February 2025 arXiv papers — page 2

Keqiang Yan, Xiner Li, Hongyi Ling, Kenna Ashen

We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations f

Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, Abdelrahim A. Elmadany

As large language models (LLMs) become increasingly integrated into daily life, ensuring their cultural sensitivity and inclusivity is paramount. We introduce our dataset, a year-long community-driven project covering all 22 Arab countries. The dataset includes instructions (input, response pairs) in both Modern Standard Arabic (MSA) and dialectal Arabic (DA

Dor Polonsky

The phenomenon of stable lift oscillations occurring on an elliptic wing section utilizing circulation control at transonic speeds was evaluated using numerical simulations. As the momentum of the jet increases beyond a prescribed magnitude, periodic detachment occurs from the trailing-edge. This behavior conforms to a bi-stable state, consistent with prior

Mengzhu Katie Chen, Isabella Pedraza Pineros, Arvind Satyanarayan, Jonathan Zong

Tactile charts are essential for conveying data to blind and low vision (BLV) readers but are difficult for designers to construct. Non-expert designers face barriers to entry due to complex guidelines, while experts struggle with fragmented and time-consuming workflows that involve extensive customization. Inspired by formative interviews with expert tactil

Christophe Ponsard

Designing sustainable systems involves complex interactions between environmental resources, social impact/adoption, and financial costs/benefits. In a constrained world, achieving a balanced design across those dimensions has become challenging. However a number of strategies have emerged to tackle specific aspects such as preserving resources, improving th

Sanchayan Santra, Vishal Chudasama, Pankaj Wasnik, Vineeth N. Balasubramanian

Precise Event Spotting (PES) aims to identify events and their class from long, untrimmed videos, particularly in sports. The main objective of PES is to detect the event at the exact moment it occurs. Existing methods mainly rely on features from a large pre-trained network, which may not be ideal for the task. Furthermore, these methods overlook the issue

Najwa Alshehri, Daniele Boffi, Chayapol Chaoveeraprasit

We investigate the performance of multigrid preconditioners for solving linear systems arising from finite element discretizations of elliptic interface problems using the Fictitious Domain with Distributed Lagrange Multipliers (FD-DLM) formulation. Numerical experiments are conducted using continuous and discontinuous finite element spaces for the Lagrange

Bo Fu, Leo Tenenbaum, David Adler, Assaf Klein

In recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the design phase to facilitate rapid prototyping. This paper develop

Zihan Wu, Yicheng Tang, Barbara Ericson

AI-supported tools can help learners overcome challenges in programming education by providing adaptive assistance. However, existing research often focuses on individual tools rather than deriving broader design recommendations. A key challenge in designing these systems is balancing learner control with system-driven guidance. To explore user preferences f

Tom Pan, Evan Dramko, Mitchell D. Miller, George N. Phillips

Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce $\texttt{RecCrysFormer}$, a hybrid model that exploits the strengths of transformers with the aim of integrating experimental and ML approaches to protein structure determination from crystallographic data. $\texttt{RecCrysFormer}$ leverages

Cristiano Cantore, Giovanni Di Bartolomeo, Francesco Saverio Gaudio

This paper studies how household heterogeneity affects the level and cyclical behavior of the optimal carbon tax in a real economy. We demonstrate that an equity-efficiency trade-off arises due to income inequality and heterogeneity in the marginal disutility of pollution. Two scenarios are analyzed: one with unrestricted income redistribution to mitigate in

Gebhard Boeckle, Peter Mathias Graef, Theresa Kaiser

In this article, we describe a computational study of the action of the two natural $U$-operators acting on $\Gamma$-invariant spaces of harmonic cocycles for $\mathrm{GL}_3$ for certain congruence subgroups $\Gamma$, in a positive characteristic setting. The cocycle spaces we consider are conjecturally isomorphic to spaces of Drinfeld cusp forms of rank $3$

Abdessamad El-Kabid, El-Mahdi El-Mhamdi

Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly availability attacks -adding imperceptible perturbations to training data, thereby rendering the trained model unavailable. Prior research in distributed machine learning has demonstrated such adversarial effects throug

Kaniba Mady Keita, Younouss Hamèye Dicko

In this manuscript, we demonstrate, using several regression techniques, that the remaining independent Hodge numbers of complete intersection Calabi-Yau four-folds and five-folds can be machine learned from $h^{1,1}$ and $h^{2,1}$. Consequently, we combine the Hodge numbers $h^{1,1}$ and $h^{2,1}$ from the complete intersection Calabi-Yau three-folds, four-

Edoardo Arbib, Noemi Barrera, Paolo Biscari, Giovanni Zanzotto

We study the mechanics of temperature-driven reconstructive martensitic transformations in crystalline materials, within the framework of nonlinear elasticity theory. We focus on the prototypical case of the square-hexagonal transition in 2D crystals, using a modular Ericksen-Landau-type strain energy whose infinite and discrete invariance group originates f

Grigor Nalbandyan, Rima Shahbazyan, Evelina Bakhturina

Typical evaluations of Large Language Models (LLMs) report a single metric per dataset, often representing the model's best-case performance under carefully selected settings. Unfortunately, this approach overlooks model robustness and reliability in real-world applications. For instance, simple paraphrasing of prompts on the MMLU-Pro dataset causes accuracy

Jacopo Teneggi, J Webster Stayman, Jeremias Sulam

Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improve patient care. Beyond measuring uncertainty, it is crucial to express it in clinically meaningful terms that provide actionable insights. This work introduces a conformal risk control (CRC) procedure for organ-d

R. Loganayagam, Omkar Shetye

We extend our proposal for static patch holography made in part I (arXiv:2309.07290) to the case of electromagnetism. Using the on-shell action on de Sitter Schwinger-Keldysh (dS-SK) geometry, we derive the influence phase of an observer in dS$_{d+1}$ interacting with bulk electromagnetic fields. This influence phase, computed with appropriate boundary condi

Zhongqi Yang, Amir Rahmani

Large Language Models (LLMs) excel at general-purpose reasoning by leveraging broad commonsense knowledge, but they remain limited in tasks requiring personalized reasoning over multifactorial personal data. This limitation constrains their applicability in domains such as healthcare, where decisions must adapt to individual contexts. We introduce Personaliz

Zhixian Hu, Juan Wachs, Yu She

Tactile sensing and the manipulation of delicate objects are critical challenges in robotics. This study presents a vision-based magnetic-actuated whisker array sensor that integrates these functions. The sensor features eight whiskers arranged circularly, supported by an elastomer membrane and actuated by electromagnets and permanent magnets. A camera track

Anzhe Chen, Hongxiang Yu, Shuxin Li, Yuxi Chen

Visual servo based on traditional image matching methods often requires accurate keypoint correspondence for high precision control. However, keypoint detection or matching tends to fail in challenging scenarios with inconsistent illuminations or textureless objects, resulting significant performance degradation. Previous approaches, including our proposed C

Farouk Mokhtar, Joosep Pata, Dolores Garcia, Eric Wulff

We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample wit

S. Orlando, H. -T. Janka, A. Wongwathanarat, D. Dickinson

[Abridged] Recent JWST observations have revealed an intricate filamentary network of unshocked ejecta in the young supernova remnant (SNR) Cassiopeia A (Cas A), offering new insights into supernova (SN) explosions and ejecta evolution. We investigate the origin and evolution of this structure by (i) characterizing its 3D morphology and kinematics and (ii) i

M. B. Erdoğan, N. Tzirakis

In this paper we study the Zakharov system on the upper half--plane $U=\{(x ,y)\in \R^2: y>0\}$ with non-homogenous boundary conditions. In particular we obtain low regularity local well--posedness using the restricted norm method of Bourgain and the Fourier--Laplace method of solving initial and boundary value problems. Moreover we prove that the nonlinear

Magnus Sesodia, Alina Petrova, John Armour, Thomas Lukasiewicz

Legal systems worldwide continue to struggle with overwhelming caseloads, limited judicial resources, and growing complexities in legal proceedings. Artificial intelligence (AI) offers a promising solution, with Legal Judgment Prediction (LJP) -- the practice of predicting a court's decision from the case facts -- emerging as a key research area. However, ex

Anna Beer, Lena Krieger, Pascal Weber, Martin Ritzert

Being able to evaluate the quality of a clustering result even in the absence of ground truth cluster labels is fundamental for research in data mining. However, most cluster validation indices (CVIs) do not capture noise assignments by density-based clustering methods like DBSCAN or HDBSCAN, even though the ability to correctly determine noise is crucial fo

Gabriel Rodrigues, Rayff de Souza, Jamerson Rodrigues, Jailson Alcaniz

Recent analyses have shown that a dynamic dark energy modeled by the CPL parameterization of the dark energy equation of state (EoS) can ease constraints on the total neutrino mass compared to the standard $\Lambda$CDM model. This helps reconcile cosmological and particle physics measurements of $\sum m_\nu$. In this study, we investigate the robustness of t

Ramira van der Meulen, Rineke Verbrugge, Max van Duijn

The search for effective collaboration between humans and computer systems is one of the biggest challenges in Artificial Intelligence. One of the more effective mechanisms that humans use to coordinate with one another is theory of mind (ToM). ToM can be described as the ability to `take someone else's perspective and make estimations of their beliefs, desi

Mehrdad Phoroutan-Mehr, Tara Fetherolf

Exoplanets, with their large volumes and low temperatures, are ideal celestial detectors for probing dark matter (DM) interactions. DM particles can lose energy through scattering with the planetary interior and become gravitationally captured if their interaction with the visible sector is sufficiently strong. In the absence of annihilation, the captured DM

Nikita Soni, Pranav Chitale, Khushboo Singh, Niranjan Balasubramanian

Like most of NLP, models for human-centered NLP tasks -- tasks attempting to assess author-level information -- predominantly use representations derived from hidden states of Transformer-based LLMs. However, what component of the LM is used for the representation varies widely. Moreover, there is a need for Human Language Models (HuLMs) that implicitly mode

Thomas Massoni

This survey explores the geometry of three-dimensional Anosov flows from the perspective of contact and symplectic geometry, following the work of Mitsumatsu, Eliashberg-Thurston, Hozoori, and the author. We also present a few original results and discuss various open questions and conjectures.

Alejo Silvarrey, Pablo Negri

Biological invasions pose a significant threat to the sustainability of water sources. Efforts are increasingly being made to prevent invasions, eradicate established invaders, or control them. Remote sensing (RS) has long been recognized as a potential tool to aid in this effort, for example, by mapping the distribution of invasive species or identifying ar

Drew Weninger, Christian Duessel, Samuel Serna, Lionel Kimerling

The transition towards designs which co-package electronic and photonic die together in data center switch packages has created a scaling path to Petabyte per second (Pbps) input/output (I/O) in such systems. In a co-packaged design, the scaling of bandwidth, cost, and energy will be governed by the number of optical I/O channels and the data rate per channe

Ali Ahmad Khostovan, Jeyhan S. Kartaltepe, Mara Salvato, Olivier Ilbert

We present the COSMOS Spectroscopic Redshift Compilation encompassing ~ 20 years of spectroscopic redshifts within a 10 deg$^2$ area centered on the 2 deg$^2$ COSMOS legacy field. This compilation contains 487,666 redshifts of 266,284 unique objects from 138 individual observing programs up to $z \sim 8$ with median stellar mass $\sim 10^{8.4}$ to $10^{10}$

Arman Sauliere, Beatrice Magni, Guglielmo Lami, Xhek Turkeshi

We identify a \emph{universal functional form} that governs anticoncentration in random quantum circuits-one that holds across diverse circuit architectures and depths, and crucially remains valid even at finite system sizes and shallow depth. We support this claim through analytical results for ensembles of random tensor-network states and random-phase mode

Yuzhe Zhao, Congqiao Li, Antonios Agapitos, Dawei Fu

We present a novel method for measuring $|V_{cb}|$ at the LHC using an advanced boosted-jet tagger to identify "$bc$ signatures". By associating boosted $W \rightarrow bc$ signals with $bc$-matched jets from top-quark decays, we enable an in-situ calibration of the tagger. This approach significantly suppresses backgrounds while reducing uncertainties in fla

Pedro V. P. Cunha

Light rings (LRs) - closed circular orbits of null geodesics - are key features of both black holes and horizonless ultracompact objects. While unstable LRs are relevant for the observation of black hole images, stable LRs have been suspected to trigger instabilities, namely in exotic compact objects that could mimic black holes. The underlying mechanism beh

Alberto Colombo, Om Sharan Salafia, Giancarlo Ghirlanda, Francesco Iacovelli

The Einstein Telescope (ET), a proposed next-generation gravitational wave (GW) observatory, will expand the reach of GW astronomy of stellar-mass compact object binaries to unprecedented distances, enhancing opportunities for multi-messenger observations. Here we investigate multi-messenger emission properties of binary neutron star (NSNS) and black hole-ne

Aldana Grichener, Mathieu Renzo, Wolfgang E. Kerzendorf, Rob Farmer

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical

Matthew A. Taylor, Behzad Tahmasebzadeh, Solveig Thompson, Eugene Vasiliev

The integral-field unit mode of the Near-Infrared Spectrograph (NIRSpec+IFU) mounted on the James Webb Space Telescope has now enabled kinematic studies of smaller and less massive compact stellar systems in which to search for central massive black holes (BHs) than ever before. We present here the first such detection using NIRSpec+IFU in its highest resolu

Fabrizio Gentile, Margherita Talia, Andrea Enia, Francesca Pozzi

We present the first follow-up with JWST of radio-selected NIRfaint galaxies as part of the COSMOS-Web survey. By selecting galaxies detected at radio frequencies ($S_{\rm 3 GHz}>11.5$ $\mu$Jy; i.e. S/N$>5$) and with faint counterparts at NIR wavelengths (F150W$>26.1$ mag), we collect a sample of 127 likely dusty star-forming galaxies (DSFGs). We estimate th

Ole Wittig, Rahul Ramesh, Dylan Nelson

Based on their cosmological origin, the stars of a galaxy can be divided into two categories: those that enter through merger events (ex situ) and those born in the main progenitor (in situ). We used the TNG50 cosmological magnetohydrodynamical simulation and its Lagrangian tracer particles to explore and quantify the origin of gas that ultimately forms the

Gabriele Montefalcone, Gilly Elor, Kimberly K. Boddy, Nicola Bellomo

Leptophilic sub-MeV spin-zero dark matter (DM) decays into photons via one-loop processes, a scenario that has been in part overlooked in current literature. In this work, we provide updated and comprehensive upper limits on scalar, pseudo-scalar, and axion-like DM-electron couplings based on the latest NPIPE cosmic microwave background data from Planck. Our

Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky

We present a machine learning search for local, low-mass galaxies ($z < 0.02$ and $10^6 M_\odot < M_* < 10^9 M_\odot$) using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate the features, compare different machine learning alg

Subarna Chaki, Andrina Nicola, Alessio Spurio Mancini, Davide Piras

The simplest inflationary models predict the primordial power spectrum (PPS) of curvature perturbations to be nearly scale-invariant. However, various other models of inflation predict deviations from this behaviour, motivating a data-driven approach to reconstruct the PPS and constrain its shape. In this work, we present a novel method that employs a fully

Ivano Basile, Georgina Staudt

We investigate a pattern in the string landscape recently discovered by Castellano, Ruiz and Valenzuela, extending the analysis to subleading order in some calculable infinite-distance limits of supersymmetric compactifications. We find that in the investigated setups the proposed relation between the (gradients of the) mass gap of light towers and the speci

William McClymont, Sandro Tacchella, Aaron Smith, Rahul Kannan

Characterizing the evolution of the star-forming main sequence (SFMS) at high redshift is crucial to contextualize the observed extreme properties of galaxies in the early Universe. We present an analysis of the SFMS and its scatter in the THESAN-ZOOM simulations, where we find a redshift evolution of the SFMS normalization scaling as $\propto (1+z)^{2.64\pm

Po-Shen Hsin, Ryohei Kobayashi, Carolyn Zhang

Anomalies of global symmetries provide important information on the quantum dynamics. We show the dynamical constraints can be organized into three classes: genuine anomalies, fractional topological responses, and integer responses that can be realized in symmetry-protected topological (SPT) phases. Coset symmetry can be present in many physical systems incl

David Berenstein, Victor A. Rodriguez

We study simplified bootstrap problems for probability distributions on the infinite line and the circle. We show that the rapid convergence of the bootstrap method for problems on the infinite line is related to the fact that the smallest eigenvalue of the positive matrices in the exact solution becomes exponentially small for large matrices, while the mome

Christoph Dauer, Axel Pelster, Sebastian Eggert

Scattering by a short-range potential with time-periodic interaction strength is investigated with a Floquet-scattering theory. Sharp resonances occur, at which the s-wave scattering length can be tuned to large positive and negative values. We show that the shape of these resonances is described by a simple formula, and find that both resonance position and

Stephen J. McKay, Amy J. Barger, Lennox L. Cowie, Michael J. Nicandro Rosenthal

We identify a sample of 234 dusty star-forming galaxies (DSFGs) in the A2744 and GOODS-S fields using JWST/NIRCam-selected galaxies as priors for SCUBA-2 measurements. This method provides a large number of galaxies both above an 850$\,\mathrm{\mu}$m flux of 2 mJy (47 bright DSFGs) and below (187 faint DSFGs), representing the largest sample of individually

Jan Boruch, Gabriele Di Ubaldo, Felix M. Haehl, Eric Perlmutter

We develop a non-perturbative definition of RMT${}_2$: a generalization of random matrix theory that is compatible with the symmetries of two-dimensional conformal field theory. Given any random matrix ensemble, its $n$-point spectral correlations admit a prescribed modular-invariant lift to RMT${}_2$, which moreover reduce to the original random matrix corr

Zheng Zhou

The Julia package FuzzifiED aims at simplifying the numerical calculations on the fuzzy sphere. It supports exact diagonalisation (ED) and density matrix renormalisation group (DMRG) calculations. FuzzifiED can also apply to generic fermionic and bosonic models. This documentation provides a review of the fuzzy sphere regularisation and an instruction for us

D. M. Williams, T. Treu, S. Birrer, A. J. Shajib

Gravitational time delays offer unique, independent measurements of the Hubble constant, $H_0$. Precise measurements of $H_0$ stand as one of the most pressing challenges in modern cosmology, and to do so with time delays requires precise lens models. While much work has focused on streamlining the modeling process to keep pace with the erumpent discovery of

Koutarou Kyutoku

We assess the universal relations among second-order moments of relativistic stars, namely the moment of inertia, tidal deformability, and spin-induced quadrupole moment, via reformulated perturbation equations. After constructing the spherical background configuration by solving two ordinary differential equations as usual, these three moments are obtained

Debasish Borah, Nayan Das, Nobuchika Okada, Prantik Sarmah

We study the possibility of the highest energy neutrino event with 220 PeV energy, detected recently by the KM3NeT experiment to be originating from heavy dark matter (DM) decay. Considering a heavy right handed neutrino (RHN) DM for illustrative purpose, we show that DM mass of 440 PeV, can explain the observed flux. The required DM lifetime to produce the

Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer

Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the research community is now increasingly shifting focus toward post-training techniques to achieve further breakthroughs. While pretraining provide

Tatiana A. Bubba, Matteo Santacesaria, Andrea Sebastiani

Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with ground truth images, which can be difficult to obtain, e.g., in medical applications. We present TomoSelfDEQ, a self-supervised Deep Equilibrium (DEQ) framework for sparse-angle C

Juven Wang

Standard Model (SM) with 15 Weyl fermions per family (lacking the 16th, the sterile right-handed neutrino $\nu_R$) suffers from mixed gauge-gravitational anomalies tied to baryon number plus or minus lepton number ${\bf B} \pm {\bf L}$ symmetry. Including $\nu_R$ per family can cancel these anomalies, but when ${\bf B} \pm {\bf L}$ symmetry is preserved as d

L. Degeorge, A. Ghosh, N. Dufour, D. Picard

Recent text-to-image (T2I) generation models have achieved remarkable sucess by training on billion-scale datasets, following a `bigger is better' paradigm that prioritizes data quantity over availability (closed vs open source) and reproducibility (data decay vs established collections). We challenge this established paradigm by demonstrating that one can a

Chiheb Ben Mahmoud, Zakariya El-Machachi, Krystian A. Gierczak, John L. A. Gardner

With the rapidly growing availability of machine-learned interatomic potential (MLIP) models for chemistry, much current research focuses on the development of generally applicable and ``foundational'' MLIPs. An important question in this context is whether, and how well, such models can transfer from one application domain to another. Here, we assess this t

Jiarui Li, Daniel Jost, Ta Tang, Ruohan Wang

Recent photoemission experiments on the quasi-one-dimensional Ba-based cuprates suggest that doped holes experience an attractive potential not captured using the simple Hubbard model. This observation has garnered significant attention due to its potential relevance to Cooper pair formation in high-$T_c$ cuprate superconductors. To scrutinize this assertion

Sibo Ma, Julian Nyarko

We introduce a new method to identify emerging concepts in large text corpora. By analyzing changes in the heatmaps of the underlying embedding space, we are able to detect these concepts with high accuracy shortly after they originate, in turn outperforming common alternatives. We further demonstrate the utility of our approach by analyzing speeches in the

Zhiyu Tan, Junyan Wang, Hao Yang, Luozheng Qin

Text-to-video generation has demonstrated promising progress with the advent of diffusion models, yet existing approaches are limited by dataset quality and computational resources. To address these limitations, this paper presents a comprehensive approach that advances both data curation and model design. We introduce CFC-VIDS-1M, a high-quality video datas

Abhishek Jha, Tinne Tuytelaars, Yuki M. Asano

Following the success in NLP, the best vision models are now in the billion parameter ranges. Adapting these large models to a target distribution has become computationally and economically prohibitive. Addressing this challenge, we introduce UpStep, an Unsupervised Parameter-efficient Source-free post-pretraining approach, designed to efficiently adapt a b

Hexiang Zhang, Xuguang Zhang, Fangqi Chen, Mauro Antezza

Phase change materials (PCMs) play a pivotal role in the development of advanced thermal devices due to their reversible phase transitions, which drastically modify their thermal and optical properties. In this study, we present an effective dynamic thermal transistor with an asymmetric design that employs distinct PCMs, vanadium dioxide (VO2) and germanium

Shashwat Gupta, Sarthak Gupta, Akshan Agrawal, Mahim Naaz

Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpr

Chen-Kuan Lee

We construct stationary perturbations of a specific minimal surface with a circle of triple junctions in $\mathbb{R}^2 \times \mathbb{S}^1$, that satisfy given boundary data.

Yihong Dong, Ge Li, Xue Jiang, Yongding Tao

Periodicity, as one of the most important basic characteristics, lays the foundation for facilitating structured knowledge acquisition and systematic cognitive processes within human learning paradigms. However, the potential flaws of periodicity modeling in Transformer affect the learning efficiency and establishment of underlying principles from data for l

Thomas Waite, Yuang Geng, Trevor Turnquist, Ivan Ruchkin

It remains a challenge to provide safety guarantees for autonomous systems with neural perception and control. A typical approach obtains symbolic bounds on perception error (e.g., using conformal prediction) and performs verification under these bounds. However, these bounds can lead to drastic conservatism in the resulting end-to-end safety guarantee. This

Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill

Solar prosumers, residential electricity consumers equipped with photovoltaic (PV) systems and battery storage, are transforming electricity markets. Their interactions with the transmission grid under varying tariff designs are not yet fully understood. We explore the influence of different pricing regimes on prosumer investment and dispatch decisions and t

Ludovico Mitchener, Jon M Laurent, Alex Andonian, Benjamin Tenmann

Large Language Models (LLMs) and LLM-based agents show great promise in accelerating scientific research. Existing benchmarks for measuring this potential and guiding future development continue to evolve from pure recall and rote knowledge tasks, towards more practical work such as literature review and experimental planning. Bioinformatics is a domain wher

Carlos Calvo, Aldo Tamburrino, Claudio Falcón

We present an experimental and numerical study of linear and non-linear viscous effects in transient non-linear long wave propagation in Newtonian and shear thinning fluids in the laminar flow regime. Using optical measuring techniques (Fourier Transform Profilometry) and numerical simulations (open-source CFD library OpenFOAM), we show that the wave phase s

Andrés Jaramillo Puentes, Roberto Pirisi

Using Galois-Stiefel-Whitney classes of theta characteristics we show that over a totally real base field the moduli stack of smooth genus $g$ curves and the moduli stack of principally polarized abelian varieties of dimension $g$ have nontrivial cohomological invariants and \'etale cohomology classes in degree respectively $2^{g-2}, 2^{g-1}$ and $2^{g-1}$.

Daniel Guzman-Olivares, Philipp Schmidt, Jacek Golebiowski, Artur Bekasov

Off-policy evaluation can leverage logged data to estimate the effectiveness of new policies in e-commerce, search engines, media streaming services, or automatic diagnostic tools in healthcare. However, the performance of baseline off-policy estimators like IPS deteriorates when the logging policy significantly differs from the evaluation policy. Recent wor

Parakh M. Gupta, Ondřej Procházka, Tiago Nascimento, Martin Saska

Heterogeneous robot teams used in marine environments incur time-and-energy penalties when the marine vehicle has to halt the mission to allow the autonomous aerial vehicle to land for recharging. In this paper, we present a solution for this problem using a novel drag-aware model formulation which is coupled with MPC, and therefore, enables tracking and lan

Xuefeng Li, Nick Pant, Sheikh Ifatur Rahman, Rob Armitage

We investigate the impact of quantum well (QW) thickness on efficiency loss in c-plane InGaN/GaN LEDs using a small-signal electroluminescence (SSEL) technique. Multiple mechanisms related to efficiency loss are independently examined, including injection efficiency, carrier density vs. current density relationship, phase space filling (PSF), quantum confine

Yajnaseni Dutta, Lisa Marquand

A very good cubic fourfold is a smooth cubic fourfold that does not contain a plane, a cubic scroll, or a hyperplane section with a corank 3 singularity. We prove that the normalization of the relative compactified Prym variety associated to the universal family of hyperplanes of a very good cubic fourfold is in fact smooth, thereby extending prior results o

Kaleb Mcdowell, Nick Waytowich, Javier Garcia, Stephen Gordon

Metcalfe et al (1) argue that the greatest potential for human-AI partnerships lies in their application to highly complex problem spaces. Herein, we discuss three different forms of hybrid team intelligence and posit that across all three forms, the hybridization of man and machine intelligence can be effective under the right conditions. We foresee two sig

Vedran Brdar, Dibya S. Chattopadhyay

The KM3NeT collaboration recently reported the observation of KM3-230213A, a neutrino event with an energy exceeding 100 PeV, more than an order of magnitude higher than the most energetic neutrino in IceCube's catalog. Given its longer data-taking period and larger effective area relative to KM3NeT, IceCube should have observed events around that energy. Th

Augusto Facundes, Kayman Jhosef Goncalves, Giorgio Torrieri

Motivated both by classical physics problems associated with ``Newton's bucket'' and recent developments related to QCD in rotating frames of reference relevant to heavy ion collisions, we discuss the difference between ``active'' and ``passive'' rotations in quantum systems. We examine some relevant potentials and give general symmetry arguments to give cri

Qinghua Lei, Didier Sornette

Forecasting violent rockbursts remains a formidable challenge due to significant uncertainties involved. One major uncertainty arises from the intermittency of rock failure processes, typically characterised by a series of progressively shorter quiescent phases punctuated by sudden accelerations, rather than a smooth continuous progression towards the final

Mathieu Beau, Timothey Szczepanski, Rafael Martellini, Lionel Martellini

The probability distribution of a time measurement $T_x$ at position $x$ can be inferred from the probability distribution of a position measurement $X_t$ at time $t$ as given by the Born rule [Time-of-arrival distributions for continuous quantum systems and application to quantum backflow, Phys. Rev. A 110, 052217 (2024)]. In an application to free-fall, th

Lies Beers, Magnus Bakke Botnan

We investigate several problems concerning extremal Betti numbers and persistence in filtrations of flag complexes. For graphs on $n$ vertices, we show that $\beta_k(X(G))$ is maximal when $G=\mathcal{T}_{n,k+1}$, the Tur\'an graph on $k+1$ partition classes, where $X(G)$ denotes the flag complex of $G$. Building on this, we construct an edgewise (one edge a

Tiziana Comito, Matteo Lotriglia, Miguel D. Bustamante

We study the dynamics of the $(\alpha+\beta)$ Fermi-Pasta-Ulam-Tsingou lattice (FPUT lattice, for short) for an arbitrary number $N$ of interacting particles, in regimes of small enough nonlinearity so that a Birkhoff-Gustavson type of normal form can be found using tools from wave-turbulence theory. Specifically, we obtain the so-called Zakharov equation fo

Hongze Yu, Jeffrey A. Fessler, Yun Jiang

Deep Learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based o

Xueyun Tian, Wei Li, Bingbing Xu, Yige Yuan

Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them separately, struggling with limited high-quality data and poor generalization. However, both tasks require capturing complex visual variations while maintaining consistency between

Menghua Wu, Russell Littman, Jacob Levine, Lin Qiu

High-content perturbation experiments allow scientists to probe biomolecular systems at unprecedented resolution, but experimental and analysis costs pose significant barriers to widespread adoption. Machine learning has the potential to guide efficient exploration of the perturbation space and extract novel insights from these data. However, current approac

Seyi Oluwadare, Chunlei Liang

This paper proposes an improved Clarkson Ducted Wind Turbine (DWT) design using a new diffuser based on the Selig S1223 airfoil at an angle of attack (AoA) of 20 degrees and a smaller tip clearance. This proposed design is hereby named Selig20 Clarkson Ducted Turbine or Seyi-Chunlei Ducted Turbine (SCDT) compared to the original Clarkson Ducted Wind Turbine

Bryce Clarke

Delta lenses are functors equipped with a functorial choice of lifts, generalising the notion of split opfibration. In this paper, we introduce a Grothendieck construction (or category of elements) for delta lenses, thus demonstrating a correspondence between delta lenses and certain lax double functors into the double category of sets, functions, and split

Pierre Houdouin, Lucas Saludjian

With the digitalization of power grids, physical equations become insufficient to describe the network's behavior, and realistic but time-consuming simulators must be used. Numerical experiments, such as safety validation, that involve simulating a large number of scenarios become computationally intractable. A popular solution to reduce the computational bu

Hannah Sheats

In 1974, Erd\H{o}s asked the following question: given a graph $G$ and a directed graph $\vec{H}$, how many ways are there to orient the edges of $G$ such that it does not contain $\vec{H}$ as a subgraph? We denote this value by $D(G, \vec{H})$. Further, we let $D(n, \vec{H})$ denote the maximum of $D(G, \vec{H})$ over all graphs $G$ on $n$ vertices. In 2006

Li Yang, Mirna El Rajab, Abdallah Shami, Sami Muhaidat

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational e

Laura Pierson

In arXiv:2301.02177, Crew, Pechenik, and Spirkl defined the Kromatic symmetric function $\overline{X}_G$ as a $K$-analogue of Stanley's chromatic symmetric function $X_G$, and one question they asked was how $\overline{X}_G$ expands in their $\overline{p}_\lambda$ basis, which they defined as a $K$-analogue of the classic power sum basis $p_\lambda.$ In arXi

Federico Di Gennaro, Thibault Laugel, Vincent Grari, Marcin Detyniecki

Traditional approaches to learning fair machine learning models often require rebuilding models from scratch, typically without considering potentially existing models. In a context where models need to be retrained frequently, this can lead to inconsistent model updates, as well as redundant and costly validation testing. To address this limitation, we intr

Vittorio De Falco, Amodio Carleo, Alessandro Ridolfi, Alessandro Corongiu

Redback and black widow pulsars are two classes of peculiar binary systems characterized by very short orbital periods, very low mass companions, and, in several cases, regular eclipses in their pulsed radio signal. Long-term timing revealed systematic but unpredictable variations in the orbital period, which can most likely be explained by the so-called App

Sergey Tarima, Silvia Calderazzo, Mary Homan

Large sample behavior of dynamic information borrowing (DIB) estimators is investigated. Asymptotic properties of several DIB approaches (adaptive risk minimization, adaptive LASSO, Bayesian procedures with empirical power prior, fully Bayesian procedures, and a Bayes-frequentist compromise) are explored against shrinking to zero alternatives. As shown theor

Michael E. Ressler, Alba Aller, David Jones, Ryan M. Lau

While NGC 1514 is an elliptical, but complex, planetary nebula at optical wavelengths, it was discovered to have a pair of infrared-bright, axisymmetric rings contained within its faint outer shell during the course of the WISE all-sky survey. We have obtained JWST mid-infrared imaging and spectroscopy of the nebula through the use of simultaneous observatio

Sherlon Almeida da Silva, Davi Geiger, Luiz Velho, Moacir Antonelli Ponti

We innovate in stereo vision by explicitly providing analytical 3D surface models as viewed by a cyclopean eye model that incorporate depth discontinuities and occlusions. This geometrical foundation combined with learned stereo features allows our system to benefit from the strengths of both approaches. We also invoke a prior monocular model of surfaces to

Marius F. R. Juston, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu

Deep residual networks (ResNets) have demonstrated outstanding success in computer vision tasks, attributed to their ability to maintain gradient flow through deep architectures. Simultaneously, controlling the Lipschitz bound in neural networks has emerged as an essential area of research for enhancing adversarial robustness and network certifiability. This