March 2025 arXiv papers — page 144

Rome Samanta

In JCAP 11 (2024) 051, we discussed how different regimes (flavoured) of leptogenesis can be probed through a ``tomographic'' approach using primordial gravitational waves. By examining the theory's parameter space, we identified regions where right-handed neutrino mass-dependent non-standard cosmological expansion leaves characteristic imprints on propagati

Benjamin Remez, Moshe Goldstein

Van der Waals "sliding" ferroelectric bilayers, whose electric polarization is locked to the interlayer alignment, show promise for future non-volatile memory and other nanoelectronic devices. These applications require a fuller understanding of the polarization stability and switching properties, which present models have described in terms of an Ising-like

Sophie McKenzie

COVID-19 disrupted the professional preparation of university students, with less opportunity to engage in professional practice due to a reduced employment market. Little is known about how this period impacted upon the career confidence and career identity of university students. This research paper explores the career confidence and identity of university

Alicia Negre, Fabian Faulstich, Raehyun Kim, Thomas Ayral

Quantum embedding methods enable the study of large, strongly correlated quantum systems by (usually self-consistent) decomposition into computationally manageable subproblems, in the spirit of divide-and-conquer methods. Among these, Density Matrix Embedding Theory (DMET) is an efficient approach that enforces self-consistency at the level of one-particle r

T. A. Tyson, S. Liu, S. Amarasinghe, K. Wang

A systematic structural study of the Fe2Mo3O8 system as a function of pressure, temperature, and magnetic field reveals that the P63mc space group of this material remains stable for a broad range of these parameters. No changes are seen in the long-range structure for pressures between 0 and 10 GPa, temperatures between 11 K and 300 K, and magnetic fields u

Caleb Jordan, Jacob Bernhardt, Joseph Rahamim, Alex Kirichenko

Current superconducting quantum computing platforms face significant scaling challenges, as individual signal lines are required for control of each qubit. This wiring overhead is a result of the low level of integration between control electronics at room temperature and qubits operating at millikelvin temperatures, which raise serious doubts among technolo

Eric W. Bridgeford, Brian S. Caffo, Maya B. Mathur, Russell A. Poldrack

Over the past two decades, considerable strides have been made in advancing neuroscientific techniques, yet challenges remain in attributing causality to observed associations. This review addresses a fundamental issue in observational neuroscience studies and advocates for incorporating causal inference frameworks into standard practice. We systematically i

Juan C. Perdomo

Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? This question was discussed throughout the 20th century by authors like Merton, Morgenstern

Hariprasath Govindarajan, Maciej K. Wozniak, Marvin Klingner, Camille Maurice

Vision foundation models (VFMs) such as DINO have led to a paradigm shift in 2D camera-based perception towards extracting generalized features to support many downstream tasks. Recent works introduce self-supervised cross-modal knowledge distillation (KD) as a way to transfer these powerful generalization capabilities into 3D LiDAR-based models. However, th

Himanshu Mavani, Kai Huang, Kartik Samanta, Evgeny Y. Tsymbal

Spin-split antiferromagnets have significance for antiferromagnetic (AFM) spintronics due to their momentum dependent spin polarization which can be exploited for the control and detection of the AFM order parameter. Here, we explore the polar-layer stacking of AFM-ordered bilayers driving the emergence of reversable electric polarization and non-relativisti

Chaoming Song

The Lee-Yang circle theorem revolutionized our understanding of phase transitions in ferromagnetic systems by showing that the complex zeros of partition functions lie on the unit circle, with criticality arising as these zeros approach the real axis in the thermodynamic limit. However, in frustrated systems such as antiferromagnets and spin glasses, the zer

Eman Aldabbas, Mohammad Sababheh

Accretive partial transpose (APT) matrices have been recently defined, as a natural extension of positive partial transpose (PPT) matrices. In this paper, we discuss further properties of APT matrices in a way that extends some of those properties known for PPT matrices. Among many results, we show that if \(A,B,X\) are $n\times n$ complex matrices such that

Diana Romero, Fatima Anwar, Salma Elmalaki

Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address this by generating synthetic yet realistic collaborative MR data. MoCoMR captures individual behavioral modalities such as speaking, gaze, and locomotion during a collaborative image-

Shoaib Meraj Sami, Md Mahedi Hasan, Nasser M. Nasrabadi, Raghuveer Rao

In automatic target recognition (ATR) systems, sensors may fail to capture discriminative, fine-grained detail features due to environmental conditions, noise created by CMOS chips, occlusion, parallaxes, and sensor misalignment. Therefore, multi-sensor image fusion is an effective choice to overcome these constraints. However, multi-modal image sensors are

Antonio Gonzalez, Samuel J. Poage, Bernardo Langa,, Deepak Sapkota

The intersection of superconductivity and ferroelectricity hosts a wide range of exotic quantum phenomena. Here, we report on the observation of superconductivity in high-quality tin telluride films grown by molecular beam epitaxy. Unintentionally doped tin telluride undergoes a ferroelectric transition at ~100 K. The critical temperature of superconductivit

Xiaowen Qiu, Yian Wang, Jiting Cai, Zhehuan Chen

Automatically generating training supervision for embodied tasks is crucial, as manual designing is tedious and not scalable. While prior works use large language models (LLMs) or vision-language models (VLMs) to generate rewards, these approaches are largely limited to simple tasks with well-defined rewards, such as pick-and-place. This limitation arises be

Jeffrey Meier, Allison N. Miller

The second author and Powell asked whether there exist knots bounding infinitely many slice disks that remain pairwise nonisotopic, even after local knotting. We answer this question in the affirmative, giving many classes of examples distinguished by the kernels of the inclusion-induced maps on the fundamental group. Along the way, we give a classification

Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari

A well-known expression for the saturation throughput of heterogeneous transmitting nodes in a wireless network using p-CSMA, derived from Renewal Theory, implicitly assumes that all transmitting nodes are in range of, and therefore conflicting with, each other. This expression, as well as simple modifications of it, does not correctly capture the saturation

Phil Travis, Jacob Bortnik, Troy Carter

This study demonstrates the efficacy of ML-based trend inference using data from the Large Plasma Device (LAPD). The LAPD is a flexible basic plasma science device with a high discharge repetition rate (0.25-1 Hz) and reproducible plasmas capable of collecting high-spatial-resolution probe measurements. A diverse dataset is collected through random sampling

Stefan Sylvius Wagner, Stefan Harmeling

Object-centric understanding is fundamental to human vision and required for complex reasoning. Traditional methods define slot-based bottlenecks to learn object properties explicitly, while recent self-supervised vision models like DINO have shown emergent object understanding. We investigate the effectiveness of self-supervised representations from models

Agathe Fernandes Machado, Suzie Grondin, Philipp Ratz, Arthur Charpentier

Algorithmic fairness has received considerable attention due to the failures of various predictive AI systems that have been found to be unfairly biased against subgroups of the population. Many approaches have been proposed to mitigate such biases in predictive systems, however, they often struggle to provide accurate estimates and transparent correction me

Ole Hans, Jürgen Adamy

Remote driving of vehicles is gaining in importance in the transportation sector, especially when Automated Driving Systems (ADSs) reach the limits of their system boundaries. This study investigates the challenges faced by human Remote Drivers (RDs) during remote driving, particularly focusing on the identification and classification of human performance-re

Héctor Laria, Alexandra Gomez-Villa, Jiang Qin, Muhammad Atif Butt

Recent advances in text-to-image (T2I) diffusion models have enabled remarkable control over various attributes, yet precise color specification remains a fundamental challenge. Existing approaches, such as ColorPeel, rely on model personalization, requiring additional optimization and limiting flexibility in specifying arbitrary colors. In this work, we int

Garrelt Quandt-Wiese

A novel approach for measuring wavefunction collapse is proposed which, unlike interferometric measurements, is not affected by decoherence. A mirror of a Michelson interferometer is transferred into a quantum superposition, where the decay of the mirror superposition is measured by the fact that it affects the probability of detecting photons in the interfe

Jayson R. Vavrek, Ryan Pavlovsky, Victor Negut, Daniel Hellfeld

We have developed a CLLBC-based gamma- and neutron-sensitive multi-channel omnidirectional imaging detector, suitable for handheld or vehicle-borne operation and capable of quantitative radiation mapping in 3D. The system comprises 62 CLLBC modules in an active-masked configuration, and is coupled to a Localization and Mapping Platform (LAMP) suite of contex

Kyeong-Hun Kim, Kijung Lee, Jinsol Seo

We provide Green's function estimates for parabolic operators on polyhedrons and polyhedral cones in $\mathbb{R}^3$. These estimates incorporate mixed weights, which include appropriate powers of the distances to the vertices, the edges, and the boundary of the domains. The allowable ranges for the weight parameters are explicitly determined by the geometry

Nahid Ul Islam, DongAo Ma, Jiaxuan Pang, Shivasakthi Senthil Velan

Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical imaging, but it requires a large amount of annotated data. The advancement of deep learning has facilitated the creation of numerous medical datasets with diverse expert-level annotations. Aggregating these dataset

Georg Manten, Cecilia Casolo, Søren Wengel Mogensen, Niki Kilbertus

We develop the theory linking 'E-separation' in directed mixed graphs (DMGs) with conditional independence relations among coordinate processes in stochastic differential equations (SDEs), where causal relationships are determined by "which variables enter the governing equation of which other variables". We prove a global Markov property for cyclic SDEs, wh

Nataliya Balabanova, Adeela Bashir, Paolo Bova, Alessio Buscemi

This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes. The research explores two key mechanisms for achieving responsibl

Ofri Adiv, Bernd Krauskopf, Scott Parkins

We study the nonlinear, semiclassical dynamics of an open spin-1 (three-level) variant of the traditional Dicke model. In particular, we focus on V-type energy-level configurations with varying degrees of energy-level asymmetry. We also allow for unbalanced coupling -- where co-rotating and counter-rotating Hamiltonian terms are independently tunable. We cha

Bejan Hamawandi, Parva Parsa, Inga Pudza, Kaspars Pudzs

Thermoelectric (TE) materials can directly convert heat into electrical energy. However, they sustain costly production procedures and batch-to-batch performance variations. Therefore, developing scalable synthetic techniques for large-scale and reproducible quality TE materials is critical for advancing TE technology. This study developed a facile, high thr

Anna T. Bui, Stephen J. Cox

Spatially varying electric fields are prevalent throughout nature, such as in nanoporous materials and biological membranes, and technology, e.g, patterned electrodes and van der Waals heterostructures. While uniform fields cause free ions to migrate, for polar fluids they simply reorient the constituent molecules. In contrast, electric field gradients (EFGs

Pol Jane-Soneira, Charles Muller, Felix Strehle, Sören Hohmann

In recent times, various distributed optimization algorithms have been proposed for whose specific agent dynamics global optimality and convergence is proven. However, there exist no general conditions for the design of such algorithms. In this paper, we leverage passivity theory to fi rst establish a distributed optimization framework with local design requ

Kourosh Shahnazari, Seyed Moein Ayyoubzadeh

In personalized technology and psychological research, precisely detecting demographic features and personality traits from digital interactions becomes ever more important. This work investigates implicit categorization, inferring personality and gender variables directly from linguistic patterns in Telegram conversation data, while conventional personality

Scott A. McKinley, Gary A. Hoover

In this paper we explore the dynamic relationship between income inequality and economic mobility through a pairing of a population-scale partial differential equation (PDE) model and an associated individual-based stochastic differential equation (SDE) model. We focus on two fundamental mechanisms of income growth: (1) that annual growth is percentile-depen

An Yang, Chenyu Liu, Pengcheng Xia, Jun Du

Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style m

Alexandra Shyntar, Thomas Hillen

Spherical distributions, in particular, the von Mises-Fisher distribution, are often used for problems using or modelling directional data. Since expectation and variance-covariance matrices follow from the first and second moments of the spherical distribution, the moments often need to be approximated numerically by computing trigonometric integrals. Here,

Ali Eslamian, Qiang Cheng

Tabular data poses unique challenges for deep learning due to its heterogeneous feature types, lack of spatial structure, and often limited sample sizes. We propose TabNSA, a novel deep learning framework that integrates Native Sparse Attention (NSA) with a TabMixer backbone to efficiently model tabular data. TabNSA tackles computational and representational

Sean Dallas, Hongjiao Qiang, Motaz AbuHijleh, Wonse Jo

After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies that compared different formats of AARs have mainly focused on human teams. However, the inclusion of robotic teammates brings along new challenges in understandin

Giacomo Albi, Elisa Calzola

In this work, we present a second-order numerical scheme to address the solution of optimal control problems constrained by the evolution of nonlinear Fokker-Planck equations arising from socio-economic dynamics. In order to design an appropriate numerical scheme for control realization, a coupled forward-backward system is derived based on the associated op

Koray Aydoğan, Anthony W. Schlimgen, Kade Head-Marsden

Realistic quantum systems are affected by environmental loss, which is often seen as detrimental for applications in quantum technologies. Alternatively, weak coupling to an environment can aid in stabilizing highly entangled and mixed states, but determining optimal system-environment parameters can be challenging. Here, we describe a technique to optimize

Ariel Listo, Ercio A. Munoz, Dario Sansone

This study examines how attitudes among supervisors, co-workers, and customers contribute to discrimination against sexual minorities in the workplace. A large, nationally representative sample in Chile was recruited in collaboration with a local firm. The survey employs a series of double list experiments designed to measure attitudes on sensitive issues wh

Keshav Dahiya, Evgeny Mukhin

We discuss applications of the $q$-characters to the computation of the $R$-matrices. In particular, we describe the $R$-matrix acting in the tensor square of the first fundamental representation of E$_8$ and in a number of other cases, where the decomposition of the tensor squares with respect to non-affine quantum algebra has non-trivial multiplicities. As

Mateus M. Tarozo, Arthur A. B. Pessa, Luciano Zunino, Osvaldo A. Rosso

Quantitative analysis of visual arts has recently expanded to encompass a more extensive array of artworks due to the availability of large-scale digitized art collections. Consistent with formal analyses by art historians, many of these studies highlight the significance of encoding spatial structures within artworks to enhance our understanding of visual a

Gheorghe Craciun, Abhishek Deshpande, Jiaxin Jin

Mathematical models of reaction networks can exhibit very complex dynamics, including multistability, oscillations, and chaotic dynamics. On the other hand, under some additional assumptions on the network or on parameter values, these models may actually be toric dynamical systems, which have remarkably stable dynamics. The concept of disguised toric dynami

Joshua J. Hibbard, Jack O. Burns, Robert MacDowall, Natchimuthuk Gopalswamy

Radiowave Observations on the Lunar Surface of the photo-Electron Sheath instrument (ROLSES- 1) onboard the Intuitive Machines' Odysseus lunar lander represents NASA's first radio telescope on the Moon, and the first United States spacecraft landing on the lunar surface in five decades. Despite a host of challenges, ROLSES-1 managed to collect a small amount

David A. Brewster, Jakub Svoboda, Dylan Roscow, Krishnendu Chatterjee

We examine population structures for their ability to maintain diversity in neutral evolution. We use the general framework of evolutionary graph theory and consider birth-death (bd) and death-birth (db) updating. The population is of size $N$. Initially all individuals represent different types. The basic question is: what is the time $T_N$ until one type t

M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes, Mark DeMaria

A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network and make predictions on forecasts from the National Hurricane Center (NHC), which currently uses static error distribution

Esmaeil Ebadi

This study develops the E-Rule, a novel composite recession indicator that integrates financial market and labor market signals to improve the precision of recession forecasting. Combining the yield curve and the Sahm rule, the E-Rule provides a holistic and early-warning measure of economic downturns. Using historical data from 1976 onward, we empirically e

Hyeonsu Kang, David Chuan-en Lin, Yan-Ying Chen, Matthew K. Hong

We present BioSpark, a system for analogical innovation designed to act as a creativity partner in reducing the cognitive effort in finding, mapping, and creatively adapting diverse inspirations. While prior approaches have focused on initial stages of finding inspirations, BioSpark uses LLMs embedded in a familiar, visual, Pinterest-like interface to go bey

Ahmad Mustafa Anis, Hasnain Ali, Saquib Sarfraz

Vision Language Models (VLMs) have demonstrated significant potential in various downstream tasks, including Image/Video Generation, Visual Question Answering, Multimodal Chatbots, and Video Understanding. However, these models often struggle with basic image transformations. This paper investigates the image-level understanding of VLMs, specifically CLIP by

Godofredo Iommi, Anibal Velozo

We study a compactification of the space of invariant probability measures for a transitive countable Markov shift. We prove that it is affine homeomorphic to the Poulsen simplex. Furthermore, we establish that, depending on a combinatorial property of the shift space, the compactification contains either a single new ergodic measure or a dense set of them.

André Becker, Georgios M. Koutentakis, Peter Schmelcher

We investigate the real-time dynamics of a quenched quantum impurity immersed in a one-dimensional ultracold Fermi gas, focusing on the breakdown of the adiabatic Born-Oppenheimer approximation due to non-adiabatic effects. Despite a sizable impurity-bath mass imbalance, increasing interactions induce strong non-adiabatic couplings, disrupting adiabatic moti

Andreas Herklotz, Jonathan R. Petrie, Thomas Z. Ward

Manipulating electronic orbital states in quantum materials provides a powerful means to control their physical properties and technological functionality. Here, we demonstrate that orbital populations in strongly correlated oxide thin films can be continuously and reversibly tuned through post-synthesis He ion implantation. Using LaNiO$_3$ as a model system

Sarwar Saif, Md Jahirul Islam, Md. Zihad Bin Jahangir, Parag Biswas

The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligenc

Yihao Zhou, Tanusree Sharma

Massively multiplayer online games (MMOGs) can foster social interaction and relationship formation, but they pose specific privacy and safety challenges, especially in the context of mediating intimate interpersonal connections. To explore the potential risks, we conducted a case study on Final Fantasy XIV (FFXIV) players intimate partner seeking posts on s

Joe Whittaker

This is a comment on arXiv:2202.01553. In regression Gaussian covariate p-values (Davies and D{\"u}mbgen, arXiv:2202.01553) are used to control greedy forward subset selection by accounting for choosing the best when fitting many variables. Here we outline a simple proof of their Theorems 1 and 2, making alterations to simplify the exposition by including a

Pablo Barenbaum, Simona Ronchi Della Rocca, Cristian Sottile

It is well-known that intersection type assignment systems can be used to characterize strong normalization (SN). Typical proofs that typable lambda-terms are SN in these systems rely on semantical techniques. In this work, we study $\Lambda_\cap^e$, a variant of Coppo and Dezani's (Curry-style) intersection type system, and we propose a syntactical proof of

Feng Zhou, Pu Cao, Yiyang Ma, Lu Yang

Denoising higher-resolution latents via a pre-trained U-Net leads to repetitive and disordered image patterns. Although recent studies make efforts to improve generative quality by aligning denoising process across original and higher resolutions, the root cause of suboptimal generation is still lacking exploration. Through comprehensive analysis of position

Joohwan Seo, Soochul Yoo, Junwoo Chang, Hyunseok An

Recent advances in deep learning and Transformers have driven major breakthroughs in robotics by employing techniques such as imitation learning, reinforcement learning, and LLM-based multimodal perception and decision-making. However, conventional deep learning and Transformer models often struggle to process data with inherent symmetries and invariances, t

Ashay Patel, Michela Antonelli, Sebastien Ourselin, M. Jorge Cardoso

Deep learning has significantly advanced medical imaging analysis, yet variations in image resolution remain an overlooked challenge. Most methods address this by resampling images, leading to either information loss or computational inefficiencies. While solutions exist for specific tasks, no unified approach has been proposed. We introduce a resolution-inv

Arnold Neumaier, Phillip Josef Bachler, Arash Ghaani Farashahi

This paper defines coherent manifolds and discusses their properties and their application in quantum mechanics. Every coherent manifold with a large group of symmetries gives rise to a Hilbert space, the completed quantum space of $Z$, which contains a distinguished family of coherent states labeled by the points of the manifold. The second quantization map

Wenyi Lian, Patrick Micke, Joakim Lindblad, Nataša Sladoje

Vision Transformers (ViTs) have achieved remarkable success in standard RGB image processing tasks. However, applying ViTs to multi-channel imaging (MCI) data, e.g., for medical and remote sensing applications, remains a challenge. In particular, MCI data often consist of layers acquired from different modalities. Directly training ViTs on such data can obsc

Nizar Khalfet, Kapila W. S. Palitharathna, Symeon Chatzinotas, Ioannis Krikidis

This paper presents a comprehensive analysis of the information-energy capacity region for simultaneous lightwave information and power transfer (SLIPT) systems over lognormal fading channels. Unlike conventional studies that primarily focus on additive white Gaussian noise channels, we study the complex impact of lognormal fading, which is prevalent in opti

Sabina Werren, Hermann Grieder, Christopher Scherb

As we progress toward Society 5.0's vision of a human-centered digital society, ensuring digital accessibility becomes increasingly critical, particularly for citizens with visual impairments and other disabilities. This paper examines the implementation challenges of accessible digital public services within Swiss public administration. Through Design Scien

Kevin Liao, Shreya Thipireddy, Daniel Weitzner

This paper offers a new privacy approach for the growing ecosystem of services -- ranging from open banking to healthcare -- dependent on sensitive personal data sharing between individuals and third parties. While these services offer significant benefits, individuals want control over their data, transparency regarding how their data is used, and accountab

Sameer Neupane, Jeevan Chapagain, Nobal B. Niraula, Diwa Koirala

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has significantly advanced Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), which involves identifying entities like person, location, and organization names in text. LLMs are especially promising for low-resource languages due to their abil

M. A. Mojarro, Sergio E. Ulloa

We study kagome lattices with on-site and extended spin-singlet s-wave superconducting pairing and show that the inclusion of Rashba spin-orbit (RSO) interaction allows time-reversal-invariant topological superconducting states which support helical Majorana pairs at the edge. We calculate the Z2 topological invariant as a function of the pairing parameters

Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne, Jing Liang

We introduce ViLAM, a novel method for distilling vision-language reasoning from large Vision-Language Models (VLMs) into spatial attention maps for socially compliant robot navigation. Unlike traditional methods that rely on expert demonstrations or human-annotated datasets, ViLAM performs knowledge distillation and fine-tuning at the intermediate layer rep

Yuwei Zhang, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang

Large Language Models (LLMs) often exhibit substantially shorter effective context lengths than their claimed capacities, especially when handling complex reasoning tasks that require integrating information from multiple parts of a long context and performing multi-step reasoning. Although Chain-of-Thought (CoT) prompting has shown promise in reducing task

Negar Mohammadnejad

In this paper, we investigate the existence of positive singular solutions for a system of partial differential equations on a bounded domain \begin{equation} \label{main equation of the thesis} \left\{ \begin{array}{lr} -\Delta u = (1+\kappa_1(x)) | \nabla v |^p & \text{in}~~ B_1 \backslash \{0\},\\ -\Delta v = (1+\kappa_2(x)) | \nabla u |^p & \text{in}~~ B

Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rémi Munos

Predictive models of the future are fundamental for an agent's ability to reason and plan. A common strategy learns a world model and unrolls it step-by-step at inference, where small errors can rapidly compound. Geometric Horizon Models (GHMs) offer a compelling alternative by directly making predictions of future states, avoiding cumulative inference error

Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann, Sebastien Gros

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints expressed by signal temporal logic (STL). We formulate the control synthesis as a chance-constrained program (CCP) with STL specifications that must be satisfied with high probability

Hadil Ben Amor, Manel Abdellatif, Taher Ghaleb

Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, cloud providers offer ML services to ease the integration of ML components in software systems. The evolving business requirements and the popularity of ML services have led practitioners of all skill levels to im

Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review pape

Aleksander P. Woźniak, Ludwik Adamowicz, Thomas Bondo Pedersen, Simen Kvaal

When time-propagating a wave packet representing a molecular system interacting with strong attosecond laser pulses, one needs to use an approach that is capable of describing intricate coupled electronic-nuclear events that require departure from the conventional adiabatic Born-Oppenheimer (BO) approximation. Hence, the propagation should be carried out sim

Matteo D'Alessandro, Magne Thoresen

Selective inference aims at providing valid inference after a data-driven selection of models or hypotheses. It is essential to avoid overconfident results and replicability issues. While significant advances have been made in this area for standard regression models, relatively little attention has been given to linear mixed models (LMMs), which are widely

Maciej J. Mrowinski, Aleksandra Buczek, Agata Fronczak

Understanding the mechanisms driving the distribution of scientific citations is a key challenge in assessing the scientific impact of authors. We investigate the influence of the preferential attachment rule (PAR) in this process by analysing individual citation events from the DBLP dataset and two Scopus-based datasets, enabling us to estimate the probabil

Talya Eden, Reut Levi, Dana Ron, Ronitt Rubinfeld

Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed problem of approximate counting has been explored within two prominent query frameworks: the standard model, which permits degree, neighbor, a

Richard Rimanyi

Thom polynomials provide universal formulas for the fundamental class of singularity loci in terms of characteristic classes. Ohmoto extended this notion to SSM-Thom polynomials, which refine this description by capturing the richer Segre-Schwartz-MacPherson (SSM) class of singularity loci. While previous methods for computing SSM-Thom polynomials relied on

Chenjun Li, Laurin Lux, Alexander H. Berger, Martin J. Menten

Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, and most public datasets contain no clinical reasoning or interpretation beyond image-level labels. In this paper, we present a novel method that integrates graph representation lear

Qingwu Liu, Nicolas Saunier, Guillaume-Alexandre Bilodeau

Image-based multi-object detection (MOD) and multi-object tracking (MOT) are advancing at a fast pace. A variety of 2D and 3D MOD and MOT methods have been developed for monocular and stereo cameras. Road safety analysis can benefit from those advancements. As crashes are rare events, surrogate measures of safety (SMoS) have been developed for safety analyse

Kyle Sonandres, Thomas Palazzo, Jonathan P. How

This paper presents an optimal control solution for an aerocapture vehicle with two control inputs, bank angle and angle of attack, referred to as augmented bank angle modulation (ABAM). We derive the optimal control profiles using Pontryagin's Minimum Principle, validate the result numerically using the Gauss pseudospectral method (implemented in GPOPS), an

Jordan Taylor, Joel Mire, Franchesca Spektor, Alicia DeVrio

Queer people are often discussed as targets of bias, harm, or discrimination in research on generative AI. However, the specific ways that queer people engage with generative AI, and thus possible uses that support queer people, have yet to be explored. We conducted a workshop study with 13 queer artists, during which we gave participants access to GPT-4 and

Peter H. Handel, Klara E. Splett

We derive the first analytical formula for the density of "Dark Matter" (DM) at all length scales, thus also for the rotation curves of stars in galaxies, for the baryonic Tully-Fisher relation and for planetary systems, from Einstein's equations (EE) and classical approximations, in agreement with observations. DM is defined in Part I as the energy of the c

Enes Özeren, Arka Bhowmick

The increasing applications of autonomous driving systems necessitates large-scale, high-quality datasets to ensure robust performance across diverse scenarios. Synthetic data has emerged as a viable solution to augment real-world datasets due to its cost-effectiveness, availability of precise ground-truth labels, and the ability to model specific edge cases

Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Sihan Liu

We study the task of list-decodable linear regression using batches. A batch is called clean if it consists of i.i.d. samples from an unknown linear regression distribution. For a parameter $\alpha \in (0, 1/2)$, an unknown $\alpha$-fraction of the batches are clean and no assumptions are made on the remaining ones. The goal is to output a small list of vect

Benjamín Borquez, Rayssa Caju, Hanne Van Den Bosch

This paper addresses the quantitative stability for a Yamabe-type functional on compact manifolds with boundary introduced by Escobar. Minimizers of the functional correspond to scalar-flat metrics with constant mean curvature on the boundary. We prove that the deficit controls the distance to the minimizing set to a suitable power by reducing the problem to

Themba Hodge, Tuan Kieu, Jasmin Bedow, Eric Mascot

Braiding and fusion of Majorana zero modes are key elements of any future topological Majorana-based quantum computer. Here, we investigate the fusion dynamics of Majorana zero modes in the spinless Kitaev model, as well as in a spinful model describing magnet-superconductor hybrid structures. We consider various scenarios allowing us to reproduce the fusion

Zachary Charles, Gabriel Teston, Lucio Dery, Keith Rush

As we scale to more massive machine learning models, the frequent synchronization demands inherent in data-parallel approaches create significant slowdowns, posing a critical challenge to further scaling. Recent work develops an approach (DiLoCo) that relaxes synchronization demands without compromising model quality. However, these works do not carefully an

Roland Schäfer, Philipp Weitkamp, Otgonbayar Erdene-Ochir, Klaus Meerholz

We demonstrate strong light-matter interaction for a layer of templated merocyanine molecules in a planar microcavity. Using a single layer of graphene nanoribbons as a templating layer, we obtain an aligned layer of aggregated molecules. The molecular layer displays anisotropic optical properties resembling those of a biaxial crystal. The anisotropic excito

Bikram Das, Rupchand Sutradhar, D C Dalal

Accurate forecasting of viral disease outbreaks is crucial for guiding public health responses and preventing widespread loss of life. In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a promising framework that can capture the intricate dynamics of viral infection and reliably predict its future progression. However, despite notable

Benjamin Towle, Xin Chen, Ke Zhou

Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, this trend has extended to medical imaging. Yet, often these methods only produce a single prediction for a given image, neglecting inherent uncertainty in medical images, due to unclear object boundaries and errors caused by the annotation tool. Multiple Choice

Farrukh A. Chishtie

In ``On restricting to one-loop order the radiative effects in quantum gravity" (Brandt, Frenkel, and McKeon, 2020) \cite{Brandt2020}, a Lagrange multiplier (LM) field is introduced into the Einstein-Hilbert action, removing all multi-loop graviton diagrams and confining quantum-gravity corrections to just one loop. The resulting one-loop effective action ca

Geoffrey Boyer, Wayne Goddard

An isolating set of a graph is a set of vertices $S$ such that, if $S$ and its neighborhood is removed, only isolated vertices remain; and the isolation number is the minimum size of such a set. It is known that for every connected graph apart from $K_2$ and $C_5$, the isolation number is at most one-third the order and indeed such a graph has three disjoint

Mariana Fernandez-Espinosa, Diego Gomez-Zara

As Augmented Reality (AR) and Artificial Intelligence (AI) continue to converge, new opportunities emerge for AI agents to actively support human collaboration in immersive environments. While prior research has primarily focused on dyadic human-AI interactions, less attention has been given to Human-AI Teams (HATs) in AR, where AI acts as an adaptive teamma

Shailendra P. Joshi, Ashley Bucsek, Darren C. Pagan, Samantha Daly

The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length & time scales in the mesoscale between atomistic & continuum approaches. This is leading

Thomas W. Pfeifer, Harrison A. Walker, Henry T. Aller, Samuel Graham

As momentum-resolved Electron Energy Loss Spectroscopy (q-EELS) becomes more widely used for phonon measurements, better understanding of the intricacies of the acquired signal is necessary. Selection rules limit the allowed scattering, which may prohibit the appearance of specific phonon branches in some measurements. Simultaneous sampling of the lattice ac

Joni-Kristian Kämäräinen

Transformer is the state-of-the-art model for many natural language processing, computer vision, and audio analysis problems. Transformer effectively combines information from the past input and output samples in auto-regressive manner so that each sample becomes aware of all inputs and outputs. In sequence-to-sequence (Seq2Seq) modeling, the transformer pro

Michael Cardei, Jacob K Christopher, Thomas Hartvigsen, Bhavya Kailkhura

Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly growing ability to generate coherent natural language, these models present a new and important opportunity to enforce sequence-level constraints, a capability that current autoregr