March 2026 arXiv papers — page 106

Jianan Nie, Peng Gao

Mass spectrometry (MS) stands as a cornerstone analytical technique for molecular identification, yet de novo structure elucidation from spectra remains challenging due to the combinatorial complexity of chemical space and the inherent ambiguity of spectral fragmentation patterns. Recent deep learning approaches, including autoregressive sequence models, sca

Deyan P. Mihaylov, Jan Eberhardt, Trifon Trifonov, Rafael Brahm

TOI-1232 is a G-dwarf star with a mass of $1.06_{-0.06}^{+0.07} M_\odot$, a radius of $1.07\pm 0.05 R_\odot$, and slightly higher metallicity than solar of Fe/H = $0.18 \pm 0.05$. The star hosts a transiting warm Jovian-mass planet, TOI-1232 b, with an orbital period of $P_{b} = 14.256_{-0.001}^{+0.001}$ days, identified with data from multiple sectors of th

Xinyu Liu

This paper establishes a natural quantum counterpart of weak equilibration for statistical ensembles in integrable systems. For quantum systems with pure point spectrum, single-time expectation values under unitary evolution are typically quasiperiodic, and hence generally do not admit a pointwise limit as $t\to\infty$. To overcome this difficulty, we introd

Lawrence Arkoh, Daniel Feitosa, Wesley K. G. Assunção

Design decisions are at the core of software engineering and appear in Q\&A forums, mailing lists, pull requests, issue trackers, and commit messages. Design discussions spanning a project's history provide valuable information for informed decision-making, such as refactoring and software modernization. Machine learning techniques have been used to detect d

Takuji Nakamura, Yasutaka Nakanishi, Shin Satoh, Kodai Wada

We study Fox colorings of tangle diagrams by $R=\mathbb{Z}$ or $\mathbb{Z}/p\mathbb{Z}$, where $p\geq3$ is an odd integer. For an $R$-colored $m$-string tangle diagram, the colors at the $2m$ boundary points form a vector $v\in R^{2m}$. We show that for classical tangle diagrams, such vectors are completely characterized by the alternating sum condition $\De

Mingda Qiao

The distance from calibration, introduced by B{\l}asiok, Gopalan, Hu, and Nakkiran (STOC 2023), has recently emerged as a central measure of miscalibration for probabilistic predictors. We study the fundamental problems of computing and estimating this quantity, given either an exact description of the data distribution or only sample access to it. We give a

Zhelin Xu, Shuhei Yamamoto, Atsuyuki Morishima

Corporate recruiters often need to screen many resumes within a limited time, which increases their burden and may cause suitable candidates to be overlooked. To address these challenges, prior work has explored LLM-based automated resume screening. However, some methods rely on commercial LLMs, which may pose data privacy risks. Moreover, since companies ty

Lingfeng Zhang, Taoyong Cui, Dongzhan Zhou, Lei Bai

Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slow

Xiaojing Ye

This draft book offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks, the theory and algorithms of optimal control and reinforcement learning integrated with deep learning techniques, to contemporary gen

Frank Nijhoff, Linyu Peng, Cheng Zhang, Da-jun Zhang

In this paper, we present a unified approach to constructing continuous and discrete $\mathrm{PGL}(3)$-invariant integrable systems, formulated in terms of the common dependent variables $z_1,z_2$, from linear spectral problems and their factorisation. Starting from third-order spectral problems, we first provide explicit forms of the differential and differ

Mathew Joseph, Davar Khoshnevisan, Kunwoo Kim, Carl Mueller

Consider a parabolic SPDE \[ \partial_t u = \Delta u + \sigma(u)\eta, \] on $(0\,,\infty)\times\mathbb{R}^d$, where $\eta$ is a centered, generalized Gaussian noise with $\text{Cov}[\eta(t\,,x)\,,\eta(s\,,y)]=\delta_0(t-s)\Lambda(x-y)$ for a tempered Borel measure $\Lambda$ that is positive definite and satisfies a mild weak-noise. The existence of invariant

Grant Wilkins, Fiodar Kazhamiaka, Ram Rajagopal

Datacenter operators and electrical utilities rely on power traces at different spatiotemporal scales. Operators use fine-grained traces for provisioning, facility management, and scheduling, while utilities use site-level load profiles for capacity and interconnection planning. Existing datacenter power models do not capture LLM inference workloads, in whic

Petr Sramek

Mid-circuit measurements are essential primitives for dynamic circuits and quantum error correction, yet characterizing their induced disturbance on spectator qubits remains a central practical problem. Device-level benchmarking often compresses this disturbance into low-order proxy metrics such as $T_1$, $T_2$, readout assignment error, and pairwise crossta

Aizierjiang Aiersilan, Raeli Savitt

Multi-agent AI systems exhibit emergent risks that no single agent produces in isolation. Existing safety frameworks rely on binary classifications of agent behavior, discarding the uncertainty inherent in proxy-based evaluation. We introduce SWARM (\textbf{S}ystem-\textbf{W}ide \textbf{A}ssessment of \textbf{R}isk in \textbf{M}ulti-agent systems), a simulat

Saurabh Sharma, Ambuj Singh

Understanding how complex behaviors, opinions, and innovations spread in online social networks remains a central challenge in computational social science. Existing models of complex contagion typically rely on stylized threshold mechanisms based solely on the number of infected neighbors and do not account for the interaction between individual preferences

Sijian Fan, Ray Bai

Biclustering is a powerful unsupervised learning technique for simultaneously identifying coherent subsets of rows and columns in a data matrix, thus revealing local patterns that may not be apparent in global analyses. However, most biclustering methods are developed for continuous data and are not applicable for binary datasets such as single-nucleotide po

Guangsheng Yu, Qin Wang, Rui Lang, Shuai Su

Cloud-hosted large language models (LLMs) have become the de facto planners in agentic systems, coordinating tools and guiding execution over local environments. In many deployments, however, the environment being planned over is private, containing source code, files, credentials, and metadata that cannot be exposed to the cloud. Existing solutions address

Sayan Saha, Jacqueline E. McCleary, Spencer W. Everett, Maya Amit

We present the weak gravitational lensing dataset from the Super-pressure Balloon-Borne Imaging Telescope (SuperBIT), which imaged 30 galaxy clusters during its 45 night flight in April to May 2023. SuperBIT is a first-of-its-kind balloon-borne imaging telescope that achieved near diffraction-limited observations in near-space conditions above 98% of the Ear

Kabilan Mahathevan, Yining Zhang, Muhammad Ali Gulzar, Kirshanthan Sundararajah

Sparse Tensor Compilers (STCs) have emerged as critical infrastructure for optimizing high-dimensional data analytics and machine learning workloads. The STCs must synthesize complex, irregular control flow for various compressed storage formats directly from high-level declarative specifications, thereby making them highly susceptible to subtle correctness

M. Uria, C. Hermann-Avigliano, P. Solano, A. Delgado

A quantum mirror is a device whose optical response, that is, transmission and reflection, can be controlled by a single qubit. Here, we propose the use of quantum mirrors as nodes in quantum networks. Propagating coherent states mediate the interaction between the control qubits of each quantum mirror. This allows implementing quantum teleportation, quantum

Chengxiao He, Wenhui Yang, Hongliang Zhao, Jiacheng Lv

Stable and reliable grasp is critical to robotic manipulations especially for fragile and glazed objects, where the grasp force requires precise control as too large force possibly damages the objects while small force leads to slip and fall-off. Although it is assumed the objects to manipulate is grasped firmly in advance, slip detection and timely preventi

Zelalem Arega Worku, David C. Del Rey Fernández, David W. Zingg

The Lax equivalence theorem guarantees convergence of stable and consistent discretizations for linear hyperbolic partial differential equations (PDEs). For nonlinear problems, however, stability and consistency alone do not generally guarantee convergence, even for smooth solutions, and existing convergence results typically rely either on projection-based

Kenji Tokuo

The decidability of a logical system refers to the existence of an algorithm that can determine whether any given formula in that system is a theorem. In this paper, Harrop's lemma is used to prove the decidability of quantum modal logic.

Guangqiang Lan, Fansai Meng

In this paper, we will investigate the moment exponential stabilization of highly nonlinear hybrid stochastic differential delay equations. A periodically intermittent controller based on discrete time state observations with asynchronous switching is designed. The upper bound of observation period as well as the lower bound of the control width are all obta

Yinuo Zhao, Donghan Liu, Baoqi Shi, Zhiyuan Huang

The recent breakthroughs in laser-driving 229Th nuclear transition have created an urgent demand for coherent vacuum-ultraviolet (VUV) sources delivering high spectral brightness at the critical 148.38 nm isomer energy. However, generating sufficient photon flux to overcome the low nuclear excitation probability remains a challenge for compact setups. While

Richard G. Baird, Boyden Myers, Erik M. Jorgensen, Rajesh Menon

Event-based image sensors provide microsecond temporal resolution but lack spectral discrimination, whereas diffractive spectral imagers encode wavelength information at conventional frame rates. We introduce a fluorescence microscopy architecture that fuses asynchronous event streams with diffraction-encoded CMOS measurements to decouple temporal and spectr

Yeongjun Jang, Kaoru Teranishi, Junsoo Kim

In this paper, we develop a distributionally robust optimal control approach for differentially private dynamical systems, enabling a plant to securely outsource control computation to an untrusted remote server. We consider a plant that ensures differential privacy of its state trajectory by injecting calibrated noise into its output measurements. Unlike pr

Evangelia Vanezi, Georgia M. Kapitsaki, Anna Philippou

The application of the General Data Protection Regulation (GDPR) has significantly affected privacy requirements elicitation, modelling, and verification in Software Engineering (SE). One of the affected areas is requirements visualisation through modelling diagrams, which plays a crucial role in ensuring privacy compliance, as functional system requirements

Gaoxiang Cao, Wenke Yuan, Yunpeng Hou, Huasen He

Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as frequent link disconnections and subnet fragmentation, which hinder reliable connectivity. To address these issues, we dynamically deploy multiple Unmanned Aerial Vehicles (UAVs) as

Shasha Yu, Fiona Carroll, Barry L. Bentley

Large language models (LLMs) are increasingly deployed as agents with access to executable tools, enabling direct interaction with external systems. However, most safety evaluations remain text-centric and assume that compliant language implies safe behavior, an assumption that becomes unreliable once models are allowed to act. In this work, we empirically e

Sergei Dyda, Randall C. Dannen, Shane W. Davis, Daniel Proga

We apply novel developments in photoionization modeling and multi-frequency radiation hydrodynamics to the study of line driven AGN disc winds. We use a flux-averaged force multiplier approach to compute the radiation force due to lines for hydrodynamics simulations using 4 frequency bands - infrared (IR), optical (O), ultraviolet (UV) and X-rays. Though lin

Alexander Venner, Chelsea X. Huang, David W. Latham, Samuel N. Quinn

The detection of exoplanets using astrometry has long been an area of interest, but is fraught with challenges. The Gaia mission is fundamentally reshaping this field thanks to its unprecedentedly precise all-sky astrometric observations. The 2022 release of Gaia DR3 brought the first exoplanets discovered from the Gaia astrometry, including a new candidate

Mao Tian Tan, Tomaž Prosen

We study a so-called semi-ergodic brickwork dual-unitary circuits where, in the infinite volume limit, the two-point correlation functions of single-site operators exhibit ergodic behavior along one light ray and non-ergodic behavior along the other light ray. Here, however, we study intermediate and long-time dynamics of a system in a finite, large volume.

Maoyang Xiang, Bo Wang

Non-linear activation functions play a pivotal role in on-device inference and training, as they not only consume substantial hardware resources but also impose a significant impact on system performance and energy efficiency. In this work, we propose Distribution-Aware Piecewise Activation (DAPA), a differentiable and hardware-friendly activation function f

Davide Castaldo, Markus Reiher

Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational

Marta González, Isabelle Joncour, Estelle Moraux, Frédérique Motte

We provide the community with a homogeneous catalogue of small, significant substructures (henceforth NESTs) extracted from the spatial distribution of Young Stellar Objects (YSOs) in a large, consistent sample of star-forming regions. The catalog allows us to explore the relevance of small scale spatial substructure and discuss the interpretation of NESTs a

Sara Rutten, Thomas Neyens, Elisa Duarte, Antonio Gasparrini

Distributed lag non-linear models (DLNMs) are a popular approach to flexibly model the effect of time-delayed exposures. Classical DLNMs specify a common exposure-lag-response relationship across geographical areas. However, this relationship might be altered by an effect modifier that differs between spatial units. Although some methods have been proposed t

T. Jocteur, K. Martens, R. Mari, E. Bertin

Oscillatory sheared suspensions, when observed stroboscopically, exhibit a reversible-irreversible transition as a function of the strain amplitude, which is a kind of absorbing phase transition. So far studies of this transition focused on global quantities, e.g. quantifying the irreversibility on one side of the transition or the time to reach a reversible

Anirudh Sivakumar, Sanjay Shukla, Rahul Pandit, Pankaj Kumar Mishra

We investigate turbulent spin-down dynamics in self-gravitating Bose-Einstein condensates, comparing purely bosonic and axionic (higher-order interacting) systems. Through simulations of the Gross-Pitaevskii-Poisson system, we study condensates pinned to a crust potential undergoing rapid rotation slowdown. We find that axionic condensates exhibit more unifo

Jonas Sinjan, Tino L. Riethmüller, Achim Gandorfer, Alex Feller

Context. The High Resolution Telescope of the Polarimetric and Helioseismic Imager on Solar Orbiter (SO/PHI-HRT) operates in an extreme observational environment, observing the Sun as close as $0.28$ au. The high thermal load and large illuminating field puts high demands on the instrument in terms of both imaging performance and false light control. Aims. T

Marco Cè, Leonardo Giusti, Michele Pepe, Pietro Rescigno

Novel theoretical and computational strategies have opened the possibility of exploring thermal QCD at the non-perturbative level at unprecedented temperatures, reaching from the GeV scale up to the electroweak scale. A number of observable quantities are now being investigated in this regime. Key ones are the hadronic screening masses, which encode the corr

Nikola Antonijević, Bernhard Etzlinger, Dave Singelée, Bart Preneel

Ranging and localisation have become critical for many applications and services. The Wi-Fi (IEEE 802.11) standard is a natural candidate for providing these functions across diverse environments, given its widespread deployment. The IEEE 802.11az amendment, finalised in 2023, introduces "Next Generation Positioning" mechanisms to secure and harden t

Arturo Moncho-Jordá, José López-Molina, Joachim Dzubiella

We investigate the non-equilibrium compression of a confined hard-sphere colloidal fluid driven by a mobile boundary within dynamical density functional theory. The system consists of a fluid confined between two parallel walls, one acting as an overdamped piston subjected to a sudden increase in external pressure. The piston motion is controlled by a mobili

Renjie Zhang, Bei Jiang, Xiangqi Liu, Hengxin Tan

The interplay between local and itinerant electrons underpins many correlated and topological quantum states. Kagome lattices provide an ideal platform by hosting both flat (localized states) and dispersive bands (itinerant states), yet direct spectroscopic evidence of their dynamical coupling has remained elusive. Here we report the long-sought flat band re

Masayuki Kawarada, Kodai Watanabe, Soichiro Murakami

We introduce GAIN (Goal-Aligned Decision-Making under Imperfect Norms), a benchmark designed to evaluate how large language models (LLMs) balance adherence to norms against business goals. Existing benchmarks typically focus on abstract scenarios rather than real-world business applications. Furthermore, they provide limited insights into the factors influen

Jonathan Cook, Sabih ur Rehman, M. Arif Khan

SIMON and SPECK were among the first efficient encryption algorithms introduced for resource-constrained applications. SIMON is suitable for Internet of Things (IoT) devices and has rapidly attracted the attention of the research community to understand its structure and analyse its security. To analyse the security of an encryption algorithm, researchers of

Ding-An Chen, Kai-You Huang, Chun-Yen Hsu, Meng-Cheng Xie

The generation of a trapping potential for dark-state polaritons in a two-dimensional electromagnetically induced transparency system is theoretically studied. We show that such a trap can arise from a spatially inhomogeneous effective mass of the dark-state polariton. Because this mass inhomogeneity can be engineered by tuning the parameters of the control

Md. Tanvir Hossain, Mohd Ruhul Ameen, Akif Islam, Md. Omar Faruqe

Efficient text entry remains a primary bottleneck preventing Virtual Reality (VR) from evolving into a viable productivity platform. To address this, we conducted an empirical comparison of six physical input systems across three interaction styles Controller Driven, Free Hand, and Virtual Touch evaluating both discrete tap typing and continuous gesture typi

Masayuki Kawarada, Tatsuya Ishigaki, Hiroya Takamura

Task interference, the performance degradation caused by task switches within a single conversation, has been studied exclusively in text-only settings despite the growing prevalence of multimodal dialogue systems. We introduce a benchmark for evaluating this phenomenon in multimodal LLMs, covering six tasks across text and vision with systematic variation o

Halim Shaikh, Mattia Di Mauro

The particle nature of dark matter (DM) remains one of the central open problems in modern physics. Among the most extensively studied candidates are weakly interacting massive particles, whose parameter space is now under strong pressure from direct detection, indirect detection, and collider searches. In this work we revisit the Higgs-portal scenario with

Arun Govindankutty, Sudarshan K. Srinivasan

We present a scalable formal verification methodology for Quantum Phase Estimation (QPE) circuits. Our approach uses a symbolic qubit abstraction based on quantifier-free bit-vector logic, capturing key quantum phenomena, including superposition, rotation, and measurement. The proposed methodology maps quantum circuit functional behaviour from Hilbert space

Francesco Laiti, Davide Talon, Jacopo Staiano, Elisa Ricci

Image memorability, i.e., how likely an image is to be remembered, has traditionally been studied in computer vision either as a passive prediction task, with models regressing a scalar score, or with generative methods altering the visual input to boost the image likelihood of being remembered. Yet, none of these paradigms supports users at capture time, wh

Hiu Yung Wong

The superconducting qubit quantum computer is one of the most promising quantum computing architectures for large-scale integration due to its maturity and close proximity to the well-established semiconductor manufacturing infrastructure. From an education perspective, it also bridges classical microwave electronics and quantum electrodynamics. In this pape

James Brock, Ce Zhang, Nantheera Anantrasirichai

The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central challenges in this domain are pixel-level change detection and semantic change interpretation, particularly for complex forest dynamics. While large language models (LLMs) are increa

Zehang Bao, Zitian Zhu, Yang-Ren Liu, Zixuan Song

Periodically driven quantum many-body systems exhibit a wide variety of exotic nonequilibrium phenomena and provide a promising pathway for quantum applications. A fundamental challenge for stabilizing and harnessing these highly entangled states of matter is system heating by energy absorption from the drive. Here, we propose and demonstrate a disorder-free

Mehdi Djellabi, Matthias Hecker, Shaheen Acheche

We extend the quantum-feature kernel framework, which relies on measurements of graph-dependent observables, along three directions. First, leveraging neutral-atom quantum processing units (QPUs), we introduce a scheme that incorporates attributed graphs by embedding edge features into atomic positions and node features into local detuning fields of a Rydber

Michael Dymond, Vojtěch Kaluža

We provide a new characterisation of the decades old open problem of extending bilipschitz mappings given on a Euclidean separated net. In particular, this allows for the complete positive solution of the open problem in dimension two. Along the way, we develop a set of tools for bilipschitz extensions of mappings between subsets of Euclidean spaces.

Samuel Rot, Iulius Dragonu, Christina Triantafyllou, Matthew Grech-Sollars

Purpose: The clinical feasibility and translation of many advanced quantitative MRI (qMRI) techniques are inhibited by their restriction to 'research mode', due to resource-intensive, offline parameter estimation. This work aimed to achieve 'clinical mode' qMRI, by real-time, inline parameter estimation with a trained neural network (NN) full

Guillaume Olikier, Petar Mlinarić, P. -A. Absil, André Uschmajew

The set of real matrices of upper-bounded rank is a real algebraic variety called the real generic determinantal variety. An explicit description of the tangent cone to that variety is given in Theorem 3.2 of Schneider and Uschmajew [SIAM J. Optim., 25 (2015), pp. 622-646]. The present paper shows that the proof therein is incomplete and provides a proof. It

Friedrich Bauermeister

Let $(M,h)$ be a connected, complete Riemannian manifold, $x\in M$, and $l>0$. Then $M$ is called a $Z^x$ manifold if all geodesics starting at $x$ return to $x$, and it is called a $Y^x_l$ manifold if every unit-speed geodesic starting at $x$ returns to $x$ at time $l$. It is unknown whether there are $Z^x$ manifolds that are not $Y^x_l$ manifolds for any $

Michael Dymond, Vojtěch Kaluža

We prove that every $L$-bilipschitz mapping $\mathbb{Z}^2\to\mathbb{R}^2$ can be extended to a $C(L)$-bilipschitz mapping $\mathbb{R}^2\to\mathbb{R}^2$ and provide a polynomial upper bound for $C(L)$. Moreover, we extend the result to every separated net in $\mathbb{R}^2$ instead of $\mathbb{Z}^2$, with the upper bound gaining a polynomial dependence on the

Heinz-Jürgen Schmidt

Completely positive transformations play an important role in the description of state changes in quantum mechanics, including the time evolution of open quantum systems. One useful tool to describe them is the so-called Choi isomorphism, which maps completely positive transformations to positive semi-definite matrices. Accordingly, there are numerous propos

Ingvars Vitenburgs, Niels R. Walet

A study of correlation effects in twisted bilayer graphene, using the extended coupled cluster method, is presented. This approach considers both self-consistent mean-field and beyond mean-field contributions, and can describe phase transitions in such strongly correlated systems, without further inputs or assumptions. Detailed expressions and a suitable imp

Silvain Rideau-Kikuchi, Mariana Vicaría

We classify the imaginaries in a large class of equicharacteristic zero henselian valued fields that contain all those with bounded inertia group, and more. To do so, we consider a mix of sorts introduced in earlier works of the two authors and prove elimination of imaginaries down to the field, the k-linear imaginaries and the imaginaries of the value group

Lev Steinberg

We present a geometric formulation of classical Cosserat elasticity in which the coframe and rotational connection are treated as independent variational fields. In contrast to conventional metric-based approaches, this formulation makes the underlying geometric structure explicit and separates translational and rotational degrees of freedom at the level of

Tianhui Zhang, Bei Peng, Danushka Bollegala

Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative scenarios, reflecting the diversity in their responses. Despite the growing need to train diverse commonsense generators, the progress of this line of work has been significantly hin

Harish K. Dureppagari, Harikumar Krishnamurthy, Chiranjib Saha, Xiaofeng Wang

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) enables a new class of positioning capabilities based on cellular signals transmitted by Low-Earth Orbit (LEO) satellites. In this paper, we investigate joint delay-and-carrier-phase positioning for LEO-based NR-NTN systems and provide a convergence-centric comparison with Global Naviga

Muhammad Ammar

Nevanlinna theory studies the value distribution of meromorphic functions and provides powerful results in the form of the First and Second Main Theorems. In this paper, we introduce quaternionic analogues of the Nevanlinna functions. Starting from the Jensen formula due to Perotti (arXiv:1902.06485), we derive a notion of total order and an associated integ

Evangelia Zve, Gauvain Bourgne, Benjamin Icard, Jean-Gabriel Ganascia

Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce a temporal taxonomy of news-document trajectories that defines how documents relate to topic formation over time. It distinguishes anticipatory outliers, which precede the topics they later join, from documents

Aryana Haghjoo, Shoubaneh Hemmati, Bahram Mobasher, Nima Chartab

The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral super-resolution that enhances low-resolution galaxy spectra by a factor of $\sim$10 in resolving power ($R\sim100$ to $R\sim1000$).

Athira J. Jacob, Puneet Sharma, Daniel Rueckert

Segmentation of enhancement in LGE cardiac MRI is critical for diagnosing various ischemic and non-ischemic cardiomyopathies. However, creating pixel-level annotations for these images is challenging and labor-intensive, leading to limited availability of annotated data. Generative models, particularly diffusion models, offer promise for synthetic data gener

Ashwin Sudhir, Zion Leonahenahe Basque, Wil Gibbs, Ati Priya Bajaj

In the ever-evolving battle against malware, binary obfuscation techniques are a formidable barrier to effective analysis by both human security analysts and automated systems. In particular, virtualization or VM-based obfuscation is one of the strongest protection mechanisms that evade automated analysis. Despite widespread use of virtualization, existing a

Lukas Cha, Ryman Hashem, Ria Prakash, Tanguy Declety

The development of wearable sensing systems for sports performance tracking, rehabilitation, and injury prevention has driven growing demand for smart garments that combine comfort, durability, and accurate motion detection. This paper presents a textile-compatible fabrication workflow that integrates multi-material direct ink writing with automated embroide

Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan

Language models encode task-relevant knowledge in internal representations that far exceeds their output performance, but whether mechanistic interpretability methods can bridge this knowledge-action gap has not been systematically tested. We compared four mechanistic interpretability methods -- concept bottleneck steering (Steerling-8B), sparse autoencoder

Yue Chen, Reinhard H. W. Friedel, Richard M. Kippen

This report describes the method used to derive fluxes of the trapped proton belt along the GPS orbit (i.e., a Medium-Earth Orbit) during 2000-2010, a period almost covering a solar cycle. This method utilizes a newly developed empirical proton radiation-belt model, with the model output scaled by GPS in-situ measurements, to generate proton fluxes that cove

Andre Juliao, Wenura Withanage, Nikolya Cadavid, Anatolii Polyanskii

Multiple pairs of bronze pieces were joined along a common seam and then exposed to Nb vapor via sputter deposition during heating at $\sim$715 $^\circ$C to form a diffusion bond between the pieces. Polishing and alignment of the pieces created smooth surfaces normal to the Nb flux with seams perpendicular to the surface (i.e. parallel to the Nb flux). Conve

Yutong Ren, Arnav Reddy, Michael Nebeling

Gaze-based selection in XR requires visual confirmation due to eye-tracking limitations and target ambiguity in 3D contexts. Current designs for wide-FOV displays use world-locked, central overlays, which are not conducive to always-on AR glasses. This paper introduces PeriphAR (per-ree-far), a visualization technique that leverages peripheral vision for fee

Julia Jose, Meghna Manoj Nair, Rachel Greenstadt

We present an NLP-based study of political propaganda on Moltbook, a Reddit-style platform for AI agents. To enable large-scale analysis, we develop LLM-based classifiers to detect political propaganda, validated against expert annotation (Cohen's $\kappa$= 0.64-0.74). Using a dataset of 673,127 posts and 879,606 comments, we find that political propaganda a

Muhammad Mubashar, Fabio Cuzzolin

Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range of variations. This paper introduces a novel generalization of the GAN loss function based on Dempster-Shafer theory of evidence, applied to both the generator and discriminator.

Jeanne Clelland, Kristopher Tapp

We develop effective methods for constructing an ensemble of district plans via independent sampling from a reasonable probability distribution on the space of graph partitions. We compare the performance of our algorithms to that of standard Markov Chain based algorithms in the context of grid graphs and state congressional and legislative maps. For the cas

Xin Liu

The goal of this work is to investigate the almost pressureless Euler-Poisson (EP) system with repulsive force in the large friction limit. The leading order equations in the limit are shown to be the hyperbolic-elliptic Keller-Segel (KS) system of consumption type. Under suitable assumptions on the initial data, we establish the unique global-in-time soluti

Reid Dale, Jordan Rodu, Mike Baiocchi

The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring proposition: in the best case, it generates realistic data \textit{in silico} at a fraction of the cost of authentic data which may be found \textit{in vivo} or \textit{in vitro}. Th

Hashini Senaratne, Richard Attfield, Samith Widhanapathirana, David Howard

Maintaining situational awareness (SA) is critical in human-robot teams. Yet, under high workload and dynamic conditions, operators often experience SA gaps. Automated detection of SA gaps could provide timely assistance for operators. However, conventional SA measures either disrupt task flow or cannot capture real-time fluctuations, limiting their operatio

Bo-Cheng Qiu, Yu-Fan Lin, Yu-Zhe Pien, Chia-Ming Lee

Capsule endoscopy event detection is challenging because diagnostically relevant findings are sparse, visually heterogeneous, and embedded in long, noisy video streams, while evaluation is performed at the event level rather than by frame accuracy alone. We therefore formulate the RARE-VISION task as a metric-aligned event detection problem instead of a pure

Yanchuan Tang, Taowen Wang, Yuefei Chen, Boxuan Zhang

Vision-Language-Action (VLA) models enable general-purpose robotic policies by mapping visual observations and language instructions to low-level actions, but they often lack reliable introspection. A common practice is to compute a token-level uncertainty signal and take its mean over a rollout. However, mean aggregation can dilute short-lived but safety-cr

Diego Gallego, J. Bayron Orjuela-Quintana

Exponential quintessence models motivated by string compactifications naturally involve both a dilatonic scalar and its axionic partner evolving on a curved field space, while spatial curvature enlarges the cosmological phase space and may affect late-time dynamics. We perform a systematic analysis of the minimal two-field exponential system in a curved FLRW

Raya V. Mancheva

In this work we contrast the behaviour of two spherically symmetric matter models in a class of spherically symmetric spacetimes which feature a weak null singularity. This class in particular contains spherically symmetric perturbations of subextremal Reissner-Nordstr\"{o}m under the Einstein--Maxwell--scalar field system, a system for which a $C^2$ formula

Martin Graßl, Pablo Unzueta, Andreas E. Hillers-Bendtsen, Yusong Liu

Norrish type I reactions selectively cleave carbon-carbon bonds directly adjacent to carbonyl groups. Despite their broad use in combination with aromatic carbonyls for additive manufacturing and dental UV curing applications, the nature of the photochemically active state and its population mechanism remain insufficiently understood. Detailed mechanistic in

Taichi Uyama, Luca Ricci, Marie Ygouf, Massimo Robberto

Background stars observed through a circumstellar disk provide valuable benchmarks for investigating the disk's extinction properties. The HD~163296 system is an excellent case study due to its large disk, the clearly visible extinction effects in JWST/NIRCam data, and the presence of numerous background sources within or around its disk. We present the meas

R. O. Chametla, A. Moranchel-Basurto, F. J. Sánchez-Salcedo

The release of heat by a planetary embryo modifies the local density perturbations, forming thermal lobes in its vicinity, and thereby altering the torque exerted by the disk on the embryo. In laminar disks, these thermal torques can dominate the disk-embryo interaction, rendering the classical Lindblad and corotation torques largely subdominant. The aim of

Gaotian Wang, Kejia Ren, Andrew S. Morgan, Kaiyu Hang

Dynamics models, whether simulators or learned world models, have long been central to robotic manipulation, but most focus on minimizing prediction error rather than confronting a more fundamental challenge: real-world manipulation is inherently uncertain. We argue that robust manipulation under uncertainty is fundamentally an integration problem: uncertain

Ruixuan Zhao, Guitao Yang, Thomas Parisini, Boli Chen

We present a geometric approach to designing distributed unknown input observers (DUIOs) for linear time-invariant systems, where measurements are distributed across nodes and each node is influenced by \emph{unknown inputs} through distinct channels. The proposed distributed estimation scheme consists of a network of observers, each tasked with reconstructi

Zichen Xie, Wenxi Wang

As Large Language Models (LLMs) increasingly assist secure software development, their ability to meet the rigorous demands of Rust program verification remains unclear. Existing evaluations treat Rust verification as a black box, assessing models only by binary pass or fail outcomes for proof hints. This obscures whether models truly understand the logical

Eric Cramer, Laura M. Heiser, Young Hwan Chang

Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anisti

Charlotte Fairman, Hannah R. Wakeford, Alastair B. Claringbold, James Kirk

We present the first atmospheric observation and characterisation of the aligned, 1468 K hot Jupiter, NGTS-2b, with one JWST NIRSpec/G395H transit. These observations complete the GO 3838 observing campaign of the BOWIE-ALIGN program, which aims to investigate the link between hot Jupiter atmospheric composition and formation history through the atmospheric

Hongjue Zhao, Yizhuo Chen, Yuchen Wang, Hairong Qi

Deep neural networks (DNNs) have achieved remarkable empirical success, yet the absence of a principled theoretical foundation continues to hinder their systematic development. In this survey, we present differential equations as a theoretical foundation for understanding, analyzing, and improving DNNs. We organize the discussion around three guiding questio

Lingavasan Suresh Kumar, Yang Ba, Rong Pan

Persistent Large Language Model (LLM) agents expose a critical governance gap in memory management. Standard Retrieval-Augmented Generation (RAG) frameworks treat memory as passive storage, lacking mechanisms to resolve contradictions, enforce privacy, or prevent outdated information ("zombie memories") from contaminating the context window. We introduce Mem

Zikang Ding, Qiying Hu, Yi Zhang, Hongji Li

Inference-time steering is widely regarded as a lightweight and parameter-free mechanism for controlling large language model (LLM) behavior, and prior work has often suggested that simple activation-level interventions can reliably induce targeted behavioral changes. However, such conclusions are typically drawn under relatively relaxed evaluation settings

Krishna Murari

Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems. In parallel, wavelets have been extensively used as efficient computational tools due to their strong approximation cap

Ha Na Cho, Yawen Guo, Sairam Sutari, Emilie Chow

Ambient AI generates draft clinical notes from patient-clinician conversations, often using lay or consumer-oriented phrasing to support patient understanding instead of standardized clinical terminology. How clinicians revise these drafts for professional documentation conventions remains unclear. We quantified clinician editing for consumer-to- clinical no