November 2025 arXiv papers — page 122

Domonkos Czifra, Máté Matolcsi, Ferenc Szöllősi

Based on the concept of positive definite functions on finite groups, we present a new necessary condition for the existence of Butson Hadamard matrices $BH(n,q)$. We use this condition to prove some nonexistence results for a sequence of values of $(n,q)$, and also to facilitate a computer search and discover a matrix $BH(18,14)$. Furthermore, we use cyclot

Pavlos Panagiotidis, Jocelyn Spence, Nils Jager

This paper reports a practice-based investigation into authoring responsive light and sound in immersive performance without writing code. A modular system couples live gesture, position, and speech inputs to scenographic outputs through a visual logic layer that performers can operate in rehearsal. Across six workshops with eight professional performance-ma

Sunil Vittal, Anthony Wilkie, Nika Rastegari, Mostafa Atallah

Efficient state preparation is a challenging and important problem in quantum computing. In this work, we present a recursive state preparation algorithm that combines logarithmic-depth Dicke state circuits with Hamming weight encoders for efficiently preparing ``leaf-separable" quantum states. The algorithm is built on binary partition trees, generalized we

Daniele Agostini, Andreas Höring

Let $X$ be a hyperk\"ahler manifold, and let $A$ be a nef and big divisor on $X$. We show that the fixed part of the linear system $|A|$ is reduced and as a consequence $|2A|$ is mobile. If $X$ has dimension four we also show that if the fixed part of $|A|$ is not empty, the mobile part induces a (rational) Lagrangian fibration.

Sukhsagar, Nagendra Kumar, Ambuj Kumar Mishra, Vimal Bhatia

This paper proposes a simple and effective method for constructing higher-order three-dimensional (3D) signal constellations, aiming to enhance the reliability of digital communication systems. The approach systematically extends the conventional two-dimensional hexagonal quadrature amplitude modulation (2D-HQAM) constellation into a 3D-HQAM signal space, fo

Sverre Herland, Amit Parag, Elling Ruud Øye, Fangyi Zhang

Advanced robotic manipulation of deformable, volumetric objects remains one of the greatest challenges due to their pliancy, frailness, variability, and uncertainties during interaction. Motivated by these challenges, this article introduces Sashimi-Bot, an autonomous multi-robotic system for advanced manipulation and cutting, specifically the preparation of

Isael Herrera, Miguel A. Alonso, Sophie Brasselet

Optical fields polarized along three dimensions are frequent in optical microscopy and nanophotonics, and yet retrieving their polarization distribution is challenging. We present the experimental implementation of three-dimensional (3D) Stokes polarimetric imaging of nonparaxial optical fields with nanoscale spatial resolution. This approach extends classic

Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan, Ryan Krupp

Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nuclear physics experiments. We explore sparse convolutional networks for representation learning on TPC data, finding that a sparse ResNet architecture, even with randomly set weights,

Antonio Maggio, Ignazio Pillitteri, Jorge Sanz-Forcada, Giuseppina Micela

High-energy emission spectra from the outer atmospheres of late-type stars represent an important feature of the stellar activity in several contexts, such as the photoevaporation and photochemistry of planetary atmospheres or the modeling of irradiated circumstellar disks in young objects. An accurate determination of these spectra in the EUV and soft X-ray

Chenhao Liu, Leyun Jiang, Yibo Wang, Kairan Yao

Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world interactions remain challenging. As a step toward fast-moving object interactions, we present a reinforcement-learning training pipeline that yields a unified whole-body controller for humanoid badminton, coordin

Daniel Lindenschmitt, Michael Gundall, Ainur Daurembekova, Marcos Rates Crippa

Wireless communication is evolving with the adoption of dynamic and self-organizing networks. They are expected to play a crucial role in shaping sixth-generation (6G) systems and the ongoing standardization process. The concept of non-public networks (NPNs) introduced in fifth-generation (5G) will be enhanced by nomadic non-public networks (NNPNs), extendin

Kebin Wu, Fatima Albreiki

Positional bias - where models overemphasize certain positions regardless of content - has been shown to negatively impact model performance across various tasks. While recent research has extensively examined positional bias in text generation models, its presence and effects in representation models remain underexplored. Even less is known about such biase

Toshiaki Yamanaka

Determining the integrality gap of the linear programming (LP) relaxation of the metric traveling salesman problem (TSP) remains a long-standing open problem. We introduce a transfer principle: when the integer optimum of the 2-edge-connected multisubgraph problem (2ECM) is a unique Hamiltonian cycle $T$, any $\alpha$-approximation algorithm for 2ECM that ou

Jooyoung Lee, Jader Martins Camboim de Sá

WordNet offers rich supersense hierarchies for nouns and verbs, yet adverbs remain underdeveloped, lacking a systematic semantic classification. We introduce a linguistically grounded supersense typology for adverbs, empirically validated through annotation, that captures major semantic domains including manner, temporal, frequency, degree, domain, speaker-o

Ruocheng Wu, Haolan He, Yufei Wang, Zhihao Li

3D Gaussian Splatting (3DGS) has recently gained great attention in the 3D scene representation for its high-quality real-time rendering capabilities. However, when the input comprises sparse training views, 3DGS is prone to overfitting, primarily due to the lack of intermediate-view supervision. Inspired by the recent success of Video Diffusion Models (VDM)

Mohammad Areeb Qazi, Munachiso S Nwadike, Ibrahim Almakky, Mohammad Yaqub

Foundational models are trained on extensive datasets to capture the general trends of a domain. However, in medical imaging, the scarcity of data makes pre-training for every domain, modality, or task challenging. Instead of building separate models, we propose MAFM^3 (Modular Adaptation of Foundation Models for Multi-Modal Medical AI), a framework that ena

Wei-Cheng Lee, Francesco Orabona

In this short note, we present a simple derivation of the best-of-both-world guarantee for the Tsallis-INF multi-armed bandit algorithm from J. Zimmert and Y. Seldin. Tsallis-INF: An optimal algorithm for stochastic and adversarial bandits. Journal of Machine Learning Research, 22(28):1-49, 2021. URL https://jmlr.csail.mit.edu/papers/volume22/19-753/19-753.p

Dongmei Shan, Wei Lian, Chongxia Wang

Backdoor attacks pose a critical threat to deep learning, especially in safety-sensitive 3D domains such as autonomous driving and robotics. While potent, existing attacks on 3D point clouds are predominantly limited to one-to-one paradigms. The more flexible and universal one-to-N multi-target backdoor threat remains largely unexplored, lacking both theoret

Piero Romare, Farzaneh Karegar, Simone Fischer-Hübner

IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls. Even when fine-grained controls are available, users are likely overwhelmed by the complexity of setting privacy preferences. This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users that can be se

Youngjoon Lee, Hyukjoon Lee, Jinu Gong, Yang Cao

Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, FL methods typically run for a predefined number of global rounds, often leading to unnecessary computation when optimal performance is reached earlier. In addition, training may continue even when the model fails to achieve meani

Balint Virag, Xuan Wu

Actions in the Airy line ensemble represent distances from an infinitely far object. We characterize the Airy sheet by S(x,.)=T^x(.,1), where T^x is the unique action in the Airy line ensemble satisfying a growth condition depending on x. This provides a new simple framework for establishing convergence to the Airy sheet. We present simple conceptual proofs

Amir Rosenfeld, Neta Glazer, Ethan Fetaya

Visual Language Models (VLMs) have achieved remarkable progress, yet their reliability under small, meaning-preserving input changes remains poorly understood. We present the first large-scale, systematic study of VLM robustness to benign visual and textual perturbations: pixel-level shifts, light geometric transformations, padded rescaling, paraphrasing, an

Rick Luiken, Lorenzo Pes, Manil Dev Gomony, Sander Stuijk

Bio-inspired sensors like Dynamic Vision Sensors (DVS) and silicon cochleas are often combined with Spiking Neural Networks (SNNs), enabling efficient, event-driven processing similar to biological sensory systems. To realize the low-power constraints of the edge, the SNN should run on a hardware architecture that can exploit the sparse nature of the spikes.

Luoyao Hao, Shuo Zhang, Henning Schulzrinne

We propose to enhance the dependability of large-scale IoT systems by separating the management and operation plane. We innovate the management plane to enforce overarching policies, such as safety norms, operation standards, and energy restrictions, and integrate multi-faceted management entities, including regulatory agencies and manufacturers, while the c

Daniela Cadamuro, Markus B. Fröb, Dimitrios Katsinis, Jan Mandrysch

Relative entropy serves as a fundamental measure of state distinguishability in both quantum information theory and relativistic quantum field theory. Despite its conceptual importance, however, explicit computations of relative entropy remain notoriously difficult. Thus far, results in closed form have only been obtained for ground states, coherent states,

Nadaniela Egidi, Lauro Fioretti, Josephin Giacomini, Pierluigi Maponi

Espresso coffee extraction is a complex physico-chemical process and can be modeled through a system of coupled partial differential equations. We present a numerical solution based on a meshless Collocation Method using Radial Basis Functions and Kansa's approach, which reveals to be accurate and robust in comparison to a reference numerical solution provid

Anthony Kiggundu, Bin Han, Hans D. Schotten

We study how two information feeds, a closed-form Markov estimator of residual sojourn and an online trained actor-critic, affect reneging and jockeying in a dual M/M/1 system. Analytically, for unequal service rates and total-time patience, we show that total wait grows linearly so abandonment is inevitable and the probability of a successful jockey vanishe

Yongcheng Ding

Free-electron quantum optics provides a versatile platform for manipulating electrons at the quantum level with potential applications in quantum information technologies. We propose a grating-based architecture for fully quantized free-electron quantum optics, in which photon-electron interactions map onto Jaynes-Cummings and Tavis-Cummings models via Bloch

Leonid Lantsman

In this paper we attempt to construct the topological theory of superfluid helium $4$ in the framework of the (rigid) $U(1)$ model in which the initial $U(1)$ group is destroyed with appearance of (topologically nontrivial) domains separated by domain walls treated as step voltages between domains (e.g. with neighboring topological numbers). This can explain

ShiJie Wei, Yue Zhai, Quanfeng Lu, Wentao Yang

The Riemann Hypothesis (RH), one of the most profound unsolved problems in mathematics, concerns the nontrivial zeros of the Riemann zeta function. Establishing connections between the RH and physical phenomena could offer new perspectives on its physical origin and verification. Here, we establish a direct correspondence between the nontrivial zeros of the

Shambhavi Shanker, Manikandan Padmanaban, Jagabondhu Hazra

Geospatial chain of thought (CoT) reasoning is essential for advancing Visual Question Answering (VQA) on satellite imagery, particularly in climate related applications such as disaster monitoring, infrastructure risk assessment, urban resilience planning, and policy support. Existing VQA models enable scalable interpretation of remote sensing data but ofte

Anushree Bhuskute, Kaushik Gopalan, Jeet Shah

This study presents a transfer-learning framework based on Convolutional Gated Recurrent Units (ConvGRU) for short-term rainfall prediction in the Weather4Cast 2025 competition. A single SEVIRI infrared channel (10.8 {\mu}m wavelength) is used as input, which consists of four observations over a one-hour period. A two-stage training strategy is applied to ge

Gabriele Buriola, Andreas Weiermann

The study of well quasi-orders, wqo, is a cornerstone of combinatorics and within wqo theory Kruskal's theorem plays a crucial role. Extending previous proof-theoretic results, we calculate the $\Pi^1_1$ ordinals of two different versions of labelled Kruskal's theorem: $\forall n \,$ $\mbox{KT}_\ell(n)$ and $\mbox{KT}_{\omega}(n)$; denoting, respectively, al

Huipeng Liu, Zhaoli Guo

The kinetic model with multiple integral terms based on the Enskog-Vlasov(EV) equation is widely employed to describe the inhomogeneous fluids at the nanoscale. However, previous studies have mainly focused on one-dimensional cases, partly due to the significant computational cost $O(NN_{\sigma})$ associated with direct computation of integrals, where $N$ is

Antoniorenee Barletta, Salvatore Cuomo, Nadaniela Egidi, Josephin Giacomini

This work addresses the inverse problem of espresso coffee extraction, in which one aims to reconstruct the brewing conditions that generate a desired chemical profile in the final beverage. Starting from a high-fidelity multiphysics percolation model, describing fluid flow, solute transport, solid, liquid reactions, and heat exchange within the coffee bed,

Yan Zhang, Indrakshi Dey, Nicola Marchetti

In this paper, we propose a novel blockage-aware hierarchical beamforming framework for movable antenna (MA) systems operating at millimeter-wave (mm-Wave) frequencies. While existing works on MA systems have demonstrated performance gains over conventional systems, they often neglect the design of specialized codebooks to leverage MA's unique capabilities a

Tianlang He, Zhongming Lin, Tianrui Jiang, S. -H. Gary Chan

The Long-Range (LoRa) protocol, known for its extensive range and low power, has increasingly been adopted in tags worn by mentally incapacitated persons (MIPs) and others at risk of going missing. We study the sequential decision-making process for a mobile sensor to locate a periodically broadcasting LoRa tag with the fewest moves (hops) in general, unknow

Pierre Calka, Cecilia d'Errico, Nathanaël Enriquez

We consider the typical Poisson-Voronoi cell in the Euclidean space R d and in particular the maximal distance D from a vertex of that cell to its nucleus. We provide a sharp asymptotics for the tail distribution of D. As a byproduct, we prove that the extremal index related to the sequence of such distances for all Voronoi cells included in a large box is e

Vinit Mehta, Charu Sharma, Karthick Thiyagarajan

With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines to perceive, reason and interact with complex environments through natural language and spatial understanding,

Barbara Drinovec Drnovsek, Jure Kalisnik

Given a smooth, open, oriented surface $X$ endowed with a family of complex structures $\{J_b\}_{b\in B}$ depending continuously on the parameter $b$ in a metrisable space $B$, we construct a continuous family of proper holomorphic maps $F_{b}:(X,J_b)\to\mathbb C^{2}$, $b\in B$.

Ludwig Felder, Jacob Miller, Markus Wallinger, Stephen Kobourov

Recent advances in Large Language Models have led to Large Reasoning Models, which produce step-by-step reasoning traces. These traces offer insight into how models think and their goals, improving explainability and helping users follow the logic, learn the process, and even debug errors. These traces, however, are often verbose and complex, making them cog

Heeyeon Kim, Hongseok Kim, Jaewon Song

We propose a new fermionic sum formula for the Macdonald index of a class of Argyres-Douglas theories. The formula arises naturally from a three-dimensional topological field theory obtained via a twisted dimensional reduction of the 4d theory. Such a reduction often gives rise to a 3d ${\mathcal N}=2$ abelian Chern-Simons matter theory, which is expected to

Ansh Kushwaha, Kaushik Gopalan

This paper is a submission for the Weather4Cast~2025 complementary Pollution Task and presents an efficient framework for 6-hour lead-time nowcasting of PM$_1$, PM$_{2.5}$, and PM$_{10}$ across the Indian subcontinent and surrounding regions. The proposed approach leverages analysis fields from the Copernicus Atmosphere Monitoring Service (CAMS) Global Atmos

S. M. Kovalov, I. V. Beznosenko, A. V. Vasyliev, G. V. Sotnikov

This paper presents the development of a compact leak detector for vacuum systems that operates using hydrogen as a tracer gas, detected by a semiconductor MQ-8 sensor. The sensor is connected to an Arduino microcontroller, enabling digital signal processing and real-time visualization through the Processing software environment. The designed device is capab

Victor Cochet, Axel Faccio, Georgios Stoikos, Towsif Taher

We present a Raman-based Distributed Temperature Sensor (RDTS) with centimetre-scale resolution for thermographic analysis of electronic circuits. Temperature is measured along a single-mode fiber routed across a custom printed circuit board (PCB) with 1 cm$^2$ heating elements, using optical time-domain reflectometry of Raman signals detected by superconduc

Ashutosh Jindal, Florentina Nicolau, David Martin Diego, Ravi Banavar

Differential flatness serves as a powerful tool for controlling continuous time nonlinear systems in problems such as motion planning and trajectory tracking. A similar notion, called difference flatness, exists for discrete-time systems. Although many control systems evolve in continuous time, control implementation is performed digitally, requiring discret

Dayong Liang, Xiao-Yong Wei, Changmeng Zheng

Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a promising solution by promoting consensus among multiple agents to enhance reliability, it relies on the unrealistic assumption that all debaters are rational and reflective, which is a condit

Zhenghao Zhang, Jun Xie, Xingchen Chen, Tao Yu

The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest

Farideh Abdollahi, Kourosh Malek, Thomas Kadyk, Nadiia Kulyk

Prognostics and Health Management is crucial for the reliability and lifetime assessment of Polymer Electrolyte Fuel Cells (PEFCs). Here, we review the current advances on this topic, focusing mainly on key degradation mechanisms and methodologies such as physics-aware, data-driven, and hybrid modeling approaches. Key open challenges are analyzed, including

Jun Li, Guang-Hai Guo

In this paper we use two methods to constrain the the canonical single-field slow-roll inflation model. The first method exploits the analytic slow-roll-parameter dependence of primordial perturbations, and the second consists of a phenomenological parameterization of the primordial spectra of both scalar and tensor perturbations. We constrain the slow-roll

Gianluigi Pillonetto, Akram Yazdani, Aleksandr Aravkin

Learning governing equations from data is central to understanding the behavior of physical systems across diverse scientific disciplines, including physics, biology, and engineering. The Sindy algorithm has proven effective in leveraging sparsity to identify concise models of nonlinear dynamical systems. In this paper, we extend sparsity-driven approaches t

Quoc-Huy Trinh

Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment in resource-constrained scenarios such as robotic manipulation, personal assistants, and smart cameras. Most existing methods rely on Transformer-based cross-attention, whose quadra

Sahana H Balasubramanya, Marissa Chesser, Alice Kerr, Johanna Mangahas

We extend the characterization of stable subgroups of right-angled Artin groups of Koberda, Mangahas and Taylor to the case of graph products of infinite groups. Specifically, we show that the stable subgroups of such graph products are exactly the subgroups that quasi-isometrically embed in the associated contact graph. Equivalently, they are the subgroups

Zhixin Xu, Hengyu Zhou, Yuan Liu, Wenhan Xue

Multi-view video reconstruction plays a vital role in computer vision, enabling applications in film production, virtual reality, and motion analysis. While recent advances such as 4D Gaussian Splatting (4DGS) have demonstrated impressive capabilities in dynamic scene reconstruction, they typically rely on the assumption that input video streams are temporal

Julio Aracena, Florian Bridoux, Maximilien Gadouleau, Pierre Guillon

Automata networks can be seen as bare finite dynamical systems, but their growing theory has shown the importance of the underlying communication graph of such networks. This paper tackles the question of what dynamics can be realized up to isomorphism if we suppose that the communication graph has bounded degree. We prove several negative results about para

Hinako Iseki, Shin Sasaki, Kenta Shiozawa

We study quantum mechanical wavefunctions near highly curved spaces, i.e., black holes. By utilizing the formalism developed by DeWitt, we derive the Schr\"odinger equations in the vicinity of the Schwarzschild and the Reissner-Nordstr\"om black hole geometries. The quantum electron cloud for the "black hydrogen atom" - an electron trapped by black holes - i

Mubaraka Sani Ibrahim, Isah Charles Saidu, Lehel Csato

The growing popularity of group activities increased the need to develop methods for providing recommendations to a group of users based on the collective preferences of the group members. Several group recommender systems have been proposed, but these methods often struggle due to sparsity and high-dimensionality of the available data, common in many real-w

Luıs Soeiro, Thomas Robert, Stefano Zacchiroli

Cyberattacks are becoming increasingly frequent and sophisticated, often exploiting the software supply chain (SSC) as an attack vector. Attack graphs provide a detailed representation of the sequence of events and vulnerabilities that could lead to a successful security breach in a system. MulVal is a widely used open-source tool for logical attack graph ge

Julien Collard, Pierre Gentine, Tian Zheng

This paper addresses the critical challenge of improving predictions of climate extreme events, specifically heat waves, using machine learning methods. Our work is framed as a classification problem in which we try to predict whether surface air temperature will exceed its q-th local quantile within a specified timeframe. Our key finding is that aggregating

Ayush Pandey, Jai Bardhan, Ishita Jain, Ramya S Hebbalaguppe

In the context of Visual Question Answering (VQA) and Agentic AI, calibration refers to how closely an AI system's confidence in its answers reflects their actual correctness. This aspect becomes especially important when such systems operate autonomously and must make decisions under visual uncertainty. While modern VQA systems, powered by advanced vision-l

Hangyu Li, Bofeng Cao, Zhaohui Liang, Wuzhen Li

Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets foc

Bikram Keshari Pradhan, Guy Chanfray, Hubert Hansen, Jérôme Margueron

It has been conjectured that, at sufficiently high baryon densities, the equation of state (EoS) of bulk nuclear matter can be identified with that of the nucleon core. In this work, we illustrate how the energy density and pressure distributions inside individual nucleons can be utilized to construct the EoS of supra-dense matter. In our framework, nucleons

Lasse Fischer, Konstantinos Sechidis

The knockoff filter is a powerful tool for controlled variable selection with false discovery rate (FDR) control. In this paper, we leverage e-values to allow the nominal FDR level to be switched post-hoc, after looking at the data and applying the knockoff procedure. This approach addresses a significant limitation of standard knockoffs: while frequently us

Alex George, Lyudmila Mihaylova, Sean Anderson

Explainable anomaly detection methods often have the capability to identify and spatially localise anomalies within an image but lack the capability to differentiate the type of anomaly. Furthermore, they often require the costly training and maintenance of separate models for each object category. The lack of specificity is a significant research gap becaus

Conghao Wong, Ziqian Zou, Beihao Xia, Xinge You

Bridging the past to the future, connecting agents both spatially and temporally, lies at the core of the trajectory prediction task. Despite great efforts, it remains challenging to explicitly learn and predict latencies, i.e., response intervals or temporal delays with which agents respond to various trajectory-changing events and adjust their future paths

Thomas Pethick, Kimon Antonakopoulos, Antonio Silveti-Falls, Leena Chennuru Vankadara

This article reviews modern optimization methods for training neural networks with an emphasis on efficiency and scale. We present state-of-the-art optimization algorithms under a unified algorithmic template that highlights the importance of adapting to the structures in the problem. We then cover how to make these algorithms agnostic to the scale of the pr

Zhanpeng Wang, Shuting Cao, Yuhang Lu, Yuhan Li

The Dual Diffusion Implicit Bridge (DDIB) is an emerging image-to-image (I2I) translation method that preserves cycle consistency while achieving strong flexibility. It links two independently trained diffusion models (DMs) in the source and target domains by first adding noise to a source image to obtain a latent code, then denoising it in the target domain

Yuzhen Zhao, Yating Liu, Marc Hoffmann

This paper addresses the nonparametric estimation of the drift function over a compact domain for a time-homogeneous diffusion process, based on high-frequency discrete observations from $N$ independent trajectories. We propose a neural network-based estimator and derive a non-asymptotic convergence rate, decomposed into a training error, an approximation er

Jiaxin Qi, Yan Cui, Jianqiang Huang, Gaogang Xie

Classes, as fundamental elements of Computer Vision, have been extensively studied within incremental learning frameworks. In contrast, tokens, which play essential roles in many research fields, exhibit similar characteristics of growth, yet investigations into their incremental learning remain significantly scarce. This research gap primarily stems from th

Marco Doretti, Elena Stanghellini, Alessandro Taraborrelli

In regression models with missing outcomes, selection bias can arise when the missingness mechanism depends on the outcome itself. This proposal focuses on an extension of the Heckman model to a setting where the outcome is binary and both the selection process and the outcome are modeled through logistic regression. A correction term analogous to the invers

Qiang Xu, Zhao Liu, Yanming Ma

Electron accumulation in interatomic regions is a fundamental quantum phenomenon dictating chemical bonding and material properties, yet its origin remains elusive across disciplines. Here, we report a quantum accumulation effect -- potential-barrier affinity (PBA) -- revealed by solving the Schr\"odinger equation for a crystalline potential. PBA effect driv

Omri Ben-Dov, Luiz F. O. Chamon

The forward Kullback-Leibler (KL) divergence is a ubiquitous objective for fitting a parameterized distribution to samples due to its tractability and equivalence to maximum likelihood estimation (MLE). Its inherent asymmetry, however, may lead to degenerate solutions that generalize poorly. While the symmetric Jeffreys divergence offers a more balanced alte

S. Shrivastava, M. Rathor, D. Yenurkar, S. K. Chaubey

The coagulation of blood after it is drawn from the body poses a significant challenge for hematological analysis, potentially leading to inaccurate test results and altered cellular characteristics, compromising diagnostic reliability. This paper presents a deep learning-enhanced framework for delineating anticoagulant efficacy ex vivo using Digital Hologra

Francis Bloch, Bhaskar Dutta, Marcin Dziubiński

A planner wants to select one agent out of n agents on the basis of a binary characteristic that is commonly known to all agents but is not observed by the planner. Any pair of agents can either be friends or enemies or impartials of each other. An individual's most preferred outcome is that she be selected. If she is not selected, then she would prefer that

Anand Dessai

In every odd dimension $n\geq 5$ we exhibit large classes of closed $n$-dimensional manifolds which admit infinitely many different geometries of positive Ricci curvature, i.e., manifolds for which their moduli space of metrics of positive Ricci curvature has infinitely many connected components.

Luca Benatti

In this short note, we will prove the equivalence of the isocapacitary notions of mass. This family also includes G. Huisken's isoperimetric mass and J. L. Jauregui's isocapacitary mass.

Aristotelis Magklis, Andreas Kamilaris

Disinfection byproducts are contaminants that can cause long-term effects on human health, occurring in chlorinated drinking water when the disinfectant interacts with natural organic matter. Their formation is affected by many environmental parameters, making it difficult to monitor and detect disinfection byproducts before they reach households. Due to the

Colin-Marius Koch, Michael Wilczek

Systems of active particles can show a large variety of collective behavior. In theory, two aspects determine the collective behavior: the model at the particle level and the parameter regime. While many studies consider a single model and study its parameter regime, here, we focus on the former aspect. Motivated by experiments that study dilute suspensions

Alexander Acker, Soeren Becker, Sasho Nedelkoski, Dominik Scheinert

Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distributed environments. The model trade-offs introduced by this shift remain underexplored, and our goal is to study them. We use the open-source nanochat project, a compact 8K-line full-s

Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj

Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretable deep learning framework for short-term precipitation prediction in four major Indian cities: Bengaluru, Mumbai, Delhi, and Kolkata, spanni

Miguel Aguilera, Daniele De Martino, Ivan Garashchuk, Dmitry Sinelshchikov

Continuous-Time Recurrent Neural Networks (CTRNNs) have been widely used for their capacity to model complex temporal behaviour. However, their internal dynamics often remain difficult to interpret. In this paper, we propose a new class of CTRNNs based on Hopfield-like associative memories with asymmetric couplings. This model combines the expressive power o

Kishan Deka, Pawel Bielewicz

Diffuse contamination from Galactic foreground emission is one of the main concerns for reconstruction of the cosmic microwave background (CMB) lensing potential for next-generation CMB polarisation experiments. Using realistic simulations, we investigated the impact of Galactic foreground residuals from multi-frequency foreground-cleaning methods on CMB len

Xiao Fan, Jingyan Jiang, Zhaoru Chen, Fanding Huang

Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real-world deployments often involve mixed distribution shifts, where test samples are affected by diverse and potentially conflicting domain factors, posing significant challenges eve

Meryem Bouaziz, Leonard Schue, Noeliarinala Felana Andriambelaza, Natalia Alyabyeva

The electronic structure of semiconducting 2D materials such as transition metal dichalcogenides (TMDs) is known to be tunable by its environment, from simple external fields applied with electrical contacts up to complex van der Waals heterostructure assemblies. However, conventional alloying from reference binary TMD compounds to composition-controlled ter

Hadi Barati, Ali Torkaman, Mehdi Fardmanesh

In this study, a novel method for simulating plasma dynamics using parallel programming has been developed. The equations based on Particle-in-Cell (PIC) method were utilized and adapted for this purpose. We utilized 35 processors from Sharif High Performance Computing (HPC) center and divided the plasma volume into 35 parts, with each part's PIC equation so

Julian Alfredo Mendez, Timotheus Kampik

This paper presents the AR fairness metamodel, aimed at formally representing, analyzing, and comparing fairness scenarios. The metamodel provides an abstract representation of fairness, enabling the formal definition of fairness notions. We instantiate the metamodel through several examples, with a particular focus on comparing the notions of equity and equ

Yanjia Huang, Xianshun Jiang, Xiangbo Gao, Mingyang Wu

Vision-and-Language Navigation (VLN) requires agents to follow language instructions while acting in continuous real-world spaces. Prior image imagination based VLN work shows benefits for discrete panoramas but lacks online, action-conditioned predictions and does not produce explicit planning values; moreover, many methods replace the planner with long-hor

Federico Maddanu, Tommaso Proietti, Riccardo Crupi

Detecting point anomalies in bank account balances is essential for financial institutions, as it enables the identification of potential fraud, operational issues, or other irregularities. Robust statistics is useful for flagging outliers and for providing estimates of the data distribution parameters that are not affected by contaminated observations. Howe

Jehyeok Ryu, Victor Krivenkov, Adam Olejniczak, Alexey Y. Nikitin

Metal-halide perovskite nanocrystals (PNCs) have emerged as leading candidates for next-generation quantum emitters, offering a unique combination of high photoluminescence quantum yield, tunable emission, short radiative lifetimes, and record-high single-photon purity under ambient conditions. These properties, together with low-cost and scalable solution-p

Udo Schlegel, Franziska Weeber, Jian Lan, Thomas Seidl

Contrastive Language-Image Pre-training (CLIP) is a widely used multimodal model that aligns text and image representations through large-scale training. While it performs strongly on zero-shot and few-shot tasks, its robustness to linguistic variation, particularly paraphrasing, remains underexplored. Paraphrase robustness is essential for reliable deployme

Ilya Ruchiev, Bryn Davies

Fibonacci time quasicrystals can be approximated by temporal supercells to reveal a fractal collection of $k$ gaps, in which wave energy is amplified exponentially. These estimates are validated by the observation of "super" $k$ gaps that are independent of the duration of the temporal supercell. This approach predicts the regions of parametric amplification

Yiming Rong, Yixin Zhang, Ziyi Wang, Deyang Jiang

Automatic speech recognition (ASR) systems have achieved remarkable performance in common conditions but often struggle to leverage long-context information in contextualized scenarios that require domain-specific knowledge, such as conference presentations. This challenge arises primarily due to constrained model context windows and the sparsity of relevant

Eleonora Andreotti

Lattice structures play a central role in spectral graph theory, offering analytical insight into diffusion, synchronization, and transport processes on regular discrete spaces. While their spectral properties are completely characterized in the classical graph setting, an extension to hypergraphs, where interactions involve more than two nodes, remains larg

Samuel Auroy, Pavlos Protopapas

We propose a framework for solving nonlinear partial differential equations (PDEs) by combining perturbation theory with one-shot transfer learning in Physics-Informed Neural Networks (PINNs). Nonlinear PDEs with polynomial terms are decomposed into a sequence of linear subproblems, which are efficiently solved using a Multi-Head PINN. Once the latent repres

Hussam Al Daas, Nicholas I. M. Gould

We consider an effective new method for solving trust-region and norm-regularization problems that arise as subproblems in many optimization applications. We show that the solutions to such subproblems effectively lie in a very-low-dimensional subspace as a function of their controlling parameters (trust-region radius or regularization weight). Based on this

Jingxuan Wei, Caijun Jia, Xi Bai, Xinglong Xu

The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image gen

Philipp del Hougne

Physics-consistent optimization of reconfigurable intelligent surfaces (RISs) is thwarted in practice by the difficulty of experimentally estimating the mutual coupling (MC) between RIS elements. For large RISs, experimental MC estimation is fundamentally challenging because of the quadratic scaling of the number of unknowns with the number of RIS elements.

M. E. Egwe

In this paper, we elucidate certain properties of the $(2n+1)$-dimensional Heisenberg group, and establish some theorems on the invariant differential operators on the group.

Farnaz Adib Yaghmaie, Arunava Naha

Flow $Q$-learning has recently been introduced to integrate learning from expert demonstrations into an actor-critic structure. Central to this innovation is the ``the one-step policy'' network, which is optimized through a $Q$-function that is regularized with the behavioral cloning from expert trajectories, allowing learning more expressive policies using

Paolo Cermelli, Silvia Marchese, Laura Sacerdote, Cristina Zucca

We study here the social network generated by the asynchronous visits, to a fixed set of sites, of mobile agents modelled as independent random walks on the plane lattice. The social network is constructed by assuming that a group of agents are associated if they have visited the same set of sites within a finite time interval. This construction is an instan