November 2025 arXiv papers — page 198

Zaki Panjsheeri, Douglas Q. Adams, Adil Khawaja, Saraswati Pandey

An updated flexible parametrization of the generalized parton distributions in the quark, antiquark and gluon sectors is presented using constraints from high precision electron nucleon deep inelastic scattering data, as well as from the $u$, $d$ quark and gluonic contributions to the nucleon electromagnetic elastic form factors. The latter include recently

Euclid Collaboration, L. Bazzanini, G. Angora, P. Bergamini

Strong gravitational lensing (SL) by galaxy clusters is a powerful probe of their inner mass distribution and a key test bed for cosmological models. However, the detection of SL events in wide-field surveys such as Euclid requires robust, automated methods capable of handling the immense data volume generated. In this work, we present an advanced deep learn

Haoran Zhao, Neema Nassir, Andres Fielbaum

Conventional bus services often struggle with inefficiencies including prolonged dwell times at heavily used stops, especially for through passengers. A stop-less autonomous modular bus service (SLAM) has been proposed to reduce dwell times by decoupling the front pod to serve stops and then coupling it to the next bus. However, the optimal service design an

Margarita Geleta, Daniel Mas Montserrat, Alexander G. Ioannidis

We introduce an information-theoretic generalization of the fixation statistic, the Tsallis-order $q$ F-statistic, $F_q$, which measures the fraction of Tsallis $q$-entropy lost within subpopulations relative to the pooled population. The family nests the classical variance-based fixation index $F_{\textbf{ST}}$ at $q{=}2$ and a Shannon-entropy analogue at $

Yulij Ilyashenko, Stanislav Minkov, Ivan Shilin

In this paper new numerical invariants of structurally unstable vector fields in the plane are found. One of the main tools is an improved asymptotics of sparkling saddle connections that occur when a separatrix loop of a hyperbolic saddle breaks. Another main tool is a new topological invariant of two arithmetic progressions, both perturbed and unperturbed,

Lexin Ding, Markus Reiher

Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet, this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants

Riccardo Di Sipio, Jairo Diaz-Rodriguez, Luis Serrano

We present a geometric framework for understanding Transformer-based language models, drawing an explicit analogy to General Relativity. Queries and keys induce an effective metric on representation space, and attention acts as a discrete connection that implements parallel transport of value vectors across tokens. Stacked layers provide discrete time-slices

Joan Vendrell Gallart, Russell Bent, Solmaz Kia

Microgrids offer a promising paradigm for integrating distributed energy resources, bolstering energy resilience, and reducing the impact of blackouts. However, their inherent decentralization and dynamic operation present substantial energy management complexities. These complexities, including balancing supply and demand, ensuring system stability, and min

Martina Conte, Nadia Loy

We introduce a novel linear transport equation that models the evolution of a one-particle distribution subject to free transport and two distinct scattering mechanisms: one affecting the particle's speed and the other its direction. These scattering processes occur at different time scales and with different intensities, leading to a kinetic equation where

Dev Pradeepkumar Nayak, Ali Tarokh, Muhammad Saif Ullah Khalid

Fish display remarkable swimming capabilities through the coordinated interaction of the body and caudal fin, yet the potential role of a passively pitching tail in enhancing hydrodynamic performance remains unresolved. In this work, we evaluate the performance of a carangiform swimmer equipped with either an actively pitching tail or a passively pitching ta

Victoria Ebert, Rishabh Singh, Tuochao Chen, Noah A. Smith

Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing the missing speaker's turns for rea

James Nguyen, Oleg Presnyakov, Adityakrishnan Radhakhrishnan

Among recent developments centered around Randomized Kaczmarz (RK), a row-sampling iterative projection method for large-scale linear systems, several adaptions to the method have inspired faster convergence. Focusing solely on ill-conditioned and overdetermined linear systems, we highlight inter-row relationships that can be leveraged to guide directionally

Shubhalaxmi Rath, Nicolás A. Neill

We have studied how the thermoelectric properties of the quark-gluon plasma (QGP) are affected by a weak-momentum anisotropy arising from the asymptotic expansion of matter in the initial stages of ultrarelativistic heavy-ion collisions. The highly energetic medium produced in such collisions exhibits a notable temperature difference between its central and

Ziyang Xu, Olaf Wysocki, Christoph Holst

Evaluating uncertainty is critical for reliable use of Mobile Laser Scanning (MLS) point clouds in many high-precision applications such as Scan-to-BIM, deformation analysis, and 3D modeling. However, obtaining the ground truth (GT) for evaluation is often costly and infeasible in many real-world applications. To reduce this long-standing reliance on GT in u

Henry Shugart, Jason M. Altschuler

Classically, a mainstream approach for solving a convex-concave min-max problem is to instead solve the variational inequality problem arising from its first-order optimality conditions. Is it possible to solve min-max problems faster by bypassing this reduction? This paper initiates this investigation. We show that the answer is yes in the textbook setting

Tao Zhang, Kehui Yao, Luyi Ma, Jiao Chen

Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchmarking study that systematically compares 36 LLMs, including GPT, Gemini, Claude, and Llama, across multiple product categories using a consensus-driven evaluation protocol. Our mul

Katharine E Fisher, Matthew TC Li, Youssef Marzouk, Timo Schorlepp

Gradient information is widely useful and available in applications, and is therefore natural to include in the training of neural networks. Yet little is known theoretically about the impact of Sobolev training -- regression with both function and gradient data -- on the generalization error of highly overparameterized predictive models in high dimensions.

John Bistline, Geoffrey Blanford, Adam Diamant, Arin Kaye

Voluntary carbon-free electricity (CFE) procurement has the potential to accelerate electric sector decarbonization, but procurement strategies vary widely, leading to uncertainty about emissions, investments, and costs. This study assesses the system-wide effects of voluntary CFE procurement on U.S. regional power systems using a detailed energy systems mod

Anthony Hevia, Sanjana Chintalapati, Veronica Ka Wai Lai, Thanh Tam Nguyen

We present ROBOTO2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBOTO2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users c

Emi Soroka, Tanmay Chopra, Krish Desai, Sanjay Lall

Large language models (LLMs) have seen increasing popularity in enterprise applications where AI agents and humans engage in objective-driven interactions. However, these systems are difficult to evaluate: data may be complex and unlabeled; human annotation is often impractical at scale; custom metrics can monitor for specific errors, but not previously-unde

Emi Soroka, Artem Arzyn

Recent work in financial machine learning has shown the virtue of complexity: the phenomenon by which deep learning methods capable of learning highly nonlinear relationships outperform simpler approaches in financial forecasting. While transformer architectures like Informer have shown promise for financial time series forecasting, the application of transf

Yiye Jiang

In this paper, we propose new sampling approaches for the Shrinkage Inverse-Wishart (SIW) distribution, a generalized family of the Inverse-Wishart distribution originally proposed by Berger et al. (2020, Annals of Statistics). It offers a flexible prior for covariance matrices and remains conjugate to the Gaussian likelihood, similar to the classical Invers

Apsara Adhikari, Charlotte Wertz, Anamika Dubey, Arslan Ahmad

The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learni

Qianyu Chen, Bradley Dirks, Sebastian Olano

Using Saito's theory of mixed Hodge modules, we study a generalization of Hellus-Schenzel's "cohomologically complete intersection" property. This property is equivalent to perversity of the shifted constant sheaf. We relate the generalized version to the Hodge filtration on local cohomology, depth of Du Bois complexes, Hodge-Lyubeznik numbers and prove a st

Aly A. Khan, Jason Perera, James Zou, Loïc A. Royer

For over a century, immunology has masterfully discovered and dissected the components of our immune system, yet its collective behavior remains fundamentally unpredictable. In this perspective, we argue that building on the learnings of reductionist biology and systems immunology, the field is poised for a third revolution. This new era will be driven by th

An Vuong, Minh-Hao Van, Prateek Verma, Chen Zhao

Vision-Language Models (VLMs) have shown strong performance in tasks like visual question answering and multimodal text generation, but their effectiveness in scientific domains such as materials science remains limited. While some machine learning methods have addressed specific challenges in this field, there is still a lack of foundation models designed f

R. M. Green

It has long been known that the most symmetrical solutions of Kirkman's Schoolgirl Problem can be constructed from the $240$ packings of the projective space $PG(3, 2)$, but it seems to have escaped notice that these packings have the structure of a partially ordered set. In this paper, we construct a shellable Bruhat-like graded partial order on the packing

Yiming Zheng, Haoran Qi, Lirui Yu, Zhan Shu

Incast traffic in data centers can lead to severe performance degradation, such as packet loss and increased latency. Effectively addressing incast requires prompt and accurate detection. Existing solutions, including MA-ECN, BurstRadar and Pulser, typically rely on fixed thresholds of switch port egress queue lengths or their gradients to identify microburs

Youssef Elmir, Yassine Himeur, Abbes Amira

This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images, the proposed approach enables efficient feature extraction through Convolutional Neural Networks (CNNs) while ensu

Aubrey G. J. Nyiri, Michael J. Waters, James M. Rondinelli

Second-order nonlinear optical materials enable frequency doubling of light (second-harmonic generation, SHG), which is essential for optoelectronic applications ranging from materials characterization to quantum technologies. However, comparing SHG performance across materials remains challenging as the second-order nonlinear susceptibility $\chi^{(2)}$ spa

Giuseppe Campolieti, Yaode Sui

By considering any one-dimensional time-homogeneous solvable diffusion process,this paper develops a complete analytical framework for computing the distribution of the last hitting time, to any level, and its joint distribution with the process value on any finite time horizon. Our formalism allows for regular diffusions with any type of endpoint boundaries

Ognjen Papaz, Duško Jojić

This paper provides a positive answer to the question of Mirzakhani and Vondrak that asks if there is a Sperner-admissible labeling of the simplex-lattice hypergraph such that each hyperedge uses at most 2 colors.

Yan Cathy Hua, Paul Denny, Jörg Wicker, Katerina Taškova

Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad potential, ABSA research and resources remain concentrated in commercial domains, leaving analytical needs unmet in high-

James C. Bowden, Sergey Levine, Jennifer Listgarten

In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a protein to bind its target, arrange components within a circuit to minimize latency, or find materials with certain properties. Given a property predictive model, in silico design typ

Matthew Kroesche, Lance L. Littlejohn, Graeme Reinhart

In many proofs of Fermat's Two Squares Theorem, the smallest least residue solution $x_0$ of the quadratic congruence $x^2 \equiv -1 \bmod p$ plays an essential role; here $p$ is prime and $p \equiv 1 \bmod 4$. Such an $x_0$ is called a St{\o}rmer number, named after the Norwegian mathematician and astronomer Carl St{\o}rmer (1874-1957). In this paper, we es

Kajol Kulkarni, Samuel Kemmler, Anna Schwarz, Gulcin Gedik

Energy efficiency has emerged as a central challenge for modern high-performance computing (HPC) systems, where escalating computational demands and architectural complexity have led to significant energy footprints. This paper presents the collective experience of the EuroHPC JU Center of Excellence in Exascale CFD (CEEC) in measuring, analyzing, and optimi

Mike Krebs, Alejandro Leyva

A connected Cayley graph for an Abelian group generated by a finite symmetric subset $S$ can be represented by an integer matrix, its Heuberger matrix. We call the number of columns of that matrix its rank and the number of rows its dimension. Several previous papers have dealt with the question of finding a formula for the chromatic number of an Abelian Cay

Rick van der Heijden

This paper provides a polynomial-time algorithm for solving parity games that runs in $\mathcal{O}(n^{2}\cdot(n + m))$ time-ending a search that has taken decades. Unlike previous attractor-based algorithms, the presented algorithm only removes regions with a determined winner. The paper introduces a new type of attractor that can guarantee finding the minim

Paul Sutcliffe

The discrete Nahm equation is an integrable nonlinear difference equation for complex $N\times N$ matrices defined on a one-dimensional lattice, with rank and symmetry boundary conditions at the ends of the lattice. Solutions of this system correspond to $SU(2)$ magnetic monopoles of charge $N$ in hyperbolic space, with the curvature related to the number of

Logan Murphy, Torin Viger, Alessio Di Sandro, Aren A. Babikian

In critical software engineering, structured assurance cases (ACs) are used to demonstrate how key system properties are supported by evidence (e.g., test results, proofs). Creating rigorous ACs is particularly challenging in the context of software product lines (SPLs), i.e, sets of software products with overlapping but distinct features and behaviours. Si

Euclid Collaboration, D. Vergani, S. Quai, F. Ricci

This study explores a rare population of sources in a currently uncharted region of spectroscopic redshift space in the Euclid Quick Data Release (Q1), and is intended potentially to support upcoming spectroscopic studies. Our goal is to identify and investigate a population of sources characterised by highly ionised emission lines in their spectra, which ar

Ioannis Zachos, Zhihao Zhao

We construct the Bruhat-Tits stratification of the reduced basic locus of regular ramified unitary Rapoport-Zink spaces of signature $(n\!-\!1,1)$ at vertex-stabilizer level. To study the Bruhat-Tits strata, we introduce strata models$\!-\!$simpler models that are \'{e}tale-locally isomorphic to each stratum. They admit two complementary characterizations: (

Sina Montazeri, Yunhe Feng, Kewei Sha

Open data repositories hold potential for evidence-based decision-making, yet are inaccessible to non-experts lacking expertise in dataset discovery, schema mapping, and statistical analysis. Large language models show promise for individual tasks, but end-to-end analytical workflows expose fundamental limitations: attention dilutes across growing contexts,

Lingqing Shen, Chi Heem Wong, Misaki Mito, Arnab Chakrabarti

Monitoring the internal temperature and humidity of shipping containers is essential to preventing quality degradation during cargo transportation. Sensorless monitoring -- machine learning models that predict the internal conditions of the containers using exogenous factors -- shows promise as an alternative to monitoring using sensors. However, it does not

Fathi Namouni

(Abridged) Numerical simulations of the past evolution of high-inclination Centaurs showed they originated from orbits beyond Neptune that were perpendicular to the Solar System's invariable plane in a region called the polar corridor. Recently, a study of Centaur injection in the three-body problem showed that Neptune-crossing TNOs in the polar corridor in

Fatimo Adenike Adeniya

Cyberattacks on e-commerce platforms have grown in sophistication, threatening consumer trust and operational continuity. This research presents a hybrid analytical framework that integrates statistical modelling and machine learning for detecting and forecasting cyberattack patterns in the e-commerce domain. Using the Verizon Community Data Breach (VCDB) da

Wenbo Lu

Vision-Language Pretraining (VLP) has achieved remarkable success across various downstream tasks, but such gains are largely driven by scaling up on training data. Yet, literature methods treat image-text pairs as isolated training examples; this neglects the rich relational structure naturally present in many domains, such as e-commerce product co-purchase

S. R. Berlanas

In the era of large spectroscopic surveys, a vast amount of spectra of massive stars will be gathered and supplemented by the wealth of astrometric and photometric data provided by the Gaia satellite. Released data will mean a major step forward in the study of massive stars, giving us the chance to create statistically significant samples to explore the rol

Kaustav Chatterjee, Sameer Nekkalapu, Antos Varghese, Marcelo Elizondo

In recent years, several studies conducted by both industry and U.S. Department of Energy (DOE)-funded initiatives have proposed linking North America's Eastern and Western Interconnections (EI and WI) through a multiterminal DC (MTDC) macrogrid. These studies have explored the advantages and opportunities of the proposed configuration from the perspectives

Iffat Gheyas, Muhammad Rizwan Asghar, Steve Schneider, Alan Woodward

Crowdsourced data supports real-time decision-making but faces challenges like misinformation, errors, and contributor power concentration. This study systematically examines trust management practices across platforms categorised as Volunteered Geographic Information, Wiki Ecosystems, Social Media, Mobile Crowdsensing, and Specialised Review and Environment

Ole Petersen, Marcel Kollovieh, Marten Lienen, Stephan Günnemann

Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes them difficult for traditional generative models, leading to the rise of discrete diffusion and flow matching models. In this work, we introduce GraphBSI, a novel one-shot graph gene

Minh Sao Khue Luu, Bair N. Tuchinov

We present a foundation model for brain MRI that can work with different combinations of imaging sequences. The model uses one encoder with learnable modality embeddings, conditional layer normalization, and a masked autoencoding objective that accounts for missing modalities. A variance-covariance regularizer is applied to stabilize feature learning and imp

Jose A. R. Cembranos, Eric G. Hemon, Juan J. Sanz-Cillero

In this work, we study the Quantum Field Theory version of the higher derivative Pais-Uhlenbeck oscillator. We quantize canonically this system and construct its Fock space, as well as study its path integral. We demonstrate that the inclusion of canonical coordinates in the path integral necessarily introduces a new field, a Lagrange multiplier, which is es

Hongrui Chen, Liwei Wang, Levent Burak Kara

Metamaterials are engineered materials composed of specially designed unit cells that exhibit extraordinary properties beyond those of natural materials. Complex engineering tasks often require heterogeneous unit cells to accommodate spatially varying property requirements. However, designing heterogeneous metamaterials poses significant challenges due to th

Caroline L. Evans, Cooper Downs, Donald Schmit

The Sun's magnetic field is a key driver in coronal heating and consequently solar wind acceleration. Remote measurement of the photosphere provides the magnetic surface boundary condition necessary for data-constrained 3D global coronal models. With one such model, we explore how the spatial resolution of the surface boundary condition influences the global

Mahipal Gurram

We introduce a family of deformed Bailey pairs whose $q$-series, which converge in a two-step limit ($q \to 1^-$ followed by $n \to \infty$) to Dirichlet $L$-functions scaled by $1/\sqrt{\pi}$. This construction generalizes to arbitrary bounded arithmetic progressions via character weights, providing a unified $q$-series asymptotic for $L(s,\chi)$. Our appro

Steven Senger

We explain the triangular gaps observed experimentally in the most popular sizes of the $h$-fold iterated sumset, $hA,$ when $A$ is a randomly chosen four-element subset of the first $q$ natural numbers, for $q$ much larger than $h.$

Lucas Castro, Thailsson Clementino, Rosiane de Freitas

We present an implementation and experimental analysis of the deterministic algorithm proposed by Duan et al. (2025) for the Single-Source Shortest Path (SSSP) problem, which achieves the best-known asymptotic upper bound of $O(m \log^{2/3} n)$. We provide a worst-case C++ implementation of this algorithm utilizing $O(n \log^{1/3} n)$ space, as well as a var

David Craig, Alexander W. Wray, Khellil Sefiane, Stephen K. Wilson

A mathematical model for the evolution of, and deposition from, a thin particle-laden droplet on an infinitely thick, isotropic, flooded, porous substrate with interconnected pores undergoing simultaneous evaporation and imbibition is formulated and analysed. In particular, analytical expressions for the evolution of the droplet, as well as for the flow with

Hee-Jin Lee, Zhen Guo, Luchao Jin, Morteza Moazami Goudarzi

We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer service summarization tasks. The pipeline first analyzes and categorizes common errors in summaries produced by a teacher model (GPT-3.5), then performs a targeted revision using a compa

Dakota Hester, Vitor S. Martins, Lucas B. Ferreira, Thainara M. A. Lima

Deep learning semantic segmentation methods have shown promising performance for very high 1-m resolution land cover classification, but the challenge of collecting large volumes of representative training data creates a significant barrier to widespread adoption of such models for meter-scale land cover mapping over large areas. In this study, we present a

Tânia Paulista

The aim of this paper is to see how commuting graphs interact with two semigroup constructions: the zero-union and the direct product. For both semigroup constructions, we investigate the diameter, clique number, girth, chromatic number and knit degree of their commuting graphs and, when possible, we exhibit the relationship between each one of these propert

Minseok Kang, Dongwook Choi, Gyeom Hwangbo, Seungwon Lim

Accurately synthesizing 3D scenes from user-provided text descriptions is crucial for developing embodied agents. Despite the importance of scene-description alignment, existing evaluation methods for such text-guided 3D scene synthesis either capture only coarse similarity between the synthesized scene and the user description, or ignore the spatial reasoni

A. Ravlić, Y. Saito, W. Nazarewicz

Nuclear $\beta$ decay is a key element of the astrophysical rapid neutron capture process ($r$-process). In this paper, we present state-of-the-art global $\beta$-decay calculations based on the quantified relativistic nuclear energy density functional theory and the deformed proton-neutron quasiparticle random-phase approximation. Our analysis considers con

Gennady A. Ovsyannikov, Nikita V. Dubitskiy, Georgi D. Ulev, Karen Y. Constantinian

The structural crystal features, electron transport and magnetotransport of the epitaxial strontium iridate (SrIrO$_3$) and iridate/manganite SrIrO$_3$/La$_{0.7}$Sr$_{0.3}$MnO$_3$ heterostructure have been investigated. The influence of epitaxial strain relaxation caused by the lattice mismatch between SrIrO$_3$ films and five substrates: SrTiO$_3$, NdGaO$_3

Pelin Karadal, Dilara Kekulluoglu

Large Language Models (LLMs) distinguish themselves by quickly delivering information and providing personalized responses through natural language prompts. However, they also infer user demographics, which can raise ethical concerns about bias and implicit personalization and create an echo chamber effect. This study aims to explore how inferred political v

Jon Kutasov, Chloe Loughridge, Yuqi Sun, Henry Sleight

As AI systems become more capable and widely deployed as agents, ensuring their safe operation becomes critical. AI control offers one approach to mitigating the risk from untrusted AI agents by monitoring their actions and intervening or auditing when necessary. Evaluating the safety of these protocols requires understanding both their effectiveness against

Ailar Mahdizadeh, Puria Azadi Moghadam, Xiangteng He, Shahriar Mirabbasi

Vision-language models (VLMs) have demonstrated strong cross-modal capabilities, yet most work remains limited to 2D data and assumes binary supervision (i.e., positive vs. negative pairs), overlooking the continuous and structured dependencies present in volumetric data such as CT. Existing approaches often treat volumetric scans as independent 2D slices, c

Tarcísio C. Déda, William R. Wolf, Scott T. M. Dawson, Brener L. O. Ramos

Neural network observers (NNOs) are proposed for real-time estimation of fluid flows, addressing a key challenge in flow control: obtaining real-time flow states from a limited set of sparse and noisy sensor data. For this task, we propose a generalization of the classical Luenberger observer. In the present framework, the estimation loop is composed of subs

Syed Mostaquim Ali, Taufiq Rahman, Ghazal Farhani, Mohamed H. Zaki

For developing safe Autonomous Driving Systems (ADS), rigorous testing is required before they are deemed safe for road deployments. Since comprehensive conventional physical testing is impractical due to cost and safety concerns, Virtual Testing Environments (VTE) can be adopted as an alternative. Comparing VTE-generated sensor outputs against their real-wo

Yixuan Gao, Tanvir Ahmed, Zekun Chang, Thijs Roumen

Wireless sensing technologies can now detect heartbeats using radio frequency and acoustic signals, raising significant privacy concerns. Existing privacy solutions either protect from all sensing systems indiscriminately preventing any utility or operate post-data collection, failing to enable selective access where authorized devices can monitor while unau

Mikhael Djajapermana, Moritz Reiber, Daniel Mueller-Gritschneder, Ulf Schlichtmann

Hybrids of Convolutional Neural Network (CNN) and Vision Transformer (ViT) have outperformed pure CNN or ViT architecture. However, since these architectures require large parameters and incur large computational costs, they are unsuitable for tinyML deployment. This paper introduces a new hybrid CNN-ViT search space for Neural Architecture Search (NAS) to f

Alberto Del Pia

While several classes of integer linear optimization problems are known to be solvable in polynomial time, far fewer tractability results exist for integer nonlinear optimization. In this work, we narrow this gap by identifying a broad class of discrete nonlinear optimization problems that admit polynomial-time algorithms. Central to our approach is the noti

M. Fabricius, R. Saglia, F. Balzer, L. R. Ecker

Massive early-type galaxies (ETGs) are believed to form primarily through mergers of less massive progenitors, leaving behind numerous traces of violent formation histories, such as stellar streams and shells. A particularly striking signature of these mergers is the formation of supermassive black hole (SMBH) binaries, which can create depleted stellar core

Randon J. Weaver, Robert S. Coulter, Alice M. W. Hui

We classify the parabolic unitals in regular nearfield planes of odd order $q^2$ whose linear collineation group has the maximal size of $q^3-q$. We also establish a number of more general results concerning parabolic unitals in regular nearfield planes under weaker assumptions.

James J. Bock, Asad M. Aboobaker, Joseph Adamo, Rachel Akeson

SPHEREx, a NASA explorer satellite launched on 11 March 2025, is carrying out the first all-sky near-infrared spectral survey. The satellite observes in 102 spectral bands from 0.75 to 5.0 um with a resolving power ranging from 35 to 130 in 6.2 arcsecond pixels. The observatory obtains a 5-sigma depth of 19.5 - 19.9 AB mag for 0.75 to 3.8 um and 17.8 - 18.8

Azmine Toushik Wasi, Wahid Faisal, Mst Rafia Islam, Md Rizwan Parvez

Bangladesh's low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs. Existing AI legal assistants lack Bengali-language support and jurisdiction-specific adaptation, limiting their effectiveness. To address this, we developed Mina, a multilingual LLM-based legal assistant tailor

Alan R. Vazquez, Peter Goos, Eric D. Schoen

Orthogonal minimally aliased response surface (OMARS) designs permit the study of quantitative factors at three levels using an economical number of runs. In these designs, the linear effects of the factors are neither aliased with each other nor with the quadratic effects and the two-factor interactions. Complete catalogs of OMARS designs with up to five fa

Alberto Del Pia

We study geometric characterizations of unbounded integer polynomial optimization problems. While unboundedness along a ray fully characterizes unbounded integer linear and quadratic optimization problems, we show that this is not the case for cubic polynomials. To overcome this, we introduce thin rays, which are rays with an arbitrarily small neighborhood,

Seth Bernstein, Ben Spitz

We describe the reduced formal context of the lattice of saturated transfer systems on a finite abelian group. As an application, we compute that there are 13,784,538,270,571 saturated transfer systems on the elementary abelian group $C_5^3$.

Bastien Duboeuf, Camille Eloy, Gabriel Larios

We investigate the structure of conformal manifolds around AdS$_3 \times S^3$ which lift from continuous flat directions in the scalar potential of gauged supergravity resulting from six-dimensional $\mathcal{N}=(1,1)$ supergravity. Our approach combines numerical exploration and symbolic inference. For the latter, we develop a symbolic regression algorithm

Erik M. Åsgrim, Stefano Markidis

We propose a quantum-inspired combinatorial solver that performs imaginary-time evolution (ITE) on a matrix product state (MPS), incorporating non-local couplings through structured SWAP networks and spectral qubit mapping of logical qubits. The SWAP networks, composed exclusively of local two-qubit gates, effectively mediate non-local qubit interactions. We

Esther Sun, Zichu Wu

The design and application of LLM-based personas in AI companionship is a rapidly expanding but fragmented field, spanning from virtual emotional companions and game NPCs to embodied functional robots. This diversity in objectives, modality, and technical stacks creates an urgent need for a unified framework. To address this gap, this paper systematizes the

Fuming Yang, David J. Nott, Anne M. Presanis

Evidence synthesis models combine multiple data sources to estimate latent quantities of interest, enabling reliable inference on parameters that are difficult to measure directly. However, shared parameters across data sources can induce conflicts both among the data and with the assumed model structure. Detecting and quantifying such conflicts remains a ch

Leopoldo Diaz, Harold P. Hjalmarson, Jesse J. Lutz, Peter A. Schultz

Radiation-induced defects can have a significant impact on the longevity and performance of semiconductor devices. We present an Atomistically Informed Device Engineering (AIDE) method that integrates first-principles defect properties and experimentally measured parameters into a device model to dynamically simulate the defect chemistry in semiconductors. F

Sirui Liu, Thomas E. Videbæk, W. Benjamin Rogers

Self-closing assembly is prone to polymorphism due to thermally-excited bending fluctuations, which permit the formation of off-target assemblies at the point of self-closure. One way to overcome this source of polymorphism is to use templated growth, a process in which assembly initiates from a precisely-defined seed rather than by spontaneous nucleation. W

Natalia Tziotziou

We establish new geometric inequalities comparing the volumes of sections and projections of a convex body, whose barycenter or Santal\'o point is at the origin, with those of its inner and outer regularizations. We also provide functional extensions of these inequalities to the setting of log-concave functions. Our approach relies on the recent optimal $M$-

Peiqiang Lin

In this paper, we generalize Ahlfors' lemma on logarithmic derivative to holomorphic tangent curves of directed projective manifolds intersecting closed subschemes. As a consequence, we obtain Algebro-Geometric Ahlfors' Lemma on Logarithmic Derivative (AALD for short) and General form of Algebro-Geometric Version of Ahlfors' Lemma on Logarithmic Derivative (

Yige Li, María de los Angeles Resa, José R. Zubizarreta

Inferring the causal effects of time-varying treatments is often hindered by highly variable inverse propensity weights, particularly in settings with limited covariate overlap. Building on the key framework of Imai and Ratkovic (2015), we establish sufficient balancing conditions for identification in longitudinal studies of treatment effects and propose a

M. Magliocchetti, A. La Marca, L. Bisigello, M. Bondi

Using the large statistics provided by both Euclid and the LOFAR surveys, we present the first large-scale study of the connection between radio emission, its morphology, and the merging properties of the hosts of radio sources up to z=2. By dividing the radio sample into active galactic nuclei (AGN) and star-forming galaxies, we find that radio-emitting AGN

Stergios Plataniotis, Charilaos Akasiadis, Georgios Chalkiadakis

Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $\epsilon$-greedy and Boltzmann exploration methods, often struggle to efficiently balance exploration

Md Mazharul Islam, Connor A. Good, Diego Ferrer, Juan P. Mendez

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra energy efficient data conversion architectures that remain functional at deep cryogenic temperatures.In this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a novel quantum

Victor-Alexandru Pădurean, Parameswaran Kamalaruban, Nachiket Kotalwar, Alkis Gotovos

We study the problem of aligning a generative model's response with a user's preferences. Recent works have proposed several different formulations for personalized alignment; however, they either require a large amount of user preference queries or require that the preference be explicitly specified as a text input. In this paper, we propose a novel inferen

R. Flores-Freitas, D. Wylezalek, M. Trevisan, M. Albán

Active galactic nuclei (AGN) have been observed in dwarf galaxies, yet the impact of black hole feedback in these low-mass systems remains unclear. To uncover the potential effects of AGN in the low-mass galaxy regime, we study the properties and demographics of active dwarf galaxies at $z=0$, using the IllustrisTNG simulations. We use data from the TNG50-1

Euclid Collaboration, F. Gentile, E. Daddi, D. Elbaz

(Abridged) The bimodality between star-forming discs and quiescent spheroids requires the existence of two main processes: the galaxy quenching and the morphological transformation. In this paper, we aim to understand the link between these processes and their relation with the stellar mass of galaxies and their local environment. Taking advantage of the fir

Walner Mendonça, Meysam Miralaei, Guilherme O. Mota

Given positive integers $k$ and $\ell$ we write $G \rightarrow (K_k,K_\ell)$ if every 2-colouring of the edges of $G$ yields a red copy of $K_k$ or a blue copy of $K_\ell$ and we denote by $R(k)$ the minimum $n$ such that $K_n\rightarrow (K_k,K_k)$. By using probabilistic methods and hypergraph containers we prove that for every integer $k \geq 3$, there exi

Ezequiel S. Santos, Gabriel F. Barros, Amanda C. N. Oliveira, Rômulo M. Silva

The solution of partial differential equations (PDEs) plays a central role in numerous applications in science and engineering, particularly those involving multiphase flow in porous media. Complex, nonlinear systems govern these problems and are notoriously computationally intensive, especially in real-world applications and reservoirs. Recent advances in d

Ephraim Gau, Zhongyuan Liu, Henric Krawczynski, Chong Zu

We introduce a focal-plane detector for advancing the energy and angular resolutions of current X-ray telescopes. The architecture integrates a metallic magnetic microcalorimeter (MMC) array of paramagnetic absorber pads with a thin layer of nitrogen-vacancy (NV) centers in diamond for simultaneous optical readout. An impinging X-ray photon induces a tempera

Hossein Fathollahian, Siyuan Zhao, Nafiul Nipu, G. Elisabeta Marai

High-dimensional tissue imaging generates highly complex 3D data containing multiple biomarkers, making it challenging to identify biologically relevant regions without an expert user specifying manual labels for regions of interest. We introduce an approach to automatically identifying regions of interest (ROIs) in the 3D microscopy data. Our approach is ba

Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez

Modern machine translation (MT) systems depend on large parallel corpora, often collected from the Internet. However, recent evidence indicates that (i) a substantial portion of these texts are machine-generated translations, and (ii) an overreliance on such synthetic content in training data can significantly degrade translation quality. As a result, filter