December 2025 arXiv papers — page 61

Erdao Liang

This paper presents a systematic benchmarking of the model-based microscopic traffic simulator SUMO against state-of-the-art data-driven traffic simulators using large-scale real-world datasets. Using the Waymo Open Motion Dataset (WOMD) and the Waymo Open Sim Agents Challenge (WOSAC), we evaluate SUMO under both short-horizon (8s) and long-horizon (60s) clo

Hailong Dao, Ezra Miller, Jonathan Montaño, Christopher O'Neill

Piecewise quasipolynomial growth of Presburger counting functions combines with tame persistent homology module theory to conclude piecewise quasipolynomial behavior of constructible families of finely graded modules over constructible commutative semigroup rings. Functorial preservation of constructibility for families under local cohomology, $\operatorname

Sepehr Jahangiri, H. Ali Talebi

In this paper, we study the Integrated Communication and Control (ICAC) problem. Specifically, we investigate how messages can be transmitted from the controller/encoder to the observer/decoder through the control signal in Multiple-Input Multiple-Output (MIMO) vector-state Linear Quadratic Gaussian (LQG) systems under control constraints. We provide a compu

Ahmed E. Fahmy, Archibald J. Williams, Yufei Li, Thuc T. Mai

Using a combination of band representation analysis, inelastic neutron scattering (INS), magneto-Raman spectroscopy measurements, and linear spin wave theory, we establish that the non-coplanar antiferromagnet MnTe$_2$ is a tunable Weyl magnon material, hosting symmetry-protected topological nodal lines in its magnon band structure, protected by the the non-

S Mahmudul Hasan, Shaily Roy, Akib Jawad Nafis

The proliferation of linguistically subtle political disinformation poses a significant challenge to automated fact-checking systems. Despite increasing emphasis on complex neural architectures, the empirical limits of text-only linguistic modeling remain underexplored. We present a systematic diagnostic evaluation of nine machine learning algorithms on the

Noé Stauffer, Hossein Gorji, Ivan Lunati

Model reduction techniques have emerged as a powerful paradigm across different fronts of scientific computing. Despite their success, the provided tools and methodologies remain limited if high-dimensional dynamical systems subject to initial uncertainty and/or stochastic noise are encountered; in particular if rare events are of interest. We address this o

Yuanran Zhu, Yang Yu, Efekan Kökcü, Emanuel Gull

We introduce a method for constructing global approximations to correlation functions of strongly interacting quantum field theories, starting from perturbative results. The key idea is to employ interpolation method, such as the two-point Pad\'e expansion, to interpolate the weak and strong coupling expansions of correlation function. We benchmark this many

Mohamed Seif, Atsutse Kludze, Yasaman Ghasempour, H. Vincent Poor

Explicit channel state information (CSI) feedback in IEEE~802.11 conveys \emph{transmit beamforming directions} by reporting quantized Givens rotation and phase angles that parametrize the right-singular subspace of the channel matrix. Because these angles encode fine-grained spatial signatures of the propagation environment, recent work have shown that plai

Moses Kiprono

Chronic wounds, including diabetic foot ulcers which affect up to one-third of people with diabetes, impose a substantial clinical and economic burden, with U.S. healthcare costs exceeding 25 billion dollars annually. Current wound assessment remains predominantly subjective, leading to inconsistent classification and delayed interventions. We present WoundN

Rahul Yumlembam, Biju Issac, Nauman Aslam, Eaby Kollonoor Babu

As AI-generated images become increasingly photorealistic, distinguishing them from natural images poses a growing challenge. This paper presents a robust detection framework that leverages multiple uncertainty measures to decide whether to trust or reject a model's predictions. We focus on three complementary techniques: Fisher Information, which captures t

Leonardo Bohac

Universal QRAM (U-QRAM) is a fixed, data-independent unitary interface that implements coherent random-access reads relative to a designated computational "truth-table" basis on the memory register. This work studies restricted-access inference: the memory register is persistent but inaccessible, while an experimenter may prepare and measure only accessible

Miyuki T. Nakata

Understanding learning as a dynamic process is challenging due to the interaction of multiple factors, including cognitive load, internal state change, and subjective evaluation. Existing approaches often address these elements in isolation, limiting the ability to describe learning phenomena within a unified and structurally explicit framework. This paper p

Janek Dyer, Jagdeep Ahluwalia, Javad Zarrin

The ability to discriminate between generative graph models is critical to understanding complex structural patterns in both synthetic graphs and the real-world structures that they emulate. While Graph Neural Networks (GNNs) have seen increasing use to great effect in graph classification tasks, few studies explore their integration with interpretable graph

Jonas Lammers, Federico Pegoraro, Philip Held, Nidhin Prasannan

Photonic multidimensional quantum networks (MDQN), where individual subsystems are encoded using multiple degrees of freedom and photons, are an emerging platform for quantum algorithms because they offer high scalability. The distribution of non-classical and non-local correlations between the individual subsystems in an MDQN is of fundamental interest for

Hatim M. E. Geli, Islam Omar, Mona Y. Elshinawy, David W. DuBios

Drought is a complex natural hazard that affects ecological and human systems, often resulting in substantial environmental and economic losses. Recent increases in drought severity, frequency, and duration underscore the need for effective monitoring and mitigation strategies. Predicting drought impacts rather than drought conditions alone offers opportunit

Bella Finkel, Jose Israel Rodriguez

Multiview varieties are mathematical models for the set of image feature correspondences that can be produced by a given camera arrangement. They possess an invariant known as their Euclidean distance (ED) degree, which measures the algebraic complexity of determining the 3D features that minimize the reprojection error when computing the scene structure by

Karl Zieber

This paper considers the family of Schr\"odinger operators on $\ell^2(\mathbb{Z})$ given by independent but not necessarily identically distributed and possibly unbounded potentials. We assume a finite exponential moment and allow the choice of distributions to come from any compact set away from deterministic distributions. With these assumptions we prove s

S. K. Adhikari

We study the formation of solitons in a uniform quasi-two-dimensional (quasi-2D) spin-orbit (SO) coupled self-repulsive binary dipolar and nondipolar Bose-Einstein condensate (BEC) using the mean-field Gross-Pitaevskii equation. For a weak SO coupling, in a nondipolar BEC, one can have three types of degenerate solitons: a multi-ring soliton with intrinsic v

Santhosh Kumar Ravindran

Building on the affective dream-replay reinforcement learning framework of CosmoCore, we introduce CosmoCore-Evo, an extension that incorporates evolutionary algorithms to enhance adaptability and novelty in code generation tasks. Inspired by anthropological aspects of human evolution, such as natural selection and adaptation in early hominids, CosmoCore-Evo

Kirby S. Heck, Michael F. Howland

Winds in the atmospheric boundary layer (ABL) display a wide range of velocity profiles and turbulence properties that affect wind turbine wake dynamics. However, standard concurrent-precursor large eddy simulations (LES) often neglect phenomena such as mesoscale patterns, limiting the range and controllability of inflow parameters that can be studied. Here,

Babangida Bappah, Lauren G Bristol, Lamine Noureddine, Sideeq Bello

Userland memory forensics has become a critical component of smartphone investigations and incident response, enabling the recovery of volatile evidence such as deleted messages from end-to-end encrypted apps and cryptocurrency transactions. However, these forensics tools, particularly on Android, face significant challenges in adapting to different versions

James M. Cline

Recently arXiv:2512.11011 set out to improve on previous work from 1994 by Dolgov and Freese, who used a small-angle approximation to derive the yield of spontaneous baryogenesis from a rolling phase, a pseudo-Nambu-Goldstone boson coupled to the baryon current. The goal of the recent paper was to investigate what happens when the small-angle approximation i

Sergey Salishev

This paper considers a modification of the classical Osipov--Lanchester model in which the total population of the two forces $N=R+B$ is preserved over time. It is shown that the dynamics of the ratio $y=R/B$ reduce to the Riccati equation $\dot y=\alpha y^2-\beta$, which admits a complete analytical study. The main result is that asymptotically stable invar

Bruno H. S. Mendonça, Elizane E. de Moraes, Hélio Chacham

Water nanoconfinement is known to occur inside material void spaces, such as 2D confinement between surfaces, 1D confinement inside nanotubes, and variable-dimension confinement inside nanoporous materials. In the present work we investigate, through molecular dynamics simulations, the morphologies and self-diffusion coefficient of water channels that are na

Madalina I. Sas, Fernando E. Rosas, Hardik Rajpal, Daniel Bor

A central challenge in the study of complex systems is the quantification of emergence -- understood as the ability of the system to exhibit collective behaviours that cannot be traced down to the individual components. While recent work has proposed practical measures to detect emergence, these approaches tend to double-count the contribution of shared comp

Shahar Ain Kedem, Itamar Zimerman, Eliya Nachmani

Electroencephalography (EEG) data present unique modeling challenges because recordings vary in length, exhibit very low signal to noise ratios, differ significantly across participants, drift over time within sessions, and are rarely available in large and clean datasets. Consequently, developing deep learning methods that can effectively process EEG signal

Ravishankar Ramanathan, Yuan Liu

The question of certifying quantum nonlocality under a relaxation of the assumptions in the Bell theorem has gained traction, with potential for device-independent applications under weak seeds and cross-talk. Recently, it was shown that quantum nonlocality can be certified even under a simultaneous arbitrary (but not full) relaxation of the assumptions of M

H. Hassanabadi, A. Guvendi, F. Kafikang, T. Sathiyaraj

We present a comprehensive analysis of the thermodynamic and optical properties of the Bumblebee Kerr-Newman-Anti-de Sitter (AdS) black hole, a rotating and charged configuration arising in Lorentz symmetry-violating (LSV) gravity. The influence of the black hole parameters on the horizon structure, thermodynamic stability, and geometric deformation of space

Siyi Wang, Zifan Wang, Karl Henrik Johanssson

In interactive systems, feedback is often provided in the form of preference between queried options rather than precise scores, which motivates optimization methods to learn from such comparisons. In this work, we propose a preference-based optimization algorithm that relies on noisy two-point comparisons. At each iteration, the algorithm employs a uniform-

Vincent Chang, Thee Ho, Sunishchal Dev, Kevin Zhu

With the wide-scale adoption of conversational AI systems, AI are now able to exert unprecedented influence on human opinion and beliefs. Recent work has shown that many Large Language Models (LLMs) comply with requests to persuade users into harmful beliefs or actions when prompted and that model persuasiveness increases with model scale. However, this prio

U. Ochoa-Torrentera, R. A. Vazquez-Romero, J. Sumaya-Martinez

We report suborbital in situ measurements of atmospheric thermodynamic variables and ionizing cosmic radiation obtained during a stratospheric balloon experiment conducted over the Mexican Plateau. The flight reached a maximum geometric altitude of 28.94 km above mean sea level, providing vertical sampling of the troposphere, tropopause, and lower stratosphe

Felipe Biava Cataneo

Safety architectures for language models increasingly rely on external monitors to detect errors and inject corrective signals at inference time. For such systems to function in interactive settings, models must be able to incorporate externally provided confidence information into their verbal responses. In this work, we test whether instruction-tuned langu

V. R. Shaginyan, A. Z. Msezane

In our comment we show that some of the very difficult problems have been successfully solved. We have to focus on the resolved problems, since the authors claims: Our hope, however, is that the topics we have presented will provide inspiration for others working in this field and motivation for the idea that significant progress can be made on very hard pro

Maria del V. Valera M, Ney J. Luiggi A

We synthezised an AlZnMgCu alloy through mechanical alloying and, using Xray diffraction (XRD), identified the formation of the eta prime phase after 40 hours of grinding. Using reaction-free isoconversion theory, we determined that this phase exhibits two different behaviours depending on the heating rate (beta): the eta prime phase at low beta and the eta

Yotam Svoray

This paper develops a theory of isolated hypersurface singularities in mixed characteristic $(0,p)$, focusing on quotient rings over a Discrete Valuation Ring (DVR). We introduce and study analogues of the classical Tjurina and Milnor numbers for this setting, prove a generalized analogue of the determinacy theorem and the Mather-Yau Theorem for complete Noe

Vagrant Gautam

NLP researchers regularly invoke abstract concepts like "interpretability," "bias," "reasoning," and "stereotypes," without defining them. Each subfield has a shared understanding or conceptualization of what these terms mean and how we should treat them, and this shared understanding is the basis on which operational decisions are made: Datasets are built t

Jensen Zhang, Ningyuan Liu, Keze Wang

Vision-language models (VLMs) struggle in open-world applications, where out-of-distribution (OOD) concepts often trigger cross-modal alignment collapse and severely degrade zero-shot performance. We identify the root cause as modal asymmetry: while the visual encoder can extract discriminative features from unseen images, the text encoder is constrained by

Benyamin Hosseiny, Kamirul Kamirul, Odysseas Pappas, Alin Achim

Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due to the high diversity and complexity of ship types, accurate recognition is difficult and typically requires

Mahipal Gurram

We investigate Dirichlet-type series generated by representation functions that count the number of ways an integer can be expressed as a sum of 'k' signed higher even powers. By combining generalized theta generating functions with a family of generalized cotangent series introduced in previous work, we derive two distinct explicit lower bounds for these se

Ruiting Wang, Jiaman Wu, Fabio Paparella, Scott J. Moura

Ride-hailing platforms have a profound impact on urban transportation systems, and their performance largely depends on how intelligently they dispatch vehicles in real time. In this work, we develop a new approach to online vehicle dispatch that strengthens a platform's ability to serve more requests under demand uncertainty. We introduce a novel measure ca

Santwana Sagnika, Manav Malhotra, Ishtaj Kaur Deol, Soumyajit Roy

Plant diseases pose a significant threat to agricultural productivity and global food security, accounting for 70-80% of crop losses worldwide. Traditional detection methods rely heavily on expert visual inspection, which is time-consuming, labour-intensive, and often impractical for large-scale farming operations. In this paper, we present PlantDiseaseNet-R

Sara Ditsch, Johannes M. Henn, Prashanth Raman

We introduce two novel numerical approaches for computing Feynman integrals based on their complete monotonicity (CM) and Stieltjes properties. The first method uses that scalar Feynman integrals are CM, meaning that all their derivatives have a fixed sign, in the Euclidean kinematic region. This imposes strong constraints on the function space. Simultaneous

Mustafa Bakr, Tongyu Zhang, Smain Amari

We present a complete theory of electromagnetic modes in spherical cavities, resolving fundamental questions about the nature of angular quantization. The standard result that angular indices $(\ell,m)$ must be integers is shown to be a consequence of domain constraints -- regularity at both poles and single-valuedness in the azimuthal coordinate -- rather t

Cédric Bernardin, Ana Djurdjevac, Patricia Gonçalves, Leander Schnee

We analyze the \emph{equilibrium fluctuations} of a Hamiltonian chain of oscillators on \(\mathbb{Z}\) with an exponential potential, perturbed by a conservative, symmetric noise. Under the canonical \emph{diffusive scaling} \(t \mapsto t n^2\) and an interaction strength tuned by \(n^{-1/2}\), the fluctuation field is known to converge to the \emph{energy s

Xiaoyang Guo, Keze Wang

In recent years, large-scale vision-language models (VLMs) have demonstrated remarkable performance on multimodal understanding and reasoning tasks. However, handling high-dimensional visual features often incurs substantial computational and memory costs. VoCo-LLaMA alleviates this issue by compressing visual patch tokens into a few VoCo tokens, reducing co

Rahul Yumlembam, Biju Issac, Seibu Mary Jacob

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable (PE) malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM (LGBM) malware detector by integrating Neural Networks (NN), P

Yeor Hafouta

We prove Berry-Esseen theorems, almost sure invariance principle rates and large deviations for products of independent but not identically distributed invertible matrices with some average (logarithmic) projective contraction and uniform boundedness assumptions. We also characterize the divergence of the variance of the logarithm of the norm of the product.

Zisheng Chen, Zirui Zhu, Xiangyang Li

Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware, yet even strong classical systems still miss rare or borderline attacks when the data distribution drifts. Small, near-term quantum processors are now available, but existing work rarely shows whether quantum components can improve end-to-

Annan Deng, Carole Siegel, Hyung G. Park

An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The Likely Responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framewo

Mohammadreza Saghafi, Lamine Mili, Karlton Wirsing

We present a multifractal analysis of time series data obtained by repeatedly running a single-qubit quantum circuit on IBM superconducting quantum computers, in which the measurement outcomes are recorded as the number of zeros. By applying advanced signal processing techniques, including the wavelet leader method and multifractal detrended fluctuation anal

Victor Tänzel, Fabian Coupette, Marisol Ripoll, Tanja Schilling

Carbon Black is a filler frequently used in conductive suspensions or nanocomposites, in which it forms networks supporting electric conductivity. Although Carbon Black aggregates originate from a presumably isotropic aggregation process, the resulting particles are inherently anisotropic. Therefore, they can be expected to interact with shear flow, which si

Jensen Zhang, Jing Yang, Keze Wang

Large Language Models (LLMs) demonstrate strong few-shot generalization through in-context learning, yet their reasoning in dynamic and stochastic environments remains opaque. Prior studies mainly focus on static tasks and overlook the online adaptation required when beliefs must be continuously updated, which is a key capability for LLMs acting as world mod

Joao Vitor Barros Da Silva, Arsh Gupta, Madhusudan Singh Irish Singh

Secure interoperability across heterogeneous blockchains remains one of the most pressing challenges in Web3 with existing bridge protocols vulnerable to both classical exploits and emerging quantum threats. This paper introduces QLink a quantum-safe Layer 3 interoperability protocol that integrates postquantum cryptography (PQC) quantum key distribution (QK

Jonathan Taylor

We show that the category of sober \'etale groupoids and actors admits all small limits. This is achieved by computing the limits in the equivalent category of pseudogroups with pseudogroup morphisms, which we show admits a forgetful functor to the category of sets which creates limits. We give an alternative proof of the adjunction of Cockett and Garner in

Rajavardhan Talashila

A uniqueness theorem for time-harmonic electromagnetic fields which requires the normal components of electromagnetic fields specified on a spherical surface is proposed and proved. The statement of the theorem is : "For a spherical volume $V$ that contains only perfect conductors and homogeneous lossless materials and for which the impressed currents $\math

Mihailo M. Martinovic, Kristopher G. Klein, Leon Ofman, Yogesh

Ion-driven plasma instability thresholds, derived from linear theory, constrain the distribution of solar observations in parameter space, defining boundaries of stable plasma parameters. Excursions beyond these thresholds result in the emission of energy, transferred from particles to coherent electromagnetic waves, acting to adjust the system toward a more

Shreehari Anand Bodas, Harsha Honnappa, Michel Mandjes, Liron Ravner

This paper analyzes a service system modeled as a single-server queue, in which the service provider aims to dynamically maximize the expected revenue per unit of time. This is achieved by constructing a stochastic gradient descent algorithm that dynamically adjusts the price. A key feature of our modeling framework is that customers may choose to balk - tha

Simon Roy, Samuel Barbeau, Giovanni Beltrame, Christian Desrosiers

Learning generalizable reward functions is a core challenge in embodied intelligence. Recent work leverages contrastive vision language models (VLMs) to obtain dense, domain-agnostic rewards without human supervision. These methods adapt VLMs into reward models through increasingly complex learning objectives, yet meaningful comparison remains difficult due

Igor V. Fomin, Vladimir L. Glushkov, Evgenii S. Dentsel, Gevorg D. Manucharyan

An approach to construct cosmological inflation models on the basis of a certain dependence of the scalar field evolution on the e-folds number is considered. The reconstruction of the model background parameters according to the kind of specific connection between the parameters of cosmological perturbations is proposed. By the slow-roll approximation, as f

Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang

Insider threat detection (ITD) is challenging due to the subtle and concealed nature of malicious activities performed by trusted users. This paper proposes a post-hoc ITD framework that integrates explicit and implicit graph representations with temporal modelling to capture complex user behaviour patterns. An explicit graph is constructed using predefined

Petr Jizba, Tereza Lehečková

In this paper, we study 2+2 direct product spacetimes sourced by separated electromagnetic and Yang--Mills fields within Weyl conformal gravity. We prove that all such configurations admit at least 2 independent, commuting non-null Killing vectors, which we use to find general solutions. As a special case, we obtain a generalization of the Birkhoff--Riegert

C. F. P. Avalos, G. A. Prataviera, M. C. de Oliveira

We investigate the dynamics of two trapped ions interacting with a common thermal reservoir, focusing on how cross-correlated dissipation influences heating, steady-state behavior, and parameter sensitivity. Starting from a microscopic system--reservoir model, we derive the corresponding Heisenberg--Langevin equations and show that reservoir-induced correlat

Dhruv Tiwari

The ruling method in modern Artificial Intelligence spanning from Deep Reinforcement Learning (DRL) to Large Language Models (LLMs) relies on a surge of static, externally defined reward functions. While this "extrinsic maximization" approach has rendered superhuman performance in closed, stationary fields, it produces agents that are fragile in open-ended,

Teja Chinthala

Retrieval-Augmented Generation RAG systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. We introduce Bidirectional RAG, a novel RAG architecture that enables safe corpus expansion through validated write back of high

Raphael W. Jacobs, Paul Knappe

We show that a locally finite, connected graph $G$ is $r$-locally chordal (that is, its $r/2$-balls are chordal) if and only if the unique canonical graph-decomposition $\mathcal{H}_r(G)$ of $G$ displaying its $r$-global structure is into cliques. Our proof relies on a canonical version of Halin's characterization of chordal locally finite graphs as those th

Rayleigh Lei, Yajuan Si

Small area estimation (SAE) improves estimates for local communities or groups, such as counties, neighborhoods, or demographic subgroups, when data are insufficient for each area. This is important for targeting local resources and policies, especially when national-level or large-area data mask variation at a more granular level. Researchers often fit hier

Wenjun Lin, Jensen Zhang, Kaitong Cai, Keze Wang

We present STORM (Search-Guided Generative World Models), a novel framework for spatio-temporal reasoning in robotic manipulation that unifies diffusion-based action generation, conditional video prediction, and search-based planning. Unlike prior Vision-Language-Action (VLA) models that rely on abstract latent dynamics or delegate reasoning to language comp

Jiawei Du, Yi Hong

This study focuses on forecasting the ultimate forward rate (UFR) and developing a UFRbased bond yield prediction model using data from Chinese treasury bonds and macroeconomic variables spanning from December 2009 to December 2024. The de Kort-Vellekooptype methodology is applied to estimate the UFR, incorporating the optimal turning parameter determination

V. I. Yudin, O. N. Prudnikov, A. V. Taichenachev, M. Yu. Basalaev

We investigate the shift caused by asymmetry of spectroscopic lineshape in atomic interferometers, which has not previously been discussed in the scientific literature. This asymmetry arises because laser field is frequency-chirped not only during the free-evolution intervals of atoms, but also during the Ramsey pulses. As a result, the effective detuning fr

Mykola Kuz, Ihor Lazarovych, Mykola Kozlenko, Mykola Pikuliak

This study presents a hybrid deep learning architecture that integrates LSTM, CNN, and an Attention mechanism to enhance the classification of web content based on text. Pretrained GloVe embeddings are used to represent words as dense vectors that preserve semantic similarity. The CNN layer extracts local n-gram patterns and lexical features, while the LSTM

Dmytro Kuzmenko, Nadiya Shvai

Robots must balance compliance with safety and social expectations as blind obedience can cause harm, while over-refusal erodes trust. Existing safe reinforcement learning (RL) benchmarks emphasize physical hazards, while human-robot interaction trust studies are small-scale and hard to reproduce. We present the Empathic Ethical Disobedience (EED) Gym, a sta

Khaled Berkani

We propose APC-GNN++, an adaptive patient-centric Graph Neural Network for diabetes classification. Our model integrates context-aware edge attention, confidence-guided blending of node features and graph representations, and neighborhood consistency regularization to better capture clinically meaningful relationships between patients. To handle unseen patie

Aidan Lau

We prove quenched stochastic homogenization for divergence-form elliptic equations, under the assumption that the coefficients are stationary, ergodic, integrable, and satisfy a coarse-grained ellipticity assumption. The ellipticity assumption requires that the coefficients remain bounded in a negative regularity sense on large scales. As a corollary, we rec

Daniel Berend, Michael D. Boshernitzan

We study a variant of the classical cop-robber game played on compact metric graphs, where each edge is assigned a positive length and identified with a real interval of corresponding length. In this setting, both the cop and the robber move continuously along the edges, subject to upper bounds on their speeds. The cop has no knowledge of the robber's locati

Supratim Das, Sarthak Sarkar, Subhamoy Maitra, Tridib Mukherjee

In fantasy sports, strategic thinking-not mere luck-often defines who wins and who falls short. As fantasy cricket grows in popularity across India, understanding whether success stems from skill or chance has become both an analytical and regulatory question. This study introduces a new limited-selection contest framework in which participants choose from f

Ziqi Lin, Taiyu Hou

The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual psychological attributes shape students' interaction patterns with these technologies. This qualitative study explored how college students with different attachment styles describe their interactions with ChatGPT. Us

Oraib Almegdadi, João Marcelino, Sarah Fakhreddine, João Manso

Sustainable water quality underpins ecological balance and water security. Assessing and managing lakes and reservoirs is difficult due to data sparsity, heterogeneity, and nonlinear relationships among parameters. This review examines how Self-Organizing Map (SOM), an unsupervised AI technique, is applied to water quality assessment. It synthesizes research

Youwen Liang, Bo Shen, Wan Shou

Microscale additive manufacturing of reflective copper is becoming increasingly important for microelectronics and microcomputers, due to its excellent electrical and thermal conductivity. Yet, it remains challenging for state-of-the-art commercial metal 3D printers to achieve sub-100-micron manufacturing. Two aspects are sub-optimal using commercial laser p

Mathias Schmid, Naeimeh Mohseni, Michael J. Hartmann

Combinatorial optimization is considered a promising class of problems in which quantum computers can show significant advantages. However, problems of practical relevance typically have more variables than current or foreseeable quantum computers have qubits. Here we introduce a divide and conquer approach that partitions the optimization problem into subgr

Antonio López Neumann, Juan Paucar

We introduce a quantitative version of polynomial cohomology for discrete groups and show that it coincides with usual group cohomology when combinatorial filling functions are polynomially bounded. As an application, we show that Betti numbers of nilpotent groups are invariant by mutually cobounded $\textrm L^p$-measure equivalence. We also use this to obta

Christopher Román Jaimes

Natural Language Inference (NLI) models frequently rely on spurious correlations rather than semantic reasoning. Existing mitigation strategies often incur high annotation costs or trigger catastrophic forgetting during fine-tuning. We propose an automated, scalable pipeline to address these limitations. First, we introduce Log-Frequency LMI (LF-LMI) to accu

R. Tailleux, T. Dubos, B. J. Hatton

The Boussinesq approximation is a cornerstone of geophysical fluid dynamics, yet its thermodynamic and energetic underpinnings have remained ambiguous. In standard formulations, the links with the fully compressible Navier--Stokes equations are obscured, internal energy is only implicit, and the representation of diffusion and irreversibility remains \textit

Akul Malhotra, Sumeet Kumar Gupta

The deployment of deep neural networks (DNNs) on compute-in-memory (CiM) accelerators offers significant energy savings and speed-up by reducing data movement during inference. However, the reliability of CiM-based systems is challenged by stuck-at faults (SAFs) in memory cells, which corrupt stored weights and lead to accuracy degradation. While closest val

Daniel Arnström, Gianluca Garofalo

We provide theoretical foundations and computational tools for the systematic design of optimization-based control laws with constraints that have different priorities. By introducing the concept of prioritized intersections, we extend and unify previous work on the topic. Moreover, to enable the use of prioritized intersection in real-time applications, we

Nail Akar, Ismail Cosandal, Sennur Ulukus

In this paper, we consider a single-source multi-server generate-at-will discrete-time non-preemptive status update system where update packets are transmitted using {\em only one} of the available servers, according to a server selection policy. In particular, when a transmission is complete, the update system makes a threshold-based decision on whether to

Luigi E. Picasso

Some of the problems connected with the interpretation of quantum mechanics are enumerated, in particular those related to some well known paradoxes and, above all, to the measurement process. We then show how the so called "Physics Laboratory Assumption" introduced in [1], which considers as "observables'' only the self-adjoint operators corresponding to ex

Qilong Wu, Taoran Li, Tianyang Zhou, Varun Chandrasekaran

AI-for-Code (AI4Code) systems are reshaping software engineering, with tools like GitHub Copilot accelerating code generation, translation, and vulnerability detection. Alongside these advances, however, security risks remain pervasive: insecure outputs, biased benchmarks, and susceptibility to adversarial manipulation undermine their reliability. This SoK s

Tianyang Zhang, Xinxing Cheng, Jun Cheng, Shaoming Zheng

Domain gaps arising from variations in imaging devices and population distributions pose significant challenges for machine learning in medical image analysis. Existing image-to-image translation methods primarily aim to learn mappings between domains, often generating diverse synthetic data with variations in anatomical scale and shape, but they usually ove

Jayant Lohia

Winograd convolution is the standard algorithm for efficient inference, reducing arithmetic complexity by 2.25x for 3x3 kernels. However, it faces a critical barrier in the modern era of low precision computing: numerical instability. As tiles scale to maximize efficiency (e.g., F(6,3), F(8,3)), the condition numbers of standard integer based transforms expl

Enric Boix-Adsera

Despite being one of the earliest neural network layers, the Multilayer Perceptron (MLP) is arguably one of the least understood parts of the transformer architecture due to its dense computation and lack of easy visualization. This paper seeks to understand the MLP layers in dense LLM models by hypothesizing that these layers secretly approximately perform

Ben Jacobson-Bell, Steve Croft, Ellie White, Andrew P. V. Siemion

3I/ATLAS, an interstellar object, made its closest approach to Earth on 2025 December 19. On 2025 December 18, the Breakthrough Listen program conducted a technosignature search toward 3I/ATLAS using the 100 m Robert C. Byrd Green Bank Telescope at 1-12 GHz. We report a nondetection of candidate signals down to the 100 mW level.

Vikrant Sharma, Neel Kanth Kundu

Gate-based quantum image processing is constrained by qubit scarcity and the high overhead of quantum state preparation, limiting its applicability to realistic geometric data. We introduce a quantum-native framework for image matching on neutral-atom analog quantum computers that advances our earlier Sparse-Dots Representation (SDR) approach. A classical pr

Xavier Rafael-Palou, Jose Munuera, Ana Jimenez-Pastor, Richard Osuala

Modern clinical decision support systems can concurrently serve multiple, independent medical imaging institutions, but their predictive performance may degrade across sites due to variations in patient populations, imaging hardware, and acquisition protocols. Continuous surveillance of predictive model outputs offers a safe and reliable approach for identif

Masoud Bahari, Kristian Mæland, Carsten Timm, Björn Trauzettel

We develop a symmetry-adapted multipolar $\mathbf{k}\cdot\mathbf{p}$ theory close to the bulk $\Gamma$ point for time-reversal-symmetric, noncentrosymmetric $C_{3v}$ crystals in the strong atomic spin-orbit-coupling ($jj$-coupling) limit. Using a $j\in\{1/2,3/2,5/2\}$ multiplet basis appropriate for heavy-element \textit{p}- and \textit{d}-bands, we systemat

Shivum Telang

Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide, requiring early detection to preserve sight. Limited access to physicians often leaves DR undiagnosed. To address this, AI models utilize lesion segmentation for interpretability; however, manually annotating lesions is impractical for clinicians. Physicians require a model that explains

Zongyao Li, Yongkang Wong, Satoshi Yamazaki, Jianquan Liu

Most existing video moment retrieval methods rely on temporal sequences of frame- or clip-level features that primarily encode global visual and semantic information. However, such representations often fail to capture fine-grained object semantics and appearance, which are crucial for localizing moments described by object-oriented queries involving specifi

Carlo Alberto Antonini, Filomena De Filippis, Cintia Pacchiano Camacho

We investigate the validity and the failure of modular density of smooth maps on compact manifolds.

Haaris Mian

This thesis presents a physics-informed machine learning framework for solving the Floquet-Bloch eigenvalue problem associated with particles in two-dimensional periodic potentials, with a focus on honeycomb lattice geometry, due to its distinctive band topology featuring Dirac points and its relevance to materials such as graphene. By leveraging neural netw

Amirreza Abbasi, Mohsen Hooshmand

Transformers are crucial across many AI fields, such as large language models, computer vision, and reinforcement learning. This prominence stems from the architecture's perceived universality and scalability compared to alternatives. This work examines the problem of universality in Transformers, reviews recent progress, including architectural refinements

Ian Folkins

We discuss a simple three layer model of the tropical atmosphere. The rainfall variance of the model is dominated by a rainfall mode moving parallel to the equator having the approximate size and propagation speed of the Madden-Julian Oscillation (MJO). The origin of the convective aggregation in the model is the imposition of distinct length scales for the