Tootfinder

Discovering Association Rules in High-Dimensional Small Tabular Data
Erkan Karabulut, Daniel Daza, Paul Groth, Victoria Degeler
https://arxiv.org/abs/2509.20113 https://

Discovering Association Rules in High-Dimensional Small Tabular Data
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machine learning in high-stakes decision-making. However, in high-dimensional settings, rule explosion and computational overhead render popular algorithmic approaches impractical without effective search space reduction, challenges that propagate to downstream tasks. Neurosymbolic methods, such as Aerial+, have recently b…

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Jacob Mahon, Chenxi Hou, Zhihao Yao
https://arxiv.org/abs/2507.18075 https://

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting down-stream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. This paper introduces…

An analysis and cosmological study of the propagation of gravitational radiation under a graviton of nonzero mass
Margaret Johnston, Marios Kalomenopoulos, Carl-Johan Haster
https://arxiv.org/abs/2509.18358

Attenuation Models for Extensive Air Showers Derived from Simulations
Fiona Ellwanger (Institute for Astroparticle Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany), Darko Veberi\v{c} (Institute for Astroparticle Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany)
https://arxiv.org/abs/2508.16360

Attenuation Models for Extensive Air Showers Derived from Simulations
At ultra-high energies, the flux of cosmic rays is too low for direct measurements to be meaningful. When a cosmic ray enters the atmosphere, it initiates an extensive air shower, producing a cascade of secondary particles that propagate toward the ground. Large arrays of surface detectors are used to measure these secondary particles upon arrival. The signal detected at a specific reference distance from the shower core serves as a proxy for the shower size and, consequently, as a reliable e…

Bridging the gap: consistent modeling of protoplanetary disk heating and gap formation by planet-induced spiral shocks
Satoshi Okuzumi, Takayuki Muto, Ryosuke T. Tominaga, Shizu Shimizu
https://arxiv.org/abs/2507.18283

Bridging the gap: consistent modeling of protoplanetary disk heating and gap formation by planet-induced spiral shocks
A giant planet embedded in a protoplanetary disk excites spiral density waves, which steepen into shocks as they propagate away from the planet. These shocks lead to secular disk heating and gap opening, both of which can have important implications for the evolution of solids near the planet. To date, these two effects have largely been modeled independently. In this study, we present a self-consistent model that unifies these processes by linking shock heating and angular momentum deposition …

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
Lukas P\'eron, Paolo Calafiura, Xiangyang Ju, Jay Chan
https://arxiv.org/abs/2508.16518

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the TrackML open dataset. Using the Monte Carlo Dropout method, we measure the data and model uncertainties of the pipeline steps, study how they propagate down the pipeline, and how they are impacted by the training dataset's size, the input data's geometry and physical properties. We will show that for our case study, as the…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Xin Chen, Rui Huang, Longbin Tang, Lin Zhao
https://arxiv.org/abs/2509.17340 https:/…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Agile mapless navigation in cluttered 3D environments poses significant challenges for autonomous drones. Conventional mapping-planning-control pipelines incur high computational cost and propagate estimation errors. We present AERO-MPPI, a fully GPU-accelerated framework that unifies perception and planning through an anchor-guided ensemble of Model Predictive Path Integral (MPPI) optimizers. Specifically, we design a multi-resolution LiDAR point-cloud representation that rapidly extracts spat…

Testing the Constancy of Type Ia Supernova Luminosities with Gaussian Process
Akshay Rana
https://arxiv.org/abs/2509.19494 https://arxiv.org/pdf/2509.19494…

Testing the Constancy of Type Ia Supernova Luminosities with Gaussian Process
Type Ia supernovae (SNe~Ia) are central to studies of cosmic expansion, under the assumption that their absolute magnitude $M_B$ does not evolve with redshift. Even small drifts in brightness can bias cosmological parameters such as $H_0$ and $w$. Here we test this assumption using a non-parametric Gaussian Process (GP) reconstruction of the expansion history from cosmic chronometer $H(z)$ data, which provides a model-independent baseline distance modulus, $μ_{\rm GP}(z)$. To propagate uncerta…

SPP-MIM hybridization meta-film: a biosensing structure uniting the merits of SPR and LSPR
Chenjia He, Xiaqing Sun, Tian Yang
https://arxiv.org/abs/2509.16668 https://

SPP-MIM hybridization meta-film: a biosensing structure uniting the merits of SPR and LSPR
Having a flat device-solution interface is crucial for nanophotonic biosensors to achieve stable and reproducible performance, by mitigating solid-liquid-gas interfacial processes at the nanometer scale. In this aspect, the metal-insulator-metal (MIM) film presents a capable solution, by hybridizing surface plasmon polaritons (SPP) and MIM gap plasmons, which is enabled by the latter's unique dispersion characteristic and wide tunability. In this meta-film, the SPPs propagate along a flat inter…

Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models
Jianshu Zeng, Yuxuan Liu, Yutong Feng, Chenxuan Miao, Zixiang Gao, Jiwang Qu, Jianzhang Zhang, Bin Wang, Kun Yuan
https://arxiv.org/abs/2508.12945

Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models
Video relighting is a challenging yet valuable task, aiming to replace the background in videos while correspondingly adjusting the lighting in the foreground with harmonious blending. During translation, it is essential to preserve the original properties of the foreground, e.g., albedo, and propagate consistent relighting among temporal frames. In this paper, we propose Lumen, an end-to-end video relighting framework developed on large-scale video generative models, receiving flexible textual…

XAMT: Cross-Framework API Matching for Testing Deep Learning Libraries
Bin Duan, Ruican Dong, Naipeng Dong, Dan Dongseong Kim, Guowei Yang
https://arxiv.org/abs/2508.12546 https…

XAMT: Cross-Framework API Matching for Testing Deep Learning Libraries
Deep learning powers critical applications such as autonomous driving, healthcare, and finance, where the correctness of underlying libraries is essential. Bugs in widely used deep learning APIs can propagate to downstream systems, causing serious consequences. While existing fuzzing techniques detect bugs through intra-framework testing across hardware backends (CPU vs. GPU), they may miss bugs that manifest identically across backends and thus escape detection under these strategies. To addre…

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Ning Li, Kounianhua Du, Han Zhang, Quan Gan, Minjie Wang, David Wipf, Weinan Zhang
https://arxiv.org/abs/2508.08327

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inherent structure of relational databases hinders their ability to benefit from flourishing deep learning methods. Previous research has primarily focused on exploiting the unary dependency among multiple tables in a relational database using the primary key - foreign key relationships, either joining multiple tables in…

Epidemic amplification by correlated superspreading
Noah Silva de Leonardi, Benjamin D. Dalziel
https://arxiv.org/abs/2509.15338 https://arxiv.org/pdf/2509…

Epidemic amplification by correlated superspreading
Infectious pathogens often propagate by superspreading, which focusses onward transmission on disproportionately few infected individuals. At the same time, infector-infectee pairs tend to have more similar transmission potentials than expected by chance, as risk factors assort among individuals who frequently interact. A key problem for infectious disease epidemiology, and in the dynamics of complex systems, is to understand how structured variation in individual transmission will scale to imp…

Nonlinear dispersive waves in soft elastic laminates under finite magneto-deformations
Harold Berjamin, Stephan Rudykh
https://arxiv.org/abs/2508.06324 https://

Nonlinear dispersive waves in soft elastic laminates under finite magneto-deformations
Layered media can be used as acoustic filters, allowing only waves of certain frequencies to propagate. In soft magneto-active laminates, the shear wave band gaps (i.e., the frequency intervals for which shear waves cannot propagate) can be adjusted after fabrication by exploiting the magneto-elastic coupling. In the present study, the control of shear wave propagation in magneto-active stratified media is revisited by means of homogenisation theory, and extended to nonlinear waves of moderate …

Excitation-inhibition balance in cortical networks with heterogeneous cluster sizes and its applications
Abhijit Chakraborty, Greg Morrison
https://arxiv.org/abs/2508.12541 http…

Excitation-inhibition balance in cortical networks with heterogeneous cluster sizes and its applications
Insight into how information can propagate within cortical networks is essential for a more complete understanding of neural dynamics and computation in complex networks. Networks with clustered connections have previously been shown to give rise to correlated dynamics in individual clusters. However, this same model applied to a network with highly heterogeneous cluster sizes leads to a clear breakdown of the balanced state. In this article, using a formal definition of the balance matrix, we …

Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow
Kaustav Chatterjee, Sameer Nekkalapu, Sayak Mukherjee, Ramij Raja Hossain, Marcelo Elizondo
https://arxiv.org/abs/2508.11561

Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow
Sub- and super-synchronous control interactions (SSCIs) are oscillations arising from adverse interactions between inverter-based resource (IBR) controls and the power network. SSCIs often involve multiple frequencies and propagate through complex, interconnected paths, making it difficult for model-based approaches to identify both the sources and the paths of oscillatory energy flow. This paper extends the Dissipative Energy Flow (DEF) method, originally developed for low-frequency electromec…

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Minhao Wang, Yunhang He, Cong Xu, Zhangchi Zhu, Wei Zhang
https://arxiv.org/abs/2508.10312

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate through LLM backbones layer by layer, as opposed to tradition…

Global propagation of singularities for magnetic mechanical systems
Piermarco Cannarsa, Wei Cheng, Jiahui Hong, Wenxue Wei
https://arxiv.org/abs/2509.13433 https://

Global propagation of singularities for magnetic mechanical systems
We prove that singularities propagate globally for viscosity solutions of Hamilton-Jacobi equations related to magnetic mechanical systems on closed Riemannian manifolds. Our main result shows that for any weak KAM solution $u$, the singular set $\text{Sing}\,(u)$ remains invariant under the generalized gradient flow dynamics. The proof combines three key elements: (1) reduction from magnetic to Riemannian systems, (2) analysis of reparameterized flows, and (3) regularization techniques. Compar…

A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
Curt Stutsman, Eliot W. Robson, Abhishek K. Umrawal
https://arxiv.org/abs/2509.12822 https://

A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate to others. In this paper, we propose a novel diffusion model -- the Pressure Threshold model (PT) -- for dynamically simulating the spread of influence through a social network. This new model extends the popular Linear Threshold Model (LT) by adjusting a node's outgoing influence proportional to the influence it receives from its activated neig…

Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration
Lucas Marques Sneller
https://arxiv.org/abs/2509.11970 https://

Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration
We study how sentiment shocks propagate through equity returns and investor clientele using four independent proxies with sign-aligned kappa-rho parameters. A structural calibration links a one standard deviation innovation in sentiment to a pricing impact of 1.06 basis points with persistence parameter rho = 0.940, yielding a half-life of 11.2 months. The impulse response peaks around the 12-month horizon, indicating slow-moving amplification. Cross-sectionally, a simple D10-D1 portfolio earns…

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Davide Casnici, Charlotte Frenkel, Justin Dauwels
https://arxiv.org/abs/2508.08762 https://

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Backpropagation (BP) of errors is the backbone training algorithm for artificial neural networks (ANNs). It updates network weights through gradient descent to minimize a loss function representing the mismatch between predictions and desired outputs. BP uses the chain rule to propagate the loss gradient backward through the network hierarchy, allowing efficient weight updates. However, this process requires weight updates at every layer to rely on a global error signal generated at the network…

SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Zachary E. Ross, John D. Wilding, Kamyar Azizzadenesheli, Aitaro Kato
https://arxiv.org/abs/2508.12117

SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Seismicity catalogs are larger than ever due to an explosion of techniques for enhanced earthquake detection and an abundance of high-quality datasets. Bayesian inference is a popular framework for locating earthquakes due to its ability to propagate and quantify uncertainty into the inversion results, but traditional methods do not scale well to high-dimensional parameter spaces, making them unsuitable for double-difference relocation where the number of parameters can reach the millions. Here…

Remote Interference Mitigation through Null Precoding and Fractional Programming
Xuyang Sun, Hussein A. Ammar, Israfil Bahceci, Raviraj Adve, Gary Boudreau, Zehua Li
https://arxiv.org/abs/2510.09989

Remote Interference Mitigation through Null Precoding and Fractional Programming
With the rapid deployment of 5G systems, remote interference (RI) caused by atmospheric ducting has emerged as an occasional, but critical challenge. This phenomenon occurs when the downlink (DL) signals from distant base stations (BSs) propagate over long distances through tropospheric ducting, severely disrupting uplink (UL) reception at local BSs. To address this challenge, we analyze the effect of RI on network performance, including the channel estimation phase. We then develop a solution …

Friend or Foe? Identifying Anomalous Peers in Moneros P2P Network
Yannik Kopyciok, Stefan Schmid, Friedhelm Victor
https://arxiv.org/abs/2509.10214 https://

Friend or Foe? Identifying Anomalous Peers in Moneros P2P Network
Monero, the leading privacy-focused cryptocurrency, relies on a peer-to-peer (P2P) network to propagate transactions and blocks. Growing evidence suggests that non-standard nodes exist in the network, posing as honest nodes but are perhaps intended for monitoring the network and spying on other nodes. However, our understanding of the detection and analysis of anomalous peer behavior remains limited. This paper presents a first comprehensive study of anomalous behavior in Monero's P2P network. …

Shock wave bending around a dusty plasma void
Sachin Sharma, Rauoof Wani, Prabhakar Srivastav, Meenakshee Sharma, Sayak Bose, Sanat Tiwari, Abhijit Sen
https://arxiv.org/abs/2509.12397

Shock wave bending around a dusty plasma void
We report on experimental observations of the bending of a dust acoustic shock wave around a dust void region. This phenomenon occurs as a planar shock wavefront encounters a compressible obstacle in the form of a void whose size is larger than the wavelength of the wave. As they collide, the central portion of the wavefront, that is the first to touch the void, is blocked while the rest of the front continues to propagate, resulting in an inward bending of the shock wave. The bent shock wave e…

RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Lang Qin, Zijian Gan, Xu Cao, Pengcheng Jiang, Yankai Jiang, Jiawei Han, Kaishun Wu, Jintai Chen
https://arxiv.org/abs/2510.05764

RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in w…

Perturbative Hyperboloidal Extraction of Gravitational Waves in 3 1 Numerical Relativity
Sebastiano Bernuzzi, Joan Fontbut\'e, Simone Albanesi, Anil Zengino\u{g}lu
https://arxiv.org/abs/2508.05743 …

Perturbative Hyperboloidal Extraction of Gravitational Waves in 3+1 Numerical Relativity
We present a framework to propagate to null infinity gravitational waves computed at timelike worldtubes in the interior of a 3+1 (Cauchy) numerical relativity simulations. In our method, numerical relativity data are used as the inner inflowing boundary of a perturbative time-domain Regge-Wheeler-Zerilli simulation in hyperboloidal coordinates that reaches null infinity. We showcase waveforms from (3+1)D simulations of gravitational collapse of rotating neutron stars, binary black holes merger…

Time-diffracting 2D wave vortices
Boris A. Khanikati, Konstantin Y. Bliokh
https://arxiv.org/abs/2510.10147 https://arxiv.org/pdf/2510.10147

Time-diffracting 2D wave vortices
Wave vortices constitute a large family of wave entities, closely related to phase singularities and orbital angular momentum (OAM). So far, two main classes of localized wave vortices have been explored: (i) transversely-localized monochromatic vortex beams that carry well-defined longitudinal OAM and propagate/diffract in space, and (ii) 2D-localized spatiotemporal vortex pulses that carry the more elusive transverse (or tilted) OAM and propagate/diffract in both space and time. Here we intro…

GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Search
Heng Zhang, Yuling Shi, Xiaodong Gu, Haochen You, Zijian Zhang, Lubin Gan, Yilei Yuan, Jin Huang
https://arxiv.org/abs/2510.10581

GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Search
Multi-agent systems powered by Large Language Models excel at complex tasks through coordinated collaboration, yet they face high failure rates in multi-turn deep search scenarios. Existing temporal attribution methods struggle to accurately diagnose root causes, particularly when errors propagate across multiple agents. Attempts to automate failure attribution by analyzing action sequences remain ineffective due to their inability to account for information dependencies that span agents. This …

Segmentation and Tracking of Eruptive Solar Phenomena with Convolutional Neural Networks
Oleg Stepanyuk, Kamen Kozarev
https://arxiv.org/abs/2509.05623 https://

Segmentation and Tracking of Eruptive Solar Phenomena with Convolutional Neural Networks
Solar eruptive events are complex phenomena, which most often include coronal mass ejections (CME), CME-driven compressive and shock waves, flares, and filament eruptions. CMEs are large eruptions of magnetized plasma from the Sun's outer atmosphere or corona, that propagate outward into the interplanetary space. Over the last several decades a large amount of remote solar eruption observational data has become available from ground-based and space-borne instruments. This has recently required …

Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems
Varsha Sen, Biswash Basnet
https://arxiv.org/abs/2508.10035 https:…

Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems
False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers view them as an attractive entry point to manipulate aggregated demand patterns, which can ultimately propagate and disrupt broader grid operations. These attacks undermine the integrity of smart met…

Conditional Residual Coding with Explicit-Implicit Temporal Buffering for Learned Video Compression
Yi-Hsin Chen, Kuan-Wei Ho, Martin Benjak, J\"orn Ostermann, Wen-Hsiao Peng
https://arxiv.org/abs/2508.01818

Conditional Residual Coding with Explicit-Implicit Temporal Buffering for Learned Video Compression
This work proposes a hybrid, explicit-implicit temporal buffering scheme for conditional residual video coding. Recent conditional coding methods propagate implicit temporal information for inter-frame coding, demonstrating superior coding performance to those relying exclusively on previously decoded frames (i.e. the explicit temporal information). However, these methods require substantial memory to store a large number of implicit features. This work presents a hybrid buffering strategy. For…

PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows
Renan Souza, Amal Gueroudji, Stephen DeWitt, Daniel Rosendo, Tirthankar Ghosal, Robert Ross, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2508.02866

PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows
Foundation models, such as Large Language Models (LLMs), are increasingly used as core components of AI agents in complex, large-scale workflows across federated and heterogeneous environments. In agentic workflows, autonomous agents plan tasks, interact with humans and peers, and shape scientific outcomes. This makes transparency, traceability, reproducibility, and reliability essential. However, AI-based agents can hallucinate or reason incorrectly, and their decisions may propagate errors th…

Daily fluctuations propagate damply through a black hole accretion disk
Han He, Yi Long, Bei You, Fu-Guo Xie, Zhen Yan, Andrzej A. Zdziarski, Sai-En Xu
https://arxiv.org/abs/2508.01384

Daily fluctuations propagate damply through a black hole accretion disk
Propagating fluctuations within accretion disks are known to induce multi-wavelength variability across diverse timescales. While these fluctuations have been widely invoked to explain rapid timing phenomena within the inner disk region in the frequency domain, observational signatures of outer-disk fluctuations propagating in the time domain remain sparse. Here, we present an analysis of observations by the Hard X-ray Modulation Telescope (HXMT) during the 2023 outburst of the newly discovered…

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection
Victor Livernoche, Akshatha Arodi, Andreea Musulan, Zachary Yang, Adam Salvail, Ga\'etan Marceau Caron, Jean-Fran\c{c}ois Godbout, Reihaneh Rabbany
https://arxiv.org/abs/2509.09495

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection
Deepfakes, synthetic media created using advanced AI techniques, have intensified the spread of misinformation, particularly in politically sensitive contexts. Existing deepfake detection datasets are often limited, relying on outdated generation methods, low realism, or single-face imagery, restricting the effectiveness for general synthetic image detection. By analyzing social media posts, we identify multiple modalities through which deepfakes propagate misinformation. Furthermore, our human…

Multi-Agent Taskforce Collaboration: Self-Correction of Compounding Errors in Long-Form Literature Review Generation
Zhi Zhang, Yan Liu, Zhejing Hu, Gong Chen, Sheng-hua Zhong, Jiannong Cao
https://arxiv.org/abs/2508.04306

Multi-Agent Taskforce Collaboration: Self-Correction of Compounding Errors in Long-Form Literature Review Generation
Literature reviews play an important role in scientific research. Recent advances in large language models (LLMs) have boosted the development of automated systems for the entire literature review workflow, from retrieval to manuscript drafting. However, a key challenge is that mistakes made in early stages can propagate and amplify in subsequent steps, leading to compounding errors that undermine the faithfulness of the final review. To tackle this issue, we propose the Multi-Agent Taskforce C…

Real-Time Defense Against Coordinated Cyber-Physical Attacks: A Robust Constrained Reinforcement Learning Approach
Saman Mazaheri Khamaneh, Tong Wu, Wei Sun, Cong Chen
https://arxiv.org/abs/2509.10999 …

Real-Time Defense Against Coordinated Cyber-Physical Attacks: A Robust Constrained Reinforcement Learning Approach
Modern power systems face increasing vulnerability to sophisticated cyber-physical attacks beyond traditional N-1 contingency frameworks. Existing security paradigms face a critical bottleneck: efficiently identifying worst-case scenarios and rapidly coordinating defensive responses are hindered by intensive computation and time delays, during which cascading failures can propagate. This paper presents a novel tri-level robust constrained reinforcement learning (RCRL) framework for robust power…

A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks
Sahab Zandi, Kamesh Korangi, Juan C. Moreno-Paredes, Mar\'ia \'Oskarsd\'ottir, Christophe Mues, Cristi\'an Bravo
https://arxiv.org/abs/2510.09407

A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks
Small and Medium-sized Enterprises (SMEs) are known to play a vital role in economic growth, employment, and innovation. However, they tend to face significant challenges in accessing credit due to limited financial histories, collateral constraints, and exposure to macroeconomic shocks. These challenges make an accurate credit risk assessment by lenders crucial, particularly since SMEs frequently operate within interconnected firm networks through which default risk can propagate. This paper p…

Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects
Tatsuru Kikuchi
https://arxiv.org/abs/2508.06594 http…

Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects
This paper introduces a novel framework for causal inference in spatial economics that explicitly models the stochastic transition from partial to general equilibrium effects. We develop a Denoising Diffusion Probabilistic Model (DDPM) integrated with boundary detection methods from stochastic process theory to identify when and how treatment effects propagate beyond local markets. Our approach treats the evolution of spatial spillovers as a Lévy process with jump-diffusion dynamics, where the…

Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Joachim Baumann, Paul R\"ottger, Aleksandra Urman, Albert Wendsj\"o, Flor Miriam Plaza-del-Arco, Johannes B. Gruber, Dirk Hovy
https://arxiv.org/abs/2509.08825

Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Large language models (LLMs) are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outputs vary significantly depending on the implementation choices made by researchers (e.g., model selection, prompting strategy, or temperature settings). Such variation can introduce systematic biases and random errors, which propagate to downstream analyses and cause Type I, Type II, Type S, or Type M errors. W…

Variational mode decomposition analysis of the relationship between low-frequency shock-wave oscillations and buffet cells
Yuya Ohmichi, Yosuke Sugioka
https://arxiv.org/abs/2508.04250

Variational mode decomposition analysis of the relationship between low-frequency shock-wave oscillations and buffet cells
This study investigates the relationship between low-frequency shock-wave oscillations and buffet cells on the main wing of the NASA common research model. The flow conditions were set at a Mach number of 0.85, Reynolds number of 2.27 $\times$ 10$^6$, and angle of attack of 4.82$^{\circ}$. Buffet cells are cellular patterns with spanwise periodicity that propagate toward the wing tip, with nondimensional frequencies (Strouhal numbers) of 0.2--0.6, higher than those of shock oscillations associa…

Models of topological barriers and molecular motors of bacterial DNA
Marc Joyeux
https://arxiv.org/abs/2510.05790 https://arxiv.org/pdf/2510.05790

Models of topological barriers and molecular motors of bacterial DNA
Bacterial genomes are partitioned into kilobases long domains that are topologically independent from each other, meaning that change of DNA superhelicity in one domain does not propagate to neighbours. This is made possible by proteins like the LacI repressor, which behave like topological barriers and block the diffusion of torsion along the DNA. Other proteins, like DNA gyrases and RNA polymerases, called molecular motors, use the energy released by the hydrolysis of ATP to apply forces and/…

Remarks on propagation of discontinuities in stationary radiative transfer
Daisuke Kawagoe
https://arxiv.org/abs/2510.12130 https://arxiv.org/pdf/2510.1213…

Remarks on propagation of discontinuities in stationary radiative transfer
We consider the stationary transport equation with the incoming boundary condition. We are interested in discontinuities of the solution. Under the generalized convexity condition, it is known that it has only boundary-induced discontinuities, which are discontinuities arising from discontinuous boundary data, they propagate along positive characteristic lines, and we can reconstruct the attenuation coefficient from boundary measurements by the inverse X-ray transform. In this article, we obser…

Benchmarking Diarization Models
Luca A. Lanzend\"orfer, Florian Gr\"otschla, Cesare Blaser, Roger Wattenhofer
https://arxiv.org/abs/2509.26177 https://

Benchmarking Diarization Models
Speaker diarization is the task of partitioning audio into segments according to speaker identity, answering the question of "who spoke when" in multi-speaker conversation recordings. While diarization is an essential task for many downstream applications, it remains an unsolved problem. Errors in diarization propagate to downstream systems and cause wide-ranging failures. To this end, we examine exact failure modes by evaluating five state-of-the-art diarization models, across four diarization…

Path-Based Correlation Analysis of Meteorological Factors and eLoran Signal Delay Variations
Junwoo Song, Pyo-woong Son
https://arxiv.org/abs/2509.26020 https://

Path-Based Correlation Analysis of Meteorological Factors and eLoran Signal Delay Variations
Unlike GNSS, which is vulnerable to jamming and spoofing due to its inherently weak received power, eLoran exhibits robustness owing to its high field strength. Therefore, the eLoran system can maintain reliable operation even in scenarios where GNSS becomes unavailable. However, since eLoran signals propagate through ground waves, the propagation delay is susceptible to changes in surface conditions, including both terrain and meteorological variations. This study aims to analyze the correlati…

Higher-spins on Taub-NUT and higher-spin Taub-NUT
Evgeny Skvortsov, Yihao Yin
https://arxiv.org/abs/2508.18804 https://arxiv.org/pdf/2508.18804

Higher-spins on Taub-NUT and higher-spin Taub-NUT
We consider higher-spin extensions of self-dual Yang-Mills (HS-SDYM) and self-dual Gravity (HS-SDGR), which are also truncations of chiral higher-spin gravity. Higher-spin fields can consistently propagate on gravitational instantons and we construct solutions to the higher-spin equations on the Taub-NUT background. We show that these solutions remain exact solutions of HS-SDYM. For HS-SDGR the perturbation theory converges for any given spin to give a higher-spin generalization of Taub-NUT and…

Error Detection and Correction for Interpretable Mathematics in Large Language Models
Yijin Yang, Cristina Cornelio, Mario Leiva, Paulo Shakarian
https://arxiv.org/abs/2508.03500

Error Detection and Correction for Interpretable Mathematics in Large Language Models
Recent large language models (LLMs) have demonstrated the ability to perform explicit multi-step reasoning such as chain-of-thought prompting. However, their intermediate steps often contain errors that can propagate leading to inaccurate final predictions. Additionally, LLMs still struggle with hallucinations and often fail to adhere to prescribed output formats, which is particularly problematic for tasks like generating mathematical expressions or source code. This work introduces EDCIM (Err…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
Daniele Lanzoni, Olivier Pierre-Louis, Roberto Bergamaschini, Francesco Montalenti
https://arxiv.org/abs/2507.21763

Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. Specifically, we showcase the application to a two-dimensional, many-particle system, focusing on surface-step fluctuations and on the related time-dependent roughness. After the construction of a dataset based on Kinetic Monte Carlo simulations, a conditional GAN is trained to propagate stochastically the state of the…

Perception-Consistency Multimodal Large Language Models Reasoning via Caption-Regularized Policy Optimization
Songjun Tu, Qichao Zhang, Jingbo Sun, Yuqian Fu, Linjing Li, Xiangyuan Lan, Dongmei Jiang, Yaowei Wang, Dongbin Zhao
https://arxiv.org/abs/2509.21854

Perception-Consistency Multimodal Large Language Models Reasoning via Caption-Regularized Policy Optimization
While multimodal large language models excel at tasks that integrate visual perception with symbolic reasoning, their performance is often undermined by a critical vulnerability: perception-induced errors that propagate through the reasoning chain. Current reinforcement learning (RL) fine-tuning methods, while enhancing reasoning abilities, largely fail to address the underlying misalignment between visual grounding and the subsequent reasoning process. To address this challenge, we propose \te…

CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems
Yifan Yu, Moyan Li, Shaoyuan Xu, Jinmiao Fu, Xinhai Hou, Fan Lai, Bryan Wang
https://arxiv.org/abs/2509.24088

CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems
Multi-agent systems (MAS) are increasingly capable of tackling complex real-world tasks, yet their reliance on inter-agent coordination, tool use, and long-horizon reasoning makes error recognition particularly challenging. Minor errors can propagate across agents, escalating into task failures while producing long, intertwined execution trajectories that impose significant costs for both human developers and automated systems to debug and analyze. Our key insight is that, despite surface diffe…

An automated probabilistic asteroid prediscovery pipeline
Sage Li, Alex Geringer-Sameth, Nathan Golovich
https://arxiv.org/abs/2510.07588 https://arxiv.org…

An automated probabilistic asteroid prediscovery pipeline
We present an automated and probabilistic method to make prediscovery detections of near-Earth asteroids (NEAs) in archival survey images, with the goal of reducing orbital uncertainty immediately after discovery. We refit Minor Planet Center astrometry and propagate the full six-parameter covariance to survey epochs to define search regions. We build low-threshold source catalogs for viable images and evaluate every detected source in a search region as a candidate prediscovery. We eliminate f…

Small-time asymptotics and the emergence of complex singularities for the KdV equation
Scott W. McCue, Christopher J. Lustri, Daniel J. VandenHeuvel, Jocelyn Zhang, John R. King, S. Jonathan Chapman
https://arxiv.org/abs/2509.07397

Small-time asymptotics and the emergence of complex singularities for the KdV equation
While real-valued solutions of the Korteweg--de Vries (KdV) equation have been studied extensively over the past 50 years, much less attention has been devoted to solution behaviour in the complex plane. Here we consider the analytic continuation of real solutions of KdV and investigate the role that complex-plane singularities play in early-time solutions on the real line. We apply techniques of exponential asymptotics to derive the small-time behaviour for dispersive waves that propagate in o…

HyTIP: Hybrid Temporal Information Propagation for Masked Conditional Residual Video Coding
Yi-Hsin Chen, Yi-Chen Yao, Kuan-Wei Ho, Chun-Hung Wu, Huu-Tai Phung, Martin Benjak, J\"orn Ostermann, Wen-Hsiao Peng
https://arxiv.org/abs/2508.02072

HyTIP: Hybrid Temporal Information Propagation for Masked Conditional Residual Video Coding
Most frame-based learned video codecs can be interpreted as recurrent neural networks (RNNs) propagating reference information along the temporal dimension. This work revisits the limitations of the current approaches from an RNN perspective. The output-recurrence methods, which propagate decoded frames, are intuitive but impose dual constraints on the output decoded frames, leading to suboptimal rate-distortion performance. In contrast, the hidden-to-hidden connection approaches, which propaga…

Formation of axially modulated plasma strings by filamentation of interfering femtosecond Bessel beams
Fatemeh Mansourimanesh, Amirreza Sadeghpour, Daryoush Abdollahpour
https://arxiv.org/abs/2508.10163

Formation of axially modulated plasma strings by filamentation of interfering femtosecond Bessel beams
We numerically investigate the formation of axially modulated plasma strings through the filamentation of two interfering femtosecond Bessel beams. The constituent Bessel beams have different central spot sizes and propagate collinearly. Our results show that filamentation of these beams in air leads to the formation of intense axially modulated light filaments, and corrugated plasma strings with high modulation depths and tunable modulation periods. We find that the modulation periods of the i…

Cosmic Ray Spatial Distribution and the Galactic/Extragalactic Transition
Paolo Lipari, Silvia Vernetto
https://arxiv.org/abs/2509.09028 https://arxiv.org/…

Cosmic Ray Spatial Distribution and the Galactic/Extragalactic Transition
Determining the spatial distribution of Galactic cosmic rays (CRs) is fundamental to understand how these particles propagate in interstellar space and to infer their source spectra. The most sensitive method of studying this problem is observing the gamma--ray and neutrino diffuse fluxes produced in the inelastic interactions of CR protons and nuclei with interstellar gas that encode the energy and spatial distributions of the interacting particles. Most theoretical models assume that the spat…

Search for Past Stellar Encounters and the Origin of 3I/ATLAS
Yiyang Guo, Luyao Zhang, Fabo Feng, Zhao-Yu Li, Anton Pomazan, Xiaohu Yang
https://arxiv.org/abs/2509.03361 https:/…

Search for Past Stellar Encounters and the Origin of 3I/ATLAS
3I/ATLAS, the third discovered interstellar object, has a heliocentric speed of 58 km/s and exhibits cometary activity. To constrain the origin of 3I/ATLAS and its past dynamical evolution, we propagate the orbits of 3I/ATLAS and nearby stars to search for stellar encounters. Integrating orbits in the Galactic potential and propagating the astrometric and radial-velocity uncertainties of 30 million Gaia stars, we identify 25 encounters with median encounter distances less than 1 pc. However, be…

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Osama Mohammed, Jiaxin Pan, Mojtaba Nayyeri, Daniel Hern\'andez, Steffen Staab
https://arxiv.org/abs/2508.03251

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles accelerate, brake, and change lanes relative to one another over consecutive frames. Likewise, detecting financial fraud hinges on following the flow of funds through successive transactions as they propagate through the network. Unlike classic time-series forecasting, these settings demand reasoning over who interacts…

CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series
Rahul Biswas, SuryaNarayana Sripada, Somabha Mukherjee, Reza Abbasi-Asl
https://arxiv.org/abs/2508.01920

CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series
Understanding how signals propagate through neural circuits is central to deciphering brain computation. While functional connectivity captures statistical associations, it does not reveal directionality or causal mechanisms. We introduce CITS (Causal Inference in Time Series), a non-parametric method for inferring statistically causal neural circuitry from high-resolution time series data. CITS models neural dynamics using a structural causal model with arbitrary Markov order and tests for tim…

Restoration of the Lorentz symmetry of particle propagator in the ghost condensate model
Suppanat Supanyo, Pitayuth Wongjun, Lunchakorn Tannukij
https://arxiv.org/abs/2509.03882

Restoration of the Lorentz symmetry of particle propagator in the ghost condensate model
The ghost condensate model has been proposed to protect the vacuum state of the cosmological phantom model yielding the gradient instability regime and the spontaneous Lorentz symmetry breaking in the quantum level with the dispersion relation $ω^2\sim k^4$. In this paper, we point out that the Lorentz symmetry in the propagator can be restored if the explicit Lorentz symmetry-breaking process is promoted. The particle excitation of the phantom field can propagate through spacetime with the Lo…

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Hyun-kyu Ko, Youbin Kim, Jihyeon Park, Dongheok Park, Gyeongjin Kang, Wonjun Cho, Hyung Yi, Eunbyung Park
https://arxiv.org/abs/2510.00862

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
State Space Models (SSMs)-most notably RNNs-have historically played a central role in sequential modeling. Although attention mechanisms such as Transformers have since dominated due to their ability to model global context, their quadratic complexity and limited scalability make them less suited for long sequences. Video super-resolution (VSR) methods have traditionally relied on recurrent architectures to propagate features across frames. However, such approaches suffer from well-known issue…

Human Mobility in Epidemic Modeling
Xin Lu, Jiawei Feng, Shengjie Lai, Petter Holme, Shuo Liu, Zhanwei Du, Xiaoqian Yuan, Siqing Wang, Yunxuan Li, Xiaoyu Zhang, Yuan Bai, Xiaojun Duan, Wenjun Mei, Hongjie Yu, Suoyi Tan, Fredrik Liljeros
https://arxiv.org/abs/2507.22799

Human Mobility in Epidemic Modeling
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in da…

On the fate of travelling waves at the boundary of quantum droplets
Angel Paredes, Jose Guerra-Carmenate, Humberto Michinel
https://arxiv.org/abs/2507.21376 https://

On the fate of travelling waves at the boundary of quantum droplets
We analyze quantum droplets formed in a two-dimensional symmetric mixture of Bose-Einstein condensed atoms. For sufficiently large atom numbers, these droplets exhibit a flat-top density profile with sharp boundaries governed by surface tension. Within the bulk of the droplet, traveling matter waves - localized density dips - can propagate at constant velocity while maintaining their shape. Using numerical simulations and qualitative analysis, we investigate the rich phenomenology that arises w…

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Yiming Du, Yifan Xiang, Bin Liang, Dahua Lin, Kam-Fai Wong, Fei Tan
https://arxiv.org/abs/2508.19996

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early turns can propagate across subsequent turns, undermining coherence and response quality. Existing methods typically address data quality via static prefiltering, which decouples quality control from training and fails to mitigate turn-level error propagation. In this context, we propose ReSURE (Regularizing Supervisio…

Propagation and Energy Dissipation of Shock Waves in the Solar Chromosphere
Ravi Chaurasiya, 1 Ankala Raja Bayanna, 1 Robertus Erd\'elyi
https://arxiv.org/abs/2510.00724 htt…

Propagation and Energy Dissipation of Shock Waves in the Solar Chromosphere
The solar atmosphere is permeated by various types of waves that originate from subsurface convection. As these waves propagate upward, they encounter they encounter a steep decrease in the density of the medium, leading to their steepening into shock waves. These shock waves typically exhibit a characteristic sawtooth pattern in wavelength-time ($λ$-t) plots of various chromospheric spectral lines, viz., H$α$, Ca II 8542 Å to name a few. In this study, we investigate the propagation of shoc…

Real Time Fatigue Crack Growth Monitoring Using High Precision Control and Data Acquisition Systems
Arev Hambardzumyan, Rafayel Ghasabyan
https://arxiv.org/abs/2509.23710 https:…

Real Time Fatigue Crack Growth Monitoring Using High Precision Control and Data Acquisition Systems
Fatigue cracks may initiate and propagate long before a structural component reaches the end of its nominal life. Detecting and quantifying crack growth in real time is critical for avoiding catastrophic failures in aerospace structures, nuclear reactors and civil infrastructure. This research reviews four widely used crack growth monitoring techniques: digital image correlation (DIC), acoustic emission (AE), compliance based methods and direct current potential drop (DCPD). The research evalua…

Unified understanding of Alfv\'enic superradiance and the Blandford-Znajek process in a force-free black hole magnetosphere
Sousuke Noda, Masaaki Takahashi
https://arxiv.org/abs/2508.04771

Unified understanding of Alfvénic superradiance and the Blandford-Znajek process in a force-free black hole magnetosphere
We investigate the extraction of energy from a Kerr black hole via Alfvén waves (i.e., Alfvénic superradiance) in a force-free magnetosphere, in which the plasma inertia effects are ignored. We analyze the Poynting flux generated by Alfvén waves that propagate toward the event horizon across the inner light surface, the causal boundary for the waves. We find the relationship between the energy flux associated with Alfvén waves and that of the Blandford-Znajek (BZ) process. That is, both mec…

Global Existence, Hamiltonian Conservation and Vanishing Viscosity for the Surface Quasi-Geostrophic Equation
Luigi De Rosa, Micka\"el Latocca, Jaemin Park
https://arxiv.org/abs/2509.01268

Global Existence, Hamiltonian Conservation and Vanishing Viscosity for the Surface Quasi-Geostrophic Equation
For any initial datum $θ_0\in L^{\frac{4}{3}}_x$ it is proved the existence of a global-in-time weak solution $θ\in L^\infty_t L^{\frac43}_x$ to the surface quasi-geostrophic equation whose Hamiltonian, i.e. the $\dot{H}^{-\frac{1}{2}}_x$ norm, is constant in time. The solution is obtained as a vanishing viscosity limit. Outside the classical strong compactness setting, the main idea is to propagate in time the non-concentration of the $L^{\frac{4}{3}}_x$ norm of the initial data, from which …

Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
Dingzirui Wang, Xuanliang Zhang, Keyan Xu, Qingfu Zhu, Wanxiang Che, Yang Deng
https://arxiv.org/abs/2509.21284

Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
Existing research indicates that the output of Chain-of-Thought (CoT) is significantly affected by input perturbations. Although many methods aim to mitigate such impact by optimizing prompts, a theoretical explanation of how these perturbations influence CoT outputs remains an open area of research. This gap limits our in-depth understanding of how input perturbations propagate during the reasoning process and hinders further improvements in prompt optimization methods. Therefore, in this pape…

Tight-binding photonics
Jing Li, Aodong Li, Yutao Chen, Tao Xiao, Renwen Huang, Xiaolu Zhuo, Jun Guan, Zhen Gao, Peng Zhan, Minghui Lu, Biye Xie
https://arxiv.org/abs/2508.04465

Tight-binding photonics
Photonics, dealing with the generation, manipulation, and detection of photons in various systems, lays the foundation of many advanced technologies. A key task of photonics is to know how photons propagate in complex media such as periodic and aperiodic photonic crystals. The conventional wisdom is to numerically solve the Maxwell equations either by dedicated numerical techniques or brute-force finite-element calculations. Recently, the strict analogy between photonic crystals and theoretical…

Earthquake body wave extraction using sparsity-promoting polarization filtering in the time-frequency domain
Hamzeh Mohammadigheymasi, Bahare Imanibadrbani, Ali Gholami, Ahmad Sadidkhouy
https://arxiv.org/abs/2508.20542

Earthquake body wave extraction using sparsity-promoting polarization filtering in the time-frequency domain
Seismic waves generated by earthquakes consist of multiple phases that carry critical information about Earth's internal structure as they propagate through heterogeneous media. These phases provide constraints from different regions of the Earth, such as the crust, mantle, and even the cores. The choice of phase depends on the study target and scientific objective: surface waves are suited for imaging shallow structures, whereas body waves yield higher-resolution information at depth. A key ch…

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#neurodivergent #neurospicy #adhd #AdultADHD #AuDHD #autistic
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Steady state of fast-oscillating neutrinos in an inhomogeneous medium
Manuel Goimil-Garc\'ia, Irene Tamborra
https://arxiv.org/abs/2509.22805 https://a…

Steady state of fast-oscillating neutrinos in an inhomogeneous medium
The streaming of neutrinos in an inhomogeneous medium is known to affect the physics of flavor conversion. We employ an ensemble of single-crossed angular distributions and explore the physics of flavor conversion, while neutrinos propagate across a one-dimensional inhomogeneous medium. The advective term in the neutrino equations of motion is responsible for the cascade of flavor waves towards ever smaller spatial and angular scales. However, as the system evolves, perturbations with large wav…

Coherent perfect absorption and lasing in bimodal Fabry-P\'erot interferometers
Dmitry A. Bykov, Evgeni A. Bezus, Leonid L. Doskolovich
https://arxiv.org/abs/2509.01356 http…

Coherent perfect absorption and lasing in bimodal Fabry-Pérot interferometers
Bimodal Fabry-Pérot interferometer is a model generalizing the conventional Fabry-Pérot interferometer, in which not one but two kinds of waves propagate between the interfaces. Here, we study coherent perfect absorption (CPA) and lasing at threshold in bimodal Fabry-Pérot interferometers. We show that CPA and lasing appear only in certain "allowed" regions in the parameter space, which, as we demonstrate, are described by closed-form inequalities imposed on the elements of the scattering ma…

Observations of high-frequency spectral peaks from in-situ waves in ice data: evidence for nonlinear waves in ice triad interactions?
Jean Rabault, Joey Voermans, Takehiko Nose, Graig Sutherland, Alexander Babanin, Takuji Waseda, Tsubasa Kodaira, Atle Jensen, Lars Willas Dreyer, {\O}yvind Breivik, Gaute Hope, Malte M\"uller, Zhaohui Cheng, Lichuan Wu, Aleksey Marchenko, Petra Heil, Karsten Trulsen

Observations of high-frequency spectral peaks from in-situ waves in ice data: evidence for nonlinear waves in ice triad interactions?
The propagation of waves through the marginal ice zone (MIZ) and deeper into pack ice is a key phenomenon that influences the breakup and drift of sea ice. When waves in ice propagate through a solid, non-cracked, thick enough sea ice cover, significant flexural elastic effects can be present in the dispersion relation. This results in a dispersion relation that opens up for 3-wave interactions, also known as wave triads. Here, we report the observation of high-frequency spectral peaks in the…

Momentum-mass normalized dark-bright solitons to one dimensional Gross-Pitaevskii systems
Salvador L\'opez-Mart\'inez
https://arxiv.org/abs/2508.19216 https://

Momentum-mass normalized dark-bright solitons to one dimensional Gross-Pitaevskii systems
We rigorously establish the existence of dark-bright solitons as traveling wave solutions to a one dimensional defocusing Gross-Pitaevskii system, a widely used model for describing mixtures of Bose-Einstein condensates and nonlinear optical systems. These solitons are shown to exhibit symmetry and radial monotonicity in modulus, and to propagate at subsonic speed. Our method relies on minimizing an energy functional subject to two constraints: the mass of the bright component and a modified mo…

Cosmic Rays Masquerading as Cool Cores: An Inverse-Compton Origin for Cool Core Cluster Emission
Philip F. Hopkins, Eliot Quataert, Emily M. Silich, Jack Sayers, Sam B. Ponnada, Isabel S. Sands
https://arxiv.org/abs/2507.18712

Cosmic Rays Masquerading as Cool Cores: An Inverse-Compton Origin for Cool Core Cluster Emission
X-ray bright cool-core (CC) clusters contain luminous radio sources accelerating cosmic ray (CR) leptons at prodigious rates. Near the acceleration region, high-energy leptons produce synchrotron (mini)halos and sometimes observable gamma rays, but these leptons have short lifetimes and so cannot propagate far from sources without some rejuvenation. However, low-energy (~0.1-1 GeV) CRs should survive for >Gyr, potentially reaching ~100 kpc before losing energy via inverse-Compton (IC) scatterin…