November 2025 arXiv papers — page 65

Siying Li, Lang Tong, Timothy D. Mount

We study capacity accreditation of resource-colocated large loads, defined as large demands such as data center and manufacturing loads colocated with behind-the-meter generation and storage resources, synchronously connected to the bulk power system, and capable of participating in the wholesale electricity market as an integrated unit. Because the accredit

Alex John London, Aydin Mohseni

Standard decision frameworks address uncertainty about facts but assume fixed options and values. We extend the Jeffrey-Bolker framework to model refinements in values and prove a value-of-information theorem for axiological refinement. In multi-agent settings, we establish that mutual refinement will characteristically transform zero-sum games into positive

Amr Farrag, Assegid M. Flatae, Mario Agio

The ability to address sub-picosecond events of weak optical signals is essential for progress in quantum science, nonlinear optics, and ultrafast spectroscopy. While up-conversion and optical Kerr gating (OKG) offer femtosecond resolution, they are generally limited to ensemble measurements, making ultrafast detection in nano-optics challenging. OKG, with i

Maria Gabriela Navarro, Brunella Nisini, Teresa Giannini, Patrick J. Kavanagh

We analyze the H2 emission observed in the HH46 Class I system as part of PROJECT-J (Protostellar Jets Cradle Tested with JWST), to investigate the origin and excitation of the warm molecular outflow. We used NIRSpec and MIRI spectral maps (1.6-27.9 microns) to trace the structure and physical conditions of the outflow. By fitting the H2 rotational diagrams

Hayk Hakobyan, Ludwig M. Böss, Yangyang Cai, Alexander Chernoglazov

Entity is a new-generation, fully open-source particle-in-cell (PIC) code developed to overcome key limitations in astrophysical plasma modeling, particularly the extreme separation of scales and the performance challenges associated with evolving, GPU-centric computing infrastructures. It achieves hardware-agnostic performance portability across various GPU

Haedam Im, Morgan Saidel, Heather A. Knutson, Michael Greklek-McKeon

It is relatively rare for gas giant planets to have resonant or near-resonant companions, but these systems are particularly useful for constraining planet formation and migration models. In this study, we examine Kepler-1624b, a sub-Saturn orbiting an M dwarf that was previously found to exhibit transit timing variations with an amplitude of approximately 2

Andrea Banfi, Basem Kamal El-Menoufi

Leading hadronisation corrections to two-jet global event shapes amount to a shift in the corresponding perturbative distributions. It has been recently established that this shift depends significantly on the value of the considered event shape. These analyses consider perturbative configurations with only three partons emitting an ultra-soft non-perturbati

Sushant Kumar Gupta, Anil Raghunath Iyer, Chang Yu, Neel Bagora

Low-latency message delivery is crucial for real-time systems. Data originating from a producer must be delivered to consumers, potentially distributed in clusters across metropolitan and continental boundaries. With the growing scale of computing, there can be several thousand consumers of the data. Such systems require a robust messaging system capable of

Elise Tate, Joshua A. Grochow

Graphs are a powerful tool for analyzing large data sets, but many real-world phenomena involve interactions that go beyond the simple pairwise relationships captured by a graph. In this paper we introduce and study a simple combinatorial model to capture higher order dependencies from an algorithms and computational complexity perspective. Specifically, we

Ana Trišović, Alex Fogelson, Janakan Sivaloganathan, Neil Thompson

We present the first large-scale analysis of AI foundation model usage in science - not just citations or keywords. We find that adoption has grown rapidly, at nearly-exponential rates, with the highest uptake in Linguistics, Computer Science, and Engineering. Vision models are the most used foundation models in science, although language models' share is gr

Paul Tiede, William Moses, Valentin Churavy, Michael D. Johnson

Very long baseline interferometry (VLBI) achieves the highest angular resolution in astronomy. VLBI measures corrupted Fourier components, known as visibilities. Reconstructing on-sky images from these visibilities is a challenging inverse problem, particularly for sparse arrays such as the Event Horizon Telescope (EHT) and the Very Long Baseline Array (VLBA

Pengfei Li, Stacy S. McGaugh, Marcel S. Pawlowski, Francois Hammer

Primordial dark matter halos are well understood from cold dark matter-only simulations. Since they can contract significantly as baryons settle into their centers, direct comparisons with observed galaxies are complicated. We present an approach to reversing the halo contraction by numerically calculating the halo response to baryonic infall and iterating t

Paolo Arnaudo, Benjamin Withers

We show that Price's power-law tail for perturbations of Schwarzschild, $t^{-2\ell-3}$ as $t\to \infty$, can be obtained from a sum of Schwarzschild-de Sitter quasinormal modes in the limit $\Lambda \to 0^+$.

Benedetta Spina, Cristiano Porciani, Sarah E. I. Bosman, Frederick B. Davies

Helium reionization is the most recent phase change of the intergalactic medium, yet its timing and main drivers remain uncertain. Among the probes to trace its unfolding, the 3.46 cm hyperfine line of singly-ionized helium opens the study of helium reionization to upcoming radio surveys. We aim to evaluate the detectability of the 3.46,cm signal with radio

Alisa Galishnikova, Hayk Hakobyan, Alexander Philippov, Benjamin Crinquand

Black hole environments often host plasmas that are fully collisionless or contain intrinsically collisionless regions, including relativistic jets and coronae where particle energization is ubiquitous. Capturing the physics of these systems requires numerical methods capable of modeling relativistic, magnetized, collisionless plasmas in strong gravitational

Weiwei Cai, Shuangkang Fang, Weicai Ye, Xin Dong

Instruction-guided 3D editing is a rapidly emerging field with the potential to broaden access to 3D content creation. However, existing methods face critical limitations: optimization-based approaches are prohibitively slow, while feed-forward approaches relying on multi-view 2D editing often suffer from inconsistent geometry and degraded visual quality. To

Petar Suman, Dong-Gang Wang, Wuhyun Sohn, James R. Fergusson

The search for primordial non-Gaussianities (PNG) is theoretically well motivated but remains observationally challenging. Tight constraints with low significance for the standard non-Gaussian shapes suggest that detection may lie beyond the reach of near-future experiments. However, tests of PNG are highly template-dependent. From a theory perspective, a wh

David Machado, Pietro Valigi, Tommaso Tonolo, Maria Chiara Angelini

Real ecosystems are characterized by sparse and asymmetric interactions, posing a major challenge to theoretical analysis. We introduce a new method to study the generalized Lotka-Volterra model with stochastic dynamics on sparse graphs. By deriving local Fokker-Planck equations and employing a mean-field closure, we can efficiently compute stationary states

Yifan Zhao

Given a closed immersion between arbitrary smooth complex projective varieties, we prove that the two operations: (1) taking the moduli space of stable sheaves, and (2) taking the deformation to the normal cone, commute in a precise sense. In the case of curves inside symplectic surfaces, previously studied by Donagi-Ein-Lazarsfeld, the corresponding deforma

Ana Sofía M. Uzsoy, Arjun Dey, Anand Raichoor, Douglas P. Finkbeiner

Lyman-Alpha Emitters (LAEs) are star-forming galaxies with significant Ly$\alpha$ emission and are often used as tracers of large-scale structure at high redshift. We explore the relationship between the Ly$\alpha$ line profile and environmental density with spectroscopy from the Dark Energy Spectroscopic Instrument (DESI) of LAEs selected with narrow-band p

Yuezhan Tao, Dexter Ong, Fernando Cladera, Jason Hughes

We demonstrate real-time high-altitude aerial metric-semantic mapping and exploration using a monocular camera paired with a global positioning system (GPS) and an inertial measurement unit (IMU). Our system, named HALO, addresses two key challenges: (i) real-time dense 3D reconstruction using vision at large distances, and (ii) mapping and exploration of la

Zhiyu Huang, Zewei Zhou, Tianhui Cai, Yun Zhang

Modeling realistic and interactive multi-agent behavior is critical to autonomous driving and traffic simulation. However, existing diffusion and autoregressive approaches are limited by iterative sampling, sequential decoding, or task-specific designs, which hinder efficiency and reuse. We propose Masked Denoising Generation (MDG), a unified generative fram

Spyridon Lentas

This paper provides a classification of analytic actions of the semi-orthogonal group $\text{SO}^\circ(p,q)$, for $p,q \geq 3$, on closed, connected $(p+q-1)$-dimensional manifolds. Adapting Uchida's construction of $\text{SO}^\circ(p,q)$ actions on $\text{S}^{p+q-1}$, we explicitly construct analytic actions of $\text{SO}^\circ(p,q)$ on $\text{S}^{p} \times

Yan-Qi Wang, Johannes Motruk, Andrey Grankin, Mohammad Hafezi

Edge states of chiral topologically ordered phases are commonly described by chiral Luttinger liquids, effective theories that are exact only in the hydrodynamic limit. Motivated by recent bulk observations of fractional Chern insulators (FCIs) in two-dimensional materials and by synthetic realizations in ultracold atoms, we revisit this framework and quanti

Amr Farrag, Assegid M. Flatae, Lenorah M. Stott, Alessandro Jagatti

Efficient detection of ultrafast phenomena is central to modern optical sciences, driving advances in quantum science and technology, physical chemistry, and nanophotonics. When processes occur on sub-picosecond timescales, time-resolved methods such as transient absorption, up-conversion, and optical Kerr gating (OKG) can be utilized to probe these dynamics

Weilun Li, Lei Sun, Ruixi Gao, Qi Jiang

As neuromorphic sensors, event cameras asynchronously record changes in brightness as streams of sparse events with the advantages of high temporal resolution and high dynamic range. Reconstructing intensity images from events is a highly ill-posed task due to the inherent ambiguity of absolute brightness. Early methods generally follow an end-to-end regress

Hosein Hasani, Amirmohammad Izadi, Fatemeh Askari, Mobin Bagherian

Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these foundation models represent and compute numerical information in counting tasks. We use controlled experiments with repeated textual and visual items and analyze counting in LLMs and LVLMs through a set of behav

Yolo Y. Tang, Daiki Shimada, Hang Hua, Chao Huang

Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fixed frames, leading to hallucinations and failures on fine-grained evidence. Inspired by how humans pause, zoom, and re-read critical regions, we introduce Video-R4 (Reinforcing Te

Vinay Kanakeri, Shivam Bajaj, Ashwin Verma, Vijay Gupta

It is known that reinforcement learning (RL) is data-hungry. To improve sample-efficiency of RL, it has been proposed that the learning algorithm utilize data from 'approximately similar' processes. However, since the process models are unknown, identifying which other processes are similar poses a challenge. In this work, we study this problem in the contex

Brianna Peck, Jessica R. Lu, Lianqi Wang, Brooke DiGia

We present performance simulations for a proposed visible-light, multi-conjugate adaptive optics system for the 10-meter W. M. Keck I telescope that aims to deliver near diffraction-limited angular resolution at optical wavelengths. Our proposed architecture, the Keck Optical Laser Guide Star Adaptive Optics System (KOLA), combines multiple laser guide stars

Mark Endo, Serena Yeung-Levy

Scaling up multimodal models has enabled remarkable advances in visual understanding and reasoning, but practical demands call for smaller, efficient systems. In this work, we conduct a principled analysis of downscaling intelligence in multimodal models, examining how reduced large language model (LLM) capacity affects multimodal capabilities. Our initial f

Philipp Jettkant

We study the mean-field limit of the Atlas model and its connection to SDEs with dependence on the distribution of hitting and local times. The Atlas model describes a system of Brownian particles on the real line, where only the lowest ranked particle receives a positive drift, proportional to the number of particles. We show that in the mean-field limit th

Roozbeh Bazargani, Saqib Abdullah Basar, Daniel Daly-Grafstein, Rodrigo Solis Pompa

The human spine is a complex structure composed of 33 vertebrae. It holds the body and is important for leading a healthy life. The spine is vulnerable to age-related degenerations that can be identified through magnetic resonance imaging (MRI). In this paper we propose a novel computer-vison-based deep learning method to estimate spine age using images from

Neel Sortur, Justin Goodwin, Purvik Patel, Luis Enrique Martinez

Determining the shape of 3D objects from high-frequency radar signals is analytically complex but critical for commercial and aerospace applications. Previous deep learning methods have been applied to radar modeling; however, they often fail to represent arbitrary shapes or have difficulty with real-world radar signals which are collected over limited viewi

M. Nabil Y. Lhachemi, Valentin Crépel, Jennifer Cano

We develop a symmetry indicator framework to efficiently predict the topology of superlattice-induced minibands with spin-orbit coupling. Our algorithm requires input only from the parent material before the superlattice is applied. The simplification arises by assuming a perturbatively weak superlattice potential; however, our results extend beyond the pert

Long Jiang, Yang Yang, Morgan Thornwell, Tiantian Yang

Ecohydrological models are increasingly applied across multiple scenarios, yet their application remains constrained by high computational costs of fine-resolution simulations and structural inconsistencies in cross-scale modeling. This study develops a Structure-Function Coherent Coarsening (SFCC) framework that preserves both hydrological connectivity and

Yiqing Shen, Aiza Maksutova, Chenjia Li, Mathias Unberath

World models learn to predict the temporal evolution of visual observations given a control signal, potentially enabling agents to reason about environments through forward simulation. Because of the focus on forward simulation, current world models generate predictions based on factual observations. For many emerging applications, such as comprehensive eval

Nawfel Mechiche-Alami, Eduardo Rodriguez, Jose M. Cardemil, Enrique Lopez Droguett

This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Each signal is windowed, amplitude-encoded, transformed by a QFT, then passed through a protective rotation layer to avoid the QFT/QFT adjoint cancellation; the resulting kernel is used in kernel ridge regression (KRR). Exogenous predictors a

V. Nagpal, Sebin J. Sebastian, Surya P. Patra, S. Shibash

A detailed study of the structural and magnetic properties of a spin-$5/2$ triangular lattice antiferromagnet RbFe(HPO$_{3}$)$_{2}$ is presented using x-ray diffraction, magnetization, heat capacity, and $^{31}$P nuclear magnetic resonance (NMR) experiments on a polycrystalline sample. The crystal structure features an equilateral triangular lattice of Fe$^{

Silvia Onofri, Andrey Shternshis, Stefano Marmi

Markets efficiency implies that the stock returns are intrinsically unpredictable, a property that makes markets comparable to random number generators. We present a novel methodology to investigate ultra-high frequency financial data and to evaluate the extent to which tick by tick returns resemble random sequences. We extend the analysis of ultra high-freq

Ao Wang, Junwen Yin, Félix Antoine Goudreault, Michel Côté

Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an o

Ayhan Kucukmanisa, Derya Gelmez, Sukru Selim Calik, Zeynep Hilal Kilimci

Recent advances in multimodal deep learning have greatly enhanced the capability of systems for speech analysis and pronunciation assessment. Accurate pronunciation detection remains a key challenge in Arabic, particularly in the context of Quranic recitation, where subtle phonetic differences can alter meaning. Addressing this challenge, the present study p

Himadri Halder

Quantum many-body scars are special eigenstates that violate the eigenstate thermalization hypothesis while residing at finite energy density along with thermalizing eigenstates. The spin-1 XY model is known to host a family of such exceptional states originating from long-lived quasiparticle excitations that exhibit anomalously low entanglement entropy and

Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen, Mohammad Tahir

Automated interviewers and chatbots are common in research, recruitment, customer service, and education. Many existing systems use fixed question lists, strict rules, and limited personalization, leading to repeated conversations that cause low engagement. Therefore, these tools are not effective for complex qualitative research, which requires flexibility,

Andy Wu, Sanjiva K. Lele

Neural network subgrid stress models often have a priori performance that is far better than the a posteriori performance, leading to neural network models that look very promising a priori completely failing in a posteriori Large Eddy Simulations (LES). This performance gap can be decreased by combining two different methods, training data augmentation and

Byeongjin Kim, Ian Anderson, Tzu-Hsuan Hsu, Ziqian Yao

In this work, we present the first experimental study of residual stress and post-release beam deflection in 128-degree Y-cut thin-film lithium niobate (TFLN) on Si, revealing pronounced stress anisotropy with in-plane orientation. Using optical profilometry with curvature fitting, we extract the stress gradient (sigma1) and generate orientation-resolved str

Zhen Wang, Zhifeng Gao, Guolin Ke

Test-time scaling has been shown to substantially improve large language models' (LLMs) mathematical reasoning. However, for a large portion of mathematical corpora, especially theorem proving, RLVR's scalability is limited: intermediate reasoning is crucial, while final answers are difficult to directly and reliably verify. Meanwhile, token-level SFT often

Jose Beltrán Jiménez, Federico Piazza, Javier Vecino

We study homogeneous cosmological models featuring shift-symmetric scalar fields (or, superfluids) in relative motion. In the presence of anisotropy this universe generally features rotation, in the sense that the principal axes of anisotropic expansion rotate with respect to the cosmic comoving frame. We focus in particular on the minimal case of two superf

Saikatul Haque, Rowan Killip, Monica Visan, Yunfeng Zhang

We demonstrate inflation of Fourier--Lebesgue norms for solutions to the focusing modified Korteweg--de Vries equation posed on the real line. For $p\neq 2$ and all $s\in \mathbb{R}$, we construct a sequence of solutions $u_n$ whose initial data $u_n(0)$ converges to zero in the Fourier--Lebesgue spaces $\mathcal F L^p_s(\mathbb{R})$, but whose evolutions at

Nicholas M. Falk, Gabrielle R. Leung, Leah D. Grant, Susan C. van den Heever

The individual and synergistic impacts of cold pools and land surface heterogeneity on convection initiation are investigated. Idealized large eddy simulations of deep convection over the Amazon rainforest are conducted. Simulations test realistic and homogenized vegetation, along with realistic and suppressed low-level evaporation which eliminates cold pool

Cristóbal Loyola

In this article we prove semiglobal stabilization and exact controllability results for nonlinear plate equations with hinged boundary conditions and analytic nonlinearity. These results hold when the damping or control is localized in a region where observability for the linear Schr\"odinger equation is known to hold. At the core of these results lies a new

Siqi Liang, Yudi Zhang, Yue Guo

We propose a novel framework for persona-based language model system, motivated by the need for personalized AI agents that adapt to individual user preferences. In our approach, the agent embodies the user's "persona" (e.g. user profile or taste) and is powered by a large language model (LLM). To enable the agent to leverage rich contextual information, we

Zhiyu Dong, Patrick A. Lee

We predict that the spin-polarized electrons in a two-dimensional triangular lattice with strong electron-electron repulsion gives rise to f-wave pairing. The key point is that the first-order interaction, which is usually pair-breaking, vanishes or nearly vanishes in certain f-wave channels due to symmetry constraints. As a result, these f-wave pairing chan

Dilara Erdemir, Suat Koç, Ünsal Tekir, Mesut Buğday

Let R be a commutative ring with unity and M be an R-module. In this study, we construct the \tilde{Spec}(M) topology using the prime spectrum of module M and multiplicatively closed subsets of R with the closed sets \tilde{V}(S)={P \in Spec(M) : (P : M) \cap S_i \neq \emptyset for all i \in I} with the open sets \tilde{D}(S_i):={P \in Spec(M) : (P : M) \cap

Tanzim Hossain Romel, Kawshik Kumar Paul, Tanberul Islam Ruhan, Maisha Rahman Mim

We present a patient-centric architecture for electronic health record (EHR) sharing that separates content storage from authorization and audit. Encrypted FHIR resources are stored off-chain; a public blockchain records only cryptographic commitments and patient-signed, time-bounded permissions using EIP-712. Keys are distributed via public-key wrapping, en

Jared N. Lakhani

Arnold and Arvanitis (2020) introduced a novel class of bivariate conditionally specified distributions, in which dependence between two random variables is established by defining the distribution of one variable conditional on the other. This conditioning regime was formulated through survival functions and termed the accelerated failure conditionals model

Ryan Engel, Yu Chen, Pawel Polak, Ioana Boier

Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually limit the latent factor dimension to around K=5 due to concerns that larger K can degrade performance. To overcome this challenge, we propose a scalable framework that couples a high

Jiaxi Liu, Chengyuan Ma, Hang Zhou, Weizhe Tang

Cooperative perception (CP) offers significant potential to overcome the limitations of single-vehicle sensing by enabling information sharing among connected vehicles (CVs). However, existing generic CP approaches need to transmit large volumes of perception data that are irrelevant to the driving safety, exceeding available communication bandwidth. Moreove

Yuejie Xin, Sean L. M. van der Meer, Marc Serra-Peralta, Tim H. F. Vroomans

Leakage to non-computational states is a source of correlated errors in both time and space that limits the effectiveness of quantum error correction (QEC) with superconducting circuits. We present and experimentally demonstrate a high-fidelity, leakage reduction unit (LRU) operating concurrently with transmon measurement without incurring time overhead. Ada

Poulami Sarkar, Jhinhwan Lee, Hae Ryoung Park, Anushree Datta

Emerging granularity in superconducting films by tuning disorder is a well-studied topic, both theoretically and experimentally. However, the orbital magnetic field generates a vortex lattice and contributes to the formation of periodic inhomogeneities. Here, we study superconducting films in the simultaneous presence of disorder and a magnetic field, examin

Huaichao Wang, Xuanxin Fan, Ji Liu, Haifeng Li

When performing robot/vehicle localization using ground penetrating radar (GPR) to handle adverse weather and environmental conditions, existing techniques often struggle to accurately estimate distances when processing B-scan images with minor distinctions. This study introduces a new neural network-based odometry method that leverages the similarity and di

Jessica Alessandrì, Daniel Loughran

We consider the Diophantine equation $x^4 + y^4 - w^2 = n$ for $n \in \mathbb{Z}$, which is related to near misses for the quartic case of Fermat's Last Theorem. For certain $n$ we show that the set of solutions is infinite, or more generally not thin. Our approach is via the geometry of del Pezzo surfaces of degree $2$, and we prove a more general result on

Björn Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu

Semantic segmentation networks trained under full supervision for one type of lidar fail to generalize to unseen lidars without intervention. To reduce the performance gap under domain shifts, a recent trend is to leverage vision foundation models (VFMs) providing robust features across domains. In this work, we conduct an exhaustive study to identify recipe

Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri, Matthew Fisher

We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Inspired by an artist's compositional process, illustrator's depth infers a layer index to each pixel, forming an interpretable image decomposition through a discrete, globally consis

Ivan Proskurnin

We prove a lower bound for the Milnor number of function germ invariant with respect to a finite abelian group action. It is shown that this bound is tight for functions of arbitrarily many variables. We also prove the function germs that reach this lower bound are equivariantly stable, i.e. invariant analogues of Morse singularities.

Aiman Al-Eryani, Marcel Gievers, Kilian Fraboulet

The functional renormalization group (fRG) is acknowledged as a powerful tool in quantum many-body physics and beyond. On the technical side, conventional implementations of the fRG rely on regulators for bare propagators only. Starting from Schwinger--Dyson and Bethe--Salpeter equations, we develop here an fRG formulation where both bare propagators and bar

Kostiantyn Drach, Vadim Kaloshin

For a smooth expanding map $f$ of the circle, its (unmarked) length spectrum is defined as the set of logarithms of multipliers of periodic orbits of $f$. This spectrum is analogous to the set of lengths of all closed geodesics on negatively curved surfaces -- the classical length spectrum. In the paper, we prove a length spectral rigidity result for expandi

Danko Aldunate, Julien Ricaud, Edgardo Stockmeyer

We study spectral properties of the Dirac operator $L_0$ arising as the upper-right off-diagonal block in the linearization around standing wave solutions of the one-dimensional Soler model with power nonlinearity $f(s)=s|s|^{p-1}$, $p>0$. Our main results concern the so-called gap property: we show that if $p \geq 1$, then the only eigenvalues of $L_0$ are

Yidong Huang, Zun Wang, Han Lin, Dong-Ki Kim

Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However, these methods mostly employ single-shot plans that are typically limited to simple motions, or iterative refinement which requires multiple calls to the video generator, incuring

Iñigo Robredo-Magro, Binayak Mukherjee, Hugo Aramberri, Jorge Íñiguez-González

The past decade has witnessed a spectacular development of machine-learned interatomic potentials (MLIPs), to the extent that they are already the approach of choice for most atomistic simulation studies not requiring an explicit treatment of electrons. Typical MLIP usage guidelines emphasize the need for exhaustive training sets and warn against applying th

Yuqi Li, Junhao Dong, Chuanguang Yang, Shiping Wen

Vision-Language Models (VLMs) are increasingly deployed in safety-critical applications, making their adversarial robustness a crucial concern. While adversarial knowledge distillation has shown promise in transferring robustness from teacher to student models, traditional single-teacher approaches suffer from limited knowledge diversity, slow convergence, a

Kyle M. Regan, Michael McLoughlin, Wayne A. Bryden, Gonzalo R. Arce

Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a cornerstone in biomolecular analysis, offering precise identification of pathogens through unique mass spectral signatures. Yet, its reliance on labor-intensive sample preparation and multi-shot spectral averaging restricts its use to laboratory settings, rendering it impractical f

Faheem Gul, Orest Pavlosiuk, Tetiana Romanova, Dariusz Kaczorowski

We study the origin of large topological Hall effect in the single-crystalline EuCd$_2$Sb$_2$, which orders antiferromagnetically at the N\'eel temperature $T_{\rm N}=7.4$ K. Measurements of magnetoresistance and Hall resistivity disclose anomalies that evolve with temperature and magnetic field, closely tracking the magnetization process. Analysis of these

M. P. Kozlovskii, O. A. Dobush, R. V. Romanik, I. V. Pylyuk

We study a cell fluid model of a many-particle system with Curie-Weiss-type interaction potential. It is considered as an open system in a fixed volume partitioned into a large number of congruent cubic cells. The interaction potential comprises two competing components: a global uniform attraction acting between all particle pairs in the volume and a short-

Joana Rovira Martins, Pedro Martins, Ana Boavida

Artificial Intelligence (AI) has been increasingly applied to creative domains, leading to the development of systems that collaborate with humans in design processes. In Graphic Design, integrating computational systems into co-creative workflows presents specific challenges, as it requires balancing scientific rigour with the subjective and visual nature o

Shihan Wu, Xuecheng Liu, Shaoxuan Xie, Pengwei Wang

Despite the critical role of bimanual manipulation in endowing robots with human-like dexterity, large-scale and diverse datasets remain scarce due to the significant hardware heterogeneity across bimanual robotic platforms. To bridge this gap, we introduce RoboCOIN, a large-scale multi-embodiment bimanual manipulation dataset comprising over 180,000 demonst

Omar A. Alotaibi, Brian L. Mark, Mohammad Reza Fasihi

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical model in a dual-tracking system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian transfer learning are approximated with the sum of weighted particles to improve the tracking performance of the primary s

Patryk Krukowski, Jan Miksa, Piotr Helm, Jacek Tabor

Continual learning is a fundamental challenge in artificial intelligence that requires networks to acquire new knowledge while preserving previously learned representations. Despite the success of various approaches, most existing paradigms do not provide rigorous mathematical guarantees against catastrophic forgetting. Current methods that offer such guaran

Jesse Wheeler, Aaron J. Abkemeier, Edward L. Ionides

Complex dynamic systems can be investigated by fitting mechanistic stochastic dynamic models to time series data. In this context, commonly used Monte Carlo inference procedures for model selection and parameter estimation quickly become computationally unfeasible as the system dimension grows. The increasing prevalence of panel data, characterized by multip

Sebastian Osorio Perez, Edison M. Murairi, Erik J. Gustafson, Henry Lamm

We construct a primitive gate set for the digital quantum simulation of a discrete subgroup of $SU(3)$: the 216-element $\Sigma(72\times3)$. The necessary primitives are the inversion gate, the group multiplication gate, the trace gate, and the group Fourier transform, for which we provide qubit decompositions. The resulting fault-tolerant T gate costs for a

Seth Siriya, Jingge Zhu, Dragan Nešić, Ye Pu

We consider the adaptive control problem for discrete-time, nonlinear stochastic systems with linearly parameterised uncertainty. Assuming access to a parameterised family of controllers that can stabilise the system in a bounded set within an informative region of the state space when the parameter is well-chosen, we propose a certainty equivalence learning

Zengyu Zou, Jingyuan Wang, Yixuan Huang, Junjie Wu

This paper addresses the cooperative Multi-Vehicle Dynamic Pickup and Delivery Problem with Stochastic Requests (MVDPDPSR) and proposes an end-to-end centralized decision-making framework based on sequence-to-sequence, named Multi-Agent Pointer Transformer (MAPT). MVDPDPSR is an extension of the vehicle routing problem and a spatio-temporal system optimizati

Alexander Jochim, Stefan Bornholdt

Societies experience politically stable and unstable phases along history, whereas political power is usually passed to new elite groups by these changes. Structural dynamics of the elites in a society have been proposed to be one of the core drivers shaping long term behavior. As current models and data are rather macroscopic, the emergence of macroscopic b

Shrikant Kendre, Austin Xu, Honglu Zhou, Michael Ryoo

Traditional evaluation metrics for textual and visual question answering, like ROUGE, METEOR, and Exact Match (EM), focus heavily on n-gram based lexical similarity, often missing the deeper semantic understanding needed for accurate assessment. While measures like BERTScore and MoverScore leverage contextual embeddings to address this limitation, they lack

Ziwen Zhong, Zhitao Shu, Yue Zhao

Emotion recognition is a fundamental component of next-generation human-computer interaction (HCI), enabling machines to perceive, understand, and respond to users' affective states. However, existing systems often rely on single-modality analysis such as facial expressions, speech tone, or textual sentiment, resulting in limited robustness and poor generali

Douglas C. Schmidt, Dan Runfola

Mastering one or more programming languages has historically been the gateway to implementing ideas on a computer. Today, that gateway is widening with advances in large language models (LLMs) and artificial intelligence (AI)-powered coding assistants. What matters is no longer just fluency in traditional programming languages but the ability to think comput

Ziqian Yao, Heather Chang, Eric Stolt, Clarissa Daniel

In this work, we demonstrate the first two-port radial-mode Rosen transformer based on 36$^{\circ}$Y-cut lithium niobate (LN) for piezoelectric power conversion. The device achieves a high transformation ratio (TF) of 16, a high electromechanical coupling factor ($k^2$) of 16.8\% and a quality factor ($Q$) of 2500, yielding an outstanding figure of merit (Fo

Luke Tonon

We revisit the fundamentals of Circuit Complexity and the nature of efficient computation from a fresh perspective. We present a framework for understanding Circuit Complexity through the lens of Information Theory with analogies to results in Kolmogorov Complexity, viewing circuits as descriptions of truth tables, encoded in logical gates and wires, rather

Danqing Zhou, Hongmei Chen, Shiqian Ma, Junfeng Yang

The constrained gradient method (CGM) has recently been proposed to solve convex optimization and monotone variational inequality (VI) problems with general functional constraints. While existing literature has established convergence results for CGM, the assumptions employed therein are quite restrictive; in some cases, certain assumptions are mutually inco

Guilherme Coelho

This paper investigates the emerging text-to-audio paradigm in artificial intelligence (AI), examining its transformative implications for musical creation, interpretation, and cognition. I explore the complex semantic and semiotic interplays that occur when descriptive natural language prompts are translated into nuanced sound objects across the text-to-aud

Grace Liu, Dmitriy Kunisky

Several recent works have aimed to design algorithms for optimizing the Hamiltonians of spin glass models from statistical physics. While Montanari (2018) eventually gave a sophisticated message-passing algorithm to do this nearly optimally for the Sherrington-Kirkpatrick (SK) model, the recent work of Erba, Behrens, Krzakala, and Zdeborov\'a (2024) also obs

Etienne Meunier, Said Ouala, Hugo Frezat, Julien Le Sommer

We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make the model fully compatible with JAX autodifferentiation framework and evaluate the numerical consistency of the resulting implementation. Two illustrative applications are then demon

Guilherme Coelho

This paper presents a pedagogical and conceptual account of the course AI in Music and Sound: Modalities, Tools and Creative Applications, offered within the Music Informatics and Media Art module of an M.Sc. in Audio Communication. The course engaged students with a range of AI modalities such as symbolic composition, voice synthesis, timbre transfer, neura

Jordana Blazek, Eric Olson, Fredrick C. Harris

The progressive second-price auction of Lazar and Semret is a decentralized mechanism for the allocation and real-time pricing of a divisible resource. Our focus is on how delays in the receipt of bid messages, asynchronous analysis by buyers of the market and randomness in the initial bids affect the $\varepsilon$-Nash equilibria obtained by the method of t

Yi Guo, Owen I. Sheekey, Trevor Arp, Kryštof Kolář

Rhombohedral multilayer graphene has recently emerged as a rich platform for studying correlation driven magnetic, topological and superconducting states. While most experimental efforts have focused on devices with N$\leq 9$ layers, the electronic structure of thick rhombohedral graphene features flat-band surface states even in the infinite layer limit. He

Christopher Boland, Sotirios Tsaftaris, Sonia Dahdouh

Deep learning models are prone to learning shortcut solutions to problems using spuriously correlated yet irrelevant features of their training data. In high-risk applications such as medical image analysis, this phenomenon may prevent models from using clinically meaningful features when making predictions, potentially leading to poor robustness and harm to

Nico Kirchner, Wonjune Choi, Frank Pollmann

Identifying experimental signatures of anyons, which exhibit fractional exchange statistics, remains a central challenge in the study of two-dimensional topologically ordered systems. Previous theoretical work has shown that the threshold behavior in linear response spectroscopy can reveal the fractional exchange statistics between an anyon and its antiparti

Yeamin Kaiser, Muhammed Tasnim Bin Anwar, Bholanath Das

Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraph-based methods provide an interpretable bridge between symbolic pattern discovery and continuous embedding learning. Yet, existing frequent or discriminative su

Houji Zhou, Ling Yang, Zhiwei Zhou, Yi Li

Memristive in-memory computing (IMC) has emerged as a promising solution for addressing the bottleneck in the Von Neumann architecture. However, the couplingbetweenthecircuitandalgorithm in IMC makes computing reliability susceptible to non-ideal effects in devices and peripheral circuits. In this respect, efficient softwarehardwareco-simulationtoolsarehighl

Soroush Javdan, Pragash Krishnamoorthy, Olga Baysal

The rapid evolution of the telecommunication industry necessitates efficient troubleshooting processes to maintain network reliability, software maintainability, and service quality. Trouble Reports (TRs), which document issues in Ericsson's production system, play a critical role in facilitating the timely resolution of software faults. However, the complex