November 2025 arXiv papers — page 119

Edward Chernysh

We establish the existence of sign-changing entire solutions to weighted critical $p$-Laplace equations of the Caffarelli-Kohn-Nirenberg type. In doing so, we investigate classes of symmetry and show that, for suitable symmetry configurations, there exists a non-trivial solution which changes sign and respects the corresponding prescribed symmetry. In additi

Tomas Oppelstrup, Nicholas Giamblanco, Delyan Z. Kalchev, Ilya Sharapov

Simulation of physical systems is essential across scientific and engineering domains. Commonly used domain decomposition methods are unable to simultaneously deliver both high simulation rate and high utilization in network computing environments. In particular, Exascale systems deliver only a small fraction their peak performance for these workloads. This

Joseph Onuegbu, Dafne Guetta, Yael Hillman, Volker Perdelwitz

We present a novel approach for characterizing nova candidates by exploiting the infrared capabilities of the Wide-field Infrared Survey Explorer (WISE) catalog. We developed a pipeline to identify novae based on well-defined infrared criteria, and leveraging this pipeline, we successfully identified 41 optically confirmed novae in the WISE catalog. In parti

S. Sabari, R. Radha, Lauro Tomio, B. A. Malomed

The nucleation and dynamics of vortices in the quasi-two-dimensional rotating dipolar Bose-Einstein condensate are explored by taking into account the Lee-Huang-Yang (LHY) correction to the mean-field (MF) theory. Assuming approximate cancellation of the MF interactions, we focus on the formation of a pure LHY superfluid. The effect of rotational frequency $

Peter Kirgis

Large language models are increasingly being used in critical domains of politics, business, and education, but the nature of their normative ethical judgment remains opaque. Alignment research has, to date, not sufficiently utilized perspectives and insights from the field of moral psychology to inform training and evaluation of frontier models. I perform a

Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu

Click-through rate (CTR) prediction, which estimates the probability of a user clicking on a given item, is a critical task for online information services. Existing approaches often make strong assumptions that training and test data come from the same distribution. However, the data distribution varies since user interests are constantly evolving, resultin

Jiayi Li, Xiao Liu, Yansong Feng

Large Language Model (LLM)-based multi-agent systems are increasingly used to simulate human interactions and solve collaborative tasks. A common practice is to assign agents with personas to encourage behavioral diversity. However, this raises a critical yet underexplored question: do personas introduce biases into multi-agent interactions? This paper prese

Diptarka Chakraborty, Kushagra Chatterjee, Debarati Das, Tien-Long Nguyen

Clustering is a fundamental task in machine learning and data analysis, but it frequently fails to provide fair representation for various marginalized communities defined by multiple protected attributes -- a shortcoming often caused by biases in the training data. As a result, there is a growing need to enhance the fairness of clustering outcomes, ideally

Mustafa Doger, Sennur Ulukus

Bitcoin's (BTC) Difficulty Adjustment Algorithm (DAA) has been a source of vulnerability for incentive attacks such as selfish mining, block withholding and coin hopping strategies. In this paper, first, we rigorously study the short-term revenue change per hashpower of the adversarial and honest miners for these incentive attacks. To study the long-term eff

Kadin Worthen, Christine H. Chen, A. Meredith Hughes, Brandon C. Johnson

We present non-simultaneous ALMA band 7 and SMA observations of the HD 166191 disk, which was recently thought to have a collision in its terrestrial planet zone. Both observations detect dust continuum emission and the ALMA observations detect the 12CO J=3-2 line from the circumstellar disk. We do not detect SiO, a potential indicator of giant collisions, b

Barun Kumar Pal

In this article we confront both large-field and small-field sectors of mutated hilltop inflation model with the recent observational results. We begin with confrontation of predictions from mutated hilltop inflation with the joint analysis of Planck-2018 and BICEP/Keck-2018 data. Subsequently, we extend our analysis by incorporating the ACT-DR6 data in comb

Jueun Kwon, Max M. Sun, Todd Murphey

Ergodic control synthesizes optimal coverage behaviors over spatial distributions for nonlinear systems. However, existing formulations model the robot as a non-volumetric point, whereas in practice a robot interacts with the environment through its body and sensors with physical volume. In this work, we introduce a new ergodic control formulation that optim

Andrew J. Peterson

Political "circuses" may undermine democratic accountability if leaders facing scandal can reliably pull media coverage toward fresh topics and away from substantive investigations or evaluations. We investigate whether politicians strategically alter their messaging during damaging media coverage ("strategic diversion") or maintain consistent provocative co

Valentin Zech, Martin Bullinger

Computing stable partitions in hedonic games is a challenging task because there exist games in which stable outcomes do not exist. Even more, these No-instances can often be leveraged to prove computational hardness results. We make this impression rigorous in a dynamic model of cardinal hedonic games by providing meta theorems. These imply hardness of deci

Marta Pérez-Casany, Ariel Duarte-López, Jordi Valero

The Zipf distribution is a probability distribution widely used by scientists from various disciplines due to its ubiquity. Some of these areas include linguistics, physics, genetics, and sociology, among others. In this paper, it is proved that the Zipf distribution is both a mixture of geometric distributions and a mixture of zero-truncated Poisson distrib

Luisa Mao, Garrett Warnell, Peter Stone, Joydeep Biswas

Successful autonomous robot navigation in off-road domains requires the ability to generate high-quality terrain costmaps that are able to both generalize well over a wide variety of terrains and rapidly adapt relative costs at test time to meet mission-specific needs. Existing approaches for costmap generation allow for either rapid test-time adaptation of

Antonio Sabbatella

The optimal assignment of Large Language Models (LLMs) to specialized roles in multi-agent systems is a significant challenge, defined by a vast combinatorial search space, expensive black-box evaluations, and an inherent trade-off between performance and cost. Current optimization methods focus on single-agent settings and lack a principled framework for th

Sebastian Schneider, Sebastian Beckert, René Hammer, Markus König

Electron beam-induced current (EBIC) imaging in the scanning transmission electron microscope (STEM), STEM-EBIC, provides direct access to carrier transport at the nanoscale. While well established in bulk SEM geometries, its application to thin TEM lamellae remains largely unexplored. Here, we present a systematic STEM-EBIC study of silicon photodiode lamel

Elso Drigo Filho, Regina Maria Ricotta

Classical computational methods, such as molecular dynamics and Monte Carlo simulations, have long been the standard for modeling viral structure and function. However, these approaches may overlook crucial quantum phenomena that operate at the nanoscale, particularly within the highly-compacted genetic material of the viral capsid. The confined, high-densit

Thien T. Duong, Jan P. Springer

Perceptual quality of audio is the combination of aural accuracy and listener-perceived sound fidelity. It is how humans respond to the accuracy, intelligibility, and fidelity of aural media. Today this fidelity is also heavily influenced by the use of audio compression codecs for storing aural media in digital form. We argue that, when choosing an audio com

Benjamin Fein-Ashley, Jacob Fein-Ashley

Vision-Language Models (VLMs) are a new family of models that align image content with natural language. Existing approaches typically fuse either (a) early: by mixing tokens/features inside the encoders, or (b) late: by comparing pooled embeddings. Many methods also tie fusion to an autoregressive decoder. However, the hidden states of both modalities alrea

Kyounghee Kim

We study the spectral radii of elements in the hyperbolic Coxeter group $W(E_{10})$ by introducing a filtration indexed by reflections conjugate to a distinguished simple reflection $s_0$. This filtration organizes $W(E_{10})$ into double cosets relative to the parabolic subgroup $W(A_9)$, and we classify the minimal representatives of these cosets via a roo

Rob Koopman

We describe a PubMed scale retrieval framework that separates semantic interpretation from metric geometry. A large language model expands a natural language query into concise biomedical phrases; retrieval then operates in a fixed, mean free, approximately isotropic embedding space. Each document and query vector is formed as a weighted mean of token embedd

S. K. Steensen, T. S. Thakur, M. Dillenz, J. M. Carlsson

Interoperability and cross-validation remains a significant challenge in the computational materials discovery community. In this context, we introduce a common input/output standard designed for internal translation by various workflow managers (AiiDA, PerQueue, Pipeline Pilot, and SimStack) to produce results in a unified schema. This standard aims to enab

Zsombor Szilágyi, Mihály Weiner

Previous studies on the geometrical properties of the state space of a finite-level quantum system have determined its volume and surface area. Building on this foundation, we derive explicit formulas for two additional intrinsic volume quantities. The question of whether a complete set of mutually unbiased bases exists in dimension $d$ can be equivalently f

Luthira Abeykoon, Ved Patel, Gawthaman Senthilvelan, Darshan Kasundra

Chess has experienced a large increase in viewership since the pandemic, driven largely by the accessibility of online learning platforms. However, no equivalent assistance exists for physical chess games, creating a divide between analog and digital chess experiences. This paper presents CVChess, a deep learning framework for converting chessboard images to

Sibylle Driezen, Fedor Levkovich-Maslyuk, Adrien Molines

Jordanian deformations offer rare integrable realisations of non-AdS holography, whose solvability methods differ from conventional AdS/CFT examples. Here we study the $\mathfrak{sl}(2,R)$ sector of the Jordanian deformed $AdS_5\times S^5$ string and its weak-coupling spin chain counterpart: the $\mathrm{XXX}_{-1/2}$ model with a non-abelian Jordanian Drinfe

Wei-Cheng Tseng, Jinwei Gu, Qinsheng Zhang, Hanzi Mao

Training generalist policies for robotic manipulation has shown great promise, as they enable language-conditioned, multi-task behaviors across diverse scenarios. However, evaluating these policies remains difficult because real-world testing is expensive, time-consuming, and labor-intensive. It also requires frequent environment resets and carries safety ri

Adam Stein, Matthew Trager, Benjamin Bowman, Michael Kleinman

Enabling agentic AI systems to adapt their problem-solving approaches based on post-training interactions remains a fundamental challenge. While systems that update and maintain a memory at inference time have been proposed, existing designs only steer the system by modifying textual input to a language model or agent, which means that they cannot change sam

Zhenyu Ding, Yuhao Wang, Tengyue Xiao, Haoying Wang

Large Language Models (LLMs) demonstrate impressive capabilities, yet their outputs often suffer from misalignment with human preferences due to the inadequacy of weak supervision and a lack of fine-grained control. Training-time alignment methods like Reinforcement Learning from Human Feedback (RLHF) face prohibitive costs in expert supervision and inherent

Jitian Liu, Nicolas Kozachuk, Subhrajit Bhattacharya

We study distributed optimization of finite-degree polynomial Laplacian spectral objectives under fixed topology and a global weight budget, targeting the collective behavior of the entire spectrum rather than a few extremal eigenvalues. By re-formulating the global cost in a bilinear form, we derive local subgraph problems whose gradients approximately alig

Michael Fucilla, Saad Nabeebaccus, Lech Szymanowski, Samuel Wallon

We consider the exclusive photoproduction of a $\pi^0 \gamma$ pair with large invariant mass, as a promising channel to study the effects of gluon saturation. It has recently been demonstrated that this process is incompatible with a collinear factorization approach in terms of generalized parton distributions (GPDs) at the leading twist. In such a situation

Paul Jonas Jost, Frank T Bergmann, Daniel Weindl, Jan Hasenauer

Motivation: Parameter estimation is a cornerstone of data-driven modeling in systems biology. Yet, constructing such problems in a reproducible and accessible manner remains challenging. The PEtab format has established itself as a powerful community standard to encode parameter estimation problems, promoting interoperability and reusability. However, its re

Max M. Sun, Jueun Kwon, Todd Murphey

Coverage motion planning is essential to a wide range of robotic tasks. Unlike conventional motion planning problems, which reason over temporal sequences of states, coverage motion planning requires reasoning over the spatial distribution of entire trajectories, making standard motion planning methods limited in computational efficiency and less amenable to

Rafael Cordoba

We implement the Gauge Theory Bootstrap (GTB) framework, initiated by He and Kruczenski in arXiv:2309.12402 and arXiv:2403.10772, using a discrete basis parametrization of the 2-to-2 pion scattering S-matrix, the spectral densities and the form factors. This approach enables a refined analysis of the convergence of the GTB and drastically reduced computation

Yiyun Zhou, Mingjing Xu, Jingwei Shi, Quanjiang Li

Tactile sensing offers rich and complementary information to vision and language, enabling robots to perceive fine-grained object properties. However, existing tactile sensors lack standardization, leading to redundant features that hinder cross-sensor generalization. Moreover, existing methods fail to fully integrate the intermediate communication among tac

Raúl Alonso, Kâzım Büyükboduk, Antonio Cauchi, Antonio Lei

Our goal in this paper is to introduce and study the Euler system of signed diagonal cycles associated with a trito-non-ordinary triple product of the form $f^B \times g^B \times \mathbf{h}^B$, where $f^B$ (resp. $g^B$) is a $p$-ordinary (resp. non-ordinary) eigenform on an indefinite quaternion algebra $B_{/\mathbb{Q}}$ of weight $2$, and $\mathbf{h}^B$ is

Xiaoyu Zheng, Xu Chen, Awais Rauf, Qifan Fu

Ultrasound (US) is one of the most widely used medical imaging modalities, thanks to its low cost, portability, real-time feedback, and absence of ionizing radiation. However, US image interpretation remains highly operator-dependent and varies significantly across anatomical regions, acquisition protocols, and device types. These variations, along with uniq

K. Z. Yang, G. Cusin, V. Mandic, C. Scarlata

We estimate the amplitude and spatial anisotropy in the stochastic gravitational wave background (SGWB) energy density due to compact binary coalescence (CBC) events: binary black holes (BBH), binary neutron stars (BNS), and black hole-neutron star (BHNS) mergers. Our starting point is the Flagship Simulation Galaxy Catalogue developed by the Euclid Consorti

Andrei A. Stepanenko, Kseniia S. Chernova, Maxim Mazanov, Maxim A. Gorlach

We present an approach to compute time-optimal control of a quantum system which combines quantum brachistochrone and Lax pair techniques and enables efficient investigation of large-scale quantum systems. We illustrate our method by finding the quantum speed limit for a single-particle excitation in a nearest-neighbor-coupled qubit lattice with switchable c

Hao Zhang, Joshua Mann, James Rosenzweig, Michael Chini

Gas-filled waveguides enable few-cycle, spatio-temporally coupled (STC) pulses with programmable structure, opening new routes to control charged particles with optical fields. This review maps the landscape of optical-field-driven photoemission, then surveys gas-based nonlinear drivers, photonic crystal fibers (PCFs) for low-energy, high-repetition operatio

Lijuan Dong, Aanal Jayesh Shah, Peter Kirton, Hadiseh Alaeian

Analogous to Coulomb blockade for electrons, photon blockade is a key quantum optical effect in which the presence of one photon prevents the transmission of subsequent ones through a nonlinear medium. Beyond its fundamental interest, photon and multi-photon blockade are actively studied as mechanisms for generating technologically-relevant quantum states of

Tara Murphy, Katarina Brlec, Giovanni Oakes, Lorenzo Peri

Spins in semiconductor quantum dots offer a scalable approach to quantum computing; however, precise control and efficient readout of large quantum dot arrays remain challenging, mainly due to the hyperdimensional voltage space required for tuning multiple gates per dot. To automate this process, large datasets are required for testing and training autotunin

Junlong Chen, Ziwei Huang, Xuesong Cai, Xiang Cheng

This paper constructs a novel multi-modal sensing-communication digital-twin dataset, named SynthSoM-Twin, which is spatio-temporally consistent with the real world, for Sim2Real transfer via Synesthesia of Machines (SoM). To construct the SynthSoM-Twin dataset, we propose a new framework that can extend the quantity and missing modality of existing real-wor

Sultan Hassan, Sambatra Andrianomena, Benjamin D. Wandelt

Systematics contaminate observables, leading to distribution shifts relative to theoretically simulated signals-posing a major challenge for using pre-trained models to label such observables. Since systematics are often poorly understood and difficult to model, removing them directly and entirely may not be feasible. To address this challenge, we propose a

Nhat Hoang-Xuan, Minh Vu, My T. Thai, Manish Bhattarai

Large vision-language models (LVLMs) are powerful, yet they remain unreliable due to object hallucinations. In this work, we show that in many hallucinatory predictions the LVLM effectively ignores the image and instead relies on previously generated output (prelim) tokens to infer new objects. We quantify this behavior via the mutual information between the

Aidan J. Kaminsky, Alexander Kashlinsky, Richard G. Arendt, Nico Cappelluti

Spitzer-based source-subtracted cosmic infrared background (CIB) fluctuations at arcminute-to-degree scales indicate the presence of new populations, whereas sub-arcminute power arises from known $z\lesssim 6$ galaxies. We reconstruct the evolution of the near-IR CIB anisotropies on sub-arcminute scales by known galaxy populations. This method is based on, a

Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan Li

Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accuracy on benchmarks, these models produce confident hallucinations, even when wrong answers carry catastrophic consequences. Our evaluations on GSM8K, MedQA and GPQA show frontier models almost never abstain despite e

Prashanti Anderson, Samuel B. Hopkins, Amit Rajaraman, David Steurer

We design new algorithms for approximating 2CSPs on graphs with bounded threshold rank, that is, whose normalized adjacency matrix has few eigenvalues larger than $\varepsilon$, smaller than $-\varepsilon$, or both. Unlike on worst-case graphs, 2CSPs on bounded threshold rank graphs can be $(1+O(\varepsilon))$-approximated efficiently. Prior approximation al

Filip Tronarp

In this article, variational state estimation is examined from the dynamic programming perspective. This leads to two different value functional recursions depending on whether backward or forward dynamic programming is employed. The result is a theory of variational state estimation that corresponds to the classical theory of Bayesian state estimation. More

Hao-Cheng Weng, John G. Rarity, Krishna C. Balram, Joe A. Smith

Nitrogen-vacancy centres (NVs) are promising solid-state nanoscale quantum sensors for applications ranging from material science to biotechnology. Using multiple sensors simultaneously offers advantages for probing spatiotemporal correlations of fluctuating fields or the dynamics of point defects. In this work, by integrating NVs with foundry silicon-nitrid

Zhengwei Jiang, Yufeng Zhou, Xusheng Zhu, Wen Chen

Transmissive reconfigurable intelligent surfaces (RIS) represent a transformative architecture for future wireless networks, enabling a paradigm shift from traditional costly base stations to low-cost, energy-efficient transmitters. This paper explores a downlink multi-user MIMO system where a transmissive RIS, illuminated by a single feed antenna, forms the

Eky Febrianto, Yiren Wang, Burigede Liu, Michael Ortiz

Quantum computing holds the promise of solving computational mechanics problems in polylogarithmic time, meaning computational time scales as $\mathscr{O}((\log N)^c)$, where $N$ is the problem size and $c$ a constant. We propose a quantum spectral method with polylogarithmic complexity for solving non-periodic boundary value problems with arbitrary Dirichle

L. Naponiello, P. Leonardi, M. Damasso, M. -L. Steinmeyer

TOI-1422 is a G2 V star ($V = 10.6$ mag) known to host a warm Neptune-sized planet, TOI-1422 b, with a mass and radius of about $9M_{\oplus}$ and $4R_{\oplus}$, on a circular orbit with a period of $12.997$ days. An outer planetary candidate in this system had previously been suggested on the basis of a residual signal in the radial velocity (RV) data with a

John R. Doyle, Alexander Galarraga

We consider the family of dynamical modular curves associated to quadratic polynomial maps and determine precisely which of these curves have infinitely many cubic points. We use this to prove a classification statement on preperiodic points for quadratic polynomials over cubic fields, extending previous work of Poonen, Faber, and the first author and Krumm.

Joan Font-Quer Roset, Devina Mohan, Anna Scaife

In this work, we estimate the intrinsic dimension (iD) of the Radio Galaxy Zoo (RGZ) dataset using a score-based diffusion model. We examine how the iD estimates vary as a function of Bayesian neural network (BNN) energy scores, which measure how similar the radio sources are to the MiraBest subset of the RGZ dataset. We find that out-of-distribution sources

Jay Shen, Yifeng Tang, Andrew Ferguson

Transformers are the go-to architecture for most data modalities due to their scalability. While they have been applied extensively to molecular property prediction, they do not dominate the field as they do elsewhere. One cause may be the lack of structural biases that effectively capture the relationships between atoms. Here, we investigate attention biase

Marta Calvi, Tommaso Fulghesu, George Hallett, Luca Hartman

The Run 3 of the LHC brings unprecedented luminosity and a surge in data volume to the LHCb detector, necessitating a critical reduction in the size of each reconstructed event without compromising the physics reach of the heavy-flavour programme. While signal decays typically involve just a few charged particles, a single proton-proton collision produces hu

Roman Kinakh, Gonzalo R. Ríos-Muñoz, Arrate Muñoz-Barrutia

Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a Bayesian segmentation framework that infers PD-L1 expression directly from H&E-stained histology images using Multimodal Posterior Sampling (MPS). Built upon nnUNet-v2, our method s

Alinda Ezgi Gerçek, Till Korten, Paul Chekhonin, Maleeha Hassan

Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline u

Mustafa Erdem Kırmızıgül, Hasan Feyzi Doğruyol, Haluk Bayram

The "Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the

Kaishen Wang, Ruibo Chen, Tong Zheng, Heng Huang

Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and inconsistency with the given prompts, particularly when textual descriptions are vague or underspecified. Existing approaches, such as prompt rewriting, best-of-N sampling, and self-

Daegeun Jo, Peter M. Oppeneer

The manipulation of magnetization in ferromagnetic metals (FMs) through orbital torque (OT) has emerged as a promising route for energy-efficient magnetic devices without relying on heavy metals. While Ti and Cu are among the most extensively studied light nonmagnetic metals (NMs) for OT devices, theoretical calculations of the resulting torque have remained

Emmanuel Lwele, Sabuni Emmanuel, Sitali Gabriel Sitali

This paper presents a deep reinforcement learning (DRL) framework for dynamic portfolio optimization under market uncertainty and risk. The proposed model integrates a Sharpe ratio-based reward function with direct risk control mechanisms, including maximum drawdown and volatility constraints. Proximal Policy Optimization (PPO) is employed to learn adaptive

Hayden R. Johnson, Anastasia N. Krouglova, Hadi Vafaii, Jacob L. Yates

Many properties of perceptual decision making are well-modeled by deep neural networks. However, such architectures typically treat decisions as instantaneous readouts, overlooking the temporal dynamics of the decision process. We present an image-computable model of perceptual decision making in which choices and response times arise from efficient sensory

Ana Inés Ennis, Johanna Hartke, Fuyan Bian, Claudia Pulsoni

Planetary nebulae (PNe) are the only single stars in galaxies outside the Local Group that can be used as kinematic tracers of the diffuse light in the extended halo. Analysing their luminosity-specific number density across galaxies of different morphologies has also shown hints that they may be used as tracers of the age and metallicity of stellar populati

Nhat Chung, Taisei Hanyu, Toan Nguyen, Huy Le

As embodied agents operate in increasingly complex environments, the ability to perceive, track, and reason about individual object instances over time becomes essential, especially in tasks requiring sequenced interactions with visually similar objects. In these non-Markovian settings, key decision cues are often hidden in object-specific histories rather t

Matthias Erbar, Marco Flaim, Eric Hupp, Zhenhao Li

We review different notions of synthetic Ricci flow that apply to time-dependent families of metric measure spaces and which are based on properties of the heat flow, ideas from optimal transport, and the asymptotic behaviour of volumes. Each notion equivalently characterises (weighted) Ricci flow for smooth families of weighted Riemannian manifolds. We disc

Angela Lopez-Cardona, Mireia Masias Bruns, Nuwan T. Attygalle, Sebastian Idesis

Effective decision-making often relies on timely insights from complex visual data. While Information Visualization (InfoVis) dashboards can support this process, they rarely adapt to users' cognitive state, and less so in real time. We present Symbiotik, an intelligent, context-aware adaptive visualization system that leverages neurophysiological signals to

Argyrios Deligkas, Gregory Gutin, Mark Jones, Philip R. Neary

In a public goods game, every player chooses whether or not to buy a good that all neighboring players will have access to. We consider a setting in which the good is indivisible, neighboring players are out-neighbors in a directed graph, and there is a capacity constraint on their number, k, that can benefit from the good. This means that each player makes

Konstantin A. Rybakov

We consider a class of functions for which the multiple Stratonovich stochastic integral or equivalent iterated Stratonovich stochastic integral with square integrable weights is defined by the orthogonal expansion. The equality of the trace of expansion coefficients matrix for these functions and the corresponding integral trace is established.

Guilin Hu, Malek Itani, Tuochao Chen, Shyamnath Gollakota

We introduce proactive hearing assistants that automatically identify and separate the wearer's conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer's self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others.

Sooyong Jang, Insup Lee

Conformal prediction constructs a set of labels instead of a single point prediction, while providing a probabilistic coverage guarantee. Beyond the coverage guarantee, adaptiveness to example difficulty is an important property. It means that the method should produce larger prediction sets for more difficult examples, and smaller ones for easier examples.

Samuel D. Tomlinson, Demetrios T. Papageorgiou

We develop a model for steady, laminar boundary layers over small-scale textured surfaces. Although the texture is small relative to the boundary-layer thickness, it modifies the flow via a slip length. We use matched asymptotic expansions to simplify the problem, dividing the flow into outer, boundary-layer and inner regions. The far-field behaviour of the

Davide Napolitano, Luca Cagliero, Fabrizio Battiloro

The evolution of Visual Large Language Models (VLLMs) has revolutionized the automatic understanding of Visually Rich Documents (VRDs), which contain both textual and visual elements. Although VLLMs excel in Visual Question Answering (VQA) on multi-page VRDs, their ability to detect unanswerable questions is still an open research question. Our research delv

Matthieu Bouyer, Irem Portakal, Javier Sendra-Arranz

This paper further develops the algebraic--geometric foundations of conditional independence (CI) equilibria, a refinement of dependency equilibria that integrates conditional independence relations from graphical models into strategic reasoning and thereby subsumes Nash equilibria. Extending earlier work on binary games, we analyze the structure of the asso

Deqiao Gan, Xiaoxia Xu, Xiaohu Ge, Yue Liu

A novel pinching antenna system (PASS) enabled wireless power transfer (WPT) framework is proposed, where energy harvesting receivers (EHRs) and information decoding receivers (IDRs) coexist. By activating pinching antennas (PAs) near both receivers and flexibly adjusting PAs' power radiation ratios, both energy harvesting efficiency and communication qualit

A. V. Smilga

In this note, we make two methodical observations. $\bullet$ We prove in a simple explicit way that a necessary and sufficient condition for a K\"ahler manifold to be hyperk\"ahler is $h_{i\bar k} h_{j\bar l } \Omega^{\bar k \bar l} \ =\ C \Omega_{ij}$, where $h_{i\bar k}$ is a complex metric, $\Omega$ is a symplectic matrix and $C$ is a positive constant. $

Sumeyye Bas, Kiymet Kaya, Elif Ak, Sule Gunduz Oguducu

The routing protocol for low-power and lossy networks (RPL) has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number manipulation. Traditional countermeasures, including protocol-le

Willy Wong

A universal theorem of sensory information, analogous to the second law of thermodynamics, is derived. Beginning from a minimal description of a sensory neuron, a state-space representation of firing rate emerges naturally from Shannon's measure of information. A special case of this formulation predicts a previously unknown inequality governing sensory adap

Kevin Chen, Kenneth W. Parker, Anish Arora

We present a pure machine learning process for synthesizing radar spectrograms from Motion-Capture (MoCap) data. We formulate MoCap-to-spectrogram translation as a windowed sequence-to-sequence task using a transformer-based model that jointly captures spatial relations among MoCap markers and temporal dynamics across frames. Real-world experiments show that

Riku Green, Huw Day, Zahraa S. Abdallah, Telmo M. Silva Filho

Multi-step forecasting is often described through a simple rule of thumb: recursive strategies are said to have high bias and low variance, while direct strategies are said to have low bias and high variance. We revisit this belief by decomposing the expected multi-step forecast error into three parts: irreducible noise, a structural approximation gap, and a

Qinghao Gao, Jiahui Qu, Wenqian Dong

Multimodal remote sensing classification often suffers from missing modalities caused by sensor failures and environmental interference, leading to severe performance degradation. In this work, we rethink missing-modality learning from a conditional computation perspective and investigate whether Mixture-of-Experts (MoE) models can inherently adapt to divers

Xiaoyin Xi, Zhe Yu

There has been a prevalence of applying AI software in both high-stakes public-sector and industrial contexts. However, the lack of transparency has raised concerns about whether these data-informed AI software decisions secure fairness against people of all racial, gender, or age groups. Despite extensive research on emerging fairness-aware AI software, up

Xenofon Chiotopoulos, Miriam Lucio Martinez, Davide Nicotra, Jacco A. de Vries

In the future high-luminosity LHC era, high-energy physics experiments face unprecedented computational challenges for event reconstruction. Employing the LHCb vertex locator as a case study we investigate a novel approach for charged particle track reconstruction. The algorithm hinges on minimizing an Ising-like Hamiltonian using matrix inversion. Solving t

Xuyang Zhang, Jiaqi Jiang, Zhuo Chen, Yongqiang Zhao

Tactile sensing in biological organisms is deeply intertwined with morphological form, such as human fingers, cat paws, and elephant trunks, which enables rich and adaptive interactions through a variety of geometrically complex structures. In contrast, vision-based tactile sensors in robotics have been limited to simple planar geometries, with biomorphic de

María Josefa Cánovas, Masao Fukushima, Juan Parra

This paper was initially motivated by the computation of the Lipschitz modulus of the metric projection on polyhedral convex sets in the Euclidean space when both the reference point and the polyhedron where it is projected are subject to perturbations. The paper tackles the more general problem of computing the Lipschitz modulus of the argmin mapping in the

María Blanco de Paz, Juan R. Deop-Ruano, Diego M. Solís, Alejandro Manjavacas

Lattice resonances are collective optical modes supported by periodic arrays of scatterers, arising from their coherent interaction enabled by the underlying periodicity. Owing to their collective nature, these resonances produce optical responses that are both stronger and spectrally narrower than those of individual scatterers. While such phenomena have be

Seth Alain Chang, Muhammad Mueez Amjad, Noorul Wahab, Ethar Alzaid

Multimodal deep learning (MDL) has emerged as a transformative approach in computational pathology. By integrating complementary information from multiple data sources, MDL models have demonstrated superior predictive performance across diverse clinical tasks compared to unimodal models. However, the assumption that combining modalities inherently improves p

Ya Liu, Junbin Liu, Wing-Kin Ma, Aritra Konar

Given an undirected graph and a size parameter $k$, the Densest $k$-Subgraph (D$k$S) problem extracts the subgraph on $k$ vertices with the largest number of induced edges. While D$k$S is NP--hard and difficult to approximate, penalty-based continuous relaxations of the problem have recently enjoyed practical success for real-world instances of D$k$S. In thi

Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff, Fabian Isensee

We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences, to 3D masks. Trained on 62K+ CT, MRI, and PET volumes spanning over 1K anatomical and pathological classes, VoxTell uses multi-stage vision-language fusion across decoder layers t

Chibuzor Henry Amadi, Akaniyene Benard Obot, Kufre Monday Udofia, Olaoluwa Ayodeji Adegboye

This study presents an analytical model of foliage cover impact on propagation loss in smart farming IoT applications.

Liza Daly, Matteo Cargnelutti, Catherine Brobston, John Hess

Publicly launched in 2004, the Google Books project has scanned tens of millions of items in partnership with libraries around the world. As part of this project, Google created the Google Return Interface (GRIN). Through this platform, libraries can access their scanned collections, the associated metadata, and the ongoing OCR and metadata improvements that

Farhana Amin, Sabiha Afroz, Kanchon Gharami, Mona Moghadampanah

Diffusion models produce high quality images but inference is costly due to many denoising steps and heavy matrix operations. We present DiffPro, a post-training, hardware-faithful framework that works with the exact integer kernels used in deployment and jointly tunes timesteps and per-layer precision in Diffusion Transformers (DiTs) to reduce latency and m

Ian Benson, Alexei Semenov

We consider learning mathematics through action research, hacking, discovery, inquiry, learning-by-doing as opposed to the instruct and perform, industrial model of the 19th century. A learning model based on self-awareness, types, functions, structured drawing and formal diagrams addresses the weaknesses of drill and practice and the pitfalls of statistical

Daniel Caro, Marco D'Addezio

For smooth affine varieties in positive characteristic, we identify a slope obstruction to the injectivity of the comparison morphism from rigid cohomology to rationalised crystalline cohomology. This yields a negative answer to a question of Esnault--Kisin--Petrov concerning the injectivity of the de Rham-to-crystalline comparison map for smooth affine sche

Xuejie Li, Yuzhou Hao, Yujie Liu, Shengying Yue

The search for materials with intrinsically low thermal conductivity ($\kappa_L$) is critical for energy applications, yet conventional descriptors often fail to capture the complex interplay between bonding and lattice dynamics. Here, first-principles calculations are used to contrast the thermal transport in covalent zincblende (zb) and metavalent rocksalt

Benedetta Marmiroli, Sumea Klokic, Barbara Sartori, Marie Reissenbuechel

Microfluidic devices are increasingly used in synchrotron-based experiments to deliver and probe liquid samples, offering advantages such as minimal sample consumption and reduced radiation damage. Despite their growing use, few devices have been specifically designed for monitoring liquids under photoexcitation, a promising approach for fast structural tran

Rodrigo Navarro-Betancourt

As a consequence of the proof of the Kashiwara-Vergne conjecture of Alekseev and Torossian, the authors obtained an injection $\mathrm{GRT} \hookrightarrow \mathrm{KRV}$. The group $\mathrm{GRT}$ can be regarded as the group of automorphisms of the operad of parenthesized chord diagrams, while $\mathrm{KRV}$ can be recovered from the automorphism group of th

Yiling He, Junchi Lei, Hongyu She, Shuo Shao

Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve and code representations shift. While continual learning (CL) offers a natural solution through sequential updates, most existing approaches rely on data replay or unconstrained u