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…

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)…

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 …

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 …

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…

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, …

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…

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…

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…

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…

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, …

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…

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…

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…

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…

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…

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…

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…

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.
3/

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, …

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…

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…

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…

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…

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…

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…

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…

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…

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,…

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…

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…

Keywords are not always the key: A metadata field analysis for natural language search on open data portals
Lisa-Yao Gan, Arunav Das, Johanna Walker, Elena Simperl
https://arxiv.org/abs/2509.14457

Keywords are not always the key: A metadata field analysis for natural language search on open data portals
Open data portals are essential for providing public access to open datasets. However, their search interfaces typically rely on keyword-based mechanisms and a narrow set of metadata fields. This design makes it difficult for users to find datasets using natural language queries. The problem is worsened by metadata that is often incomplete or inconsistent, especially when users lack familiarity with domain-specific terminology. In this paper, we examine how individual metadata fields affect the…

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…

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…

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…

Energy Conversion and Electron Acceleration and Transport in 3D Simulations of Solar Flares
Xiaocan Li, Chengcai Shen, Xiaoyan Xie, Fan Guo, Bin Chen, Ivan Oparin, Yuqian Wei, Sijie Yu, Jeongbhin Seo
https://arxiv.org/abs/2508.12990

Energy Conversion and Electron Acceleration and Transport in 3D Simulations of Solar Flares
Recent observations and simulations indicate that solar flares undergo extremely complex three-dimensional (3D) evolution, making 3D particle transport models essential for understanding electron acceleration and interpreting flare emissions. In this study, we investigate this problem by solving Parker's transport equation with 3D MHD simulations of solar flares. By examining energy conversion in the 3D system, we evaluate the roles of different acceleration mechanisms, including reconnection c…

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…

🔌 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.

Extracting Interpretable Higher-Order Topological Features across Multiple Scales for Alzheimer's Disease Classification
Dengyi Zhao, Shanyong Li, Yunping Wang, Chenfei Wang, Zhiheng Zhou, Guiying Yan, Xingqin Qi
https://arxiv.org/abs/2509.14634

Extracting Interpretable Higher-Order Topological Features across Multiple Scales for Alzheimer's Disease Classification
Brain network topology, derived from functional magnetic resonance imaging (fMRI), holds promise for improving Alzheimer's disease (AD) diagnosis. Current methods primarily focus on lower-order topological features, often overlooking the significance of higher-order features such as connected components, cycles, and cavities. These higher-order features are critical for understanding normal brain function and have been increasingly linked to the pathological mechanisms of AD. However, their qua…

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 …

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

On the Late-Time Instability of MOT solution to the Time-Domain PMCHWT Equation
Van Chien Le, Viviana Giunzioni, Pierrick Cordel, Francesco P. Andriulli, Kristof Cools
https://arxiv.org/abs/2509.14995 …

On the Late-Time Instability of MOT solution to the Time-Domain PMCHWT Equation
This paper investigates the late-time instability of marching-on-in-time solution to the time-domain PMCHWT equation. The stability analysis identifies the static solenoidal nullspace of the time-domain electric field integral operator as the primary cause of instability. Furthermore, it reveals that the instability mechanisms of the time-domain PMCHWT equation are fundamentally different from those of the time-domain electric field integral equation. In particular, the PMCHWT's instability is …

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…

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…

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…

The origin of extreme N-emitters in star-forming galaxies at z$<$0.5 with DESI DR1
Souradeep Bhattacharya, Chiaki Kobayashi
https://arxiv.org/abs/2508.11998 https://

The origin of extreme N-emitters in star-forming galaxies at z$<$0.5 with DESI DR1
Extreme nitrogen enhancement relative to oxygen, recently found in very high-redshift galaxies, has been seen in local star-forming galaxies displaying high log(N/O) values ($\geq\!-1.1$) at relatively low O abundances, 12+log(O/H)$\leq$8. Understanding the physical origins of these extreme N-emitters at low redshifts enables us to better constrain chemical enrichment mechanisms that drove such high log(N/O) values in the early Universe. With direct N and O abundances derived for 944 SFGs with …

As usual we watched the "Stoertebekker" stage play at Ralswiek on Rügen Island. The stage setups and wardrobes were as awesome as always. They really try to improve on that every year and come up with clever mechanisms to transform the building for the various acts of the play. The script this year left room for desire ;) Nevertheless a happing worth attending
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Dynamic Forcing Behind Rapid Intensification of Hurricane Lidia
Mauricio L\'opez-Reyes, M. L. Mart\'in-P\'erez, C. Calvo-Sancho, J. J. Gonz\'alez-Alem\'an
https://arxiv.org/abs/2508.12481

Dynamic Forcing Behind Rapid Intensification of Hurricane Lidia
This study examines Hurricane Lidia rapid intensification (RI) in the understudied northeastern Pacific, focusing on its interaction with an upper-level trough. Using IFS-ECMWF ensemble forecasts and ERA5 reanalysis, we analyze the large-scale dynamical mechanisms driving Lidias intensification. Results show that the trough played a crucial role in promoting RI by enhancing synoptic-scale ascent, upper-level divergence, and eddy flux convergence. In the higher-intensification ensemble group, st…

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…

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…

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…

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…

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…

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…

TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang (Shanghai Jiaotong University), Changfa Wu (Alibaba Inc), Dunxian Huang (Alibaba Inc), Weiqiang Sun (Shanghai Jiaotong University), Ziyang Wang (Alibaba Inc), Yuliang Yan (Alibaba Inc), Jian Wu (Alibaba Inc), Yuning Jiang (Alibaba Inc), Bo Zheng (Alibaba Inc), Ke Chen (Alibaba Inc), Silu Zhou (Alibaba Inc), Yu Zhang (Alibaba Inc)

TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative models have demonstrated potential in enhancing recommendation systems; however, these models often exhibit limitations in optimizing retrieval tasks, primarily due to their reliance on autoregressive generation mechanisms. Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they a…

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…

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…

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…

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…

Embodied sensorimotor control: computational modeling of the neural control of movement
Muhammad Noman Almani, John Lazzari, Jeff Walker, Shreya Saxena
https://arxiv.org/abs/2509.14360

Embodied sensorimotor control: computational modeling of the neural control of movement
We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynamically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior t…

Stochastic Modeling of Filtration with Sieving in Graded Pore Networks
Binan Gu, Pejman Sanaei, Lou Kondic, Linda J. Cummings
https://arxiv.org/abs/2508.11820 https://

Stochastic Modeling of Filtration with Sieving in Graded Pore Networks
We model filtration of a feed solution, containing both small and large foulant particles, by a membrane filter. The membrane interior is modeled as a network of pores, allowing for the simultaneous adsorption of small particles and sieving of large particles, two fouling mechanisms typically observed during the early stages of commercial filtration applications. In our model, first-principles continuum partial differential equations model transport of the small particles and adsorptive fouling…

Hydrodynamic Attraction and Hindered Diffusion Govern First-passage Times of Swimming Microorganisms
Yanis Baouche, Magali Le Goff, Thomas Franosch, Christina Kurzthaler
https://arxiv.org/abs/2509.14765

Hydrodynamic Attraction and Hindered Diffusion Govern First-passage Times of Swimming Microorganisms
The motion of microorganisms in their natural habitat is strongly influenced by their propulsion mechanisms, geometrical constraints, and random fluctuations. Here, we study numerically the first-passage-time (FPT) statistics of microswimmers, modeled as force-dipoles, to reach a no-slip wall. Our results demonstrate that hindered diffusion near the wall can increase the median FPT by orders of magnitude compared to "dry" agents, while the intricate interplay of active motion and hydrodynamic a…

The Rosetta Stone project. III. ALMA synthetic observations of fragmentation in high-mass star-forming clumps
Alice Nucara, Alessio Traficante, Ugo Lebreuilly, Ngo-Duy Tung, Sergio Molinari, Patrick Hennebelle, Leonardo Testi, Ralf S. Klessen, Veli-Matti Pelkonen, Adam Avison, Milena Benedettini, Alessandro Coletta, Fabrizio De Angelis, Davide Elia, Gary A. Fuller, Bethany M. Jones, Seyma Mercimek, Chiara Mininni, Stefania Pezzuto, Thushara Pillai, Veronica Roccatagliata, Eugenio Schis…

The Rosetta Stone project. III. ALMA synthetic observations of fragmentation in high-mass star-forming clumps
The physical mechanisms that regulate the collapse of high-mass parsec-scale clumps and allow them to form clusters of new stars represent a crucial aspect of star formation. To investigate these mechanisms, we developed the Rosetta Stone project: an end-to-end (simulations-observations) framework that is based on the systematic production of realistic synthetic observations of clump fragmentation and their comparison with real data. In this work, we compare ALMA 1.3mm continuum dust emission o…

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…

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 …

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
Haoning Xue, Brian Nishimine, Martin Hilbert, Drew Cingel, Samantha Vigil, Jane Shawcroft, Arti Thakur, Zubair Shafiq, Jingwen Zhang
https://arxiv.org/abs/2507.12571

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
The prevalence of short form video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harms facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-leve…

Simulation of a generalized asset exchange model with investment and income mechanisms
Jan Tobochnik, Harvey Gould, William Klein
https://arxiv.org/abs/2508.05810 https://

Simulation of a generalized asset exchange model with investment and income mechanisms
An agent-based model of the economy is generalized to incorporate investment and guaranteed income mechanisms in addition to the exchange and distribution mechanisms considered in earlier models. We find realistic wealth distributions and realistic values of the Gini coefficients and the Pareto index. We also show that although the system reaches a steady state, the system is not in thermal equilibrium. The nonequilibrium behavior is associated with the multiplicative noise generated by the inv…

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…

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 …

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…

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…

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
S M Rafiuddin, Sadia Kamal, Mohammed Rakib, Arunkumar Bagavathi, Atriya Sen
https://arxiv.org/abs/2507.12695

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, i…

A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals
Zachery Dahm, Vasileios Theos, Konstantinos Vasili, William Richards, Konstantinos Gkouliaras, Stylianos Chatzidakis
https://arxiv.org/abs/2508.12428

A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals
The transition of next generation advanced nuclear reactor systems from analog to fully digital instrumentation and control will necessitate robust mechanisms to safeguard against potential data integrity threats. One challenge is the real-time characterization of false data injections, which can mask sensor signals and potentially disrupt reactor control systems. While significant progress has been made in anomaly detection within reactor systems, potential false data injections have been show…

Tera-Hertz Counterparts to Fast Radio Bursts from Coherent Cherenkov Radiation by Tilted Bunches
Ze-Nan Liu, Wei-Yang Wang, Yu-Chen Huang, Zi-Gao Dai
https://arxiv.org/abs/2508.11736

Tera-Hertz Counterparts to Fast Radio Bursts from Coherent Cherenkov Radiation by Tilted Bunches
Fast radio bursts (FRBs) are millisecond-duration extragalactic transients characterized by ultrahigh brightness temperatures, suggesting coherent emission mechanisms in extreme astrophysical processes. In this paper, we extend the bunched coherent Cherenkov radiation (CChR) framework by incorporating bunch inclination and geometric configuration parameters, enabling it to more rigorously model FRB emission and tera-Hertz (THz) emission from magnetars. When relativistic bunches are injected int…

Aligned Stellar Obliquities for Two Hot Jupiter-hosting M Dwarfs Revealed by MAROON-X: Implications for Hot Jupiter Formation
Drew Weisserman, Erik Gillis, Ryan Cloutier, Nina Brown, Jacob L. Bean, Andreas Seifahrt, Tanya Das, Madison Brady, Bertram Bitsch, Emily Deibert, Thomas M. Evans-Soma, Noah Fenlon, Laura Kreidberg, Michael Line, Ralph Pudritz, Evgenya L. Shkolnik, Luis Welbanks

Aligned Stellar Obliquities for Two Hot Jupiter-hosting M Dwarfs Revealed by MAROON-X: Implications for Hot Jupiter Formation
Hot Jupiters (HJs) are $2-3\times$ less common around early M dwarfs than around AFGK stars, suggesting that HJs may form and/or migrate via distinct pathways around different types of stars. One source of insight into HJ formation mechanisms is to trace their dynamical histories through measurements of host stellar obliquities via the Rossiter-McLaughlin (RM) effect. Here we present measurements of the RM effect for the HJs TOI-3714 b and TOI-5293 A b using the Gemini-North/MAROON-X spectrogra…

A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance
Jie Su, Weiwei Wang, Zhaotian Gu, Dahui Wang, Tianyi Qian
https://arxiv.org/abs/2508.12702

A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance
Robust information representation and its persistent maintenance are fundamental for higher cognitive functions. Existing models employ distinct neural mechanisms to separately address noise-resistant processing or information maintenance, yet a unified framework integrating both operations remains elusive -- a critical gap in understanding cortical computation. Here, we introduce a recurrent neural circuit that combines divisive normalization with self-excitation to achieve both robust encodin…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Jiayue Jin, Lang Qian, Jingyu Zhang, Chuanyu Ju, Liang Song
https://arxiv.org/abs/2508.11453 ht…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Recent years have witnessed remarkable progress in autonomous driving, with systems evolving from modular pipelines to end-to-end architectures. However, most existing methods are trained offline and lack mechanisms to adapt to new environments during deployment. As a result, their generalization ability diminishes when faced with unseen variations in real-world driving scenarios. In this paper, we break away from the conventional "train once, deploy forever" paradigm and propose EvoPSF, a nove…

Chiral twist-bend liquid crystals
E. I. Kats
https://arxiv.org/abs/2509.14686 https://arxiv.org/pdf/2509.14686

Chiral twist-bend liquid crystals
Recently published preprint ( A. Ashkinazi, H.Chhabra, A.El.Moumane, M.M.C.Tortora, J.P.K. Doye, ''Chirality transfer in lyotropic twist-bend nematics'', arXiv:2508.03544v1 (2025)) has reawakened also interest to various mechanisms of chirality transfer from microscopic (molecular) level into the macroscopic chirality of the structure. In this communication we present a simple theoretical analysis how the transfer occurs for the Landau model of phase transition between cholesteric and chiral tw…

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…

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…

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…

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games
Mingjia He, Andrea Censi, Emilio Frazzoli, Gioele Zardini
https://arxiv.org/abs/2508.12059

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games
Network-based systems are inherently interconnected, with the design and performance of subnetworks being interdependent. However, the decisions of self-interested operators may lead to suboptimal outcomes for users and the overall system. This paper explores cooperative mechanisms that can simultaneously benefit both operators and users. We address this challenge using a game-theoretical framework that integrates both non-cooperative and cooperative game theory. In the non-cooperative stage, w…

Runaway stars and the Galactic supernova remnant landscape: non-thermal emission and observational evidence
Rowan Batzofin, Kathrin Egberts, Dominique M. -A. Meyer, Constantin Steppa
https://arxiv.org/abs/2508.12721

Runaway stars and the Galactic supernova remnant landscape: non-thermal emission and observational evidence
Context. A significant fraction (~30%) of massive stars in our Galaxy are moving supersonically through the interstellar medium, which strongly governs their location at the time they end their lives, e.g. die as a supernova and give birth to a supernova remnant (SNR). These dead stellar environments accelerate particles, emitting by non-thermal mechanisms up to the TeV range, and they are considered as a major contributor to the very-high-energy band of the local cosmic-ray spectrum. Aims. T…

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…

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…

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…

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping
Zijian An, Ran Yang, Yiming Feng, Lifeng Zhou
https://arxiv.org/abs/2509.14143 https://

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping
Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natural language instructions into visuomotor actions. However, current VLAs often struggle to satisfy precise task constraints, such as stopping based on numeric thresholds, since their observation-to-action mappings are implicitly shaped by training data and lack explicit mechanisms for condition monitoring. In this work, we propose CLAW (CLIP-Languag…

Hierarchical Learning for Maze Navigation: Emergence of Mental Representations via Second-Order Learning
Shalima Binta Manir, Tim Oates
https://arxiv.org/abs/2509.14195 https://…

Hierarchical Learning for Maze Navigation: Emergence of Mental Representations via Second-Order Learning
Mental representation, characterized by structured internal models mirroring external environments, is fundamental to advanced cognition but remains challenging to investigate empirically. Existing theory hypothesizes that second-order learning -- learning mechanisms that adapt first-order learning (i.e., learning about the task/domain) -- promotes the emergence of such environment-cognition isomorphism. In this paper, we empirically validate this hypothesis by proposing a hierarchical architec…

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…

Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Suzie Kim, Hye-Bin Shin, Seong-Whan Lee
https://arxiv.org/abs/2507.13171

Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, rein forcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with human-derived evaluation signals. However, most existing RLHF methods depend on explicit feedback mechanisms such as button presses or pr…

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…

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…

Fast attention mechanisms: a tale of parallelism
Jingwen Liu, Hantao Yu, Clayton Sanford, Alexandr Andoni, Daniel Hsu
https://arxiv.org/abs/2509.09001 https://

Fast attention mechanisms: a tale of parallelism
Transformers have the representational capacity to simulate Massively Parallel Computation (MPC) algorithms, but they suffer from quadratic time complexity, which severely limits their scalability. We introduce an efficient attention mechanism called Approximate Nearest Neighbor Attention (ANNA) with sub-quadratic time complexity. We prove that ANNA-transformers (1) retain the expressive power previously established for standard attention in terms of matching the capabilities of MPC algorithms,…