February 2025 arXiv papers — page 20

Ivan Cheltsov, Yuri Tschinkel, Zhijia Zhang

We study unirationality of actions of finite groups on Fano threefolds.

Joni-Kristian Kämäräinen

Understanding the transformer architecture and its workings is essential for machine learning (ML) engineers. However, truly understanding the transformer architecture can be demanding, even if you have a solid background in machine learning or deep learning. The main working horse is attention, which yields to the transformer encoder-decoder structure. Howe

Nayoung Choi, Grace Byun, Andrew Chung, Ellie S. Paek

Proprietary corporate documents contain rich domain-specific knowledge, but their overwhelming volume and disorganized structure make it difficult even for employees to access the right information when needed. For example, in the automotive industry, vehicle crash-collision tests, each costing hundreds of thousands of dollars, produce highly detailed docume

Moonkwang Jeon, Xiangzhou Tan, Felix Fischer, Tian Qiu

Small-scale robots offer significant potential in minimally-invasive medical procedures. Due to the nature of soft biological tissues, however, robots are exposed to complex environments with various challenges in locomotion, which is essential to overcome for useful medical tasks. A single mini-robot often provides insufficient force on slippery biological

Yi-Min Huang, Justin Kin Jun Hew, Andrew Brown, Amitava Bhattacharjee

This work presents the first numerical investigation of using Voigt regularization as a method for obtaining magnetohydrodynamic (MHD) equilibria without the assumption of nested magnetic flux surfaces. Voigt regularization modifies the MHD dynamics by introducing additional terms that vanish in the infinite-time limit, allowing for magnetic reconnection and

Ricardo Santos, André V. Carreiro, Xi Peng, Hugo Gamboa

The multivariate, asynchronous nature of real-world clinical data, such as that generated in Intensive Care Units (ICUs), challenges traditional AI-based decision-support systems. These often assume data regularity and feature independence and frequently rely on limited data scopes and manual feature engineering. The potential of generative AI technologies h

Hongrui Zhao, Boris Ivanovic, Negar Mehr

Multi-agent neural implicit mapping allows robots to collaboratively capture and reconstruct complex environments with high fidelity. However, existing approaches often rely on synchronous communication, which is impractical in real-world scenarios with limited bandwidth and potential communication interruptions. This paper introduces RAMEN: Real-time Asynch

Yeping Wang, Michael Gleicher

Many robotic applications, such as sanding, polishing, wiping and sensor scanning, require a manipulator to dexterously cover a surface using its end-effector. In this paper, we provide an efficient and effective coverage path planning approach that leverages a manipulator's redundancy and task tolerances to minimize costs in joint space. We formulate the pr

Jungho Kim, Taeyong Kim

Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or frequently observed failure scenarios, limiting the effectiveness in machine learning-based prediction. To address this chall

Sil Hamilton, Rebecca M. M. Hicke, David Mimno, Matthew Wilkens

We release 70,509 high-quality social networks extracted from multilingual fiction and nonfiction narratives. We additionally provide metadata for $\sim$30,000 of these texts (73\% nonfiction and 27\% fiction) written between 1800 and 1999 in 58 languages. This dataset provides information on historical social worlds at an unprecedented scale, including data

Federico Mazzola, Wojciech Brzezicki, Chiara Bigi, Armando Consiglio

The kagome lattice stands as a rich platform for hosting a wide array of correlated quantum phenomena, ranging from charge density waves and superconductivity to electron nematicity and loop current states. Direct detection of loop currents in kagome systems has remained a formidable challenge due to their intricate spatial arrangements and the weak magnetic

Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella, Riccardo Marin

Deep Reinforcement Learning (RL) models often fail to generalize when even small changes occur in the environment's observations or task requirements. Addressing these shifts typically requires costly retraining, limiting the reusability of learned policies. In this paper, we build on recent work in semantic alignment to propose a zero-shot method for mappin

Daan Vrancken, Simon Ganne, Daan Verraes, Tom Braeckevelt

We present a high-accuracy procedure for electronic structure calculations of strongly correlated materials. To address limitations in current electronic structure methods, we employ density functional theory in combination with the constrained random phase approximation to construct an effective multi-band Hubbard model, which is subsequently solved using t

Lola Le Breton, Quentin Fournier, Mariam El Mezouar, John X. Morris

Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of large auto-regressive language models such as LLaMA and DeepSeek. In contrast, encoders like BERT and RoBERTa have not seen the same level of progress despite being foundational for many downstream NLP applications. To b

Qinan Zhou, Gabrielle Vuylsteke, R. Dyche Anderson, Jing Sun

Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current (CC) charging profiles. This research removes this limitation and proposes an approach that extends ICA/DVA-based degradation mo

Thomas J. Fauchez, Elsa Ducrot, Benjamin V. Rackham, Kevin B. Stevenson

Stellar contamination has long been recognized as a major bottleneck in transmission spectroscopy, limiting our ability to accurately characterize exoplanet atmospheres-particularly for terrestrial worlds. In response, significant observational efforts have shifted toward emission spectroscopy as a potentially more robust alternative, exemplified by initiati

Dragan Marković, Mihailo Čubrović

We study the evolution of two-point correlation functions of one-dimensional Bose-Hubbard model in the semiclassical regime in the framework of Truncated Wigner Approximation (TWA) with quantum jumps as first-order corrections. At early times, the correlation functions show strong superdiffusion with universal integer exponents determined solely by the initi

Alberto Cattaneo, Varun Shankar, M. Keith Ballard

While approaches to model the progression of fracture have received significant attention, methods to find the solution to the associated nonlinear equations have not. In general, nonlinear solution methods and optimization methods have a rich body of work spanning back to at least the first century, providing the opportunity for advancement in the field of

Ife Adebara, Hawau Olamide Toyin, Nahom Tesfu Ghebremichael, AbdelRahim Elmadany

Africa's rich linguistic heritage remains underrepresented in NLP, largely due to historical policies that favor foreign languages and create significant data inequities. In this paper, we integrate theoretical insights on Africa's language landscape with an empirical evaluation using Sahara - a comprehensive benchmark curated from large-scale, publicly acce

Luofu Liu, Rui Wang

The system of polymers in solvent mixtures is a widely-used model to represent biomolecular condensates in intracellular environments. Here, we apply a variational theory to control the center-of-mass of two polymers and perform the first quantification of their interactions in solvent mixtures. Even both solvent and cosolvent are good to the polymer, we dem

Josh Alman, Jingxun Liang

For an $N \times N$ matrix $A$, its rank-$r$ rigidity, denoted $\mathcal{R}_A(r)$, is the minimum number of entries of $A$ that one must change to make its rank become at most $r$. Determining the rigidity of interesting explicit families of matrices remains a major open problem, and is central to understanding the complexities of these matrices in many diff

Lorenzo Caprini, Umberto Marini Bettolo Marconi

A wide range of physical and biological systems, including colloidal magnets, granular spinners, and starfish embryos, are characterized by strongly rotating units that give rise to odd viscosity and odd elasticity. These active systems can be described using a coarse-grained model in which the pairwise forces between particles include a transverse component

Alec Dektor, Peter DelMastro, Erika Ye, Roel Van Beeumen

We propose inexact subspace iteration for solving high-dimensional eigenvalue problems with low-rank structure. Inexactness stems from low-rank compression, enabling efficient representation of high-dimensional vectors in a low-rank tensor format. A primary challenge in these methods is that standard operations, such as matrix-vector products and linear comb

Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo, Christian Lovis

Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In this sense, self-explainable models (SEM) aim to provide interpretable classifiers that decompose predictions into a weighted sum of interpretable concepts. Despite their promise,

Ruojing Peng, Garnet Kin-Lic Chan

Many quantum many-body wavefunctions, such as Jastrow-Slater, tensor network, and neural quantum states, are studied with the variational Monte Carlo technique, where stochastic optimization is usually performed to obtain a faithful approximation to the ground-state of a given Hamiltonian. While first-order gradient descent methods are commonly used for such

Henri Cohen

Our main result is that any real cubic algebraic number has a continued fraction expansion with polynomial coefficients. Some generalizations are mentioned.

Taylor A Phillips, Alejandro W. Huskey, Patrick T. Huskey, Seth L. Robia

Large language models (LLMs) have shown promise in various natural language processing tasks, including their application to proteomics data to classify protein fragments. In this study, we curated a limited mass spectrometry dataset with 1000s of protein fragments, consisting of proteins that appear to be attached to the endoplasmic reticulum in cardiac cel

Gabriele Prato, Jerry Huang, Prasanna Parthasarathi, Shagun Sodhani

Large Language Models (LLMs) have emerged as highly capable systems and are increasingly being integrated into various uses. However, the rapid pace of their deployment has outpaced a comprehensive understanding of their internal mechanisms and a delineation of their capabilities and limitations. A desired attribute of an intelligent system is its ability to

Davide Addona, Davide Augusto Bignamini

Let $U,H$ be two separable Hilbert spaces. The main goal of this paper is to study the weak uniqueness of the Stochastic Differential Equation evolving in $H$ \begin{align*} dX(t)=AX(t)dt+\mathcal{V}B(X(t))dt+GdW(t), \quad t>0, \quad X(0)=x \in H, \end{align*} where $\{W(t)\}_{t\geq 0}$ is a $U$-cylindrical Wiener process, $A:D(A)\subseteq H\to H$ is the inf

Yehonathan Refael, Iftach Arbel, Ofir Lindenbaum, Tom Tirer

We study robust parameter-efficient fine-tuning (PEFT) techniques designed to improve accuracy and generalization while operating within strict computational and memory hardware constraints, specifically focusing on large-language models (LLMs). Existing PEFT methods often lack robustness and fail to generalize effectively across diverse tasks, leading to su

Van-Dung Nguyen, Erin Kuci, Michel Rasquin, Ludovic Noels

This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation. At each optimisation iteration of an iterative process, two minimisation problems are formulated one for the underlying mechanical boundary value problem through the minimisation potential energy principle and one for the minimisation probl

Mark Pustilnik, Francesco Borrelli

In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper presents a novel method based on the Mixed Complement

Bo Li, Bob Zhang, Chengyang Zhang, Minghao Zhou

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challe

Marcin Spoczynski, Marcela S. Melara, Sebastian Szyller

The rapid adoption of open source machine learning (ML) datasets and models exposes today's AI applications to critical risks like data poisoning and supply chain attacks across the ML lifecycle. With growing regulatory pressure to address these issues through greater transparency, ML model vendors face challenges balancing these requirements against confide

Agniva Dasgupta, Rizwanur Khan

Let $E$ be an elliptic curve defined over $\mathbb{Q}$. We show that the analytic rank of $E$ over the cyclotomic extension $\mathbb{Q}(e^{2\pi i/q})$ is bounded above by $q^{45/52+\varepsilon}$, as $q\to \infty$ through the primes. This improves the bound $q^{7/8+\varepsilon}$ established by Chinta.

Bruno R. R. Boaretto, Elbert E. N. Macau, Cristina Masoller

Extreme events are rare, large-scale deviations from typical system behavior that can occur in nonlinear dynamical systems. In this study, we explore the emergence of extreme events within a network of identical stochastic Hodgkin-Huxley neurons with mean-field coupling. The neurons are exposed to uncorrelated noise, which introduces stochastic electrical fl

Nicholas Ioannidis, Daniele Reda, Setareh Cohan, Michiel van de Panne

Diffusion models can be used as a motion planner by sampling from a distribution of possible futures. However, the samples may not satisfy hard constraints that exist only implicitly in the training data, e.g., avoiding falls or not colliding with a wall. We propose learned viability filters that efficiently predict the future success of any given plan, i.e.

Mónica Clapp, Jorge Faya, Alberto Saldaña

We establish the existence and nonexistence of entire solutions to a semilinear elliptic problem whose nonlinearity is the critical power multiplied by a function that takes the value 1 in an open bounded region and the value -1 in its complement. The existence or not of solutions depends on the geometry of the bounded region, in a way analogous to what happ

Eleftheria Katsiri, Alexandros Gazis, Angelos Protopapas

We present a novel form of scalable knowledge representation about agents in a simulated democracy, e-polis, where real users respond to social challenges associated with democratic institutions, structured as Smart Spatial Types, a new type of Smart Building that changes architectural form according to the philosophical doctrine of a visitor. At the end of

Sergey Afonin, Alisa Tsymbal

The light mesons tend to cluster near certain values of mass. As was noticed almost twenty years ago, the emergent degeneracy is of the same type as the dynamical $O(4)$-symmetry of the Coulomb potential in the hydrogen atom. The meson mass spectrum can be well approximated by the linear Regge trajectories of the kind $M^2=al+bn_r+c$, where $l$ and $n_r$ are

Samuel Ozechi

Human attribute identification and classification are crucial in computer vision, driving the development of innovative recognition systems. Traditional gender classification methods primarily rely on facial recognition, which, while effective, struggles under non-ideal conditions such as blurriness, side views, or partial occlusions. This study explores an

Bastian Castorene, Vinicius Gomez de Paula, Francisco J. Peña, Clebson Cruz

This work explores the thermodynamic performance of a quantum Stirling heat engine implemented with an anisotropic spin-1 Heisenberg dimer as the working medium. Using the Hamiltonian of the system, we analyze the interplay of anisotropy, magnetic field, and exchange interactions and their influence on the energy spectrum and the quantum level crossing. Our

Henry F. Legg

The topological gap protocol (TGP) is presented as "a series of stringent experimental tests" for the presence of topological superconductivity and associated Majorana bound states. Here, we show that the TGP, 'passed' by Microsoft Quantum [PRB 107, 245423 (2023)], lacks a consistent definition of 'gap' or 'topological', and even utilises different parameter

Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan, Jan Philip Wahle

Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. However, these methods show limitations when solving complex problems that require longer reasoning chains. We analyze how multi-agent debate drifts away from the initial problem over mu

A. Denis, A. Vigan, J. Costes, G. Chauvin

Since the recent discovery of the directly imaged super-Jovian planet AF Lep b, several studies have been conducted to characterize its atmosphere and constrain its orbital parameters. AF Lep b has a measured dynamical mass of $3.68 \pm 0.48$ MJup, a radius of $1.3 \pm 0.15$ RJup, a nearly circular orbit in spin-orbit alignment with the host star, a relative

Yudi Zhang, Lu Wang, Meng Fang, Yali Du

Distilling large language models (LLMs) typically involves transferring the teacher model's responses through supervised fine-tuning (SFT). However, this approach neglects the potential to distill both data (output content) and reward signals (quality evaluations). Extracting reliable reward signals directly from teacher models is challenging, as LLMs are op

Joshua Aurand, Christopher Pang, Sina Mokhtar, Henry Lei

This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimiz

A. Möller, E. E. O. Ishida, J. Peloton, O. Vidal Velázquez

Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples that contain a limited number of spectroscopically confirmed events. Here, we present the first real-time application of Active Learning to optimise spectroscopic follow-up with the

Antoine Deza, Zhongyuan Liu, Lionel Pournin

It is shown that the smallest possible distance between two disjoint lattice polytopes contained in the cube $[0,k]^3$ is exactly $$ \frac{1}{\sqrt{2(2k^2-4k+5)(2k^2-2k+1)}} $$ for every integer $k$ at least $4$. The proof relies on modeling this as a minimization problem over a subset of the lattice points in the hypercube $[-k,k]^9$. A precise characteriza

Jack Hirsch, Eric Tang

We study the effect of providing information to agents who queue before a scarce good is distributed at a fixed time. Many information policies reveal "sudden bad news," when agents learn the queue is longer than previously believed. Sudden bad news causes assortative inefficiency by prompting multiple agents to simultaneously join the queue. If the value di

Achille Nazaret, David Blei

The goal of causal discovery is to learn a directed acyclic graph from data. One of the most well-known methods for this problem is Greedy Equivalence Search (GES). GES searches for the graph by incrementally and greedily adding or removing edges to maximize a model selection criterion. It has strong theoretical guarantees on infinite data but can fail in pr

Connor Robertson, Cosmin Safta, Nicholson Collier, Jonathan Ozik

Accurate calibration of stochastic agent-based models (ABMs) in epidemiology is crucial to make them useful in public health policy decisions and interventions. Traditional calibration methods, e.g., Markov Chain Monte Carlo (MCMC), that yield a probability density function for the parameters being calibrated, are often computationally expensive. When applie

Jungho Kim, Maijia Su, Ziqi Wang, Marco Broccardo

This paper presents a comparative analysis of structural seismic responses under two types of ground motion inputs: (i) synthetic motions generated by stochastic spectral-compatible ground motion models and (ii) recorded motions from an earthquake database. Both ground motion datasets are calibrated to a shared target response spectrum to ensure consistent s

Zhengdong Yang, Shuichiro Shimizu, Yahan Yu, Chenhui Chu

Recent advancements in large language models (LLMs) have spurred interest in expanding their application beyond text-based tasks. A large number of studies have explored integrating other modalities with LLMs, notably speech modality, which is naturally related to text. This paper surveys the integration of speech with LLMs, categorizing the methodologies in

Seolhwa Kim, Per Kraus, Zhengdi Sun

We study various aspects of codimension one defects in free scalar field theory, with particular emphasis on line defects in two-dimensions. These defects are generically non-conformal, but include conformal and topological defects as special cases. Our analysis is based on the interplay between two complementary descriptions, the first involving matching co

Anton Alyakin, Jaden Stryker, Daniel Alexander Alber, Jin Vivian Lee

General-purpose VLMs demonstrate impressive capabilities, but their opaque training on uncurated internet data poses critical limitations for high-stakes decision-making, such as in neurosurgery. We present CNS-Obsidian, a neurosurgical VLM trained on peer-reviewed literature, and demonstrate its clinical utility versus GPT-4o in a real-world setting. We com

Ashley Lewis, Michael White, Jing Liu, Toshiaki Koike-Akino

The deployment of Large Language Models (LLMs) in customer support is constrained by hallucination (generating false information) and the high cost of proprietary models. To address these challenges, we propose a retrieval-augmented question-answering (QA) pipeline and explore how to balance human input and automation. Using a dataset of questions about a Sa

Biao Yuan, He Wang, Yanjie Song, Ana Heitor

Modelling complex multiphysics systems governed by nonlinear and strongly coupled partial differential equations (PDEs) is a cornerstone in computational science and engineering. However, it remains a formidable challenge for traditional numerical solvers due to high computational cost, making them impractical for large-scale applications. Neural operators'

Diogo C. Cabanas, Kendrick M. Shepherd, Deepesh Toshniwal, Rafael Vázquez

The de Rham complex arises naturally when studying problems in electromagnetism and fluid mechanics. Stable numerical methods to solve these problems can be obtained by using a discrete de Rham complex that preserves the structure of the continuous one. This property is not necessarily guaranteed when the discrete function spaces are hierarchical B-splines,

Kaitlyn Hohmeier, Erik Slivken

Permutons are probability measures on the unit square with uniform marginals that provide a natural way to describe limits of permutations. We are interested in the permuton limits for permutations sampled uniformly from certain pattern-avoiding classes that are in bijection with the class of permutations avoiding the increasing pattern of length $d+1$. In p

Tiezheng Zhang, Qihang Yu, Alan Yuille, Ju He

In this work, we present CoCal, an interpretable and consistent object parsing framework based on dictionary-based mask transformer. Designed around Contrastive Components and Logical Constraints, CoCal rethinks existing cluster-based mask transformer architectures used in segmentation; Specifically, CoCal utilizes a set of dictionary components, with each c

Arash Kazemi, Kshiteej J Deshmukh, Susan Trolier-McKinstry, Shad Roundy

Flexoelectricity, a coupling between strain gradients and electric polarization, has attracted significant interest due to its critical role in enhanced effects at small scales and its applicability across a diverse range of materials. Modeling flexoelectricity is challenging, especially in 3D, due to the higher-order equations involved, which require contin

Sam Stites, John M. Li, Steven Holtzen

There are many different probabilistic programming languages that are specialized to specific kinds of probabilistic programs. From a usability and scalability perspective, this is undesirable: today, probabilistic programmers are forced up-front to decide which language they want to use and cannot mix-and-match different languages for handling heterogeneous

Ranjan Sapkota, Manoj Karkee

This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards based on the model training completed entirely on synthetic images generated by Large Language Models (LLMs). The YOLOv12n configuration achieved the highest precision at 0.916, the h

Joshua Kazdan, Abhay Puri, Rylan Schaeffer, Lisa Yu

Leading language model (LM) providers like OpenAI and Anthropic allow customers to fine-tune frontier LMs for specific use cases. To prevent abuse, these providers apply filters to block fine-tuning on overtly harmful data. In this setting, we make three contributions: First, while past work has shown that safety alignment is "shallow", we correspondingly de

Phila Rembold, Santiago Beltrán-Romero, Alexander Preimesberger, Sergei Bogdanov

Transmission electron microscopes (TEMs) enable atomic-scale imaging and characterisation, driving advances across fields from materials science to biology. Quantum correlations, specifically entanglement, may provide a basis for novel hybrid sensing techniques to make TEMs compatible with sensitive samples prone to radiation damage. We present a protocol to

Mustafa Muhibullah, Mark Brodwin, Michael McDonald, Anthony H. Gonzalez

We present an analysis of the cluster X-ray morphology and active galactic nucleus (AGN) activity in nine $z\sim1$ galaxy clusters from the Massive and Distant Clusters of $WISE$ Survey (MaDCoWS) observed with $Chandra$. Using photon asymmetry ($A_{\text{phot}}$) to quantify X-ray morphologies, we find evidence that the four most dynamically disturbed cluste

Sam Pastoriza, Iman Yousfi, Christopher Redino, Marc Vucovich

We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight deployment of a behavioral network anomaly detection model. This methodology is easily integrable to similar domains that require a premium on throughput while maintaining high pr

Peiyi Chen, Irene M. Gamba, Qin Li, Li Wang

For nano-materials, heat conductivity is an ill-defined concept. This classical concept assumes the validity of Fourier's law, which states the heat flux is proportional to temperature gradient, with heat conductivity used to denote this ratio. However, this macroscopic constitutive relation breaks down at nano-scales. Instead, heat is propagated using phono

Matthew Kasemer, Paul R. Dawson

In this paper, we discuss a method to calculate the topology of the rate independent single crystal yield surface for materials with arbitrary slip systems and arbitrary slip strengths. We describe the general problem, as motivated by Schmid's law, and detail the calculation of hyperplanes in deviatoric stress space, $\mathbb{D}^5$, which describe the criter

A. Sopczak

A key focus of the physics program at the LHC is the study of head-on proton-proton collisions. However, an important class of physics can be studied for cases where the protons narrowly miss one another and remain intact. In such cases, the electromagnetic fields surrounding the protons can interact producing high-energy photon-photon collisions. Alternativ

Edith Haim, Lars van den Bergh, Cynthia S. Q. Siew, Yoed N. Kenett

Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects. This study uses behavioural forma mentis networks (BFMNs) to investigate the STEM-focused mindset, i.e. ways of associating and perceiving ideas, of 177 human participants and 17

Arwa Alanqary, Chao Zhang, Yechen Li, Neha Arora

This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge i

Andrew Kolmer Forbes, Ivan H. Deutsch

We present a protocol for generating nonclassical states of atomic spin ensembles through the backaction induced by a hybrid measurement of light that is entangled with atoms, combining both homodyne and single photon detection. In phase-I of the protocol we create a spin squeezed state by measuring the light's polarization rotation due to the Faraday effect

Henry Lei, Zachary S. Lippay, Anonto Zaman, Joshua Aurand

The design and deployment of autonomous systems for space missions require robust solutions to navigate strict reliability constraints, extended operational duration, and communication challenges. This study evaluates the stability and performance of a hierarchical deep reinforcement learning (DRL) framework designed for multi-agent satellite inspection task

Jatin Arora, Mingkuan Xu, Sam Westrick, Pengyu Liu

Recent advances in quantum architectures and computing have motivated the development of new optimizing compilers for quantum programs or circuits. Even though steady progress has been made, existing quantum optimization techniques remain asymptotically and practically inefficient and are unable to offer guarantees on the quality of the optimization. Because

Yuxin Liu, M. Amin Rahimian

In settings like vaccination registries, individuals act after observing others, and the resulting public records can expose private information. We study privacy-preserving sequential learning, where agents add endogenous noise to their reported actions to conceal private signals. Efficient social learning relies on information flow, seemingly in conflict w

Letizia Ferbel, Stefano Veronesi, Antonio Rossi, Stiven Forti

Graphene on SiC intercalated with two-dimensional metal layers, such as Pt, offers a versatile platform for applications in spintronics, catalysis, and beyond. Recent studies have demonstrated that Pt atoms can intercalate at the heterointerface between SiC(0001) and the C-rich $(6\sqrt{3}\times6\sqrt{3})$R30{\deg} reconstructed surface (hereafter referred a

David Broadhurst, Xavier Roulleau

For integers $n,k,s$, we give a formula for the number $T(n,k,s)$ of order $k$ subsets of the ring $\mathbb{Z}/n\mathbb{Z}$ whose sum of elements is $s$ modulo $n$. To do so, we describe explicitly a sequence of matrices $M(k)$, for positive integers $k$, such that the size of $M(k)$ is the number of divisors of $k$, and for two coprime integers $k_{1},k_{2}

Jessica Finocchiaro, Sanket Shah, Milind Tambe

There is a prevalent debate in machine learning about whether practitioners should train models to optimize a task-agnostic objective (e.g., cross entropy) or incorporate the downstream decision task into the optimization objective (e.g., weighted cross entropy). In ideal settings, like those with infinite data and infinite model capacity, the two approaches

Tarek Yehia

The generalized uncertainty connection between the fluctuations of a quantum observable and its temporal derivative is derived in this study, we demonstrate that the product of an observable's uncertainties and its time derivative is bounded by half the modulus of the expectation value of the commutator between the observable and its derivative, using the Ca

Rajesh Vedala, Harkirat Kaur, Palak Kapoor

Hardware impairments and system non-linearities impacting communication signal is one of key aspect for having harmonics and RF desense which overall causing the lower quality and integrity of the modulated signal, resulting in I/Q imbalance, further bit error and spectral efficiency degradation. This presentation outlines the RF Desense results, EVM Measure

Cristian Ciulică

Endo-Pajitnov manifolds are generalizations to higher dimensions of the Inoue surfaces $S^M$. We study the existence of complex submanifolds in Endo-Pajitnov manifolds. We identify a class of these manifolds that do contain compact complex submanifolds and establish an algebraic condition under which an Endo-Pajitnov manifold contains no compact complex curv

Nicolai Hejlesen Jørgensen, Sarmilan Tharmabalan, Ilhan Aslan, Nicolai Brodersen Hansen

This paper presents a game master AI for single-player role-playing games. The AI is designed to deliver interactive text-based narratives and experiences typically associated with multiplayer tabletop games like Dungeons & Dragons. We report on the design process and the series of experiments to improve the functionality and experience design, resulting in

Jason M. Pittman

Cyber information influence, or disinformation in general terms, is widely regarded as one of the biggest threats to social progress and government stability. From US presidential elections to European Union referendums and down to regional news reporting of wildfires, lies and post-truths have normalized radical decision-making. Accordingly, there has been

L. P. Franciscatto Guerra, N. Ernst

Large Language Models (LLMs) have demonstrated significant promise in automating software development tasks, yet their capabilities with respect to software design tasks remains largely unclear. This study investigates the capabilities of an LLM in understanding, reproducing, and generating structures within the complex VIPER architecture, a design pattern f

Mohannad Shehadeh, William Kingsford, Frank R. Kschischang

We provide some difference triangle sets with scopes that improve upon the best known values. These are found with purpose-built digital circuits realized with field-programmable gate arrays (FPGAs) rather than software algorithms running on general-purpose processors.

Jose Marco Arias, Eric F. Bell, Katya Gozman, In Sung Jang

We present the discovery of Andromeda XXXV, the faintest Andromeda satellite galaxy discovered to date, identified as an overdensity of stars in the Pan-Andromeda Archaeological Survey and confirmed via Hubble Space Telescope imaging. Located at a heliocentric distance of $927^{+76}_{-63}$ kpc and $158^{+57}_{-45}$ kpc from Andromeda, Andromeda XXXV is an ex

Daron Weekley, Jace Duckworth, Anastasiia Sukhanova, Ananya Jana

Intraoral scans are widely used in digital dentistry for tasks such as dental restoration, treatment planning, and orthodontic procedures. These scans contain detailed topological information, but manual annotation of these scans remains a time-consuming task. Deep learning-based methods have been developed to automate tasks such as tooth segmentation. A typ

Or Abramovich, Hadas Pizem, Jonathan Fhima, Eran Berkowitz

Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The traditional diagnostic approach for GON involves a set of ophthalmic examinations, which are time-consuming and require a visit to an ophthalmologist. Recent deep learning models for automating GON detection from

Zexin Li, Jiancheng Zhang, Yufei Li, Yinglun Zhu

Incorporating self-supervised learning (SSL) before standard supervised learning (SL) has become a widely used strategy to enhance model performance, particularly in data-limited scenarios. However, this approach introduces a trade-off between computation and performance: while SSL helps with representation learning, it requires a separate, often time-consum

Yucheng Zhang, Beatrice Bevilacqua, Mikhail Galkin, Bruno Ribeiro

Fully inductive knowledge graph models can be trained on multiple domains and subsequently perform zero-shot knowledge graph completion (KGC) in new unseen domains. This is an important capability towards the goal of having foundation models for knowledge graphs. In this work, we introduce a more expressive and capable fully inductive model, dubbed TRIX, whi

Amr AlBarqawy, Adel Awad, Esraa Elkhateeb, Mohamed Tharwat

Motivated by the recent developments in the thermodynamics of Taub-NUT spaces and the absence of Misner strings in Taub-NUT solutions with flat horizons, we investigated the phase structure of dyonic Taub-NUT solutions. We follow the treatment proposed in arXiv:2206.09124 and arXiv:2304.06705 to introduce the nut parameter as a conserved charge to the first

Eric Bonnetier, Carlos Brito-Pacheco, Charles Dapogny, Rafael Estevez

This article deals with a particular class of shape and topology optimization problems: the optimized design is a region $G$ of the boundary $\partial \Omega$ of a given domain $\Omega$, which supports a particular type of boundary conditions in the considered physical problem. In our analyses, we develop adapted versions of the notions of shape and topologi

Marco Antônio do Couto Fernandes, Samuel Paulino dos Santos

Let f = 0 be an implicit singular plane curve. When deforming f = 0, inflections and vertex emerge from the singularities. In this papper, we classify the deformations of f = 0 with respect to the inflections and the vertices in the cases of codimension less than or equal to 2, that is, in the cases that occur generically in families of implicit curves with

M. Sénoville, F. Delaunay, N. L. Achouri, N. A. Orr

A series of measurements have been performed with low-energy monoenergetic neutrons to characterise cross-talk between two organic scintillator detectors. Cross-talk time-of-flight spectra and probabilities were determined for neutron energies from 1.4 to 15.5 MeV and effective scattering angles ranging from $\sim$50{\deg} to $\sim$100{\deg}. Monte-Carlo sim

Nirmal Gelal, Aastha Gautam, Soheil Abadifard, Nico Giordano

In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store. Using the Knowledge Acquisition and Representation Methodology (KNARM), with domain experts in th

Giuseppe Di Giulio, Xhek Turkeshi, Sara Murciano

Monitoring a quantum system can profoundly alter its dynamical properties, leading to nontrivial emergent phenomena. In this work, we demonstrate that dynamical measurements strongly influence the evolution of symmetry in many-body quantum systems. Specifically, we demonstrate that monitored systems governed by non-Hermitian dynamics exhibit a quantum Mpemba

Mark Colarusso, William Q. Erickson, Andrew Frohmader, Jeb F. Willenbring

Let $K$ be one of the complex classical groups ${\rm O}_k$, ${\rm GL}_k$, or ${\rm Sp}_{2k}$. Let $M \subseteq K$ be the block diagonal embedding ${\rm O}_{k_1} \times \cdots \times {\rm O}_{k_r}$ or ${\rm GL}_{k_1} \times \cdots \times {\rm GL}_{k_r}$ or ${\rm Sp}_{2k_1} \times \cdots \times {\rm Sp}_{2k_r}$, respectively. By using Howe duality and seesaw r

David Aram Korbany, Michael J. Gullans, Lorenzo Piroli

Long-range nonstabilizerness can be defined as the amount of nonstabilizerness which cannot be removed by shallow local quantum circuits. In this work, we study long-range nonstabilizerness in the context of many-body quantum physics, a task with possible implications for quantum-state preparation protocols and implementation of quantum-error correcting code