November 2025 arXiv papers — page 90

Kashaf Gulzar, Dominik Wagner, Sebastian P. Bayerl, Florian Hönig

Automatic transcription of stuttered speech remains a challenge, even for modern end-to-end (E2E) automatic speech recognition (ASR) frameworks. Dysfluencies and fluency-shaping artifacts are often overlooked, resulting in non-verbatim transcriptions with limited clinical and research value. We propose a parameter-efficient adaptation method to decode dysflu

Dwipam Katariya, Snehita Varma, Akshat Shreemali, Benjamin Wu

Transformer-based architectures are widely adopted in sequential recommendation systems, yet their application in Financial Services (FS) presents distinct practical and modeling challenges for real-time recommendation. These include:a) long-range user interactions (implicit and explicit) spanning both digital and physical channels generating temporally hete

S. D. von Fellenberg, R. Arcodia, P. Benke, A. Goodwin

Quasi-periodic eruptions (QPEs) are repeating soft X-ray flares associated with galactic nuclei. Several recent works have found evidence that the accretion flow in the galactic nuclei of QPEs is of recent origin, and that it is unlike canonical active galactic nuclei (AGN). A precursor tidal disruption event has been observed in a few cases. In this work we

Phuong N. Hoàng, Kevin McGoff, Andrew B. Nobel, Yang Xiang

We introduce an optimal transport based approach for comparing undirected graphs with non-negative edge weights and general vertex labels, and we study connections between the resulting linear program and the graph isomorphism problem. Our approach is based on the notion of a joining of two graphs $G$ and $H$, which is a product graph that preserves their ma

Bilal Alilou, Clément Duval, Frederick Del Pozo, Nicolas Cherroret

We theoretically investigate the nonequilibrium relaxation of a spatial density modulation in a one-dimensional, weakly interacting Bose gas, and its connection to the equilibrium scattering rate $\smash{\gamma_k\propto k^{3/2}}$ of the system's phononic excitations. We show that the relaxation is generally governed by a nonequilibrium scattering rate $\gamm

Julie Y. A. Cachia, Xuan Zhao, John Hunter, Delancey Wu

Young adults today face unprecedented mental health challenges, yet many hesitate to seek support due to barriers such as accessibility, stigma, and time constraints. Bite-sized well-being interventions offer a promising solution to preventing mental distress before it escalates to clinical levels, but have not yet been delivered through personalized, intera

Aashish Ghimire, Jun Zeng, Roshan Paudel, Nikhil Kumar Tomar

Accurate identification and segmentation of dental caries in panoramic radiographs are critical for early diagnosis and effective treatment planning. Automated segmentation remains challenging due to low lesion contrast, morphological variability, and limited annotated data. In this study, we present the first comprehensive benchmarking of convolutional neur

A. Plati, G. Petrillo, L. de Arcangelis, A. Gnoli

We study the rheology of dense granular materials subjected to vertical vibration {by} using numerical simulations of a stress-imposed vane rheometer. The effective viscosity increases with confining pressure, decreases with vibration amplitude, and exhibits a non-monotonic dependence on frequency: weakening is observed at intermediate frequencies but is los

Vidushi Sharma, Ronit Agarwala, Judith L. Racusin, Leo P. Singer

The General Coordinates Network (GCN) is NASA's time-domain and multimessenger alert system. GCN distributes two data products: automated "Notices" and human-generated "Circulars" that report the observations of high-energy and multimessenger astronomical transients. The flexible and nonstructured format of GCN Circulars, comprising more than 40,500 Circular

Garv Chauhan, Cecilia Lunardini

The formation of a hot and dense core in a core-collapse supernova (SN) can produce massive Beyond Standard Model (BSM) particles. These particles can decay in the stellar envelope, generating positrons either directly or through secondary processes involving neutrinos or photons. We show for the first time that such positrons regardless of their production

Tamojeet Roychowdhury, Sebastiano D. von Fellenberg, Joseph M. Michail, S. P. Willner

JWST/MIRI observations can place photometric limits on the presence of an intermediate-mass black hole (IMBH) near the Galactic Centre. The stellar complex IRS 13E, a co-moving conglomerate of young and massive stars, is a prime location to study because it has been speculated to be bound by an IMBH. Assuming a standard radiatively inefficient accretion flow

Carla M. Quispe Flores, Raphael Kaubruegger, Minh C. Tran, Xun Gao

We investigate the fundamental time complexity, as constrained by Lieb-Robinson bounds, for preparing entangled states useful in quantum metrology. We relate the minimum time to the Quantum Fisher Information ($F_Q$) for a system of $N$ quantum spins on a $d$-dimensional lattice with $1/r^\alpha$ interactions with $r$ being the distance between two interacti

Emma Albertini, Michael L. Graesser, Gabriel Herczeg

There are a number of classical double copies, each providing a prescription for generating solutions to the Maxwell and scalar wave equations from exact solutions of Einstein's equations. Two such prescriptions are the Kerr-Schild and twistorial double copies. We argue that for a broad class of self-dual vacuum solutions of the Kerr-Schild form, which we re

Robab Aghazadeh Chakherlou, Siddartha Khastgir, Xingyu Zhao, Jerein Jeyachandran

Assuring the trustworthiness and safety of AI systems, e.g., autonomous vehicles (AV), depends critically on the data-related safety properties, e.g., representativeness, completeness, etc., of the datasets used for their training and testing. Among these properties, this paper focuses on representativeness-the extent to which the scenario-based data used fo

Mingkun Yu, Heming Zhong, Dan Huang, Yutong Lu

Kolmogorov-Arnold Networks (KANs) promise higher expressive capability and stronger interpretability than Multi-Layer Perceptron, particularly in the domain of AI for Science. However, practical adoption has been hindered by low GPU utilization of existing parallel implementations. To address this challenge, we present a GPU-accelerated operator library, nam

Shengyi Liu, Kun-Feng Lyu, Jie Meng, Jing Shu

We propose using the ultra-narrow 88 keV M\"ossbauer transition in $^{109}$Ag to search for QCD axion dark matter. The sub-eV axion field oscillates coherently, inducing a time-varying effective $\bar{\theta}_{\rm QCD}$ angle. This, in turn, modulates the nuclear binding energy. From existing linewidth measurements, we derive constraints on the $f_a^{-1}$-$m

Sebastiano D. von Fellenberg, Joseph M. Michail, S. P. Willner, Braden Seefeldt-Gail

We determine the mid-infrared (MIR, $\sim$5~\mu m--22~\mu m) extinction towards the Galactic center using MIRI/MRS integral field unit (IFU) observations of the central $3''\times3''$ region (near 5~\mu m) to $7''\times7''$ region (near 22~\mu m). To measure the MIR extinction, we employ two approaches: modeling the intrinsic-to-observed dust thermal spectru

Adeel Mahmood, Aaron B. Wagner

Through refined asymptotic analysis based on the normal approximation, we study how higher-order coding performance depends on the mean power as well as on finer statistics of the input power. We introduce a multifaceted power model in which the expectation of an arbitrary (but finite) number of arbitrary functions of the normalized average power is constrai

Yarin Bekor, Gal Michael Harari, Or Perel, Or Litany

We present Gaussian See, Gaussian Do, a novel approach for semantic 3D motion transfer from multiview video. Our method enables rig-free, cross-category motion transfer between objects with semantically meaningful correspondence. Building on implicit motion transfer techniques, we extract motion embeddings from source videos via condition inversion, apply th

Yifeng Ding, Hung Le, Songyang Han, Kangrui Ruan

Training Large Language Models (LLMs) for multi-turn Tool-Integrated Reasoning (TIR) - where models iteratively reason, generate code, and verify through execution - remains challenging for existing reinforcement learning (RL) approaches. Current RL methods, exemplified by Group Relative Policy Optimization (GRPO), suffer from coarse-grained, trajectory-leve

D. Fernández Gil, J. A. Fernández-Ontiveros, C. López-Sanjuan, F. Arizo-Borillo

We introduce J-HERTz (J-PLUS Heritage Exploration of Radio Targets at $z < 5$), a new multi-wavelength catalog that combines optical narrow-band photometry from J-PLUS, infrared observations from WISE, and deep low-frequency radio data from LoTSS for nearly half a million sources across 2,100 deg$^2$ of the northern sky. Key innovations of J-HERTz include Ba

Emmet Golden-Marx, Zheng Cai, Dongdong Shi, Xin Wang

As galaxies evolve in dense cluster and protocluster environments, they interact and quench their star formation, which gradually transforms the galaxy population from star-forming galaxies to quiescent galaxies. This transformation is identifiable by observing galaxy colors and can be seen in the morphological transformation of late-type galaxies into early

S. Kaviraj, D. De Cicco, I. Lazar, B. Bichang'a

We use the VST-COSMOS survey to identify, via their optical broadband variability, 30 AGN in nearby (z<0.4) dwarf (10^8 MSun < M < 10^10 MSun) galaxies. VST-COSMOS offers a 1 deg^2 survey footprint, a single visit depth of 24.6 mag and 68 r-band visits spanning an eleven-year temporal baseline. Compared to a control sample matched in stellar mass and redshif

Vitor Cardoso, Shauvik Biswas, Subhodeep Sarkar

Ten short years ago, we had the rare privilege of witnessing the onset of a renaissance in science: humanity finally succeeded in its arduous quest to directly detect gravitational waves. This breakthrough did not occur in a vacuum: it was the natural culmination of decades of research dedicated towards understanding the nature of gravitation based on Einste

Eric Kubischta, Ian Teixeira

We introduce an intrinsic formulation of quantum error correction based on representation theory, in which error-protection structure is encoded directly in a unitary group representation, rather than being tied to a particular embedding into a larger Hilbert space. In this framework, error models are classified according to the isotypic decomposition of the

Weam Abou Hamdan, Damián A. Galante

We analyse a class of SYK models whose Hamiltonian is the sum of two SYK Hamiltonians with different numbers of fermions $q, \tilde q$ in each interaction. We consider both Euclidean and Lorentzian probes of the quantum system in the large $N$ limit. In the strong coupling phase, the entropy provides a diagnostic of the thermal renormalisation group flow. Un

Madyson G. Barber, Andrew W. Mann, Marshall C. Johnson, Mayuko Mori

Despite the wide range of planet-star (mis)alignments in the mature population of transiting exoplanets, the small number of known young transiting planets are nearly all aligned with the rotation axes of their host stars, as determined by the sky-projected obliquity angle. The small number of young systems with measured obliquities limits statistical conclu

Matthew Mackinnon, Mauro Paternostro

The quantum Mpemba effect (QME) is a phenomenon observed in many-body systems where initial systems configurations farther from equilibrium can be observed to equilibrate faster than configurations that are closer to it. By considering noise induced error in the initial system state preparation, we analyse the robustness of various models exhibiting the QME.

Joseph M. Michail, Sebastiano D. von Fellenberg, Garrett K. Keating, Ramprasad Rao

S. D. von Fellenberg et al. (2025a, Paper I) reported the first mid-infrared detection of a flare from Sgr A*. The JWST/MIRI/MRS observations were consistent with an orbiting hotspot undergoing electron injection with a spectrum that subsequently breaks from synchrotron cooling. However, mid-infrared extinction measurements appropriate for these data were no

M. A. Arroyo-Ureña, O. Félix-Beltrán, J. Hernández-Sánchez, C. G. Honorato

We show the outstanding potential of the High-Luminosity LHC (HL-LHC) to discover charged lepton flavor violation (cLFV) via the ultra-peripheral process $\gamma\gamma \to e^\pm\mu^\mp$. Using a gauge-invariant Effective Field Theory (EFT) framework -consistent with the most stringent bounds from radiative decays- we perform a full Monte Carlo analysis with

Samuel Ruthven Ward, Tiago Costa, Chris M. Harrison, Vincenzo Mainieri

Active galactic nuclei (AGN) drive powerful, multiphase outflows that are thought to play a key role in galaxy evolution. The hot, shocked phase of these outflows ($T \gtrsim 10^{6} \rm{\ K}$) is expected to dominate the energy content, but is challenging to observe due to its long cooling time and low emissivity. The cool phase ($T \lesssim 10^{4} \rm{\ K}$

Jaeyeon Kim, Adam K. Leroy, Karin Sandstrom, Sharon E. Meidt

Polycyclic aromatic hydrocarbon (PAH) emission is widely used to trace the distribution of molecular gas in the interstellar medium, exhibiting a tight correlation with CO(2-1) emission across nearby galaxies. Using PHANGS-JWST and PHANGS-ALMA data, we identify localized regions where this correlation fails, with CO flux exceeding that predicted from 7.7$\mu

Marie Hein, Gregor Kasieczka, Michael Krämer, Louis Moureaux

Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is e

Thomas Biekötter, Andrii Dashko, Maximilian Löschner, Georg Weiglein

We present a detailed analysis of strong first-order electroweak phase transitions within the extension of the Standard Model by a complex scalar singlet (cxSM). Focusing on the impact of renormalization scale and gauge dependence, we systematically compare commonly used perturbative frameworks for predicting thermodynamic observables that characterize the p

Gökhan Yücel, Neslihan Alan, Timothy Banks, Remziye Canbay

This study presents a comprehensive analysis of the detached binary system V570\,Per through combined photometric, spectroscopic, and astrometric observations. By disentangling the composite spectra, precise fundamental parameters and detailed chemical abundances were determined for both stars. The primary component has a mass of $1.4569_{-0.0100}^{+0.0094}$

Takatoshi Ko, Ryosuke Hirai, Taiga Sasaoka, Toshikazu Shigeyama

Pa 30 is the recently identified remnant of the historical supernova SN 1181, likely a Type Iax event, and a nebula surrounding the central white dwarf launching a fast wind ($\sim10^9~\cm~\s^{-1}$) is observed in optical and infrared bands. X-ray observations show that this wind collides with the surrounding material and produces a termination shock, and th

Keya Hu, Ali Cy, Linlu Qiu, Xiaoman Delores Ding

The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although the puzzle-like tasks in ARC are inherently visual, existing

Rui Tian, Mingfei Gao, Haiming Gang, Jiasen Lu

We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing. Building upon UniGen, we comprehensively enhance the model architecture and training pipeline to strengthen the image understanding and generation capabilities while unlocking strong image editing ability. Especially, we propose a

Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna

We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP), that provides for RL training of VLAs via advantage conditioning. Our method incorporates heterogeneous data into the s

Dawn Lawrie, James Mayfield, Eugene Yang, Andrew Yates

To measure advances in retrieval, test collections with relevance judgments that can faithfully distinguish systems are required. This paper presents NeuCLIRBench, an evaluation collection for cross-language and multilingual retrieval. The collection consists of documents written natively in Chinese, Persian, and Russian, as well as those same documents mach

Viktor Nilsson, Pierre Nyquist

In this paper, we consider the large deviations for dynamical Schr\"odinger problems, using the variational approach developed by Dupuis, Ellis, Budhiraja, and others. Recent results on scaled families of Schr\"odinger problems, in particular by Bernton, Ghosal, and Nutz, and the authors, have established large deviation principles for the static problem. Fo

Lai Wei, Xuanbin Peng, Ri-Zhao Qiu, Tianshu Huang

Learning from real-world robot demonstrations holds promise for interacting with complex real-world environments. However, the complexity and variability of interaction dynamics often cause purely positional controllers to struggle with contacts or varying payloads. To address this, we propose a Heterogeneous Meta-Control (HMC) framework for Loco-Manipulatio

Albert Lin, Alessandro Pinto, Somil Bansal

As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despite perceptual uncertainty. Unfortunately, the verification of such systems remains challenging, largely due to the complexity of the controllers, which are often nonlinear, nonconv

Frances Herr

Curve stitching is a classic educational activity where one constructs elegant curves from a family of straight lines. We perform curve stitching around a circle to make a modular stitch graph. Take $m$ points equally spaced around a circle, choose an integer multiplier $a$, and draw a chord from point $p$ to $a p \mod m$. What design will appear as the enve

Leon Kleebank, Frank Vewinger, Arturo Camacho-Guardian, Victor Romero-Rochín

Critical exponents characterize the divergent scaling of thermodynamic quantities near phase transitions and allow for the classification of physical systems into universality classes. While quantum gases thermalizing by interparticle interactions fall into the XY model universality class, the ideal Bose gas has been predicted to form a distinct universality

Junfeng Wu, Hadjer Benmeziane, Kaoutar El Maghraoui, Liu Liu

Spatiotemporal data mining (STDM) has a wide range of applications in various complex physical systems (CPS), i.e., transportation, manufacturing, healthcare, etc. Among all the proposed methods, the Convolutional Long Short-Term Memory (ConvLSTM) has proved to be generalizable and extendable in different applications and has multiple variants achieving stat

Justin Ganiban, Natalia Pavlasek, Behcet Acikmese

Trajectory optimization methods provide an efficient and reliable means of computing feasible trajectories in nonconvex solution spaces. However, a well-known limitation of these algorithms is that they are inherently local in nature, and typically converge to a solution in the neighborhood of their initial guess. This paper presents a sequential operator-sp

Fan Gao, Baiying Liu, Chi-Heng Lo, Freydoon Shahidi

In this paper, we start by defining a covering Barbasch-Vogan duality and prove some of its properties. Then, for genuine representations of $p$-adic covering groups we formulate an upper bound conjecture for their wavefront sets using this covering Barbasch-Vogan duality and reduce it to anti-discrete representations. The formulation generalizes that of Ciu

Alexander Vedernikov, Puneet Kumar, Haoyu Chen, Tapio Seppänen

Engagement recognition in video datasets, unlike traditional image classification tasks, is particularly challenged by subjective labels and noise limiting model performance. To overcome the challenges of subjective and noisy engagement labels, we propose a framework leveraging Vision Large Language Models (VLMs) to refine annotations and guide the training

Zhaoheng Li, Wei Ding, Silu Huang, Zikang Wang

Vector search has been widely employed in recommender system and retrieval-augmented-generation pipelines, commonly performed with vector indexes to efficiently find similar items in large datasets. Recent growths in both data and task complexity have motivated placing vector indexes onto remote storage -- cloud-native vector search, which cloud providers ha

Peter Halmos, Boris Hanin

Wasserstein gradient flow provides a general framework for minimizing an energy functional $J$ over the space of probability measures on a Riemannian manifold $(M,g)$. Its canonical time-discretization, the Jordan-Kinderlehrer-Otto (JKO) scheme, produces for any step size $\eta>0$ a sequence of probability distributions $\rho_k^\eta$ that approximate to firs

Rahil N. Valani, Sumesh Thampi, Julia M. Yeomans

We investigate channel-confined, nematic liquid crystals using the Beris-Edwards model of nematohydrodynamics. Using strong homeotropic anchoring at the walls, we find multistability i.e. multiple coexisting states where the uniform nematic state coexists with states having spatially varying scalar nematic order and director fields. When a pressure gradient

Maxime Lapointe-Major, Boyan Torosov, Bohdan Kulchytskyy, Pooya Ronagh

We present a gradient-based method to construct memory-efficient, high-fidelity, single-qubit gates for fluxonium qubits. These gates are constructed using a sequence of single-flux quantum (SFQ) pulses that are sent to the qubit through either capacitive or inductive coupling. The schedule of SFQ pulses is constructed with an on-ramp and an off-ramp applied

Haiqing Zhu, Tijana Zrnic, Celestine Mendler-Dünner

On many learning platforms, the optimization criteria guiding model training reflect the priorities of the designer rather than those of the individuals they affect. Consequently, users may act strategically to obtain more favorable outcomes. While past work has studied strategic user behavior on learning platforms, the focus has largely been on strategic re

Antonia Ebner, Christoph Bartmann, Sonja Topf, Sohvi Luukkonen

Deep learning's rise since the early 2010s has transformed fields like computer vision and natural language processing and strongly influenced biomedical research. For drug discovery specifically, a key inflection - akin to vision's "ImageNet moment" - arrived in 2015, when deep neural networks surpassed traditional approaches on the Tox21 Data Challenge. Th

Christof Naumzik, Abdurahman Maarouf, Stefan Feuerriegel, Markus Weinmann

Online ratings influence customer decision-making, yet standard aggregation methods, such as the sample mean, fail to adapt to quality changes over time and ignore review heterogeneity (e.g., review sentiment, a review's helpfulness). To address these challenges, we demonstrate the value of using the Gaussian process (GP) framework for rating aggregation. Sp

Stefan Cobeli, Kazi Shahrukh Omar, Rodrigo Valença, Nivan Ferreira

Despite the growing availability of 3D urban datasets, extracting insights remains challenging due to computational bottlenecks and the complexity of interacting with data. In fact, the intricate geometry of 3D urban environments results in high degrees of occlusion and requires extensive manual viewpoint adjustments that make large-scale exploration ineffic

Robert W. Batterman, James F. Woodward

This paper argues that dataset structure is important in image recognition tasks (among other tasks). Specifically, we focus on the nature and genesis of correlational structure in the actual datasets upon which DNNs are trained. We argue that DNNs are implementing a widespread methodology in condensed matter physics and materials science that focuses on mes

Jared N. Lakhani

Arnold & Manjunath (2021) claim that the bivariate pseudo-Poisson distribution is well suited to bivariate count data with one equidispersed and one overdispersed marginal, owing to its parsimonious structure and straightforward parameter estimation. In the formulation of Leiter & Hamdan (1973), the conditional mean of $X_2$ was specified as a function of $X

Matthew Aldridge

A random number of items each independently marked with one of a collection of colours gives rise to the multinomial marking, which generalises binomial thinning. A multivariate version, where previously marked items are then re-marked, has similar properties to taking a linear transformation of a random vector.

Sourabh Magare, Abhinav Roy, Shasvath J. Kapadia, Nishikanta Khandai

Gravitational waves (GWs) from massive black hole (MBH) mergers will provide a novel way to probe the high-redshift universe and are key to understanding galactic dynamics and evolution. In this work, we analyze MBH mergers, their GW signals and detectability, as well as their population properties, using the cosmological hydrodynamical simulation - NINJA Si

Tzu-Hsuan Chou, Chun-Nan Chou

Large language models (LLMs) have shown a remarkable ability to generalize beyond their pre-training data, and fine-tuning LLMs can elevate performance to human-level and beyond. However, in real-world scenarios, lacking labeled data often prevents practitioners from obtaining well-performing models, thereby forcing practitioners to highly rely on prompt-bas

Paul Renault, Patrick Yard, Raphael Pooser, Hussain Zaidi

We present an architecture for the generation of GKP states in which quadrature squeezing operations are used to control the average photon number statistics of probabilistic photon number measurements on Gaussian resource states. Specifically, we present an architecture employing a teleportation-based squeezing protocol and polynomial-gate applications inte

Andrés Chirre, Harald Andrés Helfgott

Let $A(s) = \sum_n a_n n^{-s}$ be a Dirichlet series with meromorphic continuation. Say we are given information on the poles of $A(s)$ with $|\Im s| \leq T$ for some large constant $T$. What is the best way to use such finite spectral data to give explicit estimates on sums $\sum_{n\leq x} a_n$? The problem of giving explicit bounds on the Mertens function

Junsheng Zhang

We prove a weaker version of the transcendental base-point freeness on compact K\"ahler manifolds. As a consequence, we derive the diameter lower bound for finite time singularities of K\"ahler-Ricci flow with non-Fano initial data.

Hao Zhang, Matthew Otten

Accurate quantum many-body calculations often depend on reliable reference states or good human-designed ans\"atze, yet these sources of knowledge can become unreliable in hard problems like strongly correlated systems. We introduce the Trimmed Configuration Interaction (TrimCI) method, a prior-knowledge-free algorithm that builds accurate ground states dire

Prakruti Sudarshan, Mario Flock, Alexandros Ziampras, David Melon Fuksman

Protoplanetary disks observed in millimeter continuum and scattered light show a variety of substructures. Various physical processes in the disk could trigger such features -- one of which that has been previously theorized for passive disks is the thermal wave instability -- the flared disk may become unstable as directly illuminated regions puff up and ca

Scott Bogner, Heiko Hergert, Morten Hjorth-Jensen, Ryan LaRose

We introduce a method called resolution refinement that allows one to bootstrap eigenstate preparation on a quantum computer. We first prepare an eigenstate of a low-resolution Hamiltonian using any method of choice. The eigenstate is then lifted to higher resolution and adiabatically evolved to produce the corresponding eigenstate of a higher-fidelity Hamil

Benjamin Antieau

We use derived methods to study the Gauss-Manin connection in Hochschild homology, infinitesimal cohomology, and derived de Rham cohomology. As applications, we give new approaches to nilinvariance, the Quillen spectral sequence, and the HKR filtration. We extend the results of Bhatt's work on de Rham cohomology in characteristic zero to infinitesimal cohomo

Parya Dolatyabi, Ali Farajzadeh Bavil, Mahdi Khodayar

Restoring power distribution systems (PDSs) after large-scale outages requires sequential switching actions that reconfigure feeder topology and coordinate distributed energy resources (DERs) under nonlinear constraints, including power balance, voltage limits, and thermal ratings. These challenges limit the scalability of conventional optimization and value

Antonio Alarcon, Franc Forstneric

Given an open Riemann surface $M$, we show that the branch points and the complete ends of finite total curvature of a conformal minimal surface $M\to{\mathbb R}^n$, $n\ge 3$, can be removed by an isotopy through such surfaces. The analogous result holds for null holomorphic curves $M\to{\mathbb C}^n$.

Zoltán Kovács, Xicheng Peng

We improve the complex number identity proving method to a fully automated procedure, based on elimination ideals. By using declarative equations or rewriting each real-relational hypothesis $h_i$ to $h_i-r_i$, and the thesis $t$ to $t-r$, clearing the denominators and introducing an extra expression with a slack variable, we eliminate all free and relationa

Jessie de Kruijf, Giacomo Galloni, Nicola Bartolo

The (large-scale) structures we observe in the Universe are classical, but within the inflationary scenario they do originate from quantum fluctuations. This leads to the question: ''How did this quantum-to-classical transition occur?''. A potential explanation is quantum decoherence due to interactions between different fields present during inflation. The

Barry T. Chiang, Isaque Dutra, Priyamvada Natarajan

Gravitational lensing by galaxy clusters provides a powerful probe of the spatial distribution of dark matter and its microphysical properties. Strong and weak lensing constraints on the density profiles of subhalos and their truncation radii offer key diagnostics for distinguishing between collisionless cold dark matter (CDM) and self-interacting dark matte

Vitaliano S. Amaral, Marcio Antônio de A. Bortoloti, Jurandir O. Lopes, Gilson N. Silva

This paper addresses a class of nonsmooth and nonconvex optimization problems defined on complete Riemannian manifolds. The objective function has a composite structure, combining convex, differentiable, and lower semicontinuous terms, thereby generalizing the classical framework of difference-of-convex programming. Motivated by recent advances in proximal p

Valentina Grazian, Carmine Monetta, Gareth Tracey

We classify finite groups in which the centralisers of certain non-central elements are soluble. This includes a full structural description of groups whose non-central element centralisers are all soluble, and a reduction theorem for the case in which all non-central $\pi$-elements have soluble centralisers, for a suitable collection $\pi$ of primes. Our re

Pawel Batorski, Paul Swoboda

LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually remains challenging and time-consuming. Recent automatic prompting methods mitigate this difficulty but face three key limitations: (i) for each new task, they require large datasets

Priyanka Verma, Balagopal Unnikrishnan

Fair resource division algorithms, like those implemented in Spliddit platform, have traditionally been considered difficult for the end users to manipulate due to its complexities. This paper demonstrates how Large Language Models (LLMs) can dismantle these protective barriers by democratizing access to strategic expertise. Through empirical analysis of ren

Fu-Ming Guo, Yingfang Fan

Adaptive optimizers with decoupled weight decay, such as AdamW, are the de facto standard for pre-training large transformer-based generative models. Yet the quadratic nature of the $\ell_2$ penalty embedded in weight decay drives all parameters toward the origin at the same rate, making the update vulnerable to rare but extreme gradient directions and often

Elham Binshaflout, Aymen Hamrouni, Hakim Ghazzai

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex, interconnected data, making them particularly well suited for a wide range of Intelligent Transportation System (ITS) applications. This survey presents the first comprehensive review dedicated specifically to the use of GNNs within Vehicular Social Networks (VSNs). By leveragi

Yifan Wang, Liya Ji, Zhanghan Ke, Harry Yang

We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealistic fashion. Our realism enhancement approach is a zero-shot framework that focuses on preserving the multi-level structures from synthetic videos into the enhanced one in both spatial and temporal domains, built upon a diffusion v

Bryan Harris, Majid Bani-Yaghoub

Numerous studies have utilized NCBI data for genomic analysis, gene annotation, and identifying disease-associated variants, yet NCBI's epidemiological potential remains underexplored. This study demonstrates how NCBI datasets can be systematically leveraged to extract and interpret infectious disease patterns across spatial and temporal dimensions. Using En

Benedikt Peterseim, Milan Lopuhaä-Zwakenberg

Attack trees (ATs) are popular graphical models for reasoning about the security of complex systems, allowing for the quantification of risk through so-called AT metrics. A large variety of different such AT metrics have been proposed, and despite their wide-spread practical use, no systematic treatment of attack tree metrics so far is fully satisfactory. Ex

Xiyuan Wang, Muhan Zhang

Standard Latent Diffusion Models rely on a complex, three-part architecture consisting of a separate encoder, decoder, and diffusion network, which are trained in multiple stages. This modular design is computationally inefficient, leads to suboptimal performance, and prevents the unification of diffusion with the single-network architectures common in visio

Abolfazl Younesi, Leon Kiss, Zahra Najafabadi Samani, Juan Aznar Poveda

Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who compromise model integrity through Byzantine attacks, data poisoning, or adaptive adversarial behaviors. Existing defense mechanisms rely on static thresholds and binary classification, failing to adapt to evolvi

Connor Fitchett, Ayon Mukherjee, Sofía S. Villar, David S. Robertson

The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it is unclear whether these give the best operating characteristics. By incorporating Bayesian Response-Adaptive Randomisa

Yunfeng Wu, Jiayi Song, Zhenxiong Tan, Zihao He

The quadratic time and memory complexity of the attention mechanism in modern Transformer based video generators makes end-to-end training for ultra high resolution videos prohibitively expensive. Motivated by this limitation, we introduce a training-free approach that leverages video Diffusion Transformers pretrained at their native scale to synthesize high

Aaliyah Chang, Mariam Guizani, Brittany Johnson

Motivations and challenges jointly shape how individuals enter, persist, and evolve within software engineering (SE), yet their interplay remains underexplored across the transition from education to professional practice. We conducted 15 semi-structured interviews and employed the Gioia Methodology, an adapted grounded theory methodology from organizational

Zonghao Chen, Atsushi Nitanda, Arthur Gretton, Taiji Suzuki

We establish the first global convergence result of neural networks for two stage least squares (2SLS) approach in nonparametric instrumental variable regression (NPIV). This is achieved by adopting a lifted perspective through mean-field Langevin dynamics (MFLD), unlike standard MFLD, however, our setting of 2SLS entails a \emph{bilevel} optimization proble

Hector E Mozo

QML-HCS is a research-grade framework for constructing and analyzing quantum-inspired machine learning models operating under hypercausal feedback dynamics. Hypercausal refers to AI systems that leverage extended, deep, or nonlinear causal relationships (expanded causality) to reason, predict, and infer states beyond the capabilities of traditional causal mo

Raha Aghaei, Ali A. Kiaei, Mahnaz Boush, Mahan Rofoosheh

This study analyzes the multiple functions of Large Language Models (LLMs) in transforming research and development (R&D) processes. By automating knowledge discovery, boosting hypothesis creation, integrating transdisciplinary insights, and enabling cooperation within innovation ecosystems, LLMs dramatically improve the efficiency and effectiveness of resea

Alex NieMiera, William Good, Huey-Wen Lin, Fei Yao

We present a systematic study of the nucleon gluon parton distribution function (PDF) using the self-renormalized large-momentum effective theory (LaMET) approach in lattice QCD. This work extends previous gluon-PDF extractions by performing a detailed analysis of key systematic effects, including gauge-link smearing, lattice spacing, pion mass, and nucleon

Gautham Gopinath, Emmanuel Y. Mintah, Aashrith Saraswathibhatla, Jonah J. Spencer

We perform cell segmentation on images from experimental studies of confluent, mobile cells in epithelial monolayers and show that these systems possess a broad, positively-skewed shape parameter distribution $P(\mathcal{A})$, where $\mathcal{A}=p^2/4\pi a$, $p$ is the perimeter, and $a$ is area of each cell. $P(\mathcal{A})$ is peaked at a value higher than

Junwei Ma, Bo Li, Xiangpeng Li, Ali Mostafavi

Disaster-induced power outages create cascading disruptions across urban lifelines, yet the timed coupling between grid failure and essential service access remains poorly quantified. Focusing on Hurricane Beryl in Houston (2024), this study integrates approximately 173000 15-minute outage records with over 1.25 million visits to 3187 food facilities to quan

Panpan Qi, Xuanpeng Xiao, Gongming Yu, Haitao Yang

A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $\alpha$-particle preformation factors $P_{\alpha}$ and their impact on $\alpha$-decay half-lives. The TabPFN model, trained on 498 nuclei, accurately learns the relationship between nuclear structure pr

Chloé Padois, Daniel del Ser, Friedrich Anders, João A. S. Amarante

In this paper we aim to simulate realistic exoplanet populations across different regions of the MW by combining state-of-the-art cosmological simulations of our Galaxy with exoplanet formation models and observations. We model the exoplanet populations around single stars, using planet occurrence rates and multiplicity depending on stellar mass, metallicity

Michael Reilly, Cory Shields

Let $F_2$ be the free group on two generators and let $H$ be a subgroup of $F_2$. We investigate a method for calculating the number of elements in a coset of $H$ that have a given length when written in reduced form. More specifically, taking $S_n\subseteq F_2$ to be the set of elements of length $n$, we show that for any coset $yH$ there always exists a re

Farheen Ramzan, Yusuf Kiberu, Nikesh Jathanna, Meryem Jabrane

Accurate segmentation of myocardial scar from late gadolinium enhanced (LGE) cardiac MRI is essential for evaluating tissue viability, yet remains challenging due to variable contrast and imaging artifacts. Electrocardiogram (ECG) signals provide complementary physiological information, as conduction abnormalities can help localize or suggest scarred myocard

Fabian Wolf

Today's most sensitive experiments for detecting CP-violating permanent electric dipole moments (EDM) rely on molecular spectroscopy. The high sensitivity arises from large internal electric fields that interact with the constituents of the molecule. For molecular ions it has long been assumed that experiments with static polarization from dc electric fields

Nan Liu, Yanbo Liu, Yuya Sasaki, Yuanyuan Wan

We develop methods for nonparametric uniform inference in cost-sensitive binary classification, a framework that encompasses maximum score estimation, predicting utility maximizing actions, and policy learning. These problems are well known for slow convergence rates and non-standard limiting behavior, even under point identified parametric frameworks. In no