Publications

The Cluster Evolutionary Reference Ensemble at Low- <i>z</i> (CEREAL) Sample of Galaxy Clusters. I. X-Ray Morphological Properties and Demographics
L. V. White
Michael McDonald
S. J. Allen
Marshall W. Bautz
Michael S. Calzadilla
G. P. Garmire
Julie Hlavacek-Larrondo
Ralph Kraft
Adam B. Mantz
Taweewat Somboonpanyakul
Alexey Vikhlinin
Abstract With rapid improvements in the assembly of large samples of galaxy clusters, we are approaching the ability to study clusters at z … (voir plus)≳ 2. Evolutionary studies comparing these distant clusters to the clusters in our local Universe depend heavily on the reliability of low-redshift cluster samples, most of which are subject to X-ray selection effects, biasing them to relaxed, cool-core clusters. Here, we introduce the Cluster Evolutionary Reference Ensemble at Low- z (CEREAL) sample, composed of Chandra X-ray observations of 169 galaxy clusters that have been selected from the Planck Sunyaev–Zel’dovich catalog. CEREAL has a simple and well-understood selection function, spans an order of magnitude in mass at z ∼ 0.15, and has uniform, high-resolution X-ray follow-up. We present the full sample and provide results based on X-ray surface brightness properties, finding significantly more non-cool-core systems than in X-ray-selected samples. We use surface brightness concentration ( c SB ) as a proxy for cool-core strength and centroid shift ( w ) to measure dynamical state. Over the full sample, we find a cool-core ( c SB > 0.075) fraction of 0.3 9 − 0.04 + 0.04 , a strong cool-core ( c SB > 0.155) fraction of 0.1 3 − 0.03 + 0.03 , and a dynamically relaxed ( w < 0.01) frac
2026-03-26
Astrophysical Journal (publié)
doi.org
arxiv.org
1113 GPR124 Alleviates Blood-Brain Barrier Disruption Through Improving the Function of Microvascular Endothelial After Traumatic Brain Injury
Chen Wang
Jun Ding
Hengli Tian
Hao Chen
Xinyu Niu
2026-03-25
Neurosurgery (publié)
doi.org
Adversarial-Robust Multivariate Time-Series Anomaly Detection via Joint Information Retention
Hadi Hojjati
Narges Armanfard
Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are oft… (voir plus)en highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via joint information retention), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, task-relevant temporal perturbations that maximally increase the detector's anomaly score, while the detector is optimized to remain stable under these structured perturbations. The resulting masks characterize the detector's sensitivity to adversarial temporal corruptions and can serve as explanatory signals for the detector's decisions. This adversarial training strategy exposes brittle decision pathways and encourages the detector to rely on distributed and stable temporal patterns rather than spurious localized artifacts. We conduct extensive experiments on the TSB-AD benchmark, demonstrating that ARTA consistently improves anomaly detection performance across diverse datasets and exhibits significantly more graceful degradation under increasing noise levels compared to state-of-the-art baselines.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
Maeva Guerrier
Karthik Soma
Jana Pavlasek
Giovanni Beltrame
Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing … (voir plus)real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and outdoor settings. Beyond success rate, we combine path-based metrics with vision-based goal-recognition scores and assess robustness through controlled image perturbations (motion blur, sunflare). Our analysis uncovers three systematic limitations: (a) even architecturally sophisticated diffusion and transformer-based models exhibit frequent collisions, indicating limited geometric understanding; (b) models fail to discriminate between different locations that are perceptually similar, however some semantics differences are present, causing goal prediction errors in repetitive environments; and (c) performance degrades under distribution shift. We will publicly release our evaluation codebase and dataset to facilitate reproducible benchmarking of VNMs.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets
Alex Koran
Dimitrios Sinodinos
Hadi Hojjati
Takuya Nanri
Fangge Chen
Narges Armanfard
High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving, as evidenced by the low driving scores on the C… (voir plus)ARLA Leaderboard. Despite collision-related infractions being the dominant failure mode in closed-loop evaluations, collision-aware representation learning has received limited attention. To address this gap, we first develop a Video-Language-Augmented Anomaly Detector (VLAAD), leveraging a Multiple Instance Learning (MIL) formulation to obtain stable, temporally localized collision signals for proactive prediction. To transition these capabilities into closed-loop simulations, we must overcome the limitations of existing simulator datasets, which lack multimodality and are frequently restricted to simple intersection scenarios. Therefore, we introduce CARLA-Collide, a large-scale multimodal dataset capturing realistic collision events across highly diverse road networks. Trained on this diverse simulator data, VLAAD serves as a collision-aware plug-in module that can be seamlessly integrated into existing E2E driving models. By integrating our module into a pretrained TransFuser++ agent, we demonstrate a 14.12% relative increase in driving score with minimal fine-tuning. Beyond closed-loop evaluation, we further assess the generalization capability of VLAAD in an open-loop setting using real-world driving data. To support this analysis, we introduce Real-Collide, a multimodal dataset of diverse dashcam videos paired with semantically rich annotations for collision detection and prediction. On this benchmark, despite containing only 0.6B parameters, VLAAD outperforms a multi-billion-parameter vision-language model, achieving a 23.3% improvement in AUC.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
A Compression Perspective on Simplicity Bias
Tom Marty
Eric Elmoznino
Leo Gagnon
Tejas Kasetty
Mizu Nishikawa-Toomey
Sarthak Mittal
Guillaume Lajoie
Dhanya Sridhar
Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cas… (voir plus)t new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias governs feature selection in neural networks through a fundamental trade-off between model complexity (the cost of describing the hypothesis) and predictive power (the cost of describing the data). Our framework predicts that as the amount of available training data increases, learners transition through qualitatively different features -- from simple spurious shortcuts to complex features -- only when the reduction in data encoding cost justifies the increased model complexity. Consequently, we identify distinct data regimes where increasing data promotes robustness by ruling out trivial shortcuts, and conversely, regimes where limiting data can act as a form of complexity-based regularization, preventing the learning of unreliable complex environmental cues. We validate our theory on a semi-synthetic benchmark showing that the feature selection of neural networks follows the same trajectory of solutions as optimal two-part compressors.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset
Hadi Hojjati
Christopher Roth
Rory Woods
Ken Sills
Narges Armanfard
The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scal… (voir plus)e, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset comprising high-resolution sensor telemetry collected from a fleet of 25 commercial vehicles over a six-month period. Unlike synthetic datasets, EngineAD features authentic operational data labeled with expert annotations, distinguishing normal states from subtle indicators of incipient engine faults. We preprocess the data into
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community
Maxime C. Cohen
Tinglong Dai
Georgia Perakis
Narendra Agrawal
Gad Allon
Robert N. Boute
Gérard P. Cachon
Zhe Chen
Morris A. Cohen
Rares Cristian
Vinayak Deshpande
Francis de Véricourt
Jan C. Fransoo
Joren Gijsbrechts
Pavithra Harsha
Ming Hu
Pınar Keskinocak
Caleb Kwon
Hau L. Lee
Sheng Liu … (voir 22 de plus)
Konstantina Mellou
Ishai Menache
Jason W. Miller
Serguei Netessine
Tava Lennon Olsen
Jeevan Pathuri
Robert Peels
Yongzhi Qi
Ananth Raman
Anne G. Robinson
Zuo-Jun Max Shen
Masha Shunko
David Simchi-Levi
Hannah K. Smalley
Jing-Sheng Song
Jayashankar M. Swaminathan
Christopher S. Tang
Sridhar Tayur
Maximiliano Udenio
Jan Van Mieghem
Yuqian Xu
Dennis Zhang
Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impac… (voir plus)t depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.
2026-03-25
Manufacturing & Service Operations Management (publié)
doi.org
On the Objective and Feature Weights of Minkowski Weighted k-Means
Renato Cordeiro De Amorim
Vladimir Makarenkov
The Minkowski weighted k-means (mwk-means) algorithm extends classical k-means by incorporating feature weights and a Minkowski distance. De… (voir plus)spite its empirical success, its theoretical properties remain insufficiently understood. We show that the mwk-means objective can be expressed as a power-mean aggregation of within-cluster dispersions, with the order determined by the Minkowski exponent p. This formulation reveals how p controls the transition between selective and uniform use of features. Using this representation, we derive bounds for the objective function and characterise the structure of the feature weights, showing that they depend only on relative dispersion and follow a power-law relationship with dispersion ratios. This leads to explicit guarantees on the suppression of high-dispersion features. Finally, we establish convergence of the algorithm and provide a unified theoretical interpretation of its behaviour.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Unboundedness in Bilevel Optimization
Bárbara Rodrigues
Margarida Carvalho
Miguel Anjos
Nagisa Sugishita
Abstract Bilevel optimization has garnered growing interest over the past decade. However, little attention has been paid to detecting and d… (voir plus)ealing with unboundedness in these problems, with most research assuming a bounded high-point relaxation. In this paper, we address unboundedness in bilevel and multilevel optimization by studying its computational complexity. We show that deciding whether an optimistic linear bilevel problem is unbounded is strongly NP-complete, even without coupling constraints. Furthermore, we extend the hardness result to the linear multilevel case, by showing that for each extra level added, the decision problem of checking unboundedness moves up a level in the polynomial hierarchy. Deciding unboundedness of a mixed-integer multilevel problem is shown to be one level higher in the polynomial complexity hierarchy than the decision problem for linear multilevel problem with the same number of levels. Finally, we introduce two algorithmic approaches to determine whether a linear bilevel problem is unbounded and, if so, return a certificate of unboundedness. This certificate consists of a direction of unboundedness and corresponding bilevel feasible point. We present a proof of concept of these algorithmic approaches on some relevant examples, and provide a brief computational comparison.
2026-03-25
Mathematical Programming (publié)
doi.org
Multiscale reorganization of brain and behavior under large-scale electrical perturbation
Sarah Kreuzer
Juergen Dukart
Justine Y. Hansen
Hoang K. Nguyen
Michael Bentsch
Sophia Zieger
Katrin Sakreida
Thomas C. Baghai
Caroline Nothdurfter
Michael Groezinger
Bogdan Draganski
Bratislav Misic
Danilo Bzdok
Simon B. Eickhoff
Timm B. Poeppl
2026-03-24
bioRxiv (prépublication)
doi.org
PrismBench: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
The rapid advancement of LLMs' code generation capabilities is outpacing traditional evaluation methods. Static benchmarks fail to capture t… (voir plus)he depth and breadth of LLM capabilities and eventually become obsolete, while most dynamic approaches either rely too heavily on LLM-based evaluation or remain constrained by predefined test sets. To address these issues, we introduce PrismBench, a multi-agent, dynamic benchmarking framework designed to systematically expose and analyze LLM failure modes in code generation tasks. We formulate evaluation as a Markov Decision Process over a structured tree of coding challenges, leveraging a customized Monte Carlo Tree Search algorithm to traverse this tree and discover high-failure scenarios. Our multi-agent setup orchestrates task generation, model response, and analysis, enabling scalable assessment across diverse coding challenges. Additionally, we propose metrics that combine structural traversal patterns with performance across different tasks and difficulty levels to enable diagnostic and systematic comparison of LLMs' performance. We conduct extensive experiments on eight state-of-the-art LLMs and analyze how model architecture and scale influence code generation performance across varying coding tasks. All code, evaluation trees, and a public leaderboard are available at https://prismbench.github.io/Demo/
2026-03-24
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net