November 2025 arXiv papers — page 75

Michele Mascherpa, Axel Ringh, Amirhossein Taghvaei, Johan Karlsson

We address the problem of identifying the dynamical law governing the evolution of a population of indistinguishable particles, when only aggregate distributions at successive times are observed. Assuming a Markovian evolution on a discrete state space, the task reduces to estimating the underlying transition probability matrix from distributional data. We f

Yoshiaki Horiike, Yuki Kawaguchi

Simulated annealing provides a heuristic solution to combinatorial optimization problems. The cost function of a problem is mapped onto the energy function of a physical many-body system, and, by using thermal or quantum fluctuations, the system explores the state space to find the ground state, which corresponds to the optimal solution of the problem. Studi

German Mino Galaz, Javier Patino Baez, Nicolas Mino Berdu, Jose Gonzalez Suarez

This research reports the entropy transfer throughout the tridimensional structure of PDZ-2 and TIM barrel structures using the dynamic Gaussian Network Model. The model predicts the allocation of the allosteric pathways of the PDZ-2. Moreover. A visualization analysis reveals that entropy and information is transported towards the effector site in PDZ-2 and

Pei Mu, Anderson Chaves Carniel, Antonio Barbalace, Amir Shaikhha

Unreliable cardinality estimation remains a critical performance bottleneck in database management systems (DBMSs). Adaptive Query Processing (AQP) strategies address this limitation by providing a more robust query execution mechanism. Specifically, plan-based AQP achieves this by incrementally refining cardinality using feedback from the execution of sub-p

Doriand Petit, Steve Bourgeois, Vincent Gay-Bellile, Florian Chabot

Developing a multi-modal language model capable of understanding 3D scenes remains challenging due to the limited availability of 3D training data, in contrast to the abundance of 2D datasets used for vision-language models (VLM). As an alternative, we introduce LLaVA$^3$ (pronounced LLaVA-Cube), a novel method that improves the 3D scene understanding capabi

Isaak Mengesha, Meiqi Sun, Debraj Roy

Why do maladaptive perceptions and norms, such as zero-sum interpretations of interaction, persist even when they undermine cooperation and investment? We develop a framework where bounded rationality and heterogeneous cognitive biases shape the evolutionary dynamics of norm coordination. Extending evolutionary game theory with quantal response equilibria an

Stelios Sachpazis

Let $x\geqslant 2$ and assume that $a$ and $q$ are coprime positive integers. As usual, $\psi(x;q,a):=\sum_{n\leqslant x,n\equiv a(\!\!\!\mod{\!\!q})}\Lambda(n)$, where $\Lambda$ is the von Mangoldt function. In 2003, Friedlander and Iwaniec assumed the existence of exceptional characters corresponding to "extreme" Landau-Siegel zeroes and established a mean

L. J. Milligan

SABRE aims to provide a test of the signal observed by DAMA/LIBRA through two separate detectors that rely on joint ultra-high NaI(Tl) purity crystal R&D activities: SABRE South at SUPL Australia and SABRE North at LNGS Italy. SABRE South is designed to disentangle seasonal and site-related effects from the dark matter-like modulated signal. Ultra-high purit

Ziyue Xu, Zhihong Zhang, Holger R. Roth, Chester Chen

Federated Learning (FL) offers a promising solution for training machine learning models across distributed data sources while preserving data privacy. However, FL faces critical challenges related to communication overhead and local resource constraints, especially in the era of Large Language Models (LLMs) with billions of parameters. The sheer size of the

Anne E Blackwell, Joel N Bregman, Sophia Chan Davis

Observed metal abundances in the intracluster medium (ICM) of galaxy groups and clusters, $Z_{ICM}$, exceed what is expected from present-day stellar populations alone. Galaxy clusters are presumed to be near closed-box systems, allowing constraints to be placed on the origins of metals and stellar populations responsible for $Z_{ICM}$. We present a uniform

Gian Mario Sangiovanni, Gianluca Mastrantonio, Daniele Ventura, Alessio Pollice

In ecology, photogrammetry is a crucial method for efficiently collecting non-destructive samples of natural environments. When estimating the spatial distribution of animals, detecting objects in large-scale images becomes crucial. Object detection models enable large-scale analysis but introduce uncertainty because detection probability depends on various

Joy Lai, Alex Mihailidis

People living with dementia (PLwD) often show gradual shifts in how they communicate, becoming less expressive, more repetitive, or drifting off-topic in subtle ways. While caregivers may notice these changes informally, most computational tools are not designed to track such behavioral drift over time. This paper introduces PersonaDrift, a synthetic benchma

Changming Huang, Qidong Fu, Li Ma

We study the existence and dynamical properties of vortex solitons in Kerr media supported by azimuthally modulated waveguide lattices with localized gain and nonlinear loss. In this dissipative system, we find that the accessible topological charge of vortex solitons is strongly determined by the number of waveguide channels, with higher-order charges requi

Tuuli Sillanpää, Sanni Nousiainen, Lasse Laurson

Elastic interfaces in quenched random media driven by external forces exhibit a continuous depinning phase transition between pinned and moving phases at a critical external force. Recent work [Phys. Rev. Lett. 129, 175701 (2022)] has shown that the distribution of local interface heights at depinning displays negative skewness. Here, by considering local, l

Benoît Loridant, Jörg M. Thuswaldner, Shu-Qin Zhang

Although the theory of self-affine tiles and the theory of Rauzy fractals are quite different from each other, they have some common features. Both, self-affine tiles and Rauzy fractals have tiling properties and these tiling properties can be checked and described by certain graphs, so-called {\it contact graphs} and {\it neighbor graphs}. The contact graph

Benjamin J. Farcy, Darryl Z. Seligman, Kathleen E. Mandt, John W. Noonan

The breadth of topics that encompass the search for life has expanded and evolved significantly since the emergence of the field of astrobiology. Initial astrobiology centered investigations focused on detecting biosignatures in the Martian soil with the Viking lander. The field now encompasses identification of biosignatures throughout the galaxy and habita

Jianing Zhang, Rumei Liu

In this work, a nonlinear momentum method is introduced to enhance the convergence performance of momentum-based gradient optimization algorithms. Classical momentum methods, such as the Heavy Ball method, can be viewed as a dynamical system with quadratic kinetic energy and linear damping. By extending this analogy to non-Newtonian dynamical systems, we con

Diogo J. Paulo, João Martins, Hugo Proença, João C. Neves

Urban waste management remains a critical challenge for the development of smart cities. Despite the growing number of litter detection datasets, the problem of monitoring overflowing waste containers, particularly from images captured by garbage trucks, has received little attention. While existing datasets are valuable, they often lack annotations for spec

Idan Fonea, Amir Peles, Sivan Niv, Goren Gordon

We analyze an ensemble-based approach for uncertainty quantification (UQ) in atomistic neural networks. This method generates an epistemic uncertainty signal without requiring changes to the underlying multi-headed regression neural network architecture, making it suitable for sealed or black-box models. We apply this method to molecular systems, specificall

Sherine George, Nithish Saji

We present ESGBench, a benchmark dataset and evaluation framework designed to assess explainable ESG question answering systems using corporate sustainability reports. The benchmark consists of domain-grounded questions across multiple ESG themes, paired with human-curated answers and supporting evidence to enable fine-grained evaluation of model reasoning.

Meike Driessen, Selina Khan, Gonçalo Marcelino

This project explores how we engage with AI-generated content through the lens of the jutter: Dutch coastal foragers who comb the shoreline after storms, gathering and repurposing what the sea leaves behind. Reflecting how our lives are increasingly shaped by AI-generated media, we create a beach-like installation that blends real shoreline debris with AI-tr

A. O. Korotkevich, A. O. Prokofiev

Through a massive computation we reached the fourth superharmonic instability branch of the Stokes' wave. Using the obtained results we checked phenomenological formulae for the dependence of the instability growth rates corresponding to different branches of instability on the nonlinearity parameter (steepness, defined as the wave \red{hight} to wavelength

Jin Wang, Bingfeng Zhang, Jian Pang, Mengyu Liu

Few-shot segmentation (FSS) aims to segment novel classes under the guidance of limited support samples by a meta-learning paradigm. Existing methods mainly mine references from support images as meta guidance. However, due to intra-class variations among visual representations, the meta information extracted from support images cannot produce accurate guida

Chenming Wu, Xiaofan Li, Chengkai Dai

Current text-to-3D models prioritize visual fidelity but often neglect physical fabricability, resulting in geometries requiring excessive support structures. This paper introduces SEG (\textit{\underline{S}upport-\underline{E}ffective \underline{G}eneration}), a novel framework that integrates Direct Preference Optimization with an Offset (ODPO) into the 3D

Simin Pang, Zhengyi Li, Ziyu Wang, Yanpei Lv

Magnetostatic surface spin wave (MSSW) solitons hold great promise for magnonic information processing, but their existence has long been debated. In this work, we resolve this issue by advanced time-resolved Brillouin light scattering (TR-BLS) spectroscopy. We observe long-period MSSW soliton trains in yttrium iron garnet (YIG) thin films by demonstrating t

Claudius Gros

The big strides seen in generative AI are not based on somewhat obscure algorithms, but due to clearly defined generative principles. The resulting concrete implementations have proven themselves in large numbers of applications. We suggest that it is imperative to thoroughly investigate which of these generative principles may be operative also in the brain

Yihan Li, Nikhil Churamani, Maria Robu, Imanol Luengo

Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle with occlusions, long-range dependencies, and capturing the fine-scale geometry of rare structures. This work addresses these challenges by introducing graph-based segmentation appr

Samer Abualhanud, Christian Grannemann, Max Mehltretter

Self-supervised surround-view depth estimation enables dense, low-cost 3D perception with a 360{\deg} field of view from multiple minimally overlapping images. Yet, most existing methods suffer from depth estimates that are inconsistent across overlapping images. To address this limitation, we propose a novel geometry-guided method for calibrated, time-synch

Muhammad Aslanimoghanloo, Ahmed ElGazzar, Marcel van Gerven

Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant challenges due to irregular sampling, complex latent physiology, and inherent uncertainties in both measurements and disease

Seyed Mohamad Moghadas, Bruno Cornelis, Adrian Munteanu

Multivariate time-series (MTS) forecasting is fundamental to applications ranging from urban mobility and resource management to climate modeling. While recent generative models based on denoising diffusion have advanced state-of-the-art performance in capturing complex data distributions, they suffer from significant computational overhead due to iterative

Ákos M. Bokor, Tamás Dózsa, Felix Biertümpfel, Ádám Szabó

This paper presents a computationally efficient approach for robust Model Predictive Control of nonlinear systems by combining Random Fourier Features with tube-based MPC. Tube-based Model Predictive Control provides robust constraint satisfaction under bounded model uncertainties arising from approximation errors and external disturbances. The Random Fourie

Samuel Mallick, Filippo Airaldi, Azita Dabiri, Bart De Schutter

The state of the art for model predictive control (MPC)-based distributed Q-learning is limited to first-order gradient updates of the MPC parameterization. In general, using secondorder information can significantly improve the speed of convergence for learning, allowing the use of higher learning rates without introducing instability. This work presents a

Li Zhang, Zhongxuan Han, XiaoHua Feng, Jiaming Zhang

Efficient and lightweight adaptation of pre-trained Vision-Language Models (VLMs) to downstream tasks through collaborative interactions between local clients and a central server is a rapidly emerging research topic in federated learning. Existing adaptation algorithms are typically trained iteratively, which incur significant communication costs and increa

V. Sunko, J. Orenstein

Altermagnets are a class of collinear magnets that exhibit non-relativistic spin splitting (NRSS) of electronic bands in the absence of net magnetization. Their potential to generate large spin polarization without spin-orbit coupling has created strong interest in probes that access the underlying order parameter directly. In this Perspective, we show that

Shaked Regev, Eve Tsybina, Slaven Peles

The rapid growth of data centers increasingly requires data center operators to "bring own generation" to complement the available utility power plants to supply all or part of data center load. This practice sharply increases the number of generators on the bulk power system and shifts operational focus toward fuel costs rather than traditional startup and

Hai Lan, Zongyan Li, Jianmin Hu, Jialing Yang

Marker-based optical motion capture (MoCap), while long regarded as the gold standard for accuracy, faces practical challenges, such as time-consuming preparation and marker identification ambiguity, due to its reliance on dense marker configurations, which fundamentally limit its scalability. To address this, we introduce a novel fundamental unit for MoCap,

Yan Chen, Yu Zou, Jialei Zeng, Haoran You

Environmental, Social, and Governance (ESG) principles are reshaping the foundations of global financial governance, transforming capital allocation architectures, regulatory frameworks, and systemic risk coordination mechanisms. However, as the core medium for assessing corporate ESG performance, the ESG reports present significant challenges for large-scal

Connor McElroy, Thiago E. A. de Oliveira, Chris Brogly

This study explored whether supervised machine learning and deep learning models can effectively distinguish perceived lower-quality news articles from perceived higher-quality news articles. 3 machine learning classifiers and 3 deep learning models were assessed using a newly created dataset of 1,412,272 English news articles from the Common Crawl over 2018

Shohei D. Aoyama, Ken Osato, Masato Shirasaki

The matter distribution of the Universe can be mapped through the weak gravitational lensing (WL) effect: small distortions of the shapes of distant galaxies, which reflects the inhomogeneity of the cosmic density field. The most dominant contaminant in the WL effect is the shape noise; the signal is diluted due to the finite number of source galaxies. In or

Hao Liu, Le Wu, Min Hou, Han Wu

Nowadays, Large Language Models (LLMs) have shown exceptional performance in sequential recommendations, and the adoption of LLM-based recommender systems (LLMRec) is becoming increasingly widespread in existing e-commerce platforms. Despite the impressive performance, the constant high volume of new user-item interactions makes it difficult to adapt to the

Anıl Erdinç Türetken, Hakan Ersoy, Aslihan Kartci

In this study, we compare a model reference control (MRC) strategy against conventional PID controllers (tuned via metaheuristic algorithms) for surge velocity control of a thruster-driven marine system, under combined wave disturbance and sensor noise. The goal is to evaluate not only tracking performance but also control energy usage and actuator stress. A

Luca Venerando Greco

Predictive modeling of stellarator plasmas is crucial for advancing nuclear fusion energy, yet it faces unique computational difficulties. One of the main challenges is accurately simulating the dynamics of specific particle species that are not well captured by fluid models, which necessitates the use of hybrid fluid-kinetic models. The non-axisymmetric geo

Jay Anderson, Sylvia Baggett

HST is designed to use two guide stars (GSs) in the fine-guidance sensors (FGSs) to maintain its pointing and tracking during exposures. The primary GS holds the boresight fixed and the secondary GS keeps the orientation fixed. However, HST is also able to track using only a single GS by fixing the boresite on one star and maintaining the orientation using t

Mazyar Taghavi, Javad Vahidi

Multi-Agent Reinforcement Learning (MARL) has emerged as a powerfulparadigm for cooperative decision-making in connected autonomous vehicles(CAVs); however, existing approaches often fail to guarantee stability, optimality,and interpretability in systems characterized by nonlinear dynamics,partial observability, and complex inter-agent coupling. This study a

Hina Saeeda, Tommy Johansson, Mazen Mohamad, Eric Knauss

Data annotation is essential but highly error-prone in the development of AI-enabled perception systems (AIePS) for automated driving, and its quality directly influences model performance, safety, and reliability. However, the industry lacks empirical insights into how annotation errors emerge and spread across the multi-organisational automotive supply cha

Kevin Li, Luis Jorge Sánchez Saldaña

For $n\in \mathbb{N}$, a group is called $n$-coherent if every subgroup of type $\mathsf{F}_n$ is of type $\mathsf{F}_{n+1}$. For $n\ge 1$, we observe that graphs of groups with $n$-coherent vertex groups and virtually poly-cyclic edge groups are $n$-coherent. We deduce the $n$-coherence of certain right-angled Artin groups.

A. Elykov, S. Vetter, V. H. S. Wu, A. Deisting

Dual-phase liquid xenon time projection chambers are the core detector elements of many experiments that conduct searches for Dark Matter and rare events, as well as in neutrino and high-energy physics. As part of this detector technology, high-voltage electrodes are instrumental for the generation of observable signals and their physical interpretation. Thu

Xizhou Bu, Jiexi Lyu, Fulei Sun, Ruichen Yang

Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant distractors. Although incorporating action supervision can alleviate these distractions, its effectiveness is restricted by the scarcity of available action labels. Optical flow rep

Luis Luna, Isaac Chairez, Andrey Polyakov

Mobile robotic manipulators (MRMs), which integrate mobility and manipulation capabilities, present significant control challenges due to their nonlinear dynamics, underactuation, and coupling between the base and manipulator subsystems. This paper proposes a novel homogeneous Proportional-Integral-Derivative (hPID) control strategy tailored for MRMs to achi

Tau Bito, Akimasa Kataoka, Takahiro Ueda, Luca Ricci

The measurement of dust size from millimeter-wavelength spectra provides direct constraints on grain growth in protoplanetary disks. The spectral indices between 0.88 mm and 2.9 mm have been measured in multiple young star-forming regions, such as Taurus, Ophiuchus, and Lupus, which have ages of 1-3 Myr. These spectral indices are as low as 2-3, suggesting t

Joseph P. Conlon, Edmund J. Copeland, Edward Hardy, Noelia Sánchez González

When moduli roll in the early universe, all physical scales - including string tensions - simultaneously evolve. The dynamics of cosmic string loops with time-varying tension can produce cosmic string loop trackers in which most of the energy density of the universe lies in the form of string loops. This solution can exist as an attractor until the rolling m

Lucas Alland, Andrei Fridman, Thomas Michael Keller

Given a finite group $G$, its prime graph $\Gamma(G)$ (also known as its Gruenberg-Kegel graph) is the graph whose vertices are the prime divisors of $|G|$ and where edges $\{p, q\}$ exist whenever $G$ contains an element of order $pq$. We continue the study of prime graphs for $T$-solvable groups; that is, groups whose composition factors are either abelian

Jacopo Tagliabue, Federico Bianchi, Ciro Greco

Even as AI capabilities improve, most enterprises do not consider agents trustworthy enough to work on production data. In this paper, we argue that the path to trustworthy agentic workflows begins with solving the infrastructure problem first: traditional lakehouses are not suited for agent access patterns, but if we design one around transactions, governan

Muhammad Ghulam Khuwajah Khan

We explore a phenomenological model of dark energy in which space is treated as an elastic brane with uniform tension $T_s$ and supports a longitudinal phonon sector described by three scalar fields $\phi^I$. At the background level the construction reproduces a perfect fluid whose enthalpy and bulk modulus are controlled by two dimensionless parameters $\va

Yi Yao

Donaldson showed that the constant scalar curvature K\"ahler metrics can be quantized by the balanced Hermitian norms on the spaces of global sections. We explore an analogous problem in the unstable situation. For a K-unstable manifold $(X,L)$, its projective embedding via $\left|kL\right|$ will be Chow-unstable when $k$ is sufficiently large and divisible.

Wenyuan Yang

We prove that every acylindrically hyperbolic group admits a minimal and extremely proximal action on a compact metrizable space. If there are no nontrivial finite normal subgroups, then the action is topologically free. This answers positively a question of Ozawa and the applications to $C^\ast$-algebras are discussed.

Siddhesh Pimpale

Electric vehicles (EVs) have drastically changed the auto industry and developed a new era of technologies where power electronics play the leading role in traction management, energy conversion and vehicle control processes. Nevertheless, this is a digital transformation, and the cyber-attack surface area has increased considerably, to the point that EV tra

Yidong Chai, Haoxin Liu, Jiaheng Xie, Chaopeng Wang

Wearable sensor technologies and deep learning are transforming healthcare management. Yet, most health sensing studies focus narrowly on physical chronic diseases. This overlooks the critical need for joint assessment of comorbid physical chronic diseases and depression, which is essential for collaborative chronic care. We conceptualize multi-disease asses

Ren Ma, Jiantao Qiu, Chao Xu, Pei Chu

While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing step. Existing web corpora rely on heuristic-based extractors like Trafilatura, which struggle to preserve document structure and frequently corrupt structured elements such as form

Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann, Bing Li

Large Language Models (LLMs) have demonstrated remarkable potential in hardware front-end design using hardware description languages (HDLs). However, their inherent tendency toward hallucination often introduces functional errors into the generated HDL designs. To address this issue, we propose the framework CorrectHDL that leverages high-level synthesis (H

Anne O'Connor, Varun Bajaj

We examine the relative offsets of the linear terms in the geometric distortion between WFC3/IR and the Gaia DR3 catalog using the Mikulski Archive for Space Telescopes (MAST) pipeline WFC3/IR to Gaia DR3 alignment solutions to assess temporal stability over the lifetime of the WFC3 instrument (2009-2024). We find a period of increased uncertainty and offset

Anne O'Connor, Harish Khandrika

In this report, we examine the behavior of Charge Transfer Efficiency (CTE) on the WFC3/UVIS detector over time as computed by the Extended Pixel Edge Response (EPER) technique, using internal calibration data acquired from 2009 through 2025. We find that the CTE has continued to decline as expected, with a steeper loss rate for lower signal levels. The lowe

Ajith Anil Meera, Poppy Collis, Polina Arbuzova, Abián Torres

Robots today often miss a key ingredient of truly intelligent behavior: the ability to reflect on their own cognitive processes and decisions. In humans, this self-monitoring or metacognition is crucial for learning, decision making and problem solving. For instance, they can evaluate how confident they are in performing a task, thus regulating their own beh

Melanie Birke, Tim Greger

Compared to nonparametric estimators in the multivariate setting, kernel estimators for functional data models have a larger order of bias. This is problematic for constructing confidence regions or statistical tests since the bias might not be negligible. It stems from the fact that one sided kernels are used where already the first moment of the kernel is

Tom Markvart

This paper shows a fundamental thermodynamic similarity between thermoelectric and photovoltaic energy converters which, at open circuit, can be represented as isochoric engines generating a finite chemical potential which appears as voltage at the terminals of the device. We show that, allowing for the temperature variation of the Seebeck coefficient, the m

Hongjie Dong, Hongjie Li, Longjuan Xu

In this paper, we investigate wave scattering by a pair of closely spaced inclusions embedded in a homogeneous medium, characterized by a high contrast physical parameters. The system is modeled by the two-dimensional Helmholtz equation. We show that this configuration exhibits two sub-wavelength resonant modes, whose frequencies display distinct leading-ord

Changming Huang, Alexander V. Kireev, Yuxin Jiang, Victor O. Kompanets

In higher-order topological insulators (HOTIs), topologically nontrivial phases are usually associated with the shift of Wannier centers to topologically nontrivial positions on the edges of the unit cells, and the emergence of fractional spectral charges in the corners of the lattice upon its truncation that keeps the number of its unit cells integer. Here

Sathish Kumar Paramasivam, Andrea Perali, Milorad V. Milošević

We reveal the key role of the $d$-wave symmetry of the superconducting gap in strongly coupled two-dimensional superconductors in determining the properties of the Berezinskii-Kosterlitz-Thouless (BKT) transition, associated with a sizable enhancement of the phase stiffness compared to nodeless-gap superconductors. The enhanced stiffness originates from exte

Denny M. Oliveira, Mirko Piersanti, Maria-Theresia Walach, Livia R. Alves

Editorial for the Research Topic collection Impacts of the Extreme Gannon Geomagnetic Storm of May 2024 throughout the Magnetosphere-Ionosphere-Thermosphere System, published in Frontiers in Astronomy and Space Science.

Alexander Zadorojniy, Segev Wasserkrug, Eitan Farchi

Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is how to validate that the generated models are correct and satisfy the requirements defined in the natural language description. In this work, we propose a novel agent-based method f

Neil Mulcahy, Syeda Ramin Jannat, Yaqi Li, Tigran Simonian

Understanding electrochemical phenomena at complex liquid solid interfaces requires linking real time structural dynamics with atomic scale interfacial chemistry. Here, we integrate operando synchrotron X-ray fluorescence and diffraction with high resolution cryogenic electron and ion multi model microscopy to provide a mechanistic understanding of Pt based

Xing Lin, Shuang Zhang

Quantum interference underpins many quantum information protocols but is typically studied in lossless Hermitian systems. Here, we reveal an exceptional point induced phase transition in two photon Hong Ou Mandel interference within a lossy coupled waveguide system. In the PT symmetric phase. interference is ultrasensitive to coupling strength, yielding shar

Kibok Jeong, Soochang Lee

We propose a field theoretic realization of the non-unitary $\mathfrak{sl}(2,\mathbb{C})$ Witten-Reshetikhin-Turaev Topological Quantum Field Theory(WRT TQFT). The WRT TQFT at the principal root of unity is unitary. It is known to be realized by $\mathrm{SU}(2)$ Chern-Simons theory. However, the WRT TQFT at a non-principal root of unity is non-unitary. Its f

Igor Lugo, Martha G. Alatriste-Contreras, Brenda G. Coutiño-Vázquez

This study presents a theoretical model for identifying emergent scenarios of inclusiveness related to student with special education needs (SEN). Based on variations of the Shelling model of segregation, we explored the propagation of thinking about others as equals (empathy) in students with and without $SEN$ in school environments. We use the complex syst

Pan Yang, Cheng Deng, Jing Yang, Han Zhao

Compositional zero-shot learning (CZSL) aims to learn the concepts of attributes and objects in seen compositions and to recognize their unseen compositions. Most Contrastive Language-Image Pre-training (CLIP)-based CZSL methods focus on disentangling attributes and objects by leveraging the global semantic representation obtained from the image encoder. How

Hrad Ghoukasian, Shahab Asoodeh

We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first derive a closed-form optimal mechanism for binary sensitive attributes and then develop a tractable optimization framework that yields the corresponding optimal mechanism for multi-

Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun, Jeongyun Han

The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we introduce AssurAI, a new quality-controlled Korean multim

Taki E. M. Abedesselam, Fabio Giacomelli, Francesco Pasquale, Michele Salvi

We study a random process over graphs inspired by the way payments are executed in the Lightning Network, the main layer-two solution on top of Bitcoin. We first prove almost tight upper and lower bounds on the time it takes for a payment failure to occur, as a function of the number of nodes and the edge capacities, when the underlying graph is complete. Th

Marcin Kostrzewa, Oleksii Furman, Roman Furman, Sebastian Tomczak

Foundation models have shown promise across various financial applications, yet their effectiveness for corporate bankruptcy prediction remains systematically unevaluated against established methods. We study bankruptcy forecasting using Llama-3.3-70B-Instruct and TabPFN, evaluated on large, highly imbalanced datasets of over one million company records from

Abdelrahman Helaly, Nourhan Sakr, Kareem Madkour, Ilhami Torunoglu

Multipatterning is an essential decomposition strategy in electronic design automation (EDA) that overcomes lithographic limitations when printing dense circuit layouts. Although heuristic-based backtracking and SAT solvers can address these challenges, they often struggle to simultaneously handle both complex constraints and secondary objectives. In this st

Anna Luiza Gomes da Silva, Diego Kreutz, Angelo Diniz, Rodrigo Mansilha

Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. This work integrates into MalDataGen a Super-Metric that aggregates eight metrics across four fidelity dimensions, producing a single weighted score. Experiments involving ten generat

Amirreza Rouhi, Vishal Kumar, Oriol Lehmkuhl, Wen Wu

We conduct direct numerical simulations of separating turbulent boundary layers (TBLs) over triangular riblets with tip angles $90^o$ (T9) and $60^o$ (T6). Our setup follows the separating TBL study of Wu et al.\ ({\it J. Fluid Mech.}, vol.\ 883, 2020, p.\ A45). An equilibrium zero pressure-gradient (ZPG) TBL is generated at a reference location, followed by

Bowen Xu, Zexuan Yan, Minghao Lu, Xiyu Fan

Challenges in traversing dynamic clutters lie mainly in the efficient perception of the environmental dynamics and the generation of evasive behaviors considering obstacle movement. Previous solutions have made progress in explicitly modeling the dynamic obstacle motion for avoidance, but this key dependency of decision-making is time-consuming and unreliabl

V. Blavatska, Ja. Ilnytskyi, E. Lähderanta

Coagulation-flocculation of pollutants and chelation of heavy metal ions are two widely used techniques in wastewater purification. Despite the differences between their respective mechanisms and inherent length scales, they bear much similarity on a larger scale, and can both be treated as adsorption of obstacles on a polymer structure. In this regime, thei

Dag Evensberget, Aline A. Vidotto, Filip Elekes, Sandra V. Jeffers

Recent low-frequency array (LOFAR) radio signal detections bearing from the $\tau$ Bo\"otis system have been cautiously attributed to auroral emissions from the hot Jupiter $\tau$ Bo\"otis Ab. The auroral emissions are believed to be excited by interaction between the exoplanet and the winds of its host star. Since stellar winds respond to stellar surface ma

Jaron Fontaine, Mohammad Cheraghinia, John Strassner, Adnan Shahid

Recent advances in Wireless Physical Layer Foundation Models (WPFMs) promise a new paradigm of universal Radio Frequency (RF) representations. However, these models inherit critical limitations found in deep learning such as the lack of explainability, robustness, adaptability, and verifiable compliance with physical and regulatory constraints. In addition,

Yingjie Qi, Jianlei Yang, Rubing Yang, Cenlin Duan

Compute-in-memory (CIM) has emerged as a pivotal direction for accelerating workloads in the field of machine learning, such as Deep Neural Networks (DNNs). However, the effective exploitation of sparsity in CIM systems presents numerous challenges, due to the inherent limitations in their rigid array structures. Designing sparse DNN dataflows and developing

János Flesch, Christopher Kops, Dries Vermeulen, Anna Zseleva

We propose a general definition of perfect equilibrium which is applicable to a wide class of games. A key feature is the concept of completely mixed nets of strategies, based on a more detailed notion of carrier of a strategy. Under standard topological conditions, this definition yields a nonempty and compact set of perfect equilibria. For finite action se

Gustavo Laranja Thomaello, Thomaz Yeiden Busnardo Aguena, Eric Trevelato Costa, Rafael Baságlia Rosante

In this work, we present web scraping techniques to extract in- formation from patent tables, clean and structure them for future use in predictive machine learning models to develop new glasses. We extracted compositions and three properties relevant to the development of new glasses and structured them into a database to be used together with information f

Meng-Cheng Shih, Tsai-Ling Huang, Yu-Heng Shih, Hong-Han Shuai

Offline signature verification (OSV) is a frequently utilized technology in forensics. This paper proposes a new model, DetailSemNet, for OSV. Unlike previous methods that rely on holistic features for pair comparisons, our approach underscores the significance of fine-grained differences for robust OSV. We propose to match local structures between two signa

Vishal Kumar, Melissa Kozul, Wen Wu, Oriol Lehmkuhl

We computationally study the response of zero-pressure-gradient (ZPG) turbulent boundary layers (TBLs) to streamwise step changes from a smooth wall to riblets (SM_RI), and vice versa (RI_SM). To quantify the departure from equilibrium due to the step changes, we conduct reference calculations of ZPG TBLs over an entirely smooth wall, and an entirely riblet-

Arpan Bhattacharyya, Saptaswa Ghosh, Sounak Pal, Jagannath Santara

We explore several aspects of the categorical symmetry-resolved entanglement entropy (SREE) in two-dimensional Rational Conformal Field Theories (RCFTs) and express it directly in terms of the modular data of the theory. Motivated by arXiv:2409.02806, we provide a general formula for SREE that applies to symmetric (weakly/strongly) and cloaking boundary cond

Nizar Bousselmi, Zhicheng Deng, Jie Lu, Francois Glineur

The worst-case performance of an optimization method on a problem class can be analyzed using a finite description of the problem class, known as interpolation conditions. In this work, we study interpolation conditions for linear operators given scalar products between discrete inputs and outputs. First, we show that if only convex constraints on the scalar

Ziyi Gan, Chunfeng Cui

The fully-connected tensor network (FCTN) decomposition has recently exhibited strong modeling capabilities by connecting every pair of tensor factors, thereby capturing rich cross-mode correlations. However, this advantage comes with an inherent limitation: updating the factors typically requires reconstructing auxiliary sub-networks, which entails extensiv

Chengqi Zang, Gabriel P. Andrade

We study two-sided market design for goods whose utility perishes if unconsumed. Motivated by decentralized compute markets, we propose a mechanism that decouples price discovery from allocation; a load-based posted-price rule determines a per-period market price, while a greedy matching algorithm with second-price payments handles job assignment. We prove e

Cheng Li, Meihao Liao, Rong-Hua Li, Guoren Wang

Kemeny constant, defined as the expected hitting time of random walks from a source node to a randomly chosen target node, is a fundamental metric in graph data management with many real-world applications. However, computing it exactly on large graphs is highly challenging, as it requires inverting large graph matrices. Existing solutions mainly rely on app

Emanuel Dorbath, Fabian Rudolf, Adnan Gulzar, Gerhard Stock

Allostery, the intriguing phenomenon of long-range communication between distant sites in proteins, plays a central role in biomolecular regulation and signal transduction. While it is commonly attributed to conformational rearrangements, the underlying dynamical mechanisms remain poorly understood. The contact cluster model of allostery [J. Chem. Theory Com

Takumi Kuwahara, Yoshiki Uchida

We investigate a cogenesis scenario for composite asymmetric dark matter framework: a dark sector has a similar strong dynamics to quantum chromodynamics in the standard model, and the dark-sector counterpart of baryons is the dark matter candidate. The Hawking evaporation of primordial black holes plays the role of a source of heavy scalar particles whose $

Jonathan Kamp, Lisa Beinborn, Antske Fokkens

Human explanations of natural language, rationales, form a tool to assess whether models learn a label for the right reasons or rely on dataset-specific shortcuts. Sufficiency is a common metric for estimating the informativeness of rationales, but it provides limited insight into the effects of rationale information on model performance. We address this lim

Till-Yannic Müller, Frederik Zumegen, Reinhard Wiesmayr, Emre Gönültaş

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hard