Tootfinder

Hard-Stop Synthesis for Multi-DOF Compliant Mechanisms
Dean Chen, Armin Pomeroy, Brandon T. Peterson, Will Flanagan, He Kai Lim, Alexandra Stavrakis, Nelson F. SooHoo, Jonathan B. Hopkins, Tyler R. Clites
https://arxiv.org/abs/2507.13455

Hard-Stop Synthesis for Multi-DOF Compliant Mechanisms
Compliant mechanisms have significant potential in precision applications due to their ability to guide motion without contact. However, an inherent vulnerability to fatigue and mechanical failure has hindered the translation of compliant mechanisms to real-world applications. This is particularly challenging in service environments where loading is complex and uncertain, and the cost of failure is high. In such cases, mechanical hard stops are critical to prevent yielding and buckling. Convent…

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Hao Deng, Huijuan Zhan, Lijuan Hu, Hui-Hai Zhao, Ran Gao, Kannan Lu, Xizheng Ma, Zhijun Song, Fei Wang, Tenghui Wang, Feng Wu, Tian Xia, Gengyan Zhang, Xiaohang Zhang, Chunqing Deng
https://arxiv.org/abs/2508.15174

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Pinpointing the dissipation mechanisms and evaluating their impacts to the performance of Josephson junction (JJ) are crucial for its application in superconducting circuits. In this work, we demonstrate the junction-embedded resonator (JER) as a platform which enables us to identify and quantify various dissipation mechanisms of JJ. JER is constructed by embedding JJ in the middle of an open-circuit, 1/2 λ transmission-line resonator. When the 1st and 2nd harmonics of JER are excited, JJ expe…

Understanding Matching Mechanisms in Cross-Encoders
Mathias Vast, Basile Van Cooten, Laure Soulier, Benjamin Piwowarski
https://arxiv.org/abs/2507.14604 ht…

Understanding Matching Mechanisms in Cross-Encoders
Neural IR architectures, particularly cross-encoders, are highly effective models whose internal mechanisms are mostly unknown. Most works trying to explain their behavior focused on high-level processes (e.g., what in the input influences the prediction, does the model adhere to known IR axioms) but fall short of describing the matching process. Instead of Mechanistic Interpretability approaches which specifically aim at explaining the hidden mechanisms of neural models, we demonstrate that mo…

Unveiling blazar synchrotron emission: a multiwavelength polarimetric study of HSP and LSP populations
Sara Capecchiacci, Ioannis Liodakis, Riccardo Middei, Dawoon E. Kim, Laura Di Gesu, Ivan Agudo, Beatriz Agis-Gonzalez, Axel Arbet-Engels, Dmitry Blinov, Chien-Ting Chen, Steven R. Ehlert, Ephraim Gau, Lea Heckmann, Kun Hu, Svetlana G. Jorstad, Philip Kaaret, Pouya M. Kouch, Henric Krawczynski, Elina Lindfors, Frederic Marin, Alan P. Marscher, Ioannis Myserlis, Stephen L. O'Dell, L…

Unveiling blazar synchrotron emission: a multiwavelength polarimetric study of HSP and LSP populations
Polarimetric properties of blazars allow us to put constraints on the acceleration mechanisms that fuel their powerful jets. By studying the multiwavelength polarimetric behaviour of high synchrotron peaked (HSP) and low synchrotron peaked (LSP) blazars, we aim to explore differences in their emission mechanisms and magnetic field structure in the acceleration region. In this study, we take advantage of several X-ray polarisation observations of HSP by the IXPE, including four new observations …

Ultrastrong and ductile CoNiMoAl medium-entropy alloys enabled by L12 nanoprecipitate-induced multiple deformation mechanisms
Min Young Sung, Tae Jin Jang, Sang Yoon Song, Gunjick Lee, KenHee Ryou, Sang-Ho Oh, Byeong-Joo Lee, Pyuck-Pa Choi, J\"org Neugebauer, Blazej Grabowski, Fritz K\"ormann, Yuji Ikeda, Alireza Zargaran, Seok Su Sohn
https://…

Ultrastrong and ductile CoNiMoAl medium-entropy alloys enabled by L12 nanoprecipitate-induced multiple deformation mechanisms
L12 precipitates are known to significantly enhance the strength and ductility of single-phase face-centered cubic (FCC) medium- or high-entropy alloys (M/HEAs). However, further improvements in mechanical properties remain untapped, as alloy design has historically focused on systems with specific CrCoNi- or FeCoCrNi-based FCC matrix and Ni3Al L12 phase compositions. This study introduces novel Co-Ni-Mo-Al alloys with L12 precipitates by systematically altering Al content, aiming to bridge thi…

Attention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers
Krati Saxena, Federico Jurado Ruiz, Guido Manzi, Dianbo Liu, Alex Lamb
https://arxiv.org/abs/2509.16058

Attention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers
Attention mechanisms have become integral in AI, significantly enhancing model performance and scalability by drawing inspiration from human cognition. Concurrently, the Attention Schema Theory (AST) in cognitive science posits that individuals manage their attention by creating a model of the attention itself, effectively allocating cognitive resources. Inspired by AST, we introduce ASAC (Attention Schema-based Attention Control), which integrates the attention schema concept into artificial n…

An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan, Haoyu He, Feng Chen, Akide Liu, Bohan Zhuang, Jianfei Cai, Hamid Rezatofighi
https://arxiv.org/abs/2508.15360

An Empirical Study on How Video-LLMs Answer Video Questions
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However, most existing efforts focus on improving performance, with limited attention to understanding their internal mechanisms. This paper aims to bridge this gap through a systematic empirical study. To interpret existing VideoLLMs, we adopt attention knockouts as our primary analytical tool and design three variants: Video…

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Zengyi Wo, Chang Liu, Yumeng Wang, Minglai Shao, Wenjun Wang
https://arxiv.org/abs/2508.14683 https://…

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Graph Neural Networks (GNNs) have been successful in modeling graph-structured data. However, similar to other machine learning models, GNNs can exhibit bias in predictions based on attributes like race and gender. Moreover, bias in GNNs can be exacerbated by the graph structure and message-passing mechanisms. Recent cutting-edge methods propose mitigating bias by filtering out sensitive information from input or representations, like edge dropping or feature masking. Yet, we argue that such st…

Does Private Equity Hurt or Improve Healthcare Value? New Evidence and Mechanisms
Minghong Yuan, Wen Wen, Indranil Bardhan
https://arxiv.org/abs/2507.14717

Does Private Equity Hurt or Improve Healthcare Value? New Evidence and Mechanisms
What is the impact of private equity (PE) investment on healthcare value? Does PE investment hurt or improve healthcare value, and if so, can its effect be mitigated through the use of health information technologies (IT)? Given the significant investments by PE firms in the healthcare sector in recent years, these are important research questions. Stakeholders, including policy makers, care providers, and patients, need to understand their likely impact and whether PE ownership is aligned with…

GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
Amro Abdalla, Ismail Shaheen, Dan DeGenaro, Rupayan Mallick, Bogdan Raita, Sarah Adel Bargal
https://arxiv.org/abs/2507.13598

GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
We present \textbf{GIFT}: a \textbf{G}radient-aware \textbf{I}mmunization technique to defend diffusion models against malicious \textbf{F}ine-\textbf{T}uning while preserving their ability to generate safe content. Existing safety mechanisms like safety checkers are easily bypassed, and concept erasure methods fail under adversarial fine-tuning. GIFT addresses this by framing immunization as a bi-level optimization problem: the upper-level objective degrades the model's ability to represent ha…

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable
Longfei Chen, Shenghan Gao, Shiwei Wang, Ken Lin, Yun Wang, Quan Li
https://arxiv.org/abs/2508.15146 …

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable
Conversational user interfaces powered by large language models (LLMs) have significantly lowered the technical barriers to database querying. However, existing tools still encounter several challenges, such as misinterpretation of user intent, generation of hallucinated content, and the absence of effective mechanisms for human feedback-all of which undermine their reliability and practical utility. To address these issues and promote a more transparent and controllable querying experience, we…

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
Kushagra Agrawal, Polat Goktas, Anjan Bandopadhyay, Debolina Ghosh, Junali Jasmine Jena, Mahendra Kumar Gourisaria
https://arxiv.org/abs/2508.14830

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mech…

Documenting Deployment with Fabric: A Repository of Real-World AI Governance
Mackenzie Jorgensen, Kendall Brogle, Katherine M. Collins, Lujain Ibrahim, Arina Shah, Petra Ivanovic, Noah Broestl, Gabriel Piles, Paul Dongha, Hatim Abdulhussein, Adrian Weller, Jillian Powers, Umang Bhatt
https://arxiv.org/abs/2508.14119

Documenting Deployment with Fabric: A Repository of Real-World AI Governance
Artificial intelligence (AI) is increasingly integrated into society, from financial services and traffic management to creative writing. Academic literature on the deployment of AI has mostly focused on the risks and harms that result from the use of AI. We introduce Fabric, a publicly available repository of deployed AI use cases to outline their governance mechanisms. Through semi-structured interviews with practitioners, we collect an initial set of 20 AI use cases. In addition, we co-desig…

The formation and evolution of dust in the colliding-wind binary Apep revealed by JWST
Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
https://arxiv.org/abs/2507.14498

The formation and evolution of dust in the colliding-wind binary Apep revealed by JWST
Carbon-rich Wolf-Rayet (WR) stars are significant contributors of carbonaceous dust to the galactic environment, however the mechanisms and conditions for formation and subsequent evolution of dust around these stars remain open questions. Here we present JWST observations of the WR+WR colliding-wind binary Apep which reveal an intricate series of nested concentric dust shells that are abundant in detailed substructure. The striking regularity in these substructures between successive shells su…

Asymptotic Optimality in Data-driven Decision Making
Radek Sala\v{c}, Michael Kupper, Tobias Sutter
https://arxiv.org/abs/2507.15215 https://

Asymptotic Optimality in Data-driven Decision Making
Given data generated by an abstract stochastic process, we study how to construct statistically optimal decisions for general stochastic optimization problems. Our setting encompasses non-standard data structures, including data originating from heterogeneous sources or from processes with randomly evolving data-generating mechanisms. We propose a decision-making approach that identifies optimal decisions for which a specific notion of shifted regret risk, evaluated with respect to the underlyi…

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
https://arxiv.org/abs/2507.15282

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to m…

Grid-Edge Energy-Flexible Technologies: A Comparative Analysis Across Generators, Loads, and Energy Storage Systems
Jesus Silva-Rodriguez, Tianxia Zhao, Ran Mo, Xingpeng Li
https://arxiv.org/abs/2508.14297

Grid-Edge Energy-Flexible Technologies: A Comparative Analysis Across Generators, Loads, and Energy Storage Systems
This review analysis presents a comprehensive exploration of energy flexibility in modern power systems. It examines the roles and mechanisms of flexible technologies across three main categories: generators, energy storage systems (ESS), and loads. Energy flexibility is defined as the ability to dynamically adjust supply and/or demand in response to grid conditions to maintain balance and stability. This is of particular importance to facilitate the integration of the growing variable renewabl…

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding
Jiaqi Min, Xueyue Zhang, Shanshan Luo
https://arxiv.org/abs/2507.13965

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding
Proxy variables are commonly used in causal inference when unmeasured confounding exists. While most existing proximal methods assume a unidirectional causal relationship between two primary variables, many social and biological systems exhibit complex feedback mechanisms that imply bidirectional causality. In this paper, using regression-based models, we extend the proximal framework to identify bidirectional causal effects in the presence of unmeasured confounding. We establish the identifica…

Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
Eric Grinstein, Ashutosh Pandey, Cole Li, Shanmukha Srinivas, Juan Azcarreta, Jacob Donley, Sanha Lee, Ali Aroudi, Cagdas Bilen
https://arxiv.org/abs/2507.13863

Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models have achieved state-of-the-art noise suppression, their non-linear operations often introduce high speech distortion. Conversely, classical signal processing algorithms such as the Parameterized Multi-channel Wiener Filter ( PMWF) beamformer offer explicit mechanisms for controlling the suppression/distortion trade-off. …

Design and Simulation of Fault-Tolerant Network Switching System Using Python-Based Algorithms
Terlumun Gbaden, Mterorga Ukor, Grace Erdoo Ateata
https://arxiv.org/abs/2508.14305

Design and Simulation of Fault-Tolerant Network Switching System Using Python-Based Algorithms
Ensuring uninterrupted data flow in modern networks requires robust fault-tolerant mechanisms, especially in environments where reliability and responsiveness are critical. This paper presents the design and simulation of a fault-tolerant network switching system using Python-based algorithms. A simulated enterprise-level Local Area Network (LAN) was modeled using NetworkX to represent switch-router interconnectivity with redundant links. Fault scenarios, including link failure and congestion, …

Quantization Meets Spikes: Lossless Conversion in the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Qiang Yu
https://arxiv.org/abs/2508.14520 https:/…

Quantization Meets Spikes: Lossless Conversion in the First Timestep via Polarity Multi-Spike Mapping
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct training methods tackle the challenge of non-differentiable activation mechanisms in SNNs, they often suffer from high computational and energy costs during training. As a result, ANN-to-SNN conversion approach still remains a valuable and practical alternative. These conversion-based methods aim to leverage the discrete …

Brain rhythms in cognition -- controversies and future directions
Anne Keitel, Christian Keitel, Mohsen Alavash, Karin Bakardjian, Christopher S. Y. Benwell, Sophie Bouton, Niko A. Busch, Antonio Criscuolo, Keith B. Doelling, Laura Dugue, Laetitia Grabot, Joachim Gross, Simon Hanslmayr, Laura-Isabelle Klatt, Daniel S. Kluger, Gemma Learmonth, Raquel E. London, Christina Lubinus, Andrea E. Martin, Jonas Obleser, Johanna M. Rimmele, Vincenzo Romei, Manuela Ruzzoli, Felix Siebenhuhner, So…

Brain rhythms in cognition -- controversies and future directions
Brain rhythms seem central to understanding the neurophysiological basis of human cognition. Yet, despite significant advances, key questions remain unresolved. In this comprehensive position paper, we review the current state of the art on oscillatory mechanisms and their cognitive relevance. The paper critically examines physiological underpinnings, from phase-related dynamics like cyclic excitability, to amplitude-based phenomena, such as gating by inhibition, and their interactions, such as…

Inflated hot Jupiters: inferring average atmospheric velocity via Ohmic models coupled with internal dynamo evolution
Daniele Vigan\`o, Soumya Sengupta, Cl\`audia Soriano-Guerrero, Rosalba Perna, Albert Elias-L\'opez, Sandeep Kumar, Taner Akg\"un
https://arxiv.org/abs/2507.13991…

Inflated hot Jupiters: inferring average atmospheric velocity via Ohmic models coupled with internal dynamo evolution
The inflated radii observed in hundreds of hot Jupiters (HJ) represent a long-standing open issue. In this study, we quantitatively investigate this phenomenon within the framework of Ohmic dissipation arising from magnetic induction in the atmosphere, one of the most promising mechanisms for explaining the radius anomaly. We simulate the evolution of irradiated giant planets with {\tt MESA}, spanning the observed range of masses and equilibrium temperatures, incorporating an internal source of…

Resolvent model-based analyses of coherent structures in Langmuir turbulence
Anqing Xuan, Lian Shen
https://arxiv.org/abs/2508.14773 https://arxiv.org/pdf/…

Resolvent model-based analyses of coherent structures in Langmuir turbulence
We present an analysis of the coherent structures in Langmuir turbulence, a state of the ocean surface boundary layer driven by the interactions between water waves and wind-induced shear, via a resolvent framework. Langmuir turbulence is characterised by multiscale vortical structures, notably counter-rotating roll pairs known as Langmuir circulations. While classic linear stability analyses of the Craik-Leibovich equations have revealed key instability mechanisms underlying Langmuir circulati…

Cutting soft materials: how material differences shape the response
Miguel Angel Moreno-Mateos, Paul Steinmann
https://arxiv.org/abs/2507.13565 https://

Cutting soft materials: how material differences shape the response
Cutting soft materials is a complex process governed by the interplay of bulk large deformation, interfacial soft fracture, and contact forces with the cutting tool. Existing experimental characterizations and numerical models often fail to capture the variety of observed cutting behaviors, especially the transition from indentation to cutting and the roles of dissipative mechanisms. Here, we combine novel experimental cutting tests on three representative materials-a soft hydrogel, elastomer, …

Partial Symmetry Enforced Attention Decomposition (PSEAD): A Group-Theoretic Framework for Equivariant Transformers in Biological Systems
Daniel Ayomide Olanrewaju
https://arxiv.org/abs/2507.14908

Partial Symmetry Enforced Attention Decomposition (PSEAD): A Group-Theoretic Framework for Equivariant Transformers in Biological Systems
This research introduces the Theory of Partial Symmetry Enforced Attention Decomposition (PSEAD), a new and rigorous group-theoretic framework designed to seamlessly integrate local symmetry awareness into the core architecture of self-attention mechanisms within Transformer models. We formalize the concept of local permutation subgroup actions on windows of biological data, proving that under such actions, the attention mechanism naturally decomposes into a direct sum of orthogonal irreducible…

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
Dingyi Wang, Ruqiang Guo, Qian Lu
https://arxiv.org/abs/2507.15497

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
The promotion of Conservation Tillage Technology (CTT) is critical for mitigating global soil degradation, yet their actual adoption rates remain substantially lower than anticipated targets. Existing research predominantly focuses on static factor analyses, failing to adequately capture the dynamic evolutionary mechanisms of farmer strategic interactions and the impacts of information asymmetries in agricultural markets. This study constructs an evolutionary game model integrating heterogeneou…

Atomistic mechanisms of phase transitions in all-temperature barocaloric material KPF$_6$
Jiantao Wang, Yi-Chi Zhang, Yan Liu, Hongkun Deng, Mingfeng Liu, Yan Sun, Bing Li, Xing-Qiu Chen, Peitao Liu
https://arxiv.org/abs/2508.13862

Atomistic mechanisms of phase transitions in all-temperature barocaloric material KPF$_6$
Conventional barocaloric materials typically exhibit limited operating temperature ranges. In contrast, KPF$_6$ has recently been reported to achieve an exceptional all-temperature barocaloric effect (BCE) via pressure-driven phase transitions. Here, we elucidate the atomistic mechanisms underlying the phase transitions through first-principles calculations and machine-learning potential accelerated molecular dynamics simulations. We identify four distinct phases: the room-temperature cubic (C)…

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Chengqi Dong, Fenghe Tang, Rongge Mao, Xinpei Gao, S. Kevin Zhou
https://arxiv.org/abs/2508.15476

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Medical image segmentation plays a pivotal role in disease diagnosis and treatment planning, particularly in resource-constrained clinical settings where lightweight and generalizable models are urgently needed. However, existing lightweight models often compromise performance for efficiency and rarely adopt computationally expensive attention mechanisms, severely restricting their global contextual perception capabilities. Additionally, current architectures neglect the channel redundancy issu…

Evaluating Classical Software Process Models as Coordination Mechanisms for LLM-Based Software Generation
Duc Minh Ha, Phu Trac Kien, Tho Quan, Anh Nguyen-Duc
https://arxiv.org/abs/2509.13942

Evaluating Classical Software Process Models as Coordination Mechanisms for LLM-Based Software Generation
[Background] Large Language Model (LLM)-based multi-agent systems (MAS) are transforming software development by enabling autonomous collaboration. Classical software processes such asWaterfall, V-Model, and Agile offer structured coordination patterns that can be repurposed to guide these agent interactions. [Aims] This study explores how traditional software development processes can be adapted as coordination scaffolds for LLM based MAS and examines their impact on code quality, cost, and pr…

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
Salman Habib, Remi Chou, Taejoon Kim
https://arxiv.org/abs/2508.15523 https://

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require inject…

R&D on a high-performance electromagnetic calorimeter based on oriented crystalline scintillators
M. Soldani, N. Argiolas, L. Bandiera, V. Baryshevsky, L. Bomben, C. Brizzolari, N. Canale, S. Carsi, S. Cutini, F. Dav\`i, D. De Salvador, A. Gianoli, V. Guidi, V. Haurylavets, M. Korjik, G. Lezzani, A. Lobko, F. Longo, L. Malagutti, S. Mangiacavalli, V. Mascagna, A. Mazzolari, L. Montalto, P. Monti-Guarnieri, M. Moulson, R. Negrello, G. Patern\`o, L. Perna, C. Petroselli, M. Prest, D.…

R&D on a high-performance electromagnetic calorimeter based on oriented crystalline scintillators
Although inorganic scintillators are widely used in the design of electromagnetic calorimeters for high-energy physics and astrophysics, their crystalline nature and, hence, their lattice orientation are generally neglected in the detector design. However, in general, the features of the electromagnetic field experienced by the particles impinging on a crystal at a small angle with respect to a lattice axis affect their interaction mechanisms. In particular, in case of electrons/photons of $\ma…

Intraband circular photogalvanic effect in Weyl semimetals
L. E. Golub, E. L. Ivchenko
https://arxiv.org/abs/2507.13796 https://arxiv…

Intraband circular photogalvanic effect in Weyl semimetals
We apply the semiclassical theory including the Berry curvature dipole, side jumps and skew scattering for a quantitative description of the circular photogalvanic effect (CPGE) in Weyl semimetals at intraband absorption. In contrast to gapped systems where they completely exhaust all contributions to the CPGE current, all previously known semiclassical mechanisms give a result different from that obtained using a complete quantum-mechanical approach. We show that this difference in the existin…

Neural Robot Dynamics
Jie Xu, Eric Heiden, Iretiayo Akinola, Dieter Fox, Miles Macklin, Yashraj Narang
https://arxiv.org/abs/2508.15755 https://arxiv.org/p…

Neural Robot Dynamics
Accurate and efficient simulation of modern robots remains challenging due to their high degrees of freedom and intricate mechanisms. Neural simulators have emerged as a promising alternative to traditional analytical simulators, capable of efficiently predicting complex dynamics and adapting to real-world data; however, existing neural simulators typically require application-specific training and fail to generalize to novel tasks and/or environments, primarily due to inadequate representation…

Multiplicity Distributions and the Frontier between Soft and Hard Physics
H. R. Martins-Fontes, F. S. Navarra
https://arxiv.org/abs/2509.15056 https://arxi…

Multiplicity Distributions and the Frontier between Soft and Hard Physics
The multiplicity distributions measured in proton proton collisions at the LHC exhibit interesting new features. One of them is the appearance of substructures, such as the so-called "shoulder" at large multiplicities. The most natural interpretation of this behavior is the existence of two production mechanisms. The final result is then a superposition of two distributions. In a previous publication we assumed that the two production mechanisms are soft and semihard partonics scatterings. In t…

Shoddy excuse 2: “Maybe it was her personal device but she shouldn’t have agreed to let the employer install remote management software”
Could she really say no to such a demand? Not everyone has the power to refuse a coercive demand from an employer. There should be a strong legal firewall around employer demands to surveil people’s lives. And there should be mechanisms for an employee who is met with such demands to put the brakes on their employer.
I realize the word “should” is doing a lot of work there, but let’s at least be clear about how wrong that situation would be.
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Exploring the Interplay Between Quantum Entanglement and Decoherence
Samuel Marquez Gonzalez
https://arxiv.org/abs/2508.14790 https://arxiv.org/pdf/2508.14…

Exploring the Interplay Between Quantum Entanglement and Decoherence
Quantum entanglement manifests as a distinctive correlation between particles that transcends classical boundaries when their quantum states cannot be described independently. On the other hand, as quantum systems interact with their surroundings, decoherence emerges, leading to the gradual decay of quantum coherence and entanglement. In the case of entanglement, this is known as entanglement sudden death (ESD). Decoherence mechanisms are examined, focusing on how various environmental factors,…

DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints
Xuezheng Qin, Ruwei Huang, Xiaolong Tang, Feng Li
https://arxiv.org/abs/2508.14530

DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints
Federated Learning (FL) is increasingly adopted for privacy-preserving collaborative training, but its decentralized nature makes it particularly susceptible to backdoor attacks. Existing attack methods, however, often rely on idealized assumptions and fail to remain effective under real-world constraints, such as limited attacker control, non-IID data distributions, and the presence of diverse defense mechanisms. To address this gap, we propose DOPA (Divergent Optimization Path Attack), a nove…

this seminar looks plenty interesting!
Sept 19th, 3:00PM
East Hall - 4448
Lisa Fauci, Tulane University
Two Tales of Waving Tails in Biological Fluid Dynamics
The motion of undulating, flexible “tails" in a fluid environment is a common element in many biological systems, from sperm movement in reproduction to fish swimming in the ocean. This talk will focus on two collaborative projects where computational fluid dynamics has shed some light on fundamental me…

A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications
David J. Moore
https://arxiv.org/abs/2508.12683 https://

A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications
Hierarchical multi-agent systems (HMAS) organize collections of agents into layered structures that help manage complexity and scale. These hierarchies can simplify coordination, but they also can introduce trade-offs that are not always obvious. This paper proposes a multi-dimensional taxonomy for HMAS along five axes: control hierarchy, information flow, role and task delegation, temporal layering, and communication structure. The intent is not to prescribe a single "best" design but to provi…

Molecular Dynamics Simulations of Nanoscale Friction on Illite Clay: Effects of Solvent Salt Ions and Electric Double Layer
Ge Li, Astrid S. de Wijn
https://arxiv.org/abs/2507.14158

Molecular Dynamics Simulations of Nanoscale Friction on Illite Clay: Effects of Solvent Salt Ions and Electric Double Layer
Quick clay is a highly sensitive soil that transforms rapidly from solid to liquid under minor stress, as a result of long-term salt leaching that drastically reduces shear strength. Stabilizing it is both costly and carbon-intensive, significantly impacting construction emissions in regions like Norway. Developing greener stabilization methods is challenging due to limited understanding of the weakening mechanisms and the specific roles of different salts. In this study, we use molecular dynam…

Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanism
Huixin Zhong, Yanan Liu, Qi Cao, Shijin Wang, Zijing Ye, Zimu Wang, Shiyao Zhang
https://arxiv.org/abs/2508.14918

Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanism
As large language models (LLMs) integrate into collaborative teams, their social conformity -- the tendency to align with majority opinions -- has emerged as a key concern. In humans, conformity arises from informational influence (rational use of group cues for accuracy) or normative influence (social pressure for approval), with uncertainty moderating this balance by shifting from purely analytical to heuristic processing. It remains unclear whether these human psychological mechanisms apply …

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
Xiaoxiao Li, An Zhu, Youhai Jiang, Fengjie Zhu
https://arxiv.org/abs/2508.14916 https://

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
This paper presents the architecture and performance of a novel Multilingual Automatic Speech Recognition (ASR) system developed by the Transsion Speech Team for Track 1 of the MLC-SLM 2025 Challenge. The proposed system comprises three key components: 1) a frozen Whisper-large-v3 based speech encoder, leveraging large-scale pretraining to ensure robust acoustic feature extraction; 2) a trainable adaptor module using Linear-ReLU-Linear transformation mechanisms to effectively align speech and t…

Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Yehudit Aperstein, Amit Tzahar, Alon Gottlib, Tal Verber, Ravit Shagan Damti, Alexander Apartsin
https://arxiv.org/abs/2509.10348

Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected simultaneously. This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone, enhanced with two selective prediction mechanisms: entropy-based rejection and confidence interval-based rejection. Both methods enable the model to ab…

U-MARVEL: Unveiling Key Factors for Universal Multimodal Retrieval via Embedding Learning with MLLMs
Xiaojie Li, Chu Li, Shi-Zhe Chen, Xi Chen
https://arxiv.org/abs/2507.14902

U-MARVEL: Unveiling Key Factors for Universal Multimodal Retrieval via Embedding Learning with MLLMs
Universal multimodal retrieval (UMR), which aims to address complex retrieval tasks where both queries and candidates span diverse modalities, has been significantly advanced by the emergence of MLLMs. While state-of-the-art MLLM-based methods in the literature predominantly adopt contrastive learning principles, they often differ in their specific training recipes. Despite their success, the mechanisms underlying their retrieval capabilities remain largely unexplored, potentially resulting in …

Exponential asymptotics of quantum droplets and bubbles
Farrell Theodore Adriano, Hadi Susanto
https://arxiv.org/abs/2507.13632 https://

Exponential asymptotics of quantum droplets and bubbles
This research investigates the formation and stability of localized states, known as quantum droplets and bubbles, in the quadratic-cubic discrete nonlinear Schrödinger equation. Near a Maxwell point, these states emerge from two fronts connecting the bistable equilibria. By adjusting a control parameter, we identify a "pinning region" where multiple stable states coexist and are interconnected through homoclinic snaking. We analyze the system's behavior to uncover the underlying mechanisms un…

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models
Qidong Wang, Junjie Hu, Ming Jiang
https://arxiv.org/abs/2509.14837

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models
Recent advances in causal interpretability have extended from language models to vision-language models (VLMs), seeking to reveal their internal mechanisms through input interventions. While textual interventions often target semantics, visual interventions typically rely on coarse pixel-level perturbations, limiting semantic insights on multimodal integration. In this study, we introduce V-SEAM, a novel framework that combines Visual Semantic Editing and Attention Modulating for causal interpr…

Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods
Daniel Sch\"aufele, Jochen Fink, Renato L. G. Cavalcante, S{\l}awomir Sta\'nczak
https://arxiv.org/abs/2508.11792

Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods
In modern 5G user equipments (UEs), the power amplifier (PA) contributes significantly to power consumption during uplink transmissions, especially in cell-edge scenarios. While reducing power backoff can enhance PA efficiency, it introduces nonlinear distortions that degrade signal quality. Existing solutions, such as digital pre-distortion, require complex feedback mechanisms for optimal performance, leading to increased UE complexity and power consumption. Instead, in this study we explore d…

Image-Conditioned 3D Gaussian Splat Quantization
Xinshuang Liu, Runfa Blark Li, Keito Suzuki, Truong Nguyen
https://arxiv.org/abs/2508.15372 https://arxiv.…

Image-Conditioned 3D Gaussian Splat Quantization
3D Gaussian Splatting (3DGS) has attracted considerable attention for enabling high-quality real-time rendering. Although 3DGS compression methods have been proposed for deployment on storage-constrained devices, two limitations hinder archival use: (1) they compress medium-scale scenes only to the megabyte range, which remains impractical for large-scale scenes or extensive scene collections; and (2) they lack mechanisms to accommodate scene changes after long-term archival. To address these l…

Fixed time convergence guarantees for Higher Order Control Barrier Functions
Janani S K, Shishir Kolathaya
https://arxiv.org/abs/2507.13888 https://…

Fixed time convergence guarantees for Higher Order Control Barrier Functions
We present a novel method for designing higher-order Control Barrier Functions (CBFs) that guarantee convergence to a safe set within a user-specified finite. Traditional Higher Order CBFs (HOCBFs) ensure asymptotic safety but lack mechanisms for fixed-time convergence, which is critical in time-sensitive and safety-critical applications such as autonomous navigation. In contrast, our approach imposes a structured differential constraint using repeated roots in the characteristic polynomial, en…

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Mohammed Elmusrati
https://arxiv.org/abs/2508.15719 https://

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates …

🔌 UEC-LLR: The Future Of Loss Recovery In Ethernet For AI And HPC
(... not just useful for AI but these days some people can't write a heaadline without it!)
https://semiengineering.com/uec-llr-the-future-of-loss-recovery-in-ethernet-for-…

UEC-LLR: The Future Of Loss Recovery In Ethernet For AI And HPC
Link layer retry fixes packet loss locally and avoids expensive recovery mechanisms.

The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold
Naixuan Zhao, Chunli Wei, Xinyan Zhang, Jiang Li
https://arxiv.org/abs/2508.13234 https:/…

The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold
The acceleration of artificial intelligence (AI) in science is recognized and many scholars have begun to explore its role in interdisciplinary collaboration. However, the mechanisms and extent of this impact are still unclear. This study, using AlphaFold's impact on structural biologists, examines how AI technologies influence interdisciplinary collaborative patterns. By analyzing 1,247 AlphaFold-related papers and 7,700 authors from Scopus, we employ bibliometric analysis and causal inference…

Many-body theory of false vacuum decay in quantum spin chains
Christian Johansen, Alessio Recati, Iacopo Carusotto, Alberto Biella
https://arxiv.org/abs/2508.13780 https://

Many-body theory of false vacuum decay in quantum spin chains
In this work we theoretically investigate the false vacuum decay process in a ferromagnetic quantum spin-1/2 chain. We develop a many-body theory describing the nucleation and the coherent dynamics of true-vacuum bubbles that is analytically tractable and agrees with numerical matrix product state calculations in all parameter regimes up to intermediate times. This bosonic theory allows us to identify different regimes in the parameter space and unravel the underlying physical mechanisms. In pa…

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models
Yufan Song, Jiatao Zhang, Zeng Gu, Qingmiao Liang, Tuocheng Hu, Wei Song, Shiqiang Zhu
https://arxiv.org/abs/2507.14975

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models
Autonomous error correction is critical for domestic robots to achieve reliable execution of complex long-horizon tasks. Prior work has explored self-reflection in Large Language Models (LLMs) for task planning error correction; however, existing methods are constrained by inflexible self-reflection mechanisms that limit their effectiveness. Motivated by these limitations and inspired by human cognitive adaptation, we propose the Flexible Constructivism Reflection Framework (FCRF), a novel Ment…

Free electron charging of microdroplets in a plasma at atmospheric pressure
Nourhan Hendawy, Harold McQuaid, Somhairle Mag Uidhir, David Rutherford, Declan Diver, Davide Mariotti, Paul Maguire
https://arxiv.org/abs/2508.13372

Free electron charging of microdroplets in a plasma at atmospheric pressure
Gas phase microdroplets have recently demonstrated exceptional chemical properties via suspected mechanisms such as contact electrification and surface charge pinning leading, in turn, to high electric fields at the surface. In this work, microdroplets are injected into a low temperature flowing gas plasma at atmospheric pressure and during flight they are subject to an excess free electron flux. We report the first measurements of particle charge acquired in a fully collisional plasma with ave…

'They’ll chip away at it gradually with bills that disenfranchise the “wrong” sort of people and mechanisms that make voting more difficult. But we should not mistake their ultimate objective. [W]hen they hint that they are interested in getting rid of women’s suffrage, we should take them very seriously indeed.'
Women’s suffrage is apparently up for debate again in America | Arwa Mahdawi | The Guardian
https://www.theguardian.com/commentisfree/2025/sep/13/womens-suffrage-week-in-patriarchy

HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
Chentong Chen, Mengyuan Zhong, Jianyong Sun, Ye Fan, Jialong Shi
https://arxiv.org/abs/2508.13333

HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
LLM-based Automatic Heuristic Design (AHD) within Evolutionary Computation (EC) frameworks has shown promising results. However, its effectiveness is hindered by the use of static operators and the lack of knowledge accumulation mechanisms. We introduce HiFo-Prompt, a framework that guides LLMs with two synergistic prompting strategies: Foresight and Hindsight. Foresight-based prompts adaptively steer the search based on population dynamics, managing the exploration-exploitation trade-off. In a…

Enhancing Coherence with a Clock Transition and Dynamical Decoupling in the Cr$_7$Mn Molecular Nanomagnet
Guanchu Chen (Department of Physics and Astronomy, Amherst College, Amherst, USA, Department of Physics, University of Massachusetts Amherst, Amherst, USA), Brendan C. Sheehan (Department of Physics and Astronomy, Amherst College, Amherst, USA, Department of Physics, University of Massachusetts Amherst, Amherst, USA), Ilija Nikolov (Department of Physics and Astronomy, Amherst Coll…

Enhancing Coherence with a Clock Transition and Dynamical Decoupling in the Cr$_7$Mn Molecular Nanomagnet
Molecular magnets are attractive as spin qubits due to their chemical tunability, addressability through electron-spin resonance techniques, and long coherence times. Clock transitions (CTs), for which the system is immune to the effect of magnetic-field fluctuations to first order, provide a method to enhance the coherence time $T_2$, and to reveal mechanisms of decoherence that are not due to such fluctuations. Here we investigate two variants of Cr$_7$Mn, a spin-1 molecular nanomagnet, at fi…

Smart Charging Impact Analysis using Clustering Methods and Real-world Distribution Feeders
Ravi Raj Shrestha, Zhi Zhou, Limon Barua, Nazib Siddique, Karthikeyan Balasubramaniam, Yan Zhou, Lusha Wang
https://arxiv.org/abs/2508.15092

Smart Charging Impact Analysis using Clustering Methods and Real-world Distribution Feeders
The anticipated widespread adoption of electric vehicles (EVs) necessitates a critical evaluation of existing power distribution infrastructures, as EV integration imposes additional stress on distribution networks that can lead to component overloading and power quality degradation. Implementing smart charging mechanisms can mitigate these adverse effects and defer or even avoid upgrades. This study assesses the performance of two smart charging strategies - Time of Use (TOU) pricing and Load …

i am determined to read the attention/transformer paper
i even printed it out

Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
Mohamed AbdulHameed, Khadija Mahbuba, Mahmoud Yaseen, Amr Ibrahim, Daniel Moneghan, Benjamin Beeler
https://arxiv.org/abs/2508.14325

Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
Silver released from TRISO fuel particles can migrate through the SiC layer and deposit on reactor components, posing radiation hazards and operational challenges. Despite numerous proposed mechanisms, the precise pathway of silver transport through intact 3C-SiC remains unresolved. We present a physics-informed model for estimating the effective diffusivity of silver in polycrystalline 3C-SiC. Molecular dynamics (MD) simulations yield diffusivities for {Σ3} and {Σ9} grain boundaries (GBs), w…

DoubleAgents: Exploring Mechanisms of Building Trust with Proactive AI
Tao Long, Xuanming Zhang, Sitong Wang, Zhou Yu, Lydia B Chilton
https://arxiv.org/abs/2509.12626 https://

DoubleAgents: Exploring Mechanisms of Building Trust with Proactive AI
Agentic workflows promise efficiency, but adoption hinges on whether people actually trust systems that act on their behalf. We present DoubleAgents, an agentic planning tool that embeds transparency and control through user intervention, value-reflecting policies, rich state visualizations, and uncertainty flagging for human coordination tasks. A built-in respondent simulation generates realistic scenarios, allowing users to rehearse, refine policies, and calibrate their reliance before live u…

EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
Hugo Thimonier, Antony Perzo, Renaud Seguier
https://arxiv.org/abs/2508.14130 https://

EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
Emotion recognition from speech is a challenging task that requires capturing both linguistic and paralinguistic cues, with critical applications in human-computer interaction and mental health monitoring. Recent works have highlighted the ability of Large Language Models (LLMs) to perform tasks outside of the sole natural language area. In particular, recent approaches have investigated coupling LLMs with other data modalities by using pre-trained backbones and different fusion mechanisms. Thi…

Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
Ruofan Lu, Yichen Li, Yintong Huo
https://arxiv.org/abs/2508.13143 https://

Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-so…

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
https://arxiv.org/abs/2508.09996

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative analysis of three attention mechanisms (i.e., baseline multi-head attention, causal attention, and sparse attention) integrated with Convolutional Neural Networks (CNNs) for radio frequency (RF) signal classification. It proposes a novel CNN-Transformer hybrid architecture that leverages different attention patterns to…

Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
Yuzhe Liu, Xin Deng, Qiang Yu
https://arxiv.org/abs/2508.13673 https://

Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising brain-inspired models known for low power consumption and superior potential for temporal processing, but identifying suitable learning mechanisms remains a challenge. Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods typically rely on a single form of synaptic plasticity, which limits their adaptability and representational capability. In this paper, we propose a biologically inspired training…

EEG Blink Artifacts Can Identify Read Music in Listening and Imagery
Abhinav Uppal, Dillan Cellier, Min Suk Lee, Sean Bauersfeld, Yuchen Xu, Shihab A. Shamma, Gert Cauwenberghs, Virginia R. de Sa
https://arxiv.org/abs/2508.13807

EEG Blink Artifacts Can Identify Read Music in Listening and Imagery
Eye-movement related artifacts including blinks and saccades are significantly larger in amplitude than cortical activity as recorded by scalp electroencephalography (EEG), but are typically discarded in EEG studies focusing on cognitive mechanisms as explained by cortical source activity. Accumulating evidence however indicates that spontaneous eye blinks are not necessarily random, and can be modulated by attention and cognition beyond just physiological necessities. In this exploratory analy…

Earth-Mass Planets in Tandem Disks
Tokuhiro Nimura, Toshikazu Ebisuzaki
https://arxiv.org/abs/2508.13269 https://arxiv.org/pdf/2508.13269

Earth-Mass Planets in Tandem Disks
This paper presents a new terrestrial planet formation theory demonstrating that Earth-mass planets form naturally in tandem protosolar disks. Our model builds upon tandem planet formation theory (Ebisuzaki and Imaeda 2017; Imaeda and Ebisuzaki 2017a,b, 2018), incorporating magneto-rotational instability (MRI) suppression (Balbus and Hawley 1991; Hawley and Balbus 1991), porous particle aggregation (Okuzumi et al. 2012; Kataoka et al. 2013), and standard planet formation mechanisms (e.g., Safro…

NodeShield: Runtime Enforcement of Security-Enhanced SBOMs for Node.js
Eric Cornelissen, Musard Balliu
https://arxiv.org/abs/2508.13750 https://arxiv.org/p…

NodeShield: Runtime Enforcement of Security-Enhanced SBOMs for Node.js
The software supply chain is an increasingly common attack vector for malicious actors. The Node.js ecosystem has been subject to a wide array of attacks, likely due to its size and prevalence. To counter such attacks, the research community and practitioners have proposed a range of static and dynamic mechanisms, including process- and language-level sandboxing, permission systems, and taint tracking. Drawing on valuable insight from these works, this paper studies a runtime protection mechani…

Accurate and Scalable Simulation of Cavity-Based Networks in Modular Quantum Architectures
Sahar Ben Rached, Zezhou Sun, Guilu Long, Santiago Rodrigo, Carmen G. Almud\'ever, Eduard Alarc\'on, Sergi Abadal
https://arxiv.org/abs/2508.13896

Accurate and Scalable Simulation of Cavity-Based Networks in Modular Quantum Architectures
Cavity-mediated interconnects are a promising platform for scaling modular quantum computers by enabling high-fidelity inter-chip quantum state transmission and entanglement generation. In this work, we first model the dynamics of deterministic inter-chip quantum state transfer using the Stimulated Raman Adiabatic Passage (STIRAP) protocol, analyzing fidelity loss mechanisms under experimentally achievable qubit-cavity coupling and decoherence parameters. We then extend the NetSquid simulator, …

Deciphering Profile Stability in Millisecond Pulsars: Timescales, Frequency Evolution, and Implications on Emission Mechanisms
Ankita Ghosh, Bhaswati Bhattacharyya, Rahul Sharan, Patrick Weltevrede, Jayanta Roy, Sangita Kumari
https://arxiv.org/abs/2509.12781

Deciphering Profile Stability in Millisecond Pulsars: Timescales, Frequency Evolution, and Implications on Emission Mechanisms
Pulse profile stability in millisecond pulsars (MSPs) is a key factor in achieving high-precision timing essential for detecting nanohertz gravitational waves with Pulsar Timing Arrays (PTAs). In this work, we present a systematic analysis of profile stabilization timescales in MSPs using a direct method based on pulse stacking, applied to long-term multi-epoch observations. Our study utilizes data from the upgraded GMRT (uGMRT) between 300--750 MHz for nine MSPs over 3--5 years and Parkes Ultr…

Attention-Based Explainability for Structure-Property Relationships
Boris N. Slautin, Utkarsh Pratiush, Yongtao Liu, Hiroshi Funakubo, Vladimir V. Shvartsman, Doru C. Lupascu, Sergei V. Kalinin
https://arxiv.org/abs/2508.15493

Attention-Based Explainability for Structure-Property Relationships
Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterization. However, this necessitates development of methods for physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure-property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTi…

Compact Attention: Exploiting Structured Spatio-Temporal Sparsity for Fast Video Generation
Qirui Li, Guangcong Zheng, Qi Zhao, Jie Li, Bin Dong, Yiwu Yao, Xi Li
https://arxiv.org/abs/2508.12969

Compact Attention: Exploiting Structured Spatio-Temporal Sparsity for Fast Video Generation
The computational demands of self-attention mechanisms pose a critical challenge for transformer-based video generation, particularly in synthesizing ultra-long sequences. Current approaches, such as factorized attention and fixed sparse patterns, fail to fully exploit the inherent spatio-temporal redundancies in video data. Through systematic analysis of video diffusion transformers (DiT), we uncover a key insight: Attention matrices exhibit structured, yet heterogeneous sparsity patterns, whe…

Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed Variational Autoencoder Mixture Model
Sanduni Pinnawala, Annabelle Hartanto, Ivor J. A. Simpson, Peter A. Wijeratne
https://arxiv.org/abs/2509.15124

Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed Variational Autoencoder Mixture Model
Modelling the underlying mechanisms of neurodegenerative diseases demands methods that capture heterogeneous and spatially varying dynamics from sparse, high-dimensional neuroimaging data. Integrating partial differential equation (PDE) based physics knowledge with machine learning provides enhanced interpretability and utility over classic numerical methods. However, current physics-integrated machine learning methods are limited to considering a single PDE, severely limiting their application…

Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering
Satyam Kumar Navneet, Joydeep Chandra
https://arxiv.org/abs/2508.11824 https://a…

Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering
The integration of Large Language Models (LLMs) into software engineering has revolutionized code generation, enabling unprecedented productivity through promptware and autonomous AI agents. However, this transformation introduces significant risks, including insecure code generation, hallucinated outputs, irreversible actions, and a lack of transparency and accountability. Incidents like the Replit database deletion underscore the urgent need for robust safety and governance mechanisms. This p…

MACTAS: Self-Attention-Based Module for Inter-Agent Communication in Multi-Agent Reinforcement Learning
Maciej Wojtala, Bogusz Stefa\'nczyk, Dominik Bogucki, {\L}ukasz Lepak, Jakub Strykowski, Pawe{\l} Wawrzy\'nski
https://arxiv.org/abs/2508.13661

MACTAS: Self-Attention-Based Module for Inter-Agent Communication in Multi-Agent Reinforcement Learning
Communication is essential for the collective execution of complex tasks by human agents, motivating interest in communication mechanisms for multi-agent reinforcement learning (MARL). However, existing communication protocols in MARL are often complex and non-differentiable. In this work, we introduce a self-attention-based communication module that exchanges information between the agents in MARL. Our proposed approach is fully differentiable, allowing agents to learn to generate messages in …

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Churong Liang, Jinling Gan, Kairan Hong, Qiushi Tian, Zongze Wu, Runnan Li
https://arxiv.org/abs/2508.13815

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Large-scale multi-agent workflows exhibit inherent vulnerability to error propagation and quality degradation, where downstream agents compound upstream failures without corrective mechanisms. We introduce COCO (Cognitive Operating System with Continuous Oversight), a theoretically-grounded framework that implements asynchronous self-monitoring and adaptive error correction in multi-agent driven systems. COCO addresses the fundamental trade-off between quality assurance and computational effici…

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Renyuan Liu, Haoting Zhou, Chuankai Fang, Qinbing Fu
https://arxiv.org/abs/2509.13827 https://

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Anyone who has tried to swat a fly has likely been frustrated by its remarkable agility.This ability stems from its visual neural perception system, particularly the collision-selective neurons within its small brain.For autonomous robots operating in complex and unfamiliar environments, achieving similar agility is highly desirable but often constrained by the trade-off between computational cost and performance.In this context, insect-inspired intelligence offers a parsimonious route to low-p…

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
Utsav Maskey, Sumit Yadav, Mark Dras, Usman Naseem
https://arxiv.org/abs/2508.11290

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that superficially resemble harmful content. This phenomena diminishes utility in production applications that repeatedly rely on common prompt templates or applications that frequently rely on LLMs for specific tasks (e.g. sentiment analysis, language translation). Through comprehensive evaluation, we demonstrate that LLMs still tend to refuse responses to harmful instructions wh…

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Jikai Chen, Long Chen, Dong Wang, Leilei Gan, Chenyi Zhuang, Jinjie Gu
https://arxiv.org/abs/2508.13634

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interaction uncertainty and visual-semantic hierarchies. Recent methods incorporate attention mechanisms but still face two key issues: (1) ignoring processing background regions causes attention drift from the desired area, and (2) uniform labeling fails to distinguish between center and edges of the target UI element, leadin…

CAN Networks Security in Smart Grids Communication Technologies
Ayman W. Baharia, Khaled T. Naga, Hesham S. Abdelfattah, Shady A. Maged, Sherif A. Hammad
https://arxiv.org/abs/2508.12181

CAN Networks Security in Smart Grids Communication Technologies
The rapid evolution of smart grids requires effective communication protocols to transfer data reliably and securely. Controller Area Network (CAN) is one of the most recognized protocols that offer reliable data transmission in smart grids due to its robustness, real-time capabilities, and relatively low initial cost of its required hardware. However, as a smart city becomes more interconnected, it also becomes more vulnerable to cyber-attacks. As there are many mechanisms to secure the CAN no…

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Xiaoyu Yue, Zidong Wang, Yuqing Wang, Wenlong Zhang, Xihui Liu, Wanli Ouyang, Lei Bai, Luping Zhou
https://arxiv.org/abs/2509.15185

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image understanding. As a generative paradigm originally designed for natural language, autoregressive models face similar challenges. In this work, we present the first systematic investigation into the mechanisms of applying the next-token prediction paradigm to the visual domain. We identify three key properties that hinder th…

Dissipation-Induced Steady States in Topological Superconductors: Mechanisms and Design Principles
M. S. Shustin, S. V. Aksenov, I. S. Burmistrov
https://arxiv.org/abs/2508.11242

Dissipation-Induced Steady States in Topological Superconductors: Mechanisms and Design Principles
The search for conditions supporting degenerate steady states in nonequilibrium topological superconductors is important for advancing dissipative quantum engineering, a field that has attracted significant research attention over the past decade. In this study, we address this problem by investigating topological superconductors hosting unpaired Majorana modes under the influence of environmental dissipative fields. Within the Gorini-Kossakowski-Sudarshan-Lindblad framework and the third quant…

Spin Relaxation Mechanisms and Nuclear Spin Entanglement of the V$_B^{-1}$ Center in hBN
Chanaprom Cholsuk, Tobias Vogl, Viktor Iv\'ady
https://arxiv.org/abs/2507.11494

Spin Relaxation Mechanisms and Nuclear Spin Entanglement of the V$_B^{-1}$ Center in hBN
The negatively charged boron vacancy $V_B^-$ defect in hexagonal boron nitride (hBN) has recently emerged as a promising spin qubit for sensing due to its high-temperature spin control and versatile integration into van der Waals structures. While extensive experiments have explored their coherence properties, much less is known about the spin relaxation time $T_1$ and its control-parameter dependence. In this work, we develop a parameter-free spin dynamics model based on the cluster-expansion …

On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, Jose L. Hernandez-Ramos, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti
https://arxiv.org/abs/2508.13730

On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provides a comprehensive overview of more than 200 papers regarding the state-of-the-art attacks and defense mechanisms developed to address these challenges, categorizing them into security-enhancing and pr…

Thermal Degradation Mechanisms and Stability Enhancement Strategies in Perovskite Solar Cells: A Review
Arghya Paul, Kanak Raj, Prince Raj Lawrence Raj, Pratim Kumar
https://arxiv.org/abs/2509.13700

Thermal Degradation Mechanisms and Stability Enhancement Strategies in Perovskite Solar Cells: A Review
Perovskite Solar Cells (PSCs) have garnered global research interest owing to their superior photovoltaic (PV) performance. The future of photovoltaic technology lies in PSCs since they can produce power with performance on par with the best silicon solar cells while being less expensive. PSCs have enormous potential; in just ten years, their efficiency increased from 3.8% to 25.2%, and research into new developments is still ongoing. Thermal instability is PSCs' main disadvantage, despite thei…

ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks
Fei Lin, Tengchao Zhang, Ziyang Gong, Fei-Yue Wang
https://arxiv.org/abs/2508.12035 …

ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks
In recent years, generative artificial intelligence (GenAI) has demonstrated remarkable capabilities in high-stakes domains such as molecular science. However, challenges related to the verifiability and structural privacy of its outputs remain largely unresolved. This paper focuses on the task of molecular toxicity repair. It proposes a structure-private verification framework - ToxiEval-ZKP - which, for the first time, introduces zero-knowledge proof (ZKP) mechanisms into the evaluation proce…

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …

Stochastic ion emission perturbation mechanisms in atom probe tomography: Linking simulations to experiment
Aslam Shaikh, Tero M\"akinen, Fran\c{c}ois Vurpillot, Mikko Alava, Ivan Lomakin
https://arxiv.org/abs/2509.13744

Stochastic ion emission perturbation mechanisms in atom probe tomography: Linking simulations to experiment
Field evaporation in atom probe tomography (APT) includes known processes related to surface migration of atoms, such as the so-called roll-up mechanism. They lead to trajectory aberrations and artefacts on the detector. These processes are usually neglected in simulations. The inclusion of such processes is crucial for providing reliable models for the development and verification of APT reconstruction algorithms, a key part of the whole methodology. Here we include stochastic lateral velocity…

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Zihou Wu (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Yuecheng Li (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Tianchi Liao (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Jian Lou (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Chuan Chen (School of Computer Science and E…

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Federated learning (FL) faces a critical dilemma: existing protection mechanisms like differential privacy (DP) and homomorphic encryption (HE) enforce a rigid trade-off, forcing a choice between model utility and computational efficiency. This lack of flexibility hinders the practical implementation. To address this, we introduce ParaAegis, a parallel protection framework designed to give practitioners flexible control over the privacy-utility-efficiency balance. Our core innovation is a strat…

Graph Neural Diffusion via Generalized Opinion Dynamics
Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan
https://arxiv.org/abs/2508.11249 https://arxi…

Graph Neural Diffusion via Generalized Opinion Dynamics
There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical diffusion processes. However, existing methods suffer from three critical limitations: (1) they rely on homogeneous diffusion with static dynamics, limiting adaptability to diverse graph structures; (2) their depth is constrained by computational overhead and diminishing interpretability; and (3) theoretical understandi…

Overcoming Quantum Resistivity Scaling in Nanoscale Interconnects Using Delafossite PdCoO2
Seoung-Hun Kang, Youngjun Lee, Sangmoon Yoon, JongMok Ok, Mina Yoon, Ho Nyung Lee, Young-Kyun Kwon
https://arxiv.org/abs/2508.13573

Overcoming Quantum Resistivity Scaling in Nanoscale Interconnects Using Delafossite PdCoO2
Continued scaling into the sub 7 nm regime exacerbates quantum limited resistivity in Cu interconnects. We evaluated layered PdCoO2 and explicitly benchmarked it against Cu to identify mechanisms that maintain conductivity under confinement. Using a momentum resolved relaxation time formalism derived from the conductivity tensor, we link k and energy resolved velocities, life times, and mean free paths (MFPs) to thickness dependent resistivity for films and wires. PdCoO2 exhibits quasi 2D trans…

VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee, Ian Chuang, Hanchu Zhou, Hang Wang, Zhe Zhao, Junshan Zhang, Iman Soltani
https://arxiv.org/abs/2507.13231

VITA: Vision-to-Action Flow Matching Policy
We present VITA, a Vision-To-Action flow matching policy that evolves latent visual representations into latent actions for visuomotor control. Traditional flow matching and diffusion policies sample from standard source distributions (e.g., Gaussian noise) and require additional conditioning mechanisms like cross-attention to condition action generation on visual information, creating time and space overheads. VITA proposes a novel paradigm that treats latent images as the flow source, learnin…

Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction
Yuanbo Xie, Yingjie Zhang, Tianyun Liu, Duohe Ma, Tingwen Liu
https://arxiv.org/abs/2509.15202

Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction
Jailbreak attacks pose persistent threats to large language models (LLMs). Current safety alignment methods have attempted to address these issues, but they experience two significant limitations: insufficient safety alignment depth and unrobust internal defense mechanisms. These limitations make them vulnerable to adversarial attacks such as prefilling and refusal direction manipulation. We introduce DeepRefusal, a robust safety alignment framework that overcomes these issues. DeepRefusal forc…

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
https://arxiv.org/abs/2509.14113

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned outputs remains a significant challenge for forecasters. In this work, we take a further step toward addressing this critical issue by introducing the Quantile Neural Basis Model, which incorporates the interpretability principles of Quantile Generalized Additive Models into an end-to-end neural network training framewo…

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
Aaron Fl\"ototto, Benjamin Spetzler, Rose von Stackelberg, Martin Ziegler, Erich Runge, Christian Dre{\ss}ler
https://arxiv.org/abs/2508.13790

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning interatomic potentials (MLIPs) reveal key mechanisms of cooperative vacancy transport, including incorporation of vacancies into clusters of arbitrary size. The simulations provide a coherent atomistic explanation for irradiation-induced vacancy patterns observed e…

The Passwordless Authentication with Passkey Technology from an Implementation Perspective
Lien Tran, Boyuan Zhang, Ratchanon Pawanja, Rashid Hussain Khokhar
https://arxiv.org/abs/2508.11928

The Passwordless Authentication with Passkey Technology from an Implementation Perspective
With the rise of sophisticated authentication bypass techniques, passwords are no longer considered a reliable method for securing authentication systems. In recent years, new authentication technologies have shifted from traditional password-based logins to passwordless security. Among these, Time-Based One-Time Passwords (TOTP) remain one of the most widely used mechanisms, while Passkeys are emerging as a promising alternative with growing adoption. This paper highlights the key techniques u…

Kinetics of Vacancy-Assisted Reversible Phase Transition in Monolayer MoTe$_2$
Fei Shuang, Daniel Ocampo, Reza Namakian, Arman Ghasemi, Poulumi Dey, Wei Gao
https://arxiv.org/abs/2507.12565

Kinetics of Vacancy-Assisted Reversible Phase Transition in Monolayer MoTe$_2$
We investigate the kinetics of phase transition between the 2H and 1T$^\prime$ phases in monolayer MoTe$_2$ using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory to interpret the underlying mechanisms. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of 1T$^\prime$ phase is initiated by the coalescence of neighboring Te monovacancies into divacancies, which are fou…