial LiDAR Data

Mohamed Gaha, Damos Ayobo Abongo, Philippe Massicotte, Eya Ben Kacem, Wael Jaafar, Rami Langar, Christian Buteau

原文摘要

Accurate vegetation-encroachment assessment and risk mitigation require electric utilities to accurately locate and characterize distribution-network assets, including poles and power lines, and distinguish among medium-voltage, low-voltage, neutral, and telecommunication lines. Mobile and aerial Light Detection and Ranging (LiDAR) point clouds provide detailed 3D representations of these assets and their surroundings. However, power-line detection and reconstruction remain challenging because conductor returns are often sparse, incomplete, noisy, or occluded by vegetation. These challenges are especially significant in distribution networks, where lower pole heights and complex line configurations place conductors close to trees. In addition, mobile and aerial LiDAR datasets differ substantially in point density, viewing geometry, spatial coverage, and occlusion patterns. We propose a robust and automated reconstruction method combining scaled density-based clustering, robust polynomial fitting, and iterative fragment merging to recover individual conductors under severe data incompleteness and occlusion. Following semantic classification, the same reconstruction pipeline processes both mobile and aerial LiDAR data without modality-specific code or parameter tuning. The method is evaluated through degradation experiments, benchmarking, parameter sensitivity analysis, and large-scale validation. On the aerial dataset, it achieves 96.87% precision, 96.66% recall, a 96.76% F$_1$-score, and a geometric RMSE of 0.127 m. On the mobile dataset, where conductor-level reference annotations were unavailable, it achieves a point-to-model RMSE of 0.045 m and a reconstruction coverage of 95.27%. Source code, documentation, and evaluation data are publicly available at https://github.com/varen-project.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.08116

A unifying view of contrastive learning, importance sampling, and bridge sampling for energy-based models

Luca Martino

原文摘要

In the last decades, energy-based models (EBMs) have become an important class of probabilistic models in which a component of the likelihood is intractable and therefore cannot be evaluated explicitly. Consequently, parameter estimation in EBMs is challenging for conventional inference methods. This work is a review paper that brings together these different methodological contributions within a unified framework, highlighting the connections among noise contrastive estimation (NCE), reverse logistic regression (RLR), Bennett's acceptance-ratio (BAR) method, multiple importance sampling (MIS), and bridge sampling within the context of EBMs. Some of them are equivalent under specific conditions. This unified perspective clarifies relationships among existing methods and enables the development of new estimators, with the potential to improve statistical and computational efficiency. Furthermore, this study helps elucidate the success of NCE in terms of its flexibility and robustness, while also identifying scenarios in which its performance can be further improved. Our aim is to provide a self-contained and accessible treatment, with clear and straightforward derivations, that brings together these contributions within a common framework and makes the main results and their underlying ideas understandable to a broader readership. The contribution is twofold. First, this work serves as a comprehensive tutorial for researchers entering the field, introducing the fundamental concepts and derivations. Second, it offers a thorough review for experienced practitioners. The code used in the numerical experiments is available to support the reproducibility of the results.

cs.CE · eess.SP · stat.CO · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.23453

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales

Daniel Kiv, Shaowen Wang

原文摘要

Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these representations capture interpretable spatial effects. We benchmark whether GeoShapley, a game-theoretic explainer that treats all location features as a single joint player, can recover spatially varying coefficients from models built on location-encoder embeddings. Eleven encoders from the TorchSpatial framework are evaluated against a synthetic process with known coefficients, across three scales (grid, county, global), with and without raw coordinates alongside the embedding, and under untrained and contrastively trained conditions. Measuring recovery as the correlation between estimated and true coefficients, we report how it varies with scale and encoder architecture and compare the embeddings against a raw-coordinate baseline. Recovery of the primary coefficient is consistently high across encoders, whereas recovery of a secondary coefficient is more scale-dependent, differing most at the global scale; the raw-coordinate baseline remains competitive throughout.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02982

Equation Recast for Canonical Operator Learning Across Parametric PDEs

Qiyun Cheng, Valentin Duruisseaux, Cesar F. Clauser, Md Hossain Sahadath, Huihua Yang, Shaowu Pan, Nathaniel Ferraro, Anima Anandkumar 等 10 位作者

原文摘要

Learning solution operators across broad parameter ranges can require substantial coverage of both input functions and physical parameters, particularly for purely data-driven parametric models. In addition, the resulting models may fail silently outside the training distribution. We introduce equation recast, which reformulates parametric operator learning as the learning of a single canonical operator. Parameter-induced operator variations are derived analytically from the governing equation and absorbed into effective sources, enabling zero-shot prediction across new parameter regimes. Across multi-parameter, nonlinear, and singular PDE settings, equation recast supports extrapolation, integrates sparse heterogeneous datasets in a shared canonical representation, and uses loss of convergence as an internal warning signal for failure of the recast iteration. In high-fidelity tokamak simulations for nuclear fusion, the framework unifies electron-temperature data across four device geometries through canonical-domain mapping within one jointly trained operator. Equation recast provides a route toward reusable neural PDE solvers combining equation-guided transfer, data efficiency, and monitorable inference.

cs.LG · physics.comp-ph · physics.plasm-ph

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.13954

On the Solvability of Sound Distributed Verification under Byzantine Faults

Gilde Valeria Rodríguez, Borzoo Bonakdarpour, Armando Castañeda, Sergio Rajsbaum

原文摘要

Modern distributed systems, ranging from distributed tracing platforms to blockchain oracle networks, frequently rely on processes holding partial observations to collectively certify a global execution state. Under Byzantine failures, a single malicious process withholding or fabricating partial states renders sound verification impossible, as genuine observations remain indistinguishable from fabricated assertions. To overcome this impossibility, we study input overlap x: the minimum number of processes that initially witness each element of a target execution I. We formalize this challenge as the x-reconstruction problem in asynchronous message-passing systems with up to t failures, where the goal is for all correct processes to recover I completely given an input overlap x. We establish tight matching upper and lower bounds for solvability: under crash failures, x-reconstruction is solvable if and only if x >= t + 1; under Byzantine failures, x-reconstruction is solvable if and only if x >= 2t + 1. Finally, we connect these structural bounds to semantic verification. For globally dependent safety properties (such as consensus, linearizability, k-set agreement, and leader election), sound verification under Byzantine faults requires a certificate of size c = t + 1 to guarantee at least one honest report. Consequently, sound distributed verification under Byzantine faults is achievable if and only if the input overlap satisfies x >= 2t + 1.

cs.DC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.11585

Machine Learning-Based Reconstruction for Resistive Silicon Sensors

Alexander Aoki, Gaetano Barone, Leena Diehl, Gabriele Giacomini, Vagelis Gkougkousis, Hanshal Goyal, Rohan Kher, Daniel Li 等 16 位作者

原文摘要

Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented. This structure provides a 100\% fill factor and enables good spatial resolution with a relaxed readout pitch. The same signal-sharing mechanism that makes interpolation possible complicates the readout: charge spreads across multiple pads, the useful information can approach the electronic-noise threshold, and matrix-inversion approaches can become computationally challenging and sensitive to off-diagonal noise. In this work, we study machine-learning-based reconstruction and compression for resistive silicon sensors. We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information beyond what is available from binary readouts or reduced-amplitude summaries. We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction and have been tested for FPGA deployment using \hls. We also study routes towards bandwidth reduction with waveform rasterisation and window-selection methods, and extend the approach beyond the first model to topology-agnostic transformer-based architectures that use pad coordinates as part of the input. These models are designed to support arbitrary pad counts and geometries, mitigate edge distortions, preserve approximately $10~μ\mathrm{m}$ position resolution for $500~μ\mathrm{m}\times500~μ\mathrm{m}$ pitched sensors, and guide future resistive-silicon sensor designs

hep-ex · cs.LG · nucl-ex

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.12751

Show, Don't Tell: Detecting Novel Objects by Watching Human Videos

James Akl, Jose Nicolas Avendano Arbelaez, James Barabas, Jennifer L. Barry, Kalie Ching, Noam Eshed, Jiahui Fu, Michel Hidalgo 等 19 位作者

原文摘要

How can a robot quickly identify and recognize new objects shown to it during a human demonstration? Existing closed-set object detectors frequently fail at this because the objects are out-of-distribution. While open-set detectors (e.g., VLMs) sometimes succeed, they often require expensive and tedious human-in-the-loop prompt engineering to uniquely recognize novel object instances. In this paper, we present a self-supervised system that eliminates the need for tedious language descriptions and expensive prompt engineering by training a bespoke object detector on an automatically created dataset, supervised by the human demonstration itself. In our approach, "Show, Don't Tell," we show the detector the specific objects of interest during the demonstration, rather than telling the detector about these objects via complex language descriptions. By bypassing language altogether, this paradigm enables us to quickly train bespoke detectors tailored to the relevant objects observed in human task demonstrations. We develop an integrated on-robot system to deploy our "Show, Don't Tell" paradigm of automatic dataset creation and novel object-detection on a real-world robot. Empirical results demonstrate that our pipeline significantly outperforms state-of-the-art detection and recognition methods for manipulated objects, leading to improved task completion for the robot.

cs.CV · cs.LG · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.07235

Asymmetric quantum error correction efficiently tackles application-specific noise effects

Abhishek Yadav, Peter K. Schuhmacher, Michael Epping

原文摘要

Noise is a major challenge for current quantum computers. It can be broadly categorized into bit-flip and phase-flip errors. These two types do not necessarily affect the executed algorithm, thus also the application, in the same way. We illustrate this general effect for the example of the quantum approximate optimization algorithm (QAOA) applied to a small instance of the flight-gate assignment (FGA) problem. We compare bit-flip and phase-flip Pauli noise under both layer-level and gate-level noise models, using two circuit decompositions of the same ideal QAOA unitary: a CNOT-based decomposition and a native-RZZ decomposition. For the six-qubit FGA instance considered here, bit-flip noise produces a larger degradation in the optimized QAOA performance across the investigated circuit depths and noise models. The asymmetry is most pronounced in the layer-level and native-RZZ simulations. We explain this behavior through the commutation and propagation properties of the errors and their effects on the final computational-basis measurements. Since these mechanisms are not specific to the considered FGA instance, we expect similar noise asymmetries to arise in other Ising-based QAOA applications, although this remains to be investigated. We then exploit these insights to tackle noise particularly efficiently using asymmetric error-correcting codes. As an illustration, we use the quantum parity code (QPC), a generalization of the 9-qubit Shor code, and show that a smaller asymmetric code can achieve nearly the same improvement as a larger symmetric choice. This suggests that allocating error-correction resources according to algorithmic noise sensitivity, rather than physical error rates alone, can be beneficial even when the physical noise is symmetric. Finally, we discuss how information about the noise obtained through calibration can be exploited in our approach.

quant-ph · cs.ET

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.24340

DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images

Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Meliha Yetisgen, Noel Codella, Roberto Andres Novoa, Josep Malvehy

原文摘要

Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patient-authored queries and clinical context, limiting their applicability to patient-centered care. To address this gap, we introduce DermaVQA-DAS, an extension of the DermaVQA dataset that supports two complementary tasks: closed-ended question answering (QA) and dermatological lesion segmentation. Central to this work is the Dermatology Assessment Schema (DAS), a novel expert-developed framework that systematically captures clinically meaningful dermatological features in a structured and standardized form. DAS comprises 36 high-level and 27 fine-grained assessment questions, with multiple-choice options in English and Chinese. Leveraging DAS, we provide expert-annotated datasets for both closed QA and segmentation and benchmark state-of-the-art multimodal models. For segmentation, we evaluate multiple prompting strategies and show that prompt design impacts performance: the default prompt achieves the best results under Mean-of-Max and Mean-of-Mean evaluation aggregation schemes, while an augmented prompt incorporating both patient query title and content yields the highest performance under majority-vote-based microscore evaluation, achieving a Jaccard index of 0.395 and a Dice score of 0.566 with BiomedParse. For closed-ended QA, overall performance is strong across models, with average accuracies ranging from 0.729 to 0.798; o3 achieves the best overall accuracy (0.798), closely followed by GPT-4.1 (0.796), while Gemini-1.5-Pro shows competitive performance within the Gemini family (0.783). We publicly release DermaVQA-DAS, the DAS schema, and evaluation protocols to support and accelerate future research in patient-centered dermatological vision-language modeling (https://osf.io/72rp3).

cs.CV · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.18298

Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control

Nathan Mankovich, Andrei Gavrilov, Gustau Camps-Valls

原文摘要

Separating externally forced trends from internal climate variability is a fundamental challenge in climate science, particularly when analyzing observational records that exist only as a single realization. We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system, an idea grounded in pullback attractor theory. This addresses a central challenge for climate projection and for detecting and attributing the forced response. Existing single-realization methods rely on linear inverse models (LIMs), which ignore forcing, or linear regression, which omits dynamics. Here we introduce PullbackDMDc, a method grounded in non-autonomous dynamical systems theory and dynamic mode decomposition with control (DMDc), incorporating pullback attractor estimation to decompose a single climate realization into spatial modes and their associated forced and internal components, yielding a physically interpretable picture of the underlying dynamics. After testing on a synthetic system with a known forced response, we illustrate the utility of PullbackDMDc for Earth System Model (ESM) evaluation by applying it to near-surface air temperature and sea-level pressure from reanalysis and four ESM large ensembles. PullbackDMDc estimates the forced response with skill matching or exceeding established baselines and helps select forcing predictors against model-based ground truth. Its internal-variability components reveal that ESMs qualitatively capture interannual and decadal modes while exhibiting systematic differences relative to each other and to observations. Skillful forced-response estimation and a novel decomposition position PullbackDMDc as a practical tool for single-realization climate analysis and ESM evaluation.

stat.ML · cs.LG · physics.ao-ph

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07854

Towards Foundation Decision Models: Drex

Nischay Dhankhar, Alisher Amantay, Zhanibek Datbayev, Darkhan Akhmetov, Yash Shah, Nurmukhamed Ubaidullayev, Raman Saparkhan, Dos Baha 等 9 位作者

原文摘要

Autoregressive approaches with large language models, such as long chain-of-thought reasoning, have become the default for new AI applications because they reach higher accuracy on a wide range of tasks. In production, however, they give inconsistent answers, require parsing of outputs, and add cost and latency. Decision models address this: given a question and a set of options supplied by the user, they return a probability distribution over the options without autoregressive decoding. We present Drex, a family of four open decision models with 8B (DLM), 9B (v1.5), 35B with 3B active (v2.0), and 27B (Max) parameters. Each request consists of a state (a document, record, or agent context, optionally with images) and questions about it, each asking for a choice among options, a yes/no judgment, or a score on an ordered scale. Training combines supervised fine-tuning with reinforcement learning from adjusted feedback (RLAF), plus vision tuning for Max. Ablations show that RLAF improves accuracy and that consistency losses improve robustness to option order. On Decision Index 0.4.1, Max sets a new state of the art among the decision models we compare (67.52 on the public suite vs. 60.70 for Jev 1.14.0). On Decision Bench v4, it is statistically tied with d3-turbo and GPT-6 Sol and trails Claude Opus 5.5 by 1.6 points, and it has the highest accuracy among decision models on DecideBench. Max answers a typical request in 183 ms, against about 2.0 s for GPT-6 Sol and 3.5 s for Claude Opus 5.5, and its outputs are stable under option reordering. All four models are open source: https://github.com/nace-ai/drex-decision-models

cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.18784

Computing the entropy rate of a quantized stationary Gaussian process

Jeremy Magland

原文摘要

The entropy rate of a uniformly quantized stationary Gaussian process is the fundamental limit on its lossless compression. It has no closed form, and the classical high-resolution approximation breaks down whenever the spectral density is small on part of the band, as routine smoothing and filtering produce. Here we present two methods for computing the rate. The first is an analytical approximation, obtained from an exact dithering identity and the Kolmogorov-Szegő formula, in which the quantization noise power acts as a floor on the spectral density. The second is a Monte Carlo estimate of the exact rate: a minimum-phase moving-average representation makes the quantized sequence a hidden Markov process whose optimal particle filter is available in closed form, and the filter's log-likelihood on one long sequence gives the rate. The approximation agrees with the estimate to within a few millibits per sample over most of the parameter range for quantization steps up to the signal standard deviation, including processes on which the classical formula fails; for coarser steps the estimate should be used instead. At a step of a quarter of the standard deviation, linear predictive coding comes within 2 to 4 percent of the rate, FLAC within 3 to 32 percent, and general-purpose compressors remain 12 to 89 percent above it.

cs.IT

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39568

Diffusion models for the $O(3)$ nonlinear sigma model

Daniel Schubring

原文摘要

A score-based diffusion model is trained on the two-dimensional classical Heisenberg model, the lattice representation of the $O(3)$ nonlinear sigma model. Neural networks are trained on three different coarse-grainings of the same training set to implement a multiscale sampling scheme that modestly improves both the accuracy and computational cost of direct sampling on the finest lattice. Stein (or Schwinger-Dyson) diagnostics for the two-point correlation function are introduced to assess the quality of the learned score without relying on reverse diffusion sampling. These diagnostics are also incorporated into an additional training loss. We discuss several technical considerations relevant to implementing diffusion models on $S^2$. These include a consideration of lifting the network input to $S^3$, as in the $CP^1$ representation, and developing a boundary-layer approximation to the short-time spherical heat kernel that extends its usefulness to larger diffusion times.

cond-mat.stat-mech · cs.LG · hep-lat

查看论文
arXiv ↗PDF ↗