arXiv papers from August 2025

Ibrahim Khalife, Ali Abbasi, Zhe Feng, Mingda Zhou

We consider time varying MIMO fading channels with known spatial and temporal correlation and solve the problem of joint carrier frequency offset (CFO) and channel estimation with prior distributions. The maximum a posteriori probability (MAP) joint estimation is proved to be equivalent to a separate MAP estimation of the CFO followed by minimum mean square

Xiaozhou Ye, Kevin I-Kai Wang

Human Activity Recognition (HAR) using wearable sensors is crucial for healthcare, fitness tracking, and smart environments, yet cross-user variability -- stemming from diverse motion patterns, sensor placements, and physiological traits -- hampers generalization in real-world settings. Conventional supervised learning methods often overfit to user-specific

Alexis Horde-Vo, Matt Duckham, Estrid He, Rafe Benli

Who is the "Batman" behind "Batman Street" in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has shaped a community. Although place names serve as essential spatial references in gazetteers, they often lack information about place name origins. Enriching these place names in toda

Jorge Antonio Cruz Chapital

In this paper we show that for every $2\leq n\in \mathbb{N}$, the statement "there is an $n$-entangled set, but there are no $n+1$-entangled sets" is consistent. We also prove some theorems which improve our understanding of entangled sets in relation to construction schemes: (1) The axiom FCA$^\Delta$ introduced in \cite{finitizationclubch} implies the exis

Zixin Zhu, Kevin Duarte, Mamshad Nayeem Rizve, Chengyuan Xu

In text-to-image (T2I) generation, achieving fine-grained control over attributes - such as age or smile - remains challenging, even with detailed text prompts. Slider-based methods offer a solution for precise control of image attributes. Existing approaches typically train individual adapter for each attribute separately, overlooking the entanglement among

John D. Clemens

We show that a set of non-negative reals is the distance set of a separable complete metric space if and only if it is either countable or is an analytic set which has 0 as a limit point. We also consider spaces with simpler distance sets.

Kotomi Taniguchi, Ryan M. Lau, Takashi Onaka, Macarena Garcia Marin

We have analyzed the aromatic infrared bands (AIBs) in the 6-11.2 $\mu$m range around the Wolf-Rayet binary WR140 (d=1.64 kpc) obtained with the James Webb Space Telescope (JWST) Mid-Infrared Instrument (MIRI) Medium-Resolution Spectrometer (MRS). In WR140's circumstellar environment, we have detected AIBs at 6 $\mu$m and 7.7 $\mu$m which are attributed to C

Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich, Yilun Du

Masked diffusion models (MDMs) have recently emerged as a promising alternative to autoregressive models over discrete domains. MDMs generate sequences in an any-order, parallel fashion, enabling fast inference and strong performance on non-causal tasks. However, a crucial limitation is that they do not support token insertions and are thus limited to fixed-

P. L. Krapivsky, A. Yu. Plakhov

A compromise process describes the evolution of opinions through binary interactions. Opinions are real numbers, and at each step, two randomly selected agents reach a compromise by averaging their pre-interaction opinions. We prove that if the number $N$ of agents is a power of two, then consensus emerges after a finite number of compromise events with prob

Junming Xie

In this paper, we investigate the convexity of mean convex asymptotically conical self-expanders to the mean curvature flow in $\mathbb{R}^{n+1}$. Specifically, for $n\geq 3$, we show that any $n$-dimensional complete mean convex self-expander asymptotic to mean convex and weakly convex cones must be strictly convex.

Bo Fu, Zhe Chen, Rahul Chandan, Alex Barbosa

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveragin

Yukio-Pegio Gunji, Andrew Adamatzky, Panagiotis Mougkogiannis, Andrei Khrenikov

By uncovering the contrast between Artificial Intelligence and Natural-born Intelligence as a computational process, we define closed computing and open computing, and implement open computing within chemical reactions. This involves forming a mixture and invalidation of the computational process and the execution environment, which are logically distinct, a

Elena Espinosa, Rubén Rodríguez Álvarez, José Miranda, Rafael Larrosa

The advent of next-generation sequencing (NGS) has revolutionized genomic research by enabling cost-effective, high-throughput sequencing of a diverse range of organisms. This breakthrough has unleashed a "Cambrian explosion" in genomic data volume and diversity. This volume of workloads places genomics among the top four big data challenges anticipated for

Scarlett Raine, Emilio Olivastri, Benjamin Moshirian, Tobias Fischer

Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Reef restoration is crucial, but its success hinges on introducing automation to upscale efforts. In this work, we present a highly configurable AI pipeline for the real-time deployment

Matthew Varona, Karen Bonilla, Maryam Hedayati, Alark Joshi

Research in visualization literacy explores the skills required to engage with visualizations. This state-of-the-art report surveys the current literature in visualization literacy to provide a comprehensive overview of the field. We propose a taxonomy of visualization literacy that organizes the field into competency themes and research categories. To addre

Nabanita Das, Misty C. Bentz, Eugene Vasiliev, Monica Valluri

We present the stellar dynamical mass of the central black hole in the nearby Seyfert galaxy MCG$-$06-30-15 using the Schwarzschild orbit-superposition method implemented in the open-source code FORSTAND. We obtained spatially resolved $K$-band nuclear stellar spectra for this galaxy with SINFONI on the VLT. We extracted the bulk stellar kinematics using Gau

Aishni Parab, Hongjing Lu, Ying Nian Wu, Sumit Gulwani

Inductive reasoning enables humans to infer abstract rules from limited examples and apply them to novel situations. In this work, we compare an LLM-based hypothesis search framework with direct program generation approaches on few-shot rule induction tasks. Our findings show that hypothesis search achieves performance comparable to humans, while direct prog

Dragan Stankov

We introduce the ratio of the number of roots, not equal to 1 in modulus, of a reciprocal polynomial $R_d(x)$ to its degree $d$. For some sequences of reciprocal polynomials we show that the ratio has a limit $L$ when $d$ tends to infinity. Each of these sequences is defined using a two-variable polynomial $P(x,y)$ so that $R_d(x) = P(x,x^n)$. We present a f

Miguel Vanvlasselaer

Today, data and information have become overabundant resources within a global network of machines that exchange signals at speeds approaching that of light. In this highly saturated environment, communication has emerged as the most central form of interaction, supported by a rapidly evolving technical infrastructure. These new communication tools have crea

Ghazal Farhani, Taufiq Rahman, Dominique Charlebois

Rainy weather significantly increases the risk of road accidents due to reduced visibility and vehicle traction. Understanding how experienced drivers adapt their visual perception through gaze behavior under such conditions is critical for designing robust driver monitoring systems (DMS) and for informing advanced driver assistance systems (ADAS). This case

Aamod Khatiwada, Roee Shraga, Renée J. Miller

Unionable table search techniques input a query table from a user and search for data lake tables that can contribute additional rows to the query table. The definition of unionability is generally based on similarity measures which may include similarity between columns (e.g., value overlap or semantic similarity of the values in the columns) or tables (e.g

S M Rafiuddin

Ranking words is an important way to summarize a text or to retrieve information. A word graph is a way to represent the words of a sentence or a text as the vertices of a graph and to show the relationship among the words. It is also useful to determine the relative importance of a word among the words in the word-graph. In this research, the ranking of Ban

Hai-Jun Su

This paper presents analytical solvers for four common types of algebraic equations encountered in robot kinematics: single trigonometric equations, single-angle trigonometric systems, two-angle trigonometric systems, and bilinear two-angle systems. These equations arise frequently in the kinematics problems, particularly in robot kinematics. We provide deta

Fatma Chaouech, Javier Villegas, António Pereira, Carlos Baena

This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication systems, particularly focusing on 5G networks and beyond. The application of QML and QI algorithms is investigated, comparing their performance with classical Machine Learning (ML) app

J. Oliva-Maza, Y. Tomilov

We reveal new aspects of the structure of Hilbert space $C_0$-semigroups $\mathcal T = (T(t))_{t\ge 0}$ similar to semigroups of contractions. In particular, we prove that $\mathcal T$ is similar to a semigroup of contractions if and only if $\mathcal T$ is similar to a quasi-contraction $C_0$-semigroup and $T(t)$ is similar to a contraction for a single $t>

J. Oliva-Maza, Y. Tomilov

In the context of finite tensor products of Hilbert spaces, we prove that similarity of a tensor product of operator semigroups to a contraction semigroup is equivalent to the corresponding similarity for each factor, after an appropriate rescaling. A similar result holds with contractivity replaced by quasi-contractivity. This splitting phenomenon allows us

Soham Ghosh, Holger Boche, Marc Geitz

Quantum Physical Unclonable Functions (QPUFs) are hardware-based cryptographic primitives with strong theoretical security. This security stems from their modeling as Haar-random unitaries. However, implementing such unitaries on Intermediate-Scale Quantum devices is challenging due to exponential simulation complexity. Previous work tackled this using pseud

Tristan C. Collins

These lecture notes introduce conifold transitions between complex threefolds with trivial canonical bundle from the differential geometric point of view, and with a particular view towards aspects of mathematical physics and string theory. The lecture notes are aimed at beginning graduate students and non-experts, emphasizing explicit calculations and examp

Zhi Zhao, Fatih Kızılaslan, Shixiong Wang, Manuela Zucknick

Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, the

Pavle V. M. Blagojevic

We develop a topological framework in an attempt to generalize the classical colourful Caratheodory theorem by imposing an additional constraint. For that we introduce the notion of zero-avoding complexes and covering criteria for the existence of colourful transversals. Using the developed method in combination with the homological Nerve theorem of Meshulam

Zbigniew Palmowski, Paweł Stȩpniak

This paper presents a derivation of the explicit price for the perpetual American put option in the Black-Scholes model, time-capped by the first drawdown epoch beyond a predefined level. We demonstrate that the optimal exercise strategy involves executing the option when the asset price first falls below a specified threshold. The proof relies on martingale

Rachel Pries

This manuscript is about abelian varieties that are Jacobians of curves. I started writing it for a lecture series at the Arizona Winter School in 2024 on abelian varieties. A longer more descriptive title might be: The Torelli locus in the moduli space of abelian varieties, with applications to Newton polygons of curves in positive characteristic. To elabor

Shu Liu, Soujanya Ponnapalli, Shreya Shankar, Sepanta Zeighami

Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data, agents employ a high-throughput process of exploration and solution formulation for the given task, one we call agentic speculation. The sheer volume and inefficien

Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng

Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, and fine-tune adjusts the model parameters to activate specific neural pathways to align with the target manifold. Although prior fine-tuning approaches demonstrate success, their r

Brett Hungar

Anchored planar algebras, a generalized notion of Vaughan Jones' planar algebras, have recently seen use in higher category theory, functional analysis, and TQFT applications. These algebras are equipped with a natural 3-dimensional graphical calculus. We compare this graphical calculus with the 3-dimensional graphical calculus associated to tricategories, a

Raphaela Wutte

This short review surveys mass for two-dimensional asymptotically locally hyperbolic initial data sets. I explain the difficulties in defining mass in spatial dimension two, which are resolved via minimisation using a positive energy theorem, and review how gluing theorems can be used to construct novel initial data sets with controlled mass.

Liu Liu

Understanding change over time within dyads, such as mentor-mentee or therapist-client pairs, poses unique challenges, particularly in studies with small samples and distinguishable roles. This paper introduces a flexible hybrid longitudinal modeling that integrates features of the Actor-Partner Interdependence Model (APIM) and the Common Fate Growth Model (

Olusola Odeyomi, Sofiat Olaosebikan, Ajibuwa Opeyemi, Oluwadoyinsola Ige

Multi-task learning is an effective way to address the challenge of model personalization caused by high data heterogeneity in federated learning. However, extending multi-task learning to the online decentralized federated learning setting is yet to be explored. The online decentralized federated learning setting considers many real-world applications of fe

Jorge Almeida, Alfredo Costa, Herman Goulet-Ouellet

This paper is the first in a series of three, about (relatively)free profinite semigroups and S-adic representations of minimal shift spaces. We associate to each primitive S-adic directivesequence ${\boldsymbol{\sigma}}$ a $\textit{profinite image}$ in the free profinite semigroup over the alphabet of the induced minimal shift space. When this profinite ima

Deepak Bastola, Woohyeok Choi

Legal documents pose unique challenges for text classification due to their domain-specific language and often limited labeled data. This paper proposes a hybrid approach for classifying legal texts by combining unsupervised topic and graph embeddings with a supervised model. We employ Top2Vec to learn semantic document embeddings and automatically discover

Christos Anagnostopoulos, Ioulia Kapsali, Alexandros Gkillas, Nikos Piperigkos

Autonomous vehicles (AVs) rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a scalable and safe environment for robustness testing, existing frameworks typically lack comprehensive supportfor modeling multi-domain adversarial scenarios. This paper introduces

Swadhin Biswas, Imran, Tuhin Sheikh

Automatic Speech Recognition (ASR) for Bengali, the world's fifth most spoken language, remains a significant challenge, critically hindering technological accessibility for its over 270 million speakers. This challenge is compounded by two persistent and intertwined factors: the language's vast dialectal diversity and the prevalence of acoustic noise in rea

Adib Bazgir, Amir Habibdoust, Yuwen Zhang, Xing Song

Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning and generation tasks. However, their proficiency in complex causal reasoning, discovery, and estimation remains an area of active development, often hindered by issues like hallucination, reliance on spurious correlations, and difficulties in handling nuanced, domain-

Darko Sasanski, Riste Stojanov

This comprehensive review explores food data in the Semantic Web, highlighting key nutritional resources, knowledge graphs, and emerging applications in the food domain. It examines prominent food data resources such as USDA, FoodOn, FooDB, and Recipe1M+, emphasizing their contributions to nutritional data representation. Special focus is given to food entit

Alexandre P. Costa, Alexandre V. Dodonov

The interaction between atomic systems and electromagnetic fields is central to modern physics and emerging quantum technologies. The Rabi models, in their semiclassical and quantum versions, provide the simplest and most fundamental description of this interaction. In this work, we present a concise derivation of both models and show how one- and multiphoto

R. Virk

Remarks on the Hodge-Grothendieck class of the nearby cycles functor and a generalized local invariant cycles result.

Antonios Stamatogiannakis, Arsham Ghodsinia, Sepehr Etminanrad, Dilney Gonçalves

When Artificial Intelligence (AI) is used to replace consumers (e.g., synthetic data), it is often assumed that AI emulates established consumers, and more generally human behaviors. Ten experiments with Large Language Models (LLMs) investigate if this is true in the domain of well-documented biases and heuristics. Across studies we observe four distinct typ

Sadia Zaman Mishu, S M Rafiuddin

The demand for text classification is growing significantly in web searching, data mining, web ranking, recommendation systems, and so many other fields of information and technology. This paper illustrates the text classification process on different datasets using some standard supervised machine learning techniques. Text documents can be classified throug

Mateusz Wilinski, Juho Kanniainen

In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propose a modified generative architecture based on the state-space model, and apply it to limit order book data with identified investors. The model is trained on synthetic data, gener

Liancheng Zheng, Zhen Tian, Yangfan He, Shuo Liu

This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded into the MFG through a spatial influence

S. A. Paston, A. J. Ziyatdinov

We investigate the possibility of explaining the observed effects usually attributed to the existence of dark matter through a transition from GR to a modified theory of gravity - embedding gravity. Since this theory can be reformulated as GR with additional fictitious matter of embedding gravity (FMEG), which moves independently of ordinary matter, we analy

Bhima Sankar Manthina, Shreyash Gujar, Sachin Chaudhari, Kavita Vemuri1

Urban noise pollution poses a significant threat to public health, yet existing monitoring infrastructures offer limited spatial coverage and adaptability. This paper presents a scalable, low-cost, IoT-based, real-time environmental noise monitoring solution using mobile nodes (sensor nodes on a moving vehicle). The system utilizes a low-cost sound sensor in

Christiane K. M. Klein

The quantization of linearized gravity on black hole spacetimes and the construction of states for that theory is a sought-after, yet difficult achievement. One of the main reasons is the difficulty of reconciling the positivity and gauge invariance of potential states. On Kerr spacetimes, the spin-2 Teukolsky scalars express the same degrees of freedom as t

Junzo Watada, Pei-Chun Lin, Bo Wang, Jeng-Shyang Pan

In this paper, we propose a novel heuristic algorithm for constructing a Type-2 Fuzzy Set of the Linear Linguistic Regression (T2F-LLR) model, designed to address uncertainty and vagueness in real-world decision-making. We consider a practical scenario involving a cosmetic company's promotional planning across four product categories: Basic Face Care, Face C

Fabio Ancona, Laura Caravenna, Alexander J. Cliffe, Elio Marconi

We prove the H\"older regularity of continuous isentropic solutions to multi-dimensional scalar balance laws when the source term is bounded and the flux satisfies general assumptions of nonlinearity. The results are achieved by exploiting the kinetic formulation of the balance law.

J. Dorigo Jones, J. O. Burns, D. Rapetti, Shah Mohammad Bahauddin

Efforts are underway to measure the global 21 cm signal from neutral hydrogen, which is a powerful probe of the early universe, using NASA radio telescopes on the far side of the Moon. Physics-based models of the signal are computationally expensive to perform Bayesian multi-parameter inferences, for which we have developed novel, publicly-available neural n

Youssef Chakir, Iyad Lahsen-Cherif

The growing complexity of cyber incidents presents significant challenges for digital forensic investigators, especially in evidence collection and analysis. Public resources are still limited because of ethical, legal, and privacy concerns, even though realistic datasets are necessary to support research and tool developments. To address this gap, we introd

Zifeng Ding, Shenyang Huang, Zeyu Cao, Emma Kondrup

Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural approaches, such as temporal graph neural networks, achieve strong performance but lack explainability and cannot be applied to unseen graphs without retraining. Recent studies have beg

Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu, Chih-Ho Hsu

Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reasoning chains that lack factual accuracy and clinical reliability. We propose Ranked Preference Reinforcement Optimization (RPRO), a novel framework that combines reinforcement learni

Kanchon Gharami, Hansaka Aluvihare, Shafika Showkat Moni, Berker Peköz

Large Language Models (LLMs) are increasingly deployed in mission-critical systems, facilitating tasks such as satellite operations, command-and-control, military decision support, and cyber defense. Many of these systems are accessed through application programming interfaces (APIs). When such APIs lack robust access controls, they can expose full or top-k

Md Hasanuzzaman, Abhishikta Das, Sumit Som

In this article, we extend several relation-theoretic notions to topological spaces. We introduce relation preserving contraction mapping into topological spaces and utilize the same to extend Banach contraction principle in topological spaces employing a binary relation. To illustrate the validity of our main result, we provide a concrete example along with

Amin Jafarimoghaddam, Manuel Soler, María Cerezo-Magaña

Optimizing commercial aircraft cruise trajectories using the Pontryagin Maximum Principle (PMP) is particularly challenging due to the nonlinear dynamics of aircraft speed, complex costate dynamics, and the inclusion of two continuous controls, one of which (thrust) is typically a singular, affine input. We present a surrogate optimization framework, account

Jay Vaghasiya, Omkar Ghugarkar, Vishvesh Bhat, Vipul Dholaria

We introduce CoreThink, a state-of-the-art Reasoning Layer built upon a novel reasoning method called General Symbolics. This approach diverges from reasoning paradigms such as test-time scaling, Supervised Fine-Tuning (SFT), and Reinforcement Learning with Verifiable Rewards (RLVR). CoreThink General Symbolic Reasoner (GSR) is specifically structured around

Imen Jarraya, Safa Ben Atitallah, Fatimah Alahmeda, Mohamed Abdelkadera

Accurate and reliable State Of Health (SOH) estimation for Lithium (Li) batteries is critical to ensure the longevity, safety, and optimal performance of applications like electric vehicles, unmanned aerial vehicles, consumer electronics, and renewable energy storage systems. Conventional SOH estimation techniques fail to represent the non-linear and tempora

Laurent Saloff-Coste, Ruoqi Zhang

We consider several families of long jump random walks on groups of polynomial volume growth which are naturally expected to have a stable-like behavior. We then prove optimal pseudo-Poincar\'e inequalities for these walks. These pseudo-Poincar\'e inequalities allow us to show that the random walks in questions indeed have a stable-like behavior and to obtai

Xiangchen Wang, Jinrui Zhang, Teng Wang, Haigang Zhang

Recent advancements in large video-language models have revolutionized video understanding tasks. However, their efficiency is significantly constrained by processing high volumes of visual tokens. Existing token compression strategies apply a fixed compression ratio, ignoring the variability in semantic density among different video clips. Consequently, thi

Håkon Kolderup

We generalize the classical "1089-number trick", which states that a certain combination of addition, subtraction and swapping the digits of a three-digit number will always output 1089. More precisely, we show that any pair of zero divisors $fg=0$ in the group ring ${\mathbb Z}[\Sigma_n]$ on the n-th symmetric group gives rise to a partition of the set of n

Vinith Kishore, Valentin Debarnot, AmirEhsan Khorashadizadeh, Ivan Dokmanić

Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual proteins in situ. The measurements are collected by tilting the frozen specimen and exposing it to an electron beam of known dosage. As the biological samples are prone to electron d

Nadjib Achir, Philippe Jacquet

The BUBBLE-BLUE (BB) project aims to create private Bluetooth bubbles on top of smartphones and to create a kind of terrestrial STARLINK network based on users smartphones.. In each private bubble, participants will be able to communicate autonomously, without recourse to private operator networks, neither data nor cellular, relying solely on the Bluetooth t

Taufeq Mohammed Razakh, Thomas Linker, Ye Luo, Nariman Piroozan

Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electrons, and atoms over vast spatiotemporal scales on Exaflop/s computers with increased heterogeneity and low-precision focus. We present a paradigm shift that solves the multiscale/mu

Amin Jafarimoghaddam, Manuel Soler

Condensation trails (contrails) are increasingly recognized as a major contributor to aviation-induced atmospheric warming, rivaling the impact of carbon dioxide. Mitigating their climate effects requires accurate and computationally efficient models to inform avoidance strategies. Contrails evolve through distinct stages, from formation and rapid growth to

Kuranage Roche Rayan Ranasinghe, Zhaolin Wang, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu

We address the modeling and optimal beamforming (BF) design for multiple-input multiple-output (MIMO) continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. First, a comprehensive DD continuous MIMO (DDC MIMO) channel model that incorporates CAPAs at both the transmitter (TX) and receiver (RX) is derived, which is used to ob

Deepika Dash, Yeshil Bangera, Mithil Bangera, Gouthami Vadithya

Large Language Models (LLMs) are increasingly used for accessibility guidance, yet many disability groups remain underserved by their advice. To address this gap, we present taxonomy aligned benchmark1 of human validated, general purpose accessibility questions, designed to systematically audit inclusivity across disabilities. Our benchmark evaluates models

Tuck Choy, Miguel Ortuno

With the advent of quantum mechanics by Heisenberg in 1925 exactly a century ago, the quantization of the electromagnetic field became an important goal for our founding fathers, whom we are here to celebrate. It was realized very soon that a consistent picture of quantum electrodynamics (QED) requires the quantization of not just the electromagnetic field $

Yerzhan Mustafa, Berker Peköz, Selçuk Köse

Data transmission from superconducting electronic circuits, such as single flux quantum (SFQ) logic, to room-temperature electronics is susceptible to bit errors, which may result from flux trapping, fabrication defects, and process parameter variations (PPV). Due to the cooling power budget at 4.2 K and constraints on the chip area, the size of the error-co

Lun Ai, Johannes Langer, Ute Schmid, Stephen Muggleton

Ultra Strong Machine Learning (USML) refers to symbolic learning systems that not only improve their own performance but can also teach their acquired knowledge to quantifiably improve human performance. We introduce LENS (Logic Programming Explanation via Neural Summarisation), a neuro-symbolic framework that combines symbolic program synthesis with large l

Rabah Amir, Igor V. Evstigneev, Mikhail V. Zhitlukhin

The paper compares two types of industrial organization in the Cournot duopoly: (a) the classical one, where the market players maximize profits and the outcome of the game is a Cournot-Nash equilibrium; (b) a contest in which players strive to win a fixed prize/bonus employing unbeatable strategies. Passing from (a) to (b) leads to a perfect competition wit

Øven A. Grimenes, Kristian Berland

Boltzmann transport calculations based on band structures computed from first principles play an important role in modern thermoelectric materials research. Among available codes, the \textsc{BoltzTraP} code is the most widely adopted, but many recent studies contain systematic mistakes. We identify three error modes: (1) inserting the electronic thermal con

Manish Verma, Vivek Sharma, Vishal Singh

This paper introduces a novel, multi stage hybrid intelligence framework for pruning patent portfolios to identify high value assets for technology transfer. Current patent valuation methods often rely on retrospective indicators or manual, time intensive analysis. Our framework automates and deepens this process by combining a Learning to Rank (LTR) model,

Hao Wu, Haomin Zhou

We develop a numerical framework, the Deep Tangent Bundle (DTB) method, that is suitable for computing solutions of evolutionary partial differential equations (PDEs) in high dimensions. The main idea is to use the tangent bundle of an adaptively updated deep neural network (DNN) to approximate the vector field in the spatial variables while applying the tra

Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate

The Linear Quadratic Gaussian (LQG) regulator is a cornerstone of optimal control theory, yet its performance can degrade significantly when the noise distributions deviate from the assumed Gaussian model. To address this limitation, this work proposes a distributionally robust generalization of the finite-horizon LQG control problem. Specifically, we assume

Arjun Basandrai, Shourya Jain, K. Ilanthenral

Traditional resampling methods for handling class imbalance typically uses fixed distributions, undersampling the majority or oversampling the minority. These static strategies ignore changes in class-wise learning difficulty, which can limit the overall performance of the model. This paper proposes an Adaptive Resampling-based Training (ART) method that per

Sam K. Miller

For each endotrivial complex arising from Bredon homology of a representation sphere, we construct $p$-local quasi-isomorphisms, called forerunners, enabling us to extend Balmer--Gallauer's results in arXiv:2307.04398 Part II concerning the tensor-triangular geometry of permutation modules for elementary abelian $p$-groups to all $p$-groups. We construct an

H. Francisco, B. Thapa, S. B. Trickey, A. C. Cancio

Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shi

Tom Bensky, Karl Saunders

Here a genetic algorithm (GA) is presented that creates a teaching schedule for a university physics department by algorithmically assigning ${\sim}200$ classes to ${\sim}50$ professors for each of three academic terms per year. The algorithm is driven by chromosomes of the GA that encode proposed pairings between enumerated lists of professors and classes.

A. J. Nayana, Raffaella Margutti, Eli Wiston, Tanmoy Laskar

We present X-ray (0.3--79 keV) and radio (0.25--203 GHz) observations of the most luminous Fast Blue Optical Transient (LFBOT) AT\,2024wpp at $z=0.0868$, spanning 2--280 days after first light. AT 2024wpp shows luminous ($L_{\rm X} \approx 1.5 \times 10^{43}\, \rm erg\,s^{-1}$), variable X-ray emission with a Compton hump peaking at $\delta t \approx 50$ day

Natalie LeBaron, Raffaella Margutti, Ryan Chornock, A. J. Nayana

We present an extensive photometric and spectroscopic ultraviolet-optical-infrared campaign on the luminous fast blue optical transient (LFBOT) AT 2024wpp over the first ~100 d. AT 2024wpp is the most luminous LFBOT discovered to date, with $L_{\rm{pk}}\approx(2-4)\times10^{45}$ erg s$^{-1}$ (5-10 times that of the prototypical AT 2018cow). This extreme lumi

Ethan P. Honda

Results are presented from numerical simulations of the flat-space nonlinear Maxwell-Klein-Gordon-Dirac equations. The introduction of a boson-fermion interaction allows a scalar vortex to act as a harmonic trap that can confine massive Dirac bound states. A parametric analysis is performed to understand the range of boson-fermion coupling strengths, Ginzbur

Yihong Chen

The making of knowledge engines in natural language processing has been shaped by two seemingly distinct paradigms: one grounded in structure, the other driven by massively available unstructured data. The structured paradigm leverages predefined symbolic interactions, such as knowledge graphs, as priors and designs models to capture them. In contrast, the u

Denghang Hu, Taolue Chen, Philipp Rümmer, Fu Song

The theory of sequences, supported by many SMT solvers, can model program data types including bounded arrays and lists. Sequences are parameterized by the element data type and provide operations such as accessing elements, concatenation, forming sub-sequences and updating elements. Strings and sequences are intimately related; many operations, e.g., matchi

Suhas Suresh Bharadwaj, Reuben Thomas Thovelil, Rohith Chembattammal, Sradha Mishra

Carbon Nanotubes have shown to be an attractive option in the race to find a replacement to silicon-based transistors, due to its high electrical conductivity, extraordinary mechanical strength, and thermal conductivity. However, challenges with regards to controlling the purity and chirality of CNTs have raised doubts if the mass production of these transis

Bing Xie, Junqi Yin, Zhenyu Zhou, Sarp Oral

Although it has been extensively explored in theory, decentralized learning is not yet green-lighted for production use, largely due to a lack of stability, scalability, and generality in large scale DNN training. To shed light on the production use of decentralized learning, this work studies decentralized data parallel training at scale. To this end, we in

Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan

To develop and externally validate integrated ultrasound nomograms combining BIRADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and state of the art large language models in biopsy recommendation and malignancy prediction for breast lesions. In this retrospective multicenter, multinational

Christopher M. Drupieski, Jonathan R. Kujawa

We consider the finite Weyl groups of classical type -- $W(A_{r})$ for $r \geq 1$, $W(B_{r}) = W(C_{r})$ for $r \geq 2$, and $W(D_{r})$ for $r \geq 4$ -- as supergroups in which the reflections are of odd superdegree. Viewing the corresponding complex group algebras as Lie superalgebras via the graded commutator bracket, we determine the structure of the Lie

Clara Sayffaerth, Annika Köhler, Julian Rasch, Albrecht Schmidt

Transferring knowledge across generations is fundamental to human civilization, yet the challenge of passing on complex practical skills persists. Methods without a physically present instructor, such as videos, often fail to explain complex manual tasks, where spatial and social factors are critical. Technologies such as eXtended Reality and Artificial Inte

Mridul Kumar, Yevgeny Rakita

Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional tec

Zherui Yang, Shengyao Li, Shaoqin Peng, Xueyan Wang

The superconducting diode effect (SDE), combining superconductivity with diode-like nonreciprocal current flow, recently emerges as an ideal candidate for zero-dissipation electronic circuits. Such technologically advantageous diodes are achieved by intricate material engineering to disrupt inversion symmetry, which leads to the production challenges as well

Seyed Muhammad Hossein Mousavi, Atiye Ilanloo

Automatic emotion recognition has become increasingly important with the rise of AI, especially in fields like healthcare, education, and automotive systems. However, there is a lack of multimodal datasets, particularly involving body motion and physiological signals, which limits progress in the field. To address this, the MVRS dataset is introduced, featur

Xiaoyu Wang, Yingli Wang, Lingjiong Zhu

Langevin Monte Carlo (LMC) algorithms are popular Markov Chain Monte Carlo (MCMC) methods to sample a target probability distribution, which arises in many applications in machine learning. Inspired by regime-switching stochastic differential equations in the probability literature, we propose and study regime-switching Langevin dynamics (RS-LD) and regime-s

Logan Bleys, Nicholas Corkill, Yi-Feng Zhao, Gheorghe Lucian Pascut

The recent discovery of superconductivity in thin films of the bilayer Ruddlesden-Popper (RP) nickelate La$_3$Ni$_2$O$_7$ (La327) under compressive strain has generated enormous interest, opening up further opportunities to stabilize superconductivity in this class of materials at ambient pressure. To better understand the many-body normal state from which s