Tootfinder

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning
Mengyuan Liu, Xinshun Wang, Zhongbin Fang, Deheng Ye, Xia Li, Tao Tang, Songtao Wu, Xiangtai Li, Ming-Hsuan Yang
https://arxiv.org/abs/2508.10897

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning
This paper aims to model 3D human motion across domains, where a single model is expected to handle multiple modalities, tasks, and datasets. Existing cross-domain models often rely on domain-specific components and multi-stage training, which limits their practicality and scalability. To overcome these challenges, we propose a new setting to train a unified cross-domain model through a single process, eliminating the need for domain-specific components and multi-stage training. We first introd…

Omg definitiv! Verlasst euch auf keinen Fall auf andere Plattformen, schon gar nicht Spotify.
https://troet.cafe/@nSonic/115023183623242883

Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditions
Tina Raissi, Nick Rossenbach, Ralf Schl\"uter
https://arxiv.org/abs/2508.09868

Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditions
We analyze automatic speech recognition (ASR) modeling choices under domain mismatch, comparing classic modular and novel sequence-to-sequence (seq2seq) architectures. Across the different ASR architectures, we examine a spectrum of modeling choices, including label units, context length, and topology. To isolate language domain effects from acoustic variation, we synthesize target domain audio using a text-to-speech system trained on LibriSpeech. We incorporate target domain n-gram and neural …

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Anyone know what the hell's going on with the instance fediblock.lol; is the operator just trying to build a list of all users that block spam?
It appears to be just a single user instance with a rotating set of users which pump out useless spam, then a bot displays which users have reported / blocked it.
Oddly enough it doesn't support domain-level blocks, so blacklisting that entire domain, (which appears to be the route to go), seems to be efficient.
Domain regis…

GAMA: A General Anonymizing Multi-Agent System for Privacy Preservation Enhanced by Domain Rules and Disproof Method
Hailong Yang, Renhuo Zhao, Guanjin Wang, Zhaohong Deng
https://arxiv.org/abs/2509.10018

GAMA: A General Anonymizing Multi-Agent System for Privacy Preservation Enhanced by Domain Rules and Disproof Method
With the rapid advancement of Large Language Model (LLM), LLM-based agents exhibit exceptional abilities in understanding and generating natural language, facilitating human-like collaboration and information transmission in LLM-based Multi-Agent System (MAS). High-performance LLMs are often hosted on remote servers in public spaces. When tasks involve privacy data, MAS cannot securely utilize these LLMs without implementing privacy-preserving mechanisms. To address this challenge, we propose a…

WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Pretrained Speech Recognition Transformers
Akshat Pandey, Karun Kumar, Raphael Tang
https://arxiv.org/abs/2509.10452 …

WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Pretrained Speech Recognition Transformers
Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen vocabulary and parlance. In many real-world settings, collecting speech data is impractical, necessitating text-only adaptation. We propose WhisTLE, a deeply supervised, text-only adaptation method for pretrained encoder-decoder ASR models. WhisTLE trains a variational autoencoder (VAE) to model encoder outputs from text and fine-tunes the decoder using the learned…

At Berlin Buzzwords 2025, Atri Sharma & Abhishek Singh shared lessons learned building an enterprise search platform with multi-modal content, ranging from highly domain-specific content to images and unstructured content. They discussed issues of extraction, inference and relevance while demonstrating cross-domain search at scale.
Watch the full session:

from my link log —
TLD domain name renewal grace periods.
https://www.namecheap.com/support/knowledgebase/article.aspx/9916/2207/tlds-grace-periods/
saved 2025-09-09

TLDs' grace periods - Domains - Namecheap.com
Learn more about TLDs' grace periods. Find your answers at Namecheap Knowledge Base.

Quantum Algorithms for Projection-Free Sparse Convex Optimization
Jianhao He, John C. S. Lui
https://arxiv.org/abs/2507.08543 https://

Quantum Algorithms for Projection-Free Sparse Convex Optimization
This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in machine learning and data science. For the vector domain $\mathcal{D} \subset \mathbb{R}^d$, we propose two quantum algorithms for sparse constraints that finds a $\varepsilon$-optimal solution with the query complexity of $O(\sqrt{d}/\varepsilon)$ and $O(1/\varepsilon)$ by using the function value oracle, reducing a fa…

I am getting sick and tired of campfire song protests - these will have no effect on the maga-klan. (I admit that such gatherings are useful means to build networks of people and communications, but I am not sure that that is actually happening in the kind of way we need.)
Let's think of actions that may fracture the unity of the maga-nutz.
For example, were I running for mayor of NYC I would have among my campaign planks a plan to seize Trump Tower (using eminent domain and…

“How do I get to your app?”
“Oh, just enter our domain name in your URL bar.”
“I can’t find the URL bar… ?”
“In your browser app/window, it should be at the top or bottom.”
“I don’t think my browser has one.”
“It must. Which browser is it?”
“ChatGPT 6”
#TheWebWeReallyLost

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
Zihao Chen, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Xinyi Yang, Mingjie Zhou, Zhuoyao Du, Xu Yan, Zhouyang Wu, Zhenyu Xu, Jiangli Huang, Li Shang, Xuan Zeng, Fan Yang
https://arxiv.org/abs/2508.10409

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
In this paper, we propose AnalogSeeker, an effort toward an open-source foundation language model for analog circuit design, with the aim of integrating domain knowledge and giving design assistance. To overcome the scarcity of data in this field, we employ a corpus collection strategy based on the domain knowledge framework of analog circuits. High-quality, accessible textbooks across relevant subfields are systematically curated and cleaned into a textual domain corpus. To address the complex…

Heute vor 64 Jahren: Am 14. September 1961 teste die #Sowjetunion im Atomtestgebiet Nowaja Semlja eine #Atombombe mit der Testnummer 98 (1.2 Mt, atmosphärisch).

Artificial Intelligence, Domain AI Readiness, and Firm Productivity
Sipeng Zeng, Xiaoning Wang, Tianshu Sun
https://arxiv.org/abs/2508.09634 https://arxiv.…

Artificial Intelligence, Domain AI Readiness, and Firm Productivity
Although Artificial Intelligence (AI) holds great promise for enhancing innovation and productivity, many firms struggle to realize its benefits. We investigate why some firms and industries succeed with AI while others do not, focusing on the degree to which an industrial domain is technologically integrated with AI, which we term "domain AI readiness". Using panel data on Chinese listed firms from 2016 to 2022, we examine how the interaction between firm-level AI capabilities and domain AI re…

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi, Soufiane Belharbi, Houssem Ben Salem, Ali Etemad, Alessandro Lameiras Koerich, Marco Pedersoli, Simon Bacon, Eric Granger
https://arxiv.org/abs/2508.09202

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain da…

GNN-based Unified Deep Learning
Furkan Pala, Islem Rekik
https://arxiv.org/abs/2508.10583 https://arxiv.org/pdf/2508.10583…

GNN-based Unified Deep Learning
Deep learning models often struggle to maintain generalizability in medical imaging, particularly under domain-fracture scenarios where distribution shifts arise from varying imaging techniques, acquisition protocols, patient populations, demographics, and equipment. In practice, each hospital may need to train distinct models - differing in learning task, width, and depth - to match local data. For example, one hospital may use Euclidean architectures such as MLPs and CNNs for tabular or grid-…

Initial Algebras of Domains via Quotient Inductive-Inductive Types
Simcha van Collem, Niels van der Weide, Herman Geuvers
https://arxiv.org/abs/2509.10187 https://

Initial Algebras of Domains via Quotient Inductive-Inductive Types
Domain theory has been developed as a mathematical theory of computation and to give a denotational semantics to programming languages. It helps us to fix the meaning of language concepts, to understand how programs behave and to reason about programs. At the same time it serves as a great theory to model various algebraic effects such as non-determinism, partial functions, side effects and numerous other forms of computation. In the present paper, we present a general framework to construct …

On the parabolic Fatou domains
Ning Gao, Yan Gao, Wenjuan Peng
https://arxiv.org/abs/2509.09914 https://arxiv.org/pdf/2509.09914

On the parabolic Fatou domains
Let $f$ be a rational map with an infinitely-connected fixed parabolic Fatou domain $U$. We prove that there exists a rational map $g$ with a completely invariant parabolic Fatou domain $V$, such that $(f,U)$ and $(g,V)$ are conformally conjugate, and each non-singleton Julia component of $g$ is a Jordan curve which bounds a superattracting Fatou domain of $g$ containing at most one postcritical point. Furthermore, we show that if the Julia set of $f$ is a Cantor set, then the parabolic Fatou d…

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification
Weitao Tang, Johann Vargas-Calixto, Nasim Katebi, Nhi Tran, Sharmony B. Kelly, Gari D. Clifford, Robert Galinsky, Faezeh Marzbanrad
https://arxiv.org/abs/2509.10082

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification
Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is complex to acquire and difficult and laborious to interpret consistently. However, accurate sleep stage classification may aid in the early detection of abnormal brain maturation associated with pregnancy complications (e.g. hypoxia or intrauterine growth restriction). Methods: EEG electrodes were secured onto the ovine…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Gauging or extending bulk and boundary conformal field theories: Application to bulk and domain w...
Yoshiki Fukusumi

“In an era of online misinformation, declining trust and paywalled services, the [Australian Dictionary of Biography] keeps fact-based, scholarly information in the public domain. Its continued high quality and availability supports our shared mission of making knowledge freely and reliably accessible to all. #Wikimedia #Australia

Why the Australian Dictionary of Biography matters to Wikimedians (and the world)
The ADB powers Wikipedia and protects our past

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Denotational Semantics of Gradual Typing using Synthetic Guarded Domain Theory (Extended Version)
Eric Giovannini, Tingting Ding, Max S. New

Rare Events, Many Searchers, and Fast Target Reaching in a Finite Domain
Elisabetta Ellettari, Giacomo Nasuti, Alberto Bassanoni, Alessandro Vezzani, Raffaella Burioni
https://arxiv.org/abs/2507.09452

Rare Events, Many Searchers, and Fast Target Reaching in a Finite Domain
Finding a target in a complex environment is a fundamental challenge in nature, from chemical reactions to sperm reaching an egg. An effective strategy to reduce the time needed to reach a target is to deploy many searchers, increasing the likelihood that at least one will succeed by using the statistics of rare events. When the underlying stochastic process involves broadly distributed step sizes, rare long jumps dominate the dynamics, making the use of multiple searchers particularly powerful…

Carath\'eodory distance-preserving maps between bounded symmetric domains
Bas Lemmens, Cormac Walsh
https://arxiv.org/abs/2507.08639 https://

Carathéodory distance-preserving maps between bounded symmetric domains
We study the rigidity of maps between bounded symmetric domains that preserve the Carathéodory/Kobayashi distance. We show that such maps are only possible when the rank of the co-domain is at least as great as that of the domain. When the ranks are equal, and the domain is irreducible, we prove that the map is either holomorphic or antiholomorphic. In the holomorphic case, we show that the map is in fact a triple homomorphism, under the additional assumption that the origin is mapped to the o…

Explainable Ensemble Learning for Graph-Based Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins, Ali A Ghorbani
https://arxiv.org/abs/2508.09801 https…

Explainable Ensemble Learning for Graph-Based Malware Detection
Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such as control flow graphs (CFGs). However, single-model approaches may suffer from limited generalization and lack interpretability, especially in high-stakes security applications. In this paper, we prop…

Learning human-to-robot handovers through 3D scene reconstruction
Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh
https://arxiv.org/abs/2507.08726 …

Learning human-to-robot handovers through 3D scene reconstruction
Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although training using simulations offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. Gaussian Splatting visual reconstruction methods have recently provided new directions for robot manipulation by generating realistic environments. In this paper, we propose the first method f…

A comparative study of data- and image- domain LSRTM under velocity-impedance parametrization
Pengliang Yang, Zhengyu Ji
https://arxiv.org/abs/2508.10405 https://

A comparative study of data- and image- domain LSRTM under velocity-impedance parametrization
Least-squares reverse time migration (LSRTM) is one of the classic seismic imaging methods to reconstruct model perturbations within a known reference medium. It can be computed in either data or image domain using different methods by solving a linear inverse problem, whereas a careful comparison analysis of them is lacking in the literature. In this article, we present a comparative study for multiparameter LSRTM in data- and image- domain in the framework of SMIwiz open software. Different f…

Personalized Real-time Jargon Support for Online Meetings
Yifan Song, Wing Yee Au, Hon Yung Wong, Brian P. Bailey, Tal August
https://arxiv.org/abs/2508.10239 https://

Personalized Real-time Jargon Support for Online Meetings
Effective interdisciplinary communication is frequently hindered by domain-specific jargon. To explore the jargon barriers in-depth, we conducted a formative diary study with 16 professionals, revealing critical limitations in current jargon-management strategies during workplace meetings. Based on these insights, we designed ParseJargon, an interactive LLM-powered system providing real-time personalized jargon identification and explanations tailored to users' individual backgrounds. A control…

Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain
Wenkai Yu, Qi Wang, Zhen Zhang, Tiezheng Qian
https://arxiv.org/abs/2508.06830

Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain
The original Cahn-Hilliard model in an arbitrary domain with two prescribed boundary conditions is extended to a Cahn-Hilliard-type model in a larger, regular domain with homogeneous Neumann boundary conditions. The extension is based on the Onsager principle-based domain embedding (OPBDE) method, which has been developed as a systematic domain embedding framework to ensure thermodynamic consistency. By introducing a modified conservation law, the flux at the boundary of the original domain is …

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Andy Zhu, Yingjun Du
https://arxiv.org/abs/2509.09727 https://arxiv…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with domain-specific terminology, and comprehension of real-world scenarios. We present a multi-agent framework that leverages role-based prompting to enhance performance on domain-specific QA. Our framew…

Equivalent Characterizations and Applications of Fractional Sobolev Spaces with Partially Vanishing Traces on $(\epsilon,\delta,D)$-Domains Supporting $D$-Adapted Fractional Hardy Inequalities
Jun Cao, Dachun Yang, Qishun Zhang
https://arxiv.org/abs/2507.08295

Equivalent Characterizations and Applications of Fractional Sobolev Spaces with Partially Vanishing Traces on $(ε,δ,D)$-Domains Supporting $D$-Adapted Fractional Hardy Inequalities
Let $Ω\subset\mathbb{R}^n$ be an $(ε,δ,D)$-domain, with $ε\in(0,1]$, $δ\in(0,\infty]$, and $D\subset \partial Ω$ being a closed part of $\partial Ω$, which is a general open connected set when $D=\partial Ω$ and an $(ε,δ)$-domain when $D=\emptyset$. Let $s\in(0,1)$ and $p\in[1,\infty)$. If ${W}^{s,p}(Ω)$, ${\mathcal W}^{s,p}(Ω)$, and $\mathring{W}_D^{s,p}(Ω)$ are the fractional Sobolev spaces on $Ω$ that are defined respectively via the restriction of $W^{s,p}(\mathbb{R}^n)$ to $�…

Deterministic roughening in the dc-driven precessional regime of domain walls
E. F. Pusiol, V. Lecomte, S. Bustingorry, A. B. Kolton
https://arxiv.org/abs/2508.10859 https://

Deterministic roughening in the dc-driven precessional regime of domain walls
We numerically study the dynamics of extended domain walls in homogeneous ferromagnets driven by a uniform magnetic field at zero temperature. Using both micromagnetic Landau-Lifshitz-Gilbert simulations and a collective-coordinate model, we show that flat domain walls become linearly unstable above the Walker breakdown field and below a higher threshold, provided their length exceeds a characteristic value. This instability is captured by a quasi-universal spectral diagram, parameterized solel…

Wheeler-DeWitt wavefunctions for 5d BKL dynamics, automorphic L-functions and complex primon gases
Marine De Clerck, Sean A. Hartnoll, Ming Yang
https://arxiv.org/abs/2507.08788

Wheeler-DeWitt wavefunctions for 5d BKL dynamics, automorphic L-functions and complex primon gases
The near-singularity BKL dynamics of five dimensional gravity and supergravity (and also an extended four-dimensional supergravity) is known to be given by the billiard problem of a particle within a fundamental domain of the Bianchi groups $PSL(2,{\mathcal O}) \subset PSL(2,\mathbb{C})$, acting on $\mathbb{H}_3$. Here ${\mathcal O}$ are the Gaussian or Eisenstein integers, which define a square or triangular lattice in $\mathbb{C}$. The Wheeler-DeWitt wavefunctions near the singularity are, co…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift
Antoine Schieb, et al.

Optimizing Aperture Geometry in THz-TDS for Accurate Spectroscopy of Quantum Materials
Laura O. Dias, Eduardo D. Stefanato, Nicolas M. Kawahala, Felix G. G. Hernandez
https://arxiv.org/abs/2507.08609

Optimizing Aperture Geometry in THz-TDS for Accurate Spectroscopy of Quantum Materials
Terahertz time-domain spectroscopy (THz-TDS) provides a powerful platform for investigating low-energy excitations in quantum materials. Because these materials are often limited in size, experimental setups typically rely on tightly focused beams and metallic holders with small apertures. In this work, we perform a systematic study of how aperture geometry influences THz signal transmission in a standard free-space configuration. By analyzing time- and frequency-domain data for circular apertu…

CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
Yaodong Su, Yixiang Fang, Yingli Zhou, Quanqing Xu, Chuanhui Yang
https://arxiv.org/abs/2507.08445

CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
Despite the remarkable progress of Large Language Models (LLMs), their performance in question answering (QA) remains limited by the lack of domain-specific and up-to-date knowledge. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external information, often from graph-structured data. However, existing graph-based RAG methods suffer from poor graph quality due to incomplete extraction and insufficient utilization of query information during retrieval. To overcom…

Edge states in square lattice media and their deformations
Jonah Chaban, Jeremy L. Marzuola, Michael I. Weinstein
https://arxiv.org/abs/2508.09352 https://…

Edge states in square lattice media and their deformations
Edge states are time-harmonic solutions of conservative wave systems which are plane wave-like parallel to and localized transverse to an interface between two bulk media. We study a class of 2D edge Hamiltonians modeling a medium which slowly interpolates between periodic bulk media via a domain wall across a "rational" line defect. We consider the cases of (1) periodic bulk media having the symmetries of a square lattice, and (2) linear deformations of such media. Our bulk Hamiltonians break …

On Random Fields Associated with Analytic Wavelet Transform
Gi-Ren Liu, Yuan-Chung Sheu, Hau-Tieng Wu
https://arxiv.org/abs/2508.10495 https://arxiv.org/pd…

On Random Fields Associated with Analytic Wavelet Transform
Despite the broad application of the analytic wavelet transform (AWT), a systematic statistical characterization of its magnitude and phase as inhomogeneous random fields on the time-frequency domain when the input is a random process remains underexplored. In this work, we study the magnitude and phase of the AWT as random fields on the time-frequency domain when the observed signal is a deterministic function plus additive stationary Gaussian noise. We derive their marginal and joint distribu…

Time-Domain Reconstruction of the Speed of Sound in Ring-Array Ultrasound Computed Tomography with Randomized Super-Shots
Luca A. Forte
https://arxiv.org/abs/2508.09675 https://…

Time-Domain Reconstruction of the Speed of Sound in Ring-Array Ultrasound Computed Tomography with Randomized Super-Shots
Ring-array ultrasound computed tomography has recently achieved sufficient maturity for clinical applications like breast imaging. Image reconstruction is achieved with state of art iterative algorithms (full waveform inversion in the frequency domain). In this Letter, we consider a stochastic reconstruction in the time-domain. We introduce the notion of multiple super-shots and stochastic ensembles and test our inversion algorithm on publicly available experimental data. Our results show that …

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
Ian DeHaan, Neng Huang, Euiwoong Lee
https://arxiv.org/abs/2507.08693

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
We study minimum cost constraint satisfaction problems (MinCostCSP) through the algebraic lens. We show that for any constraint language $Γ$ which has the dual discriminator operation as a polymorphism, there exists a $|D|$-approximation algorithm for MinCostCSP$(Γ)$ where $D$ is the domain. Complementing our algorithmic result, we show that any constraint language $Γ$ where MinCostCSP$(Γ)$ admits a constant-factor approximation must have a \emph{near-unanimity} (NU) polymorphism …

Harmonic maps and framed $\mathrm{PSL}_2(\mathbb{C})$-representations
Subhojoy Gupta, Gobinda Sau
https://arxiv.org/abs/2508.10335 https://arxiv.org/pdf/25…

Harmonic maps and framed $\mathrm{PSL}_2(\mathbb{C})$-representations
We show that given an element $X$ of the enhanced Teichmüller space $\mathcal{T}^\pm(\mathbb{S}, \mathbb{M})$ and a type-preserving framed $\mathrm{PSL}_2(\mathbb{C})$-representation $\hatρ = (ρ,β)$, there is a $ρ$-equivariant harmonic map $f:\mathbb{H}^2 \to \mathbb{H}^3$ that is asymptotic to the framing $β$. Here, the domain is the universal cover of the punctured Riemann surface obtained from a conformal completion of $X$. Moreover, such a harmonic map is unique if one prescribes, in …

Data-driven optimization of sparse sensor placement in thermal hydraulic experiments
Xicheng Wang, Yun. Feng, Dmitry Grishchenko, Pavel Kudinov, Ruifeng Tian, Sichao Tan
https://arxiv.org/abs/2509.10055

Data-driven optimization of sparse sensor placement in thermal hydraulic experiments
Thermal-Hydraulic (TH) experiments provide valuable insight into the physics of heat and mass transfer and qualified data for code development, calibration and validation. However, measurements are typically collected from sparsely distributed sensors, offering limited coverage over the domain of interest and phenomena of interest. Determination of the spatial configuration of these sensors is crucial and challenging during the pre-test design stage. This paper develops a data-driven framework …

Empirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains
Brooke R. Weborg, Gursel Serpen
https://arxiv.org/abs/2508.10887 https:…

Empirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains
This paper examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of parameters and their values, which are applicable to problems within the same domain, to help serve to fill the experience gap needed by those entering this field of study. The influence of various parameter selections and their value adjustments, as well as architectural change…

A three-dimensional numerical scheme for modeling three-phase contact line pinning using Smoothed Particle Hydrodynamics
Subrat Kumar Nayak, Amitabh Bhattacharya, Prapanch Nair
https://arxiv.org/abs/2509.10159

A three-dimensional numerical scheme for modeling three-phase contact line pinning using Smoothed Particle Hydrodynamics
In several capillary dynamics experiments, the liquid domain is confined by pinning the three-phase contact line along a sharp edge or a discontinuity on the substrate. Simulating the dynamics of pinned droplets can offer valuable insights into capillary flow phenomena involving wetting of inhomogeneous substrates. However, Eulerian multi-phase flow solvers are usually not able to directly implement pinning of three-phase contact lines. We present the implementation of a model for pinning the c…

Cerebellar Contributions to Action and Cognition: Prediction, Timescale, and Continuity
Jonathan Tsay, Richard Ivry
https://arxiv.org/abs/2509.09818 https://

Cerebellar Contributions to Action and Cognition: Prediction, Timescale, and Continuity
The cerebellum is implicated in nearly every domain of human cognition, yet our understanding of how this subcortical structure contributes to cognition remains elusive. Efforts on this front have tended to fall into one of two camps. On one side are those who seek to identify a universal cerebellar transform, a single algorithm that can be applied across domains as diverse as sensorimotor learning, social cognition, and decision making. On the other side are those who focus on functional speci…

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment
Rini Smita Thakur, Rajeev Ranjan Dwivedi, Vinod K Kurmi
https://arxiv.org/abs/2509.10134 https:/…

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment
Accurate segmentation of the optic disc and cup is critical for the early diagnosis and management of ocular diseases such as glaucoma. However, segmentation models trained on one dataset often suffer significant performance degradation when applied to target data acquired under different imaging protocols or conditions. To address this challenge, we propose \textbf{Grad-CL}, a novel source-free domain adaptation framework that leverages a pre-trained source model and unlabeled target data to r…

Ich möchte eine info domain umziehen lassen, muss ich da was besonderes beachten ?

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Hummingbird: Fast, Flexible, and Fair Inter-Domain Bandwidth Reservations
W\"ust, Giuliari, Legner, Smith, Wyss, Bachmann, Garcia-Pardo, Perrig

https://therecord.media/angus-king-cyber-domain-cuts-cisa
Sen. Angus King (I-ME) on Tuesday said that recent cuts across the government have weakened U.S. cybersecurity even as attacks against infrastructure and business skyrocket.

Sen. King: Cyber domain is a ‘hellscape’ that will be made worse by cuts
King, a leading voice in the Senate on cybersecurity issues, honed in on the thousands of staffers and experts laid off by CISA, saying the agency has lost 30 percent of its staff and most of its seasoned leaders.

When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital
Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jennee Bryant, Melanie Molina, James Marks, Lucas Zier, Jean Feng
https://arxiv.org/abs/2508.085…

When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital
Large language models (LLMs) have the potential to address social and behavioral determinants of health by transforming labor intensive workflows in resource-constrained settings. Creating LLM-based applications that serve the needs of underserved communities requires a deep understanding of their local context, but it is often the case that neither LLMs nor their developers possess this local expertise, and the experts in these communities often face severe time/resource constraints. This crea…

Weekend #Plankton Factoid 🦠🦐
For a while, there has been concern for how the ocean's #ecosystem is being modeled. Too often it is oversimplified with #phytoplankon and

Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro
https://arxiv.org/abs/2508.10155

Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Difference image analysis (DIA) is a powerful tool for studying time-variable phenomena, and has been used by many time-domain surveys. Most DIA algorithms involve matching the spatially-varying PSF shape between science and template images, and then convolving that shape in one image to match the other. The wrong choice of which image to convolve can introduce one of the largest sources of artifacts in the final difference image. We introduce a quantitative metric to determine the optimal conv…

Heute vor 58 Jahren: Am 14. Juli 1967 zündeten die USA im Rahmen von Operation #Crosstie die 1. #Atombombe "Vito". Crosstie war eine Serie von Tests zwischen 67/68 bei der insgesamt 48 #Kernwaffentests

RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA
Bhavik Agarwal, Hemant Sunil Jomraj, Simone Kaplunov, Jack Krolick, Viktoria Rojkova
https://arxiv.org/abs/2508.09893

RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA
Regulatory compliance question answering (QA) requires precise, verifiable information, and domain-specific expertise, posing challenges for Large Language Models (LLMs). In this work, we present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG) to address these demands. First, agents build and maintain an ontology-free KG by extracting subject--predicate--object (SPO) triplets from regulatory documents and syst…

P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
Benjamin Holzschuh, Georg Kohl, Florian Redinger, Nils Thuerey
https://arxiv.org/abs/2509.10186

P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
We present a scalable framework for learning deterministic and probabilistic neural surrogates for high-resolution 3D physics simulations. We introduce a hybrid CNN-Transformer backbone architecture targeted for 3D physics simulations, which significantly outperforms existing architectures in terms of speed and accuracy. Our proposed network can be pretrained on small patches of the simulation domain, which can be fused to obtain a global solution, optionally guided via a fast and scalable sequ…

Repairing General Game Descriptions (extended version)
Yifan He, Munyque Mittelmann, Aniello Murano, Abdallah Saffidine, Michael Thielscher
https://arxiv.org/abs/2508.10438 http…

Repairing General Game Descriptions (extended version)
The Game Description Language (GDL) is a widely used formalism for specifying the rules of general games. Writing correct GDL descriptions can be challenging, especially for non-experts. Automated theorem proving has been proposed to assist game design by verifying if a GDL description satisfies desirable logical properties. However, when a description is proved to be faulty, the repair task itself can only be done manually. Motivated by the work on repairing unsolvable planning domain descript…

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
Shadikur Rahman, Aroosa Hameed, Gautam Srivastava, Syed Muhammad Danish
https://arxiv.org/abs/2509.10436

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
To optimize the reasoning and problem-solving capabilities of Large Language Models (LLMs), we propose a novel cloud-edge collaborative architecture that enables a structured, multi-agent prompting framework. This framework comprises three specialized components: GuideLLM, a lightweight model deployed at the edge to provide methodological guidance; SolverLLM, a more powerful model hosted in the cloud responsible for generating code solutions; and JudgeLLM, an automated evaluator for assessing s…

Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform
Yuankun Xie, Ruibo Fu, Xiaopeng Wang, Zhiyong Wang, Ya Li, Zhengqi Wen, Haonnan Cheng, Long Ye
https://arxiv.org/abs/2508.10559

Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform
The rapid advancement of speech generation technology has led to the widespread proliferation of deepfake speech across social media platforms. While deepfake audio countermeasures (CMs) achieve promising results on public datasets, their performance degrades significantly in cross-domain scenarios. To advance CMs for real-world deepfake detection, we first propose the Fake Speech Wild (FSW) dataset, which includes 254 hours of real and deepfake audio from four different media platforms, focusi…

EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
Yanjun Li, Yuqian Fu, Tianwen Qian, Qi'ao Xu, Silong Dai, Danda Pani Paudel, Luc Van Gool, Xiaoling Wang
https://arxiv.org/abs/2508.10729

EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
Recent advances in Multimodal Large Language Models (MLLMs) have significantly pushed the frontier of egocentric video question answering (EgocentricQA). However, existing benchmarks and studies are mainly limited to common daily activities such as cooking and cleaning. In contrast, real-world deployment inevitably encounters domain shifts, where target domains differ substantially in both visual style and semantic content. To bridge this gap, we introduce \textbf{EgoCross}, a comprehensive ben…

Real-time identification and control of influential pandemic regions using graph signal variation
Sudeepini Darapu, Subrata Ghosh, Dibakar Ghosh, Chittaranjan Hens, Santosh Nannuru
https://arxiv.org/abs/2509.10281

Real-time identification and control of influential pandemic regions using graph signal variation
The global spread of pandemics is facilitated by the mobility of populations, transforming localized infections into widespread phenomena. To contain it, timely identification of influential regions that accelerate this process is necessary. In this work, we model infection as a temporally evolving graph signal and propose graph signal variation-based metrics to capture spatio-temporal changes. Both graph domain and time domain locality are modeled. Based on this metric, we propose an online al…

AlDBaran: Towards Blazingly Fast State Commitments for Blockchains
Bernhard Kauer, Aleksandr Petrosyan, Benjamin Livshits
https://arxiv.org/abs/2508.10493 https://

AlDBaran: Towards Blazingly Fast State Commitments for Blockchains
The fundamental basis for maintaining integrity within contemporary blockchain systems is provided by authenticated databases. Our analysis indicates that a significant portion of the approaches applied in this domain fail to sufficiently meet the stringent requirements of systems processing transactions at rates of multi-million TPS. AlDBaran signifies a substantial advancement in authenticated databases. By eliminating disk I/O operations from the critical path, implementing prefetching strat…

Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
Kejun Li, Jeeseop Kim, Maxime Brunet, Marine P\'etriaux, Yisong Yue, Aaron D. Ames
https://arxiv.org/abs/2508.10269

Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

On the Birkhoff conjecture for Kepler billiards
Stefano Baranzini, Vivina L. Barutello, Irene De Blasi, Susanna Terracini
https://arxiv.org/abs/2507.08446 …

On the Birkhoff conjecture for Kepler billiards
We investigate the integrability of Kepler billiards-mechanical billiard systems in which a particle moves under the influence of a Keplerian potential and reflects elastically at the boundary of a strictly convex planar domain. Our main result establishes that, except possibly for one location of the gravitational center, analytic integrability at high energies occurs only when the domain is an ellipse and the center is placed at one of its foci. This provides a partial affirmative answer to a…

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
Lixin Jia, Zhiqing Guo, Gaobo Yang, Liejun Wang, Keqin Li
https://arxiv.org/abs/2508.10741

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
The emergence of deepfake technology has introduced a range of societal problems, garnering considerable attention. Current deepfake detection methods perform well on specific datasets, but exhibit poor performance when applied to datasets with unknown forgery techniques. Moreover, as the gap between emerging and traditional forgery techniques continues to widen, cross-domain detection methods that rely on common forgery traces are becoming increasingly ineffective. This situation highlights th…

Heute vor 63 Jahren: Am 14.07.1962 fand der "Small Boy" Test im Rahmen der Operation #Sunbeam auf dem Nevada Test Site statt. Getestet wurden taktische #Atomwaffen (Davy Crockett). Es war die letzte atmosphärische Testreihe der

Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
Hanna Abi Akl
https://arxiv.org/abs/2509.10249

Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
Recent advances in Language Models (LMs) have failed to mask their shortcomings particularly in the domain of reasoning. This limitation impacts several tasks, most notably those involving ontology engineering. As part of a PhD research, we investigate the consequences of incorporating formal methods on the performance of Small Language Models (SLMs) on reasoning tasks. Specifically, we aim to orient our work toward using SLMs to bootstrap ontology construction and set up a series of preliminar…

the safety advice following the recent npm phishing attacks reminds me of an old rant https://dotat.at/@/2016-10-19-domain-transfers-are-shocking.html
i hope things have improved since then (happily i have not needed to do any domain transfers recently)…

Specialised or Generic? Tokenization Choices for Radiology Language Models
Hermione Warr, Wentian Xu, Harry Anthony, Yasin Ibrahim, Daniel McGowan, Konstantinos Kamnitsas
https://arxiv.org/abs/2508.09952

Specialised or Generic? Tokenization Choices for Radiology Language Models
The vocabulary used by language models (LM) - defined by the tokenizer - plays a key role in text generation quality. However, its impact remains under-explored in radiology. In this work, we address this gap by systematically comparing general, medical, and domain-specific tokenizers on the task of radiology report summarisation across three imaging modalities. We also investigate scenarios with and without LM pre-training on PubMed abstracts. Our findings demonstrate that medical and domain-s…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Hummingbird: Fast, Flexible, and Fair Inter-Domain Bandwidth Reservations
W\"ust, Giuliari, Legner, Smith, Wyss, Bachmann, Garcia-Pardo, Perrig

Minimum-norm interpolation for unknown surface reconstruction
Alex Shiu Lun Chu, Leevan Ling, Ka Chun Cheung
https://arxiv.org/abs/2507.08632 https://

Minimum-norm interpolation for unknown surface reconstruction
We study algorithms to estimate geometric properties of raw point cloud data through implicit surface representations. Given that any level-set function with a constant level set corresponding to the surface can be used for such estimations, numerical methods need not specify a unique target function for the domain-type interpolation problems. In this paper, we focus on kernel-based interpolation by radial basis functions (RBF) and reformulate the uniquely solvable interpolation problem into a …

Strong and weak solutions to a structural acoustic model with a $C^1$ source term on the plate
Andrew R. Becklin (Drake University), Yanqiu Guo (Florida International University)
https://arxiv.org/abs/2509.09822

Strong and weak solutions to a structural acoustic model with a $C^1$ source term on the plate
In this manuscript, we consider a structural acoustic model consisting of a wave equation defined in a bounded domain $Ω\subset \mathbb{R}^3$, strongly coupled with a Berger plate equation acting on the flat portion of the boundary of $Ω$. The system is influenced by an arbitrary $C^1$ nonlinear source term in the plate equation. Using nonlinear semigroup theory and monotone operator theory, we establish the well-posedness of both local strong and weak solutions, along with conditions for glo…

Why Data Anonymization Has Not Taken Off
Matthew J. Schneider, James Bailie, Dawn Iacobucci
https://arxiv.org/abs/2509.10165 https://arxiv.org/pdf/2509.101…

Why Data Anonymization Has Not Taken Off
Companies are looking to data anonymization research $\unicode{x2013}$ including differential private and synthetic data methods $\unicode{x2013}$ for simple and straightforward compliance solutions. But data anonymization has not taken off in practice because it is anything but simple to implement. For one, it requires making complex choices which are case dependent, such as the domain of the dataset to anonymize; the units to protect; the scope where the data protection should extend to; and …

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Tianlong Ai, Tianzhu Liu, Haochen Jiang, Yanfeng Gu
https://arxiv.org/abs/2507.08741

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face two major challenges: 1) They predominantly adopt a flat classification paradigm, which limits their ability to generate end-to-end multi-granularity hierarchical predictions aligned with tree-structured hierarchies used in practice. 2) Most cross-domain studies focus on perform…

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Wenqi Zheng, Yutaka Arakawa
https://arxiv.org/abs/2508.10371 https://

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Reinforcement learning in large reasoning models enables learning from feedback on their outputs, making it particularly valuable in scenarios where fine-tuning data is limited. However, its application in multi-modal human activity recognition (HAR) domains remains largely underexplored. Our work extends reinforcement learning to the human activity recognition domain with multimodal large language models. By incorporating visual reinforcement learning in the training process, the model's gener…

GenOM: Ontology Matching with Description Generation and Large Language Model
Yiping Song, Jiaoyan Chen, Renate A. Schmidt
https://arxiv.org/abs/2508.10703 https://

GenOM: Ontology Matching with Description Generation and Large Language Model
Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of ontology concepts via generating textual definitions, retrieves alignment candidates with an embe…

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Christos Sgouropoulos, Christos Nikou, Stefanos Vlachos, Vasileios Theiou, Christos Foukanelis, Theodoros Giannakopoulos
https://arxiv.org/abs/2509.10074

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conducted in the image domain, few-shot learning in audio classification remains relatively underexplored. In this work, we investigate the effect of integrating supervised contrastive loss into prototypical few shot training for audio classification. In detail, we demonstrate that an…

Interleaved Transceiver Design for a Continuous- Transmission MIMO-OFDM ISAC System
Yating Chen, Cai Wen, Yan Huang, Jinye Peng, Wei Hong, Timothy N. Davidson
https://arxiv.org/abs/2508.10430

Interleaved Transceiver Design for a Continuous- Transmission MIMO-OFDM ISAC System
This paper proposes an interleaved transceiver design method for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system utilizing orthogonal frequency division multiplexing (OFDM) waveforms. We consider a continuous transmission system and focus on the design of the transmission signal and a receiving filter in the time domain for an interleaved transmission architecture. For communication performance, constructive interference (CI) is integrated into the opt…

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh
https://arxiv.org/abs/2508.09855 https://

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Human-robot teaming (HRT) systems often rely on large-scale datasets of human and robot interactions, especially for close-proximity collaboration tasks such as human-robot handovers. Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although simulation training offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. We introduce a …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/6]:
- EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Questio...
Li, Fu, Qian, Xu, Dai, Paudel, Van Gool, Wang

Leveraging Large Language Models for Rare Disease Named Entity Recognition
Nan Miles Xi, Yu Deng, Lin Wang
https://arxiv.org/abs/2508.09323 https://arxiv.o…

Leveraging Large Language Models for Rare Disease Named Entity Recognition
Named Entity Recognition (NER) in the rare disease domain poses unique challenges due to limited labeled data, semantic ambiguity between entity types, and long-tail distributions. In this study, we evaluate the capabilities of GPT-4o for rare disease NER under low-resource settings, using a range of prompt-based strategies including zero-shot prompting, few-shot in-context learning, retrieval-augmented generation (RAG), and task-level fine-tuning. We design a structured prompting framework tha…

Long-time behavior of a nonlocal Cahn-Hilliard equation with nonlocal dynamic boundary condition and singular potentials
Maoyin Lv, Hao Wu
https://arxiv.org/abs/2509.10304 https…

Long-time behavior of a nonlocal Cahn-Hilliard equation with nonlocal dynamic boundary condition and singular potentials
We investigate the long-time behavior of a nonlocal Cahn--Hilliard equation in a bounded domain $Ω\subset\mathbb{R}^d$ $(d=2,3)$, subject to a kinetic rate dependent nonlocal dynamic boundary condition. The kinetic rate $1/L$, with $L\in[0,+\infty)$, distinguishes different types of bulk-surface interactions. When $L\in[0,+\infty)$, for a general class of singular potentials including the physically relevant logarithmic potential, we establish the existence of a global attractor $\mathcal{A}_m…

from my link log —
What declarative languages are.
https://semantic-domain.blogspot.com/2013/07/what-declarative-languages-are.html
saved 2025-08-01

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography
Yuexi Du, Lihui Chen, Nicha C. Dvornek
https://arxiv.org/abs/2509.10344 https://arx…

GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography
Mammography screening is an essential tool for early detection of breast cancer. The speed and accuracy of mammography interpretation have the potential to be improved with deep learning methods. However, the development of a foundation visual language model (VLM) is hindered by limited data and domain differences between natural and medical images. Existing mammography VLMs, adapted from natural images, often ignore domain-specific characteristics, such as multi-view relationships in mammograp…

Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
Yue Zhou, Litong Feng, Mengcheng Lan, Xue Yang, Qingyun Li, Yiping Ke, Xue Jiang, Wayne Zhang
https://arxiv.org/abs/2509.10059

Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
Mathematical reasoning is critical for tasks such as precise distance and area computations, trajectory estimations, and spatial analysis in unmanned aerial vehicle (UAV) based remote sensing, yet current vision-language models (VLMs) have not been adequately tested in this domain. To address this gap, we introduce AVI-Math, the first benchmark to rigorously evaluate multimodal mathematical reasoning in aerial vehicle imagery, moving beyond simple counting tasks to include domain-specific knowl…

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Stav Armoni-Friedmann, Hana Chockler, David A. Kelly
https://arxiv.org/abs/2509.09982 https:/…

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use case of AI models predicting the values of Boolean functions. We extend the previous work in this domain by proposing a formal and precise measure of importance of variables based on actual causality, and we evaluate state-of-the-art XAI tools against this measure. We also present a novel XAI tool B-ReX, based on the e…

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
Juyuan Wang, Rongchen Zhao, Wei Wei, Yufeng Wang, Mo Yu, Jie Zhou, Jin Xu, Liyan Xu
https://arxiv.org/abs/2508.10419

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods can fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing …

A study of ferronematic thin films including a stray field energy
Shilpa Dutta, James Dalby, Apala Majumdar, Anja Schl\"omerkemper
https://arxiv.org/abs/2509.10442 https://…

A study of ferronematic thin films including a stray field energy
Ferronematic materials are colloidal suspensions of magnetic particles in liquid crystals. They are complex materials with potential applications in display technologies, sensors, microfluidics devices, etc. We consider a model for ferronematics in a 2D domain with a variational approach. The proposed free energy of the ferronematic system depends on the Landau-de Gennes (LdG) order parameter $\mathbf{Q}$ and the magnetization $\mathbf{M}$, and incorporates the complex interaction between the l…

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Jiaqi Cao, Jiarui Wang, Rubin Wei, Qipeng Guo, Kai Chen, Bowen Zhou, Zhouhan Lin
https://arxiv.org/abs/2508.09874

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Large Language Models (LLMs) have shown strong abilities in general language tasks, yet adapting them to specific domains remains a challenge. Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting. Meanwhile, Retrieval-Augmented Generation (RAG) introduces substantial inference latency due to expensive nearest-neighbor searches and longer context. This paper introduces Memory Decoder, a plug-and-play pretrained me…

Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
Mustapha Adamu, Qi Zhang, Huitong Pan, Longin Jan Latecki, Eduard C. Dragut
https://arxiv.org/abs/2509.10087

Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
The growing complexity and volume of climate science literature make it increasingly difficult for researchers to find relevant information across models, datasets, regions, and variables. This paper introduces a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts, aimed at improving how climate knowledge is accessed and used. Unlike keyword based search, our KG supports structured, semantic queries that help researchers discover precise connections…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Tajamul Ashraf, Iqra Altaf Gillani
https://arxiv.org/abs/2508.10840 https://arxiv.o…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by …

Large Language Models Meet Legal Artificial Intelligence: A Survey
Zhitian Hou, Zihan Ye, Nanli Zeng, Tianyong Hao, Kun Zeng
https://arxiv.org/abs/2509.09969 https://

Large Language Models Meet Legal Artificial Intelligence: A Survey
Large Language Models (LLMs) have significantly advanced the development of Legal Artificial Intelligence (Legal AI) in recent years, enhancing the efficiency and accuracy of legal tasks. To advance research and applications of LLM-based approaches in legal domain, this paper provides a comprehensive review of 16 legal LLMs series and 47 LLM-based frameworks for legal tasks, and also gather 15 benchmarks and 29 datasets to evaluate different legal capabilities. Additionally, we analyse the chal…

Multi-Intent Recognition in Dialogue Understanding: A Comparison Between Smaller Open-Source LLMs
Adnan Ahmad, Philine Kowol, Stefan Hillmann, Sebastian M\"oller
https://arxiv.org/abs/2509.10010

Multi-Intent Recognition in Dialogue Understanding: A Comparison Between Smaller Open-Source LLMs
In this paper, we provide an extensive analysis of multi-label intent classification using Large Language Models (LLMs) that are open-source, publicly available, and can be run in consumer hardware. We use the MultiWOZ 2.1 dataset, a benchmark in the dialogue system domain, to investigate the efficacy of three popular open-source pre-trained LLMs, namely LLama2-7B-hf, Mistral-7B-v0.1, and Yi-6B. We perform the classification task in a few-shot setup, giving 20 examples in the prompt with some i…

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Xun Yang, Meng Wang
https://arxiv.org/abs/2508.10848 …

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathematics and programming, while research in the psychological domain has predominantly emphasized emotional support and empathetic dialogue, with limited attention to reasoning mechanisms that are bene…

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong, Xinhui Wu, Lin Chen, Feng Wei
https://arxiv.org/abs/2509.09321

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training, and competition solving. However, existing benchmarks remain limited in task coverage, domain diversity, difficulty modeling, and evaluation rigor, failing to capture the full capabilities of such agents in realistic settings. We present TAM Bench, a diverse, realistic, and st…

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Peng Wang, Xuesi Hu, Jiageng Wu, Yuntao Zou, Qiancheng Zhang, Dagang Li
https://arxiv.org/abs/2507.08339

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Recently, the development of large language models (LLMs) and reasoning large language models (RLLMs) have gained considerable attention from many researchers. RLLMs enhance the reasoning capabilities of LLMs through Long Chain-of-Thought (Long CoT) processes, significantly improving the performance of LLMs in addressing complex problems. However, there are few works that systematically explore what methods can fully unlock the performance of LLMs and RLLMs within the financial domain. To inves…

Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
Sophia Maria
https://arxiv.org/abs/2509.09121 https://arxiv.org/pdf/…

Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
Large language models (LLMs) excel in general-domain applications, yet their performance often degrades in specialized tasks requiring domain-specific knowledge. E-commerce is particularly challenging, as its data are noisy, heterogeneous, multilingual, and highly dynamic. We present Compass-v3, a vertical-domain Mixture-of-Experts (MoE) model with 245B total parameters and 71B active per token, designed for Southeast Asian e-commerce. Compass-v3 adopts fewer but larger experts, combined with h…