December 2025 arXiv papers — page 40

Mansur Ziiatdinov, Igor Novikov, Farid Ablayev, Valeri Barsegov

In computational molecular science, calculation of electrostatic interactions involving charged atoms - the strongest interactions in condensed phases, is a major bottleneck. We propose a quantum-classical algorithm for fast, yet, accurate computation of the Coulomb electrostatic energy for a system of point charges. The algorithm employs the Ewald method ba

Sami Marouani, Kamal Singh, Baptiste Jeudy, Amaury Habrard

Accurate prediction of flow delay is essential for optimizing and managing modern communication networks. We investigate three levels of modeling for this task. First, we implement a heterogeneous GNN with attention-based message passing, establishing a strong neural baseline. Second, we propose FlowKANet in which Kolmogorov-Arnold Networks replace standard

Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng

Autonomous agents powered by LLMs and Retrieval-Augmented Generation (RAG) are proficient consumers of digital content but remain unidirectional, a limitation we term epistemic asymmetry. This isolation leads to redundant reasoning and stagnates collective intelligence. Current self-reflection frameworks remain largely heuristic and private, lacking a probab

Xinyi Guo, Li You, Qiong Liu, Xiqi Gao

Rate-splitting multiple access (RSMA) has emerged as a promising technique for efficient interference management in next-generation wireless networks. While most existing studies focus on downlink and single-cell designs, the modeling and analysis of uplink RSMA under large-scale deployments remain largely unexplored. On the basis of stochastic geometry (SG)

Johann Verwee

Let $(G_n)_{n\geqslant 0}$ be a linear recurrence sequence defining a numeration system and satisfying mild structural hypotheses. For real-valued G-additive functions (additive in the greedy G-digits), we establish an Erdős-Wintner-type theorem: convergence of two canonical series (a first-moment series and a quadratic digit-energy series) is necessary and

Minhyeok Kang, Jaehee Kim, William J. Munro, Seungbeom Chin

Entanglement is a fundamental feature of quantum mechanics and a key resource for quantum information processing. Among multipartite entangled states, Dicke states $|D_n^k\rangle$ are distinguished by their permutation symmetry, which provides robustness against particle loss and enables applications for quantum communication and computation. Although Dicke

Honghui Zheng, Pietro Favaro, Yury Dvorkin, Ján Drgoňa

Underground pumped hydro energy storage (UPHES) systems play a critical role in grid-scale energy storage for renewable integration, yet optimal day-ahead scheduling remains computationally prohibitive due to nonlinear turbine performance characteristics and discrete operational modes. This paper presents a decision-focused learning (DFL) framework that addr

Esmaeil Taghizadeh Sisakht, Uiseok Jeong, Xiao jiang, Jinseok Oh

Quantized charge pumping in one-dimensional chiral wires has been widely studied in the context of topological physics in a (1+1)-dimensional synthetic space, yet the role of orbital and spin degrees of freedom in such topological pumps remains largely unexplored. Here, we examine how the topologically quantized charge pump in insulators generates spin polar

SenYuan Su, Chunling Tong, Yuansheng Yang

Total colouring of 4-regular circulant graphs is an interesting but challenging topic, and has attracted much attention. However, it still remains an open question to determine the total chromatic numbers of $C_{n}(1, 3)$, a subclass of 4-regular circulant graphs, even after many efforts. In this paper, we investigate the total colouring of these graphs and

Shivraj Singh Bhatti

We present a systematic empirical study of small language models under strict compute constraints, analyzing how architectural choices and training budget interact to determine performance. Starting from a linear next-token predictor, we progressively introduce nonlinearities, self-attention, and multi-layer transformer architectures, evaluating each on char

Kanata Suzuki, Shota Shimizu, Tetsuya Ogata

From the perspective of future developments in robotics, it is crucial to verify whether foundation models trained exclusively on offline data, such as images and language, can understand the robot motion. In particular, since Vision Language Models (VLMs) do not include low-level motion information from robots in their training datasets, video understanding

Shanwlee Sow Mondal, James A. Klimchuk, Craig D. Johnston, Lars K. S. Daldorff

Determining the relationship between nanoflare energies and their delays is the key for understanding the physical mechanism of the events and the plasma response. Nanoflares analyzed in this study were generated self-consistently via prescribed photospheric motions in a 3D multi-strand simulation of a subset of active region magnetic flux. Energies and dura

Rohit Pandey, Rohan Pandey

Accurate prediction of model rocket flight performance requires estimating aerodynamic parameters that are difficult to measure directly. Traditional approaches rely on computational fluid dynamics or empirical correlations, while data-driven methods require extensive real flight data that is expensive and time-consuming to collect. We present a simulation-b

Meysam Ghanbari, Mohammad Taghi Dabiri, Osamah S. Badarneh, Mazen Hasna

Directional links in free-space optical (FSO), millimeter-wave (mmWave), and terahertz (THz) systems are a cornerstone of emerging 6G networks, yet their reliability is fundamentally limited by pointing errors and misalignment. Existing studies address this impairment using technology-specific definitions, models, and mitigation approaches, which hinders cro

Rahul Bulusu

Cyber-Physical Systems (CPS) now support critical infrastructure spanning transportation, energy, manufacturing, medical devices, and autonomous robotics. Their defining characteristic is the tight coupling between digital computation and continuous physical dynamics which enables sophisticated autonomy but also creates highly non-linear failure modes. Small

Jakir Hossain, Jue Guo, Gurvinder Singh, Lukasz Ziarek

Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures. Their utility in traditional program analysis has been well established, enabling effective classification and analysis of malicious software. In the mobile domain, especially in the And

Daichi Arai, Kyohei Unno, Yasuko Sugito, Yuichi Kusakabe

Implicit neural representations for videos (NeRV) have shown strong potential for video compression. However, applying NeRV to high-resolution 360-degree videos causes high memory usage and slow decoding, making real-time applications impractical. We propose NeRV360, an end-to-end framework that decodes only the user-selected viewport instead of reconstructi

Zhengjing Ma, Gang Mei, Nengxiong Xu, Yongshuang Zhang

Self-organizing processes shape Earth's surface, creating complex patterns from simple rules in most landforms. Rainfall-induced mass movements dramatically reshape landscapes through rapid sediment transfer, but whether they self-organize remains unknown. Here we decode their organizational principles by treating spatial changes in scar geometries as finger

Junyang Huang, Ginny Shooter, Petros Laccotripes, Andrea Barbiero

A central challenge for scalable quantum networks is the realization of coherent interfaces between stationary qubits and telecom-band photonic qubits for long-distance entanglement distribution. Semiconductor quantum dots emitting at telecom wavelengths present a promising spin-photon platform, and a precise understanding of the properties of the confined s

Aadi Patel, Nikhil Mahalingam, Rusheen Patel

The rapid expansion of artificial intelligence has significantly increased the electricity, water, and carbon demands of modern data centers, raising sustainability concerns. This study evaluates the environmental footprint of AI server operations and examines feasible technological and infrastructural strategies to mitigate these impacts. Using a literature

Felipe A. Louza, Arnaud Lefebvre

We present an extension of the in-place BWT algorithm of Crochemore et al. [8] that enables the construction of the Lyndon array using O(1) extra space. Our approach incrementally maintains the lexicographic ranks of the suffixes during the right-to-left BWT construction and then derives the Lyndon array through a simple next-smaller-value procedure. Althoug

Theodore Roberts, Bahram Zarrin

Agentic artificial intelligence systems are autonomous technologies capable of pursuing complex goals with minimal human oversight and are rapidly emerging as the next frontier in AI. While these systems promise major gains in productivity, they also raise new ethical challenges. Prior research has examined how different populations prioritize Responsible AI

Hajime Sotani

Tidal deformabilities are one of the observable quantities characterizing neutron stars, which are strongly associated with the stellar compactness, the ratio of the stellar mass to the radius. In addition to the tidal deformability, the quasinormal modes excited in a neutron star are also an important property for extracting information about the neutron st

Haotian Lv, Chao Li, Jiangbo Dai, Yuhui Zhang

To address the issues of weak correlation between multi-view features, low recognition accuracy of small-scale targets, and insufficient robustness in complex scenarios in underground pipeline detection using 3D GPR, this paper proposes a 3D pipeline intelligent detection framework. First, based on a B/C/D-Scan three-view joint analysis strategy, a three-dim

Suhyeon Lee, Dieu-Huyen Nguyen, Donghwan Lee

Blockchains offer a decentralized and secure execution environment strong enough to host cryptocurrencies, but the state-replication model makes on-chain computation expensive. To avoid heavy on-chain workloads, systems like Truebit and optimistic rollups use challenge-based protocols, performing computations off-chain and invoking the chain only when challe

Xin Bian, Riccardo Betti, Dongxiao Zhao, Hussein Aluie

The magneto-Rayleigh-Taylor instability (mRTI) is a key process in inertial confinement fusion and is thought to be widespread in the interstellar medium, where it can concentrate plasma into discrete structures. We present resistive MHD simulations of the nonlinear evolution of multi-mode mRTI in both two and three dimensions, examining the effects of unifo

Z. Zajicek, I. Paulescu, P. Reiss, R. M. Abedin

Electronic scattering is a powerful tool to identify underlying changes in electronic behavior and incipient electronic and magnetic orders. The nematic and magnetic phases are strongly intertwined under applied pressure in FeSe, however, the additional isoelectronic substitution of sulphur offers an elegant way to separate them. Here we report the detailed

Pierre Abillama, Changwoo Lee, Juechu Dong, David Blaauw

Recent advances in transformer-based foundation models have made them the default choice for many tasks, but their rapidly growing size makes fitting a full model on a single GPU increasingly difficult and their computational cost prohibitive. Block low-rank (BLR) compression techniques address this challenge by learning compact representations of weight mat

Alejandro Avina, Yashas Hariprasad, Naveen Kumar Chaudhary

Dynamic malware analysis requires executing untrusted binaries inside strongly isolated, rapidly resettable environments. In practice, many detonation workflows remain tied to heavyweight hypervisors or dedicated bare-metal labs, limiting portability and automation. This challenge has intensified with the adoption of ARM64 developer hardware (e.g., Apple Sil

Ho-Seong Lee, Jae-Hyun Ahn, Yong-Hwan Kim

Magnetocardiography system is a medical device that diagnoses cardiac disease by measuring magnetic fields generated from electric currents flowing through the myocardium. However, the accuracy of measurement data can be degraded if strong magnetic materials are present or magnetic field changes occur near the MCG system. With the widespread use of implants,

Md Zabirul Islam, Md Motaleb Hossen Manik, Ge Wang

Traditional lecture videos offer flexibility but lack mechanisms for real-time clarification, forcing learners to search externally when confusion arises. Recent advances in large language models and neural avatars provide new opportunities for interactive learning, yet existing systems typically lack lecture awareness, rely on cloud-based services, or fail

Jonathan J. Zhu

This is the second of two articles in which we investigate the geometry of free boundary and capillary minimal surfaces in balls $B_R\subset\mathbb{S}^3$. In this article, we find monotonicity formulae which imply that capillary minimal surfaces maximise a certain modified energy in their conformal orbit (preserving $B_R$). In the hemisphere, this energy is

George Bird

A systematic mismatch exists between mathematically ideal and effective activation updates during gradient descent. As intended, parameters update in their direction of steepest descent. However, activations are argued to constitute a more directly impactful quantity to prioritise in optimisation, as they are closer to the loss in the computational graph and

NVIDIA, :, Aaron Blakeman, Aaron Grattafiori

We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a Mixture-of-Experts hybrid Mamba-Transformer architecture to provide best-in-class throughput and context lengths of up to 1M tokens. Super and Ultra models are trained with NVFP4

Asaad Daher

The primary objective of this thesis is to develop a consistent theoretical framework of dissipative hydrodynamics for a relativistic fluid with spin - hereafter referred to as relativistic dissipative spin hydrodynamics. In this framework, the dynamical description of a relativistic fluid requires a new macroscopic variable, the spin density, which is assoc

Shelly Schwartz, Oleg Vasilyev, Randy Sawaya

In realistic retrieval settings with large and evolving knowledge bases, the total number of documents relevant to a query is typically unknown, and recall cannot be computed. In this paper, we evaluate several established strategies for handling this limitation by measuring the correlation between retrieval quality metrics and LLM-based judgments of respons

Zaki Panjsheeri, Saraswati Pandey, Brannon Semp, Simonetta Liuti

Using the framework of generalized parton distribution, we provide a unified interpretation of the connected and disconnected contributions from the ab-initio Euclidean path-integral formulation of the hadronic tensor in both the nucleon elastic form factors and the parton distribution functions. We develop a phenomenology to elucidate non-perturbative contr

Weibin Zhang, Xilin Liang, Sean Preins, Miguel Arratia

We study the response of a prototype Zero-Degree Calorimeter (ZDC) detector to irradiation equivalent to 10$^{11}$ 1-MeV $n_{\text{eq}}/\text{cm}^{2}$, which matches the expected exposure after one year of operation at full nominal luminosity at the future Electron-Ion Collider (EIC). The prototype, which consists of 563 channels and represents about 10 perc

Hiroaki Kaido, Kirill Ponomarev

Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have been studied on a case-by-case basis in a limited set of models. In this paper, we develop a general framework for characterizing sharp testable implications of g

Alexey Meteykin

We address the problem of combined stochastic and impulse control for a market maker operating in a limit order book. The problem is formulated as a Hamilton-Jacobi-Bellman quasi-variational inequality (HJBQVI). We propose an implicit time-discretization scheme coupled with a policy iteration algorithm. This approach removes time-step restrictions typical of

Artem Goncharov, Rajesh Ghosh, Hyou-Arm Joung, Dino Di Carlo

Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neu

Jaehong Lee, You Chan No, YoungWoo Kim, Duksu Kim

Machine learning-based computer-generated holography (ML-CGH) has advanced rapidly in recent years, yet progress is constrained by the limited availability of high-quality, large-scale hologram datasets. To address this, we present KOREATECH-CGH, a publicly available dataset comprising 6,000 pairs of RGB-D images and complex holograms across resolutions rang

Mingshu Cai, Osamu Yoshie, Yuya Ieiri

Modern surveillance systems increasingly rely on multi-wavelength sensors and deep neural networks to recognize faces in infrared images captured at night. However, most facial recognition models are trained on visible light datasets, leading to substantial performance degradation on infrared inputs due to significant domain shifts. Early feature-based metho

Salman Jan, Hassan Ali Razzaqi, Ali Akarma, Mohammad Riyaz Belgaum

The application of agentic AI systems in autonomous decision-making is growing in the areas of healthcare, smart cities, digital forensics, and supply chain management. Even though these systems are flexible and offer real-time reasoning, they also raise concerns of trust and oversight, and integrity of the information and activities upon which they are foun

Ziyuan Zheng, Xiangyu Li, Shirui Zuo, Yu Wan

Satellite networks promise wide-area 6G coverage but face two persistent barriers: blockage-induced service discontinuities and increasingly stringent spectrum coexistence across satellite layers and with terrestrial systems. Reconfigurable intelligent surfaces (RISs) act as low-power programmable apertures that redirect energy without the cost and power con

Jasper J. van Beers, Marten Scheffer, Prashant Solanki, Ingrid A. van de Leemput

Maintaining stability in feedback systems, from aircraft and autonomous robots to biological and physiological systems, relies on monitoring their behavior and continuously adjusting their inputs. Incremental damage can make such control fragile. This tends to go unnoticed until a small perturbation induces instability (i.e. loss of control). Traditional met

Yupeng Zhang, Adam G. Dunn, Usman Naseem, Jinman Kim

Medical artificial intelligence (AI) systems, particularly multimodal vision-language models (VLM), often exhibit intersectional biases where models are systematically less confident in diagnosing marginalised patient subgroups. Such bias can lead to higher rates of inaccurate and missed diagnoses due to demographically skewed data and divergent distribution

Congzhe Yan Zhijun Jiang Jinyang Ni, Guoqing Chang

The magnons in altermagnets exhibit chiral splitting even in the absence of spin-orbit coupling and external magnetic fields. Typically, this chiral splitting behavior can be well described by alternating isotropic spin exchanges (ISE) near the zero temperature. However, its finite-temperature dynamics, particularly when incorporating spin-orbit coupling eff

Perseas Christodoulidis

Open effective field theories provide a systematic framework for describing systems coupled to an environment, where dissipation, noise, and modified conservation laws naturally arise. Working within the Schwinger-Keldysh formalism, we examine open extensions of three well-studied theories: the superfluid, Maxwell theory, and Einstein gravity. In gauge and g

Wonjun Lee, Minki Hhan, Gil Young Cho, Hyukjoon Kwon

Random quantum states have various applications in quantum information science. We discover a new ensemble of quantum states that serve as an $ε$-approximate state $t$-design while possessing extremely low entanglement, magic, and coherence. These resources can reach their theoretical lower bounds, $Ω(\log (t/ε))$, which are also proven in this work. This im

Yosra Mater, Salih Demirci, V. Ongun Özçelik

Ultrathin polymer-graphene heterostructures are promising materials for next generation optoelectronic and photovoltaic technologies, while the influence of the polymer's structural variation on interfacial charge transfer remains unclear. Here, using ab initio quantum mechanical calculations we show how different forms of Poly(3-hexylthiophene) (P3HT), a wi

NVIDIA, :, Aaron Blakeman, Aaron Grattafiori

We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique tokens over Nemotron 2, followed by supervised fine tuning and large-scale RL on diverse environments. Nemotron 3 Nano achieves better accuracy than our previous

Parag Khanna, Karen Jane Dsouza, Chunyu Wang, Mårten Björkman

This paper introduces the YCB-Handovers dataset, capturing motion data of 2771 human-human handovers with varying object weights. The dataset aims to bridge a gap in human-robot collaboration research, providing insights into the impact of object weight in human handovers and readiness cues for intuitive robotic motion planning. The underlying dataset for ob

David J. Meer, Shivnag Sista, Mark D. Shattuck, Corey S. O'Hern

We describe combined experiments and simulations of droplet breakup during flow-driven interactions with a circular obstacle in a quasi-two-dimensional microfluidic chamber. Due to a lack of in-plane confinement, the droplets can also slip past the obstacle without breaking. Droplets are more likely to break when they have a higher flow velocity, larger size

Weizhang Huang, Zhuoran Wang

This work investigates inexact block Schur complement preconditioning for linear poroelasticity problems discretized using a hybrid approach: Bernardi-Raugel elements for solid displacement and lowest-order weak Galerkin elements for fluid pressure. When pure Dirichlet boundary conditions are applied to the displacement, the leading block of the resulting al

Jiao-Jiao Meng, Yu-Sen Xiao, Gen Li, Shao-Hua Liu

We report the synthesis, structure and physical properties of a new quaternary nitride LaCr$_2$Ge$_2$N. The compound crystallizes in the CeCr$_2$Si$_2$C-type structure (P4/mmm), featuring distinctive Cr$_2$N square sheets within Cr$_2$Ge$_2$N block layers. Physical characterizations reveal enhanced electron correlations evidenced by a Sommerfeld coefficient

Ratchaphat Nakarachinda, Lunchakorn Tannukij, Pitayuth Wongjun, Tanapat Deesuwan

The thermodynamic behaviours of multi-horizon black holes such as a Schwarzschild-de Sitter black hole have been one of the long-standing mysteries in gravitational physics since they involve quantum natures in gravitational systems and that the search for quantum gravity has not reached its conclusion. In this work, we seeked for a possibility of realising

Kingsley Egbo, Emily M. Garrity, Shivashree Shivamade Gowda, Saman Zare

Unintentionally formed interfacial layers are ubiquitous in semiconductor devices that operate at extreme conditions. However, these layers' structure and properties often remain unknown due to the thinness of these naturally formed interphases. Here, we report on the intentional epitaxial growth and semiconductor properties of NiGa2O4 spinel layers that for

Valeria Banica, Georg Maierhofer, Katharina Schratz

Hermite basis functions are a classical tool for the spatial discretisation of Schrödinger equations with harmonic potential. In this work, we prove that their favourable stability properties extend to Schrödinger equations without a trap: the free Schrödinger flow is stable in the weighted Sobolev spaces $Σ^k$ which govern the convergence of Hermite spectra

Putu Indah Githa Cahyani, Komang David Dananjaya Suartana, Novanto Yudistira

Vision-Language Models (VLMs) have demonstrated strong performance on multimodal reasoning tasks, but their deployment remains challenging due to high inference latency and computational cost, particularly when processing high-resolution visual inputs. While recent architectures such as FastVLM improve efficiency through optimized vision encoders, existing p

Abhishek Samlodia, Simon Catterall, Alexander F. Kemper, Yannick Meurice

We study the quantum Ising model on (2+1)-dimensional anti-de Sitter space using Matrix Product States (MPS) and Matrix Product Operators (MPOs). We explore the bulk phase diagram of the theory on regular tessellations of hyperbolic space with coordination number seven and find disordered and ordered phases separated by a phase transition. We find that the b

Jasper B. Yang, Thomas Lumley, Bryan E. Shepherd, Pamela A. Shaw

Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches generally fall into two categories: (i) designs that optimize individualized sampling rules, where unit-specific probabilities are assigned and applied indep

Lachlan McGinness

A Quantum Computing Concept Inventory is needed for the acceleration of uptake of best practice in quantum computing education required to support the quantum computing workforce for the next two decades. Eight experts in quantum computing, quantum ommunication or quantum sensing were interviewed to determine if there is substantial non-mathematical content

Mohammad Taghi Dabiri, Rula Ammuri, Mazen Hasna, Khalid Qaraqe

Optical inter-satellite links (ISLs) are becoming the principal communication backbone in modern large-scale LEO constellations, offering multi-Gb/s capacity and near speed-of-light latency. However, the extreme sensitivity of optical beams to relative satellite motion, pointing jitter, and rapidly evolving geometry makes routing fundamentally more challengi

Estefania Padilla Gonzalez, A. Joshi Bhavin, Louis G. Strolger, Bhoomika Khatri

Despite the tremendous impact of Type Ia supernovae (SNe Ia) on the field of cosmology, their underlying physics are still poorly understood. Studies have found an intriguing correlation between standardized Type Ia supernova (SN Ia) luminosities and host galaxy masses, commonly referred to as the "mass step". SNe Ia in massive galaxies appear systematically

Vidit Agrawal, John Peters, Tyler N. Thompson, Mohammad Vali Sanian

Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellular morphology are typically trained with a single microscopy imaging type. This results in specialized models that cannot be reused across biological studies because the technical

David Shlivko

We perform a Bayesian comparison between thawing quintessence and a cosmological constant, incorporating theoretically motivated priors on the phenomenological Pad\'e-w parameters used to model thawing dynamics. We find that thawing quintessence is consistently preferred over a cosmological constant when combining BAO data from DESI DR2 and CMB data from Pla

Rashmeet Kaur Nayyar, Naman Shah, Siddharth Srivastava

Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisions about continuous action parameters governing how an action is executed. Existing approaches exhibit severe limitations in this setting -- planning methods demand hand-crafted action models, and standard reinfo

Alex Yu, Huaqing Zhao, Lin Z. Li

In spectroscopic analysis, the peak-based signal-to-noise ratio (pSNR) is commonly used but suffers from limitations such as sensitivity to noise spikes and reduced effectiveness for broader peaks. We introduce the area-based signal-to-noise ratio (aSNR) as a robust alternative that integrates the signal over a defined region of interest, reducing noise vari

C. P. Stafford, O. Rybdylova

A novel method combining the fully Lagrangian approach (FLA) and kernel regression has been developed for two-way coupled simulations of evaporating sprays. The carrier phase is incompressible viscous flow described by the Navier-Stokes equations. The admixture is considered to be a cloud of monodisperse evaporating droplets, which is treated as a continuum

Henry L. Nourse, Vanessa C. Olaya-Agudelo, Ivan Kassal

Simulating chemical dynamics is computationally challenging, especially for nonadiabatic dynamics, where numerically exact classical simulations scale exponentially with system size, becoming intractable for even small molecules. On quantum computers, chemical dynamics can be simulated efficiently using either universal, qubit-only devices or specialized mix

Mohammad Taghi Dabiri, Mazen Hasna, Rula Ammuri, Saif Al-Kuwari

This paper presents a novel wireless quantum synchronization framework tailored for city-scale deployment using entangled photon pairs and passive corner cube retroreflector (CCR) arrays. A centralized quantum hub emits entangled photons, directing one toward a target device and the other toward a local reference unit. The target, equipped with a planar CCR

Simon Foucart

A linear functional of an object from a convex symmetric set can be optimally estimated, in a worst-case sense, by a linear functional of observations made on the object. This well-known fact is extended here to a nonlinear setting: other simple functionals of the object can be optimally estimated by functionals of the observations that share a similar simpl

Peter Cox, Matthew J. Dolan, Avirup Ghosh

We derive conservative upper limits on the dark-matter--nucleon scattering cross-section for sub-GeV mass dark matter. Working exclusively within the low-energy chiral effective theory, we derive bounds that are independent of the details of the dark matter interactions in the UV. Dark matter that interacts only hadronically at leading order also inevitably

Paolo Vella

We study positive subunital maps on ordered effect spaces and introduce the defect $d(T) = u - T(u)$, which satisfies a cocycle identity under composition. Using only this identity and elementary order-theoretic arguments -- requiring no spectral decomposition or dimension-dependent techniques -- we prove that in any finite composition-closed family of posit

Mohammad Taghi Dabiri, Mazen Hasna, Aiman Erbad, Rula Ammuri

During disaster response, making rapid and well-informed decisions about which areas require immediate attention can save lives. However, current coordination models often struggle with unreliable data, intentional misinformation, and the breakdown of critical communication infrastructure. A decentralized, vote-based blockchain model offers a compelling subs

Arsalan Vahi

The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation

Razine Moundir Ghorab, Emanuele Parisi, Cristian Gutierrez, Miquel Alberti-Binimelis

LLMs have shown early promise in generating RTL code, yet evaluating their capabilities in realistic setups remains a challenge. So far, RTL benchmarks have been limited in scale, skewed toward trivial designs, offering minimal verification rigor, and remaining vulnerable to data contamination. To overcome these limitations and to push the field forward, thi

Zhan Qu, Michael Färber

Large Language Models (LLMs) are increasingly applied to medicine, yet their adoption is limited by concerns over reliability and safety. Existing evaluations either test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap. We introduce MediEval, a benchmark that links MIMIC-IV electr

Marco Menale, Carmelo Filippo Munafò, Francesco Oliveri

Kinetic theory frameworks are widely used for modeling stochastic interacting systems, where the evolution primarily depends on binary interactions. Recently, in this framework the action of the external force field has been introduction in order to gain a more realistic picture of some phenomena. In this paper, we introduce nonconservative kinetic equations

Derek Shiller

This report estimates the potential number of digital minds, defined as AI systems exhibiting observable traits such as agency, personality, and intelligence, in the coming decades. It employs two complementary approaches: first, examining specific use cases for digital minds and projecting adoption rates for each; second, analyzing trends in AI chip product

Mohammad Meymani, Roozbeh Razavi-Far

Machine learning is a powerful tool enabling full automation of a huge number of tasks without explicit programming. Despite recent progress of machine learning in different domains, these models have shown vulnerabilities when they are exposed to adversarial threats. Adversarial threats aim to hinder the machine learning models from satisfying their objecti

Rachel E. Gross, Yao-Lun Yang, L. Ilsedore Cleeves, Ewine F. van Dishoeck

Sulfur dioxide (SO$_2$) ice has been tentatively detected in protostellar envelopes, but its reliability as a solid-state sulfur reservoir remains unclear. We present new measurements of SO$_2$ ice from 6.8-8.5 $\mu$m toward four Class 0 protostars observed with JWST's Mid-Infrared (MIRI) Medium Resolution Spectrometer, as part of the COMs ORigin Investigate

Adrien Sauvaget

We work over strata of meromorphic differentials with poles of order 1, and on affine subspaces defined by linear conditions on the residues. We propose a definition of the volume of these objects as the integral of a tautological class on the projectivization of the stratum. By previous work with Chen-M\"oller-Zagier, this definition agrees with the Masur-V

Maria Eleni Koutsiaki, Marina Delianidi, Chaido Mizeli, Konstantinos Diamantaras

The integration of AI chatbots into educational settings has opened new pathways for transforming teaching and learning, offering enhanced support to both educators and learners. This study investigates the design and application of an AI chatbot as an educational tool in higher education. Designed to operate in the Greek language, the chatbot addresses ling

Philip Korman, Dieter S. Schmidt

For a class of oscillatory resonant problems, involving Dirichlet problems for semilinear PDE's on balls and rectangles in $R^n$, we show the existence of infinitely many solutions, and study the global solution set. The first harmonic of the right hand side is not required to be zero, or small. We also derive asymptotic formulas in terms of the first harmon

Reeshad Khan, John Gauch

Traditional autonomous driving pipelines decouple camera design from downstream perception, relying on fixed optics and handcrafted ISPs that prioritize human viewable imagery rather than machine semantics. This separation discards information during demosaicing, denoising, or quantization, while forcing models to adapt to sensor artifacts. We present a task

Mohammadreza Rostami, Solmaz S. Kia

This paper introduces \texttt{FedMPDD} (\textbf{Fed}erated Learning via \textbf{M}ulti-\textbf{P}rojected \textbf{D}irectional \textbf{D}erivatives), a novel algorithm that simultaneously optimizes bandwidth utilization and enhances privacy in Federated Learning. The core idea of \texttt{FedMPDD} is to encode each client's high-dimensional gradient by comput

Nathaniël de Leeuw, Marceau Nahon, Mathis Reymond, Raja Chatila

Large language models (LLMs) are increasingly used in situations where human values are at stake, such as decision-making tasks that involve reasoning when performed by humans. We investigate the so-called reasoning capabilities of LLMs over novel symbolic representations by introducing an experimental framework that tests their ability to process and manipu

Rommel Cortez, Bala Krishnamoorthy

Several performance measures are used to evaluate binary and multiclass classification tasks. But individual observations may often have distinct weights, and none of these measures are sensitive to such varying weights. We propose a new weighted Pearson-Matthews Correlation Coefficient (MCC) for binary classification as well as weighted versions of related

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin, Ben P. Marchant

Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, which combine a fixed mean, expressed using independent predictors, with autocorrelated random errors, are used for inference, forecasting and filling in missing values in groundwater level time series

Jin Feng

We establish multi-scale convergence theory for a class of Hamilton-Jacobi PDEs in space of probability measures. They arise from context of hydrodynamic limit of N-particle deterministic action minimizing (global) Lagrangian dynamics. From a Lagrangian point of view, this can also be viewed as a limit result on two scale convergence of action minimizing pro

Yi Huang, Alireza Jaberi Rad, Qiangfei Xia

Hyperdimensional computing (HDC), utilizing a parallel computing paradigm and efficient learning algorithm, is well-suited for resource-constrained artificial intelligence (AI) applications, such as in edge devices. In-memory computing (IMC) systems based on memristive devices complement this by offering energy-efficient hardware solutions. To harness the ad

Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, Chenxi Whitehouse

As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. However, existing techniques, such as verbalized confidence and multi-generation methods, are often either poorly calibrated or computationally expensive. We introduce linear probes trained w

Daniyal Ganiuly, Nurzhau Bolatbek, Assel Smaiyl

Autonomous vehicles rely on LiDAR based perception to support safety critical control functions such as adaptive cruise control and automatic emergency braking. While previous research has shown that LiDAR perception can be manipulated through object based spoofing and injection attacks, the impact of such attacks on vehicle safety controllers is still not w

Jack Lee Jennings, Sanja Bojic, Lukas Breitwieser, Alex Sharpe

The cryopreservation of biological materials is a highly complex process, as it involves numerous factors such as the cooling and thawing procedures, the administration of cryoprotective agents (CPAs), as well as the type and composition of cells. While theoretical work has yielded a better understanding of the processes occurring during cryopreservation, th

Alexander Reichmann, Zahra Rajabzadeh, Sebastian Hofer, René Hammer

Simulations of SiC crystal growth using molecular dynamics (MD) have become popular in recent years. They, however, simulate very fast deposition rates, to reduce computational costs. Therefore, they are more akin to surface sputtering, leading to abnormal growth effects, including thick amorphous layers and large defect densities. A recently developed metho

Kasra Kiaee, Alexander Monin

We study the large-charge bootstrap for conformal field theories with a U(1) symmetry extending the analysis of scalar probes to include conserved currents. We formulate the bootstrap equations and analyze their solutions assuming the existence of a non-trivial macroscopic limit and that the spectrum organizes into a finite number of Regge trajectories. We s

Chenkai Qiao, Ming Li, Donghui Xie, Minyong Guo

Circular photon orbits have become an attractive topic in recent years. They play extremely important roles in black hole shadows, gravitational lensings, quasi-normal modes, and spacetime topological properties. In our recent work, \href{https://doi.org/10.1103/PhysRevD.106.L021501}{Phys. Rev. D \textbf{106}, L021501 (2022)}, a geometric approach to circula

Ralf Nötzel, Kerstin Weinberg

Radon is a naturally occurring radioactive gas that contributes significantly to human radiation exposure and must be controlled to avoid concentrations harmful to health. The paper presents an impulse-proportional ionization chamber that is suitable for the direct measurement of low radon concentrations in ambient air and achieves spectroscopic resolutions