May 2024 arXiv papers — page 8

N. Korneev, I. Ramos-Prieto, F. Soto-Eguibar, U. Ruíz

We report explicit equations and matrix representations that allow simple calculation between three different media (free space, linear and quadratic refractive index distributions) for paraxial light propagation.

Chao-Hui Huang, Sara Lichtarge, Diane Fernandez

The integration of AI in digital pathology, particularly in whole slide image (WSI) and spatial transcriptomics (ST) analysis, holds immense potential for enhancing our understanding of diseases. Despite challenges such as training pattern preparation and resolution disparities, the convergence of these technologies can unlock new insights. We introduce QuST

Jacob Fox, Janos Pach, Andrew Suk

A curve in the plane is $x$-monotone if every vertical line intersects it at most once. A family of curves are called pseudo-segments if every pair of them have at most one point in common. We construct $2^{\Omega(n^{4/3})}$ families, each consisting of $n$ labelled $x$-monotone pseudo-segments such that their intersection graphs are different. On the other

Rodrigo Francisco dos Santos, Marilton Rafael da Silva Lemes, Luis Gustavo de Almeida

We review the book of Ruben Aldrovandi and Jose Geraldo Pereira about Teleparallel Gravity. Teleparallel Gravity is an alternative to General Relativity to describe the gravitational interaction. The difference between General Relativity and Teleparallel Gravity is the fact that General Relativity associates the curvature to the gravitational interaction, wh

Asun Jiménez, Carlos Tapia Chinchay, Detang Zhou

Let $\Sigma$ be a closed embedded minimal hypersurface in the unit sphere $\mathbb{S}^{m+1}$ and let $\Lambda=\max\limits_{\Sigma}|A|$ be the norm of its second fundamental form. In this work we prove that the first eigenvalue of the Laplacian of $\Sigma$ satisfies $$\lambda_1(\Sigma)> \dfrac{m}{2}+\frac{m(m+1)}{32(12\Lambda+m+11)^2+8},$$ and $\lambda_1(\Sig

Yilin Wong, Giovanni Zocchi

We present a solid state system which spontaneously generates remarkable engraving patterns on the surface of Ge. The layered construction, with a metal film on the Ge surface, results in coupling of the metal catalyzed etching reaction with the long range stress field at the Ge - metal interface. The etching patterns generated have similarities with Turing

Frederik Wenkel, Semih Cantürk, Stefan Horoi, Michael Perlmutter

Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (and machine learning more generally) to solve combinatorial optimization (CO) problems is much less explored. Here, we introduce GCON, a novel GNN architecture that leverages a compl

D. Korucu, Reza Loloee, Norman O. Birge

Josephson junctions containing ferromagnetic (F) materials have been the subject of intense study over the past two decades. The ground state of such junctions oscillates between 0 and pi as the thickness of the ferromagnetic layer increases. For some applications, it might be beneficial to replace a very thin F layer with an unbalanced synthetic antiferroma

Wayne M Getz

Position recordings of the two-dimensional tracks of animals moving over landscapes has progressed over the past three decades from hourly to second-by-second locations. Track segmentation methods for analyzing the behavioral information in such relocation data has lagged somewhat behind, with scales of analysis currently at the sub-hourly to minute level. A

Norma Araceli Nava-Moreno, Alfredo Montaña, Itziar Aretxaga, Aldo Rodríguez-Puebla

We present a new cosmologically motivated mock redshift survey of the Dusty Star-Forming Galaxy population. Our mock survey is based on the Bolshoi-Planck dark-matter halo simulation and covers an area of 5.3 sq. degree. Using a semi-empirical approach, we generate a light cone and populate the dark-matter haloes with galaxies. Infrared properties are assign

Shota Takahashi, Akiko Takeda

In this paper, we propose the approximate Bregman proximal gradient algorithm (ABPG) for solving composite nonconvex optimization problems. ABPG employs a new distance that approximates the Bregman distance, making the subproblem of ABPG simpler to solve compared to existing Bregman-type algorithms. The subproblem of ABPG is often expressed in a closed form.

Benedikt Geiger, Peter J. Park

Non-negative matrix factorization with the generalized Kullback-Leibler divergence (NMF) and latent Dirichlet allocation (LDA) are two popular approaches for dimensionality reduction of non-negative data. Here, we show that NMF with $\ell_1$ normalization constraints on the columns of both matrices of the decomposition and a Dirichlet prior on the columns of

Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion

In this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that pruning based on the perplexity of a larger model can yield high-quality data, we investigate whether smaller models can be used for perplexity-ba

Ashok Cutkosky, Zakaria Mhammedi

We provide an online learning algorithm that obtains regret $G\|w_\star\|\sqrt{T\log(\|w_\star\|G\sqrt{T})} + \|w_\star\|^2 + G^2$ on $G$-Lipschitz convex losses for any comparison point $w_\star$ without knowing either $G$ or $\|w_\star\|$. Importantly, this matches the optimal bound $G\|w_\star\|\sqrt{T}$ available with such knowledge (up to logarithmic fa

Ethan Rathbun, Christopher Amato, Alina Oprea

Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness of RL algorithms against adversarial attacks. In this work we explore a particularly stealthy form of training-time attacks against RL -- backdoor poisoning. Here the adversary int

Lingyi Yang

Stability issues with reinforcement learning methods persist. To better understand some of these stability and convergence issues involving deep reinforcement learning methods, we examine a simple linear quadratic example. We interpret the convergence criterion of exact Q-learning in the sense of a monotone scheme and discuss consequences of function approxi

Jong Hyuk Yoon, Seung Hun Oh

We study the previously proposed quasilocal angular momentum of gravitational fields in the absence of isometries. The quasilocal angular momentum $L(\xi)$ has the following attractive properties; ({\it i}) it follows from the Einstein's constraint equations, ({\it ii}) it satisfies the Poisson algebra $\{L(\xi), L(\eta) \}_{\rm P.B.} =({1/16\pi)}\, L( [\xi,

V. M. Maslov

The reaction 243 Am (n, 2n) populates the T of 16 hours ground state 242 g Am with J of 1 or the 242 m Am isomer state J of 5 with T of 141 years. The former state 242 g Am mostly beta decays to 242 Cm, or transmutes to 242 Pu via electron capture. The absolute yield of 242 g Am is compatible with the measured data, estimated by the alpha activity of 242 Cm

Matthew Farkas, Bernard Deconinck

We derive explicit solution representations for linear, dissipative, second-order Initial-Boundary Value Problems (IBVPs) with coefficients that are spatially varying, with linear, constant-coefficient, two-point boundary conditions. We accomplish this by considering the variable-coefficient problem as the limit of a constant-coefficient interface problem, p

Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye, Xianjun Yang

Instruction Fine-Tuning (IFT) significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). While coding data is known to boost LLM reasoning abilities during pretraining, its role in activating internal reasoning capacities during IFT remains understudied. This paper investigates a key question: How does coding data impact LL

Davide Corsi, Davide Camponogara, Alessandro Farinelli

An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes in many robotic scenarios (including mobile robotics, surgical assistance, and autonomous driving) unpredictable and non-stationary environments can pose critical challenges to su

Jernej Činč

In this paper we study interval maps $f$ with zero topological entropy that are crooked; i.e. whose inverse limit with $f$ as the single bonding map is the pseudo-arc. We show that there are uncountably many pairwise non-conjugate zero entropy crooked interval maps with different sets of fixed points. We also show that there are uncountably many zero entropy

Marcela Peláez, Urko Reinosa, Julien Serreau, Matthieu Tissier

We revisit the non-Abelian dipole problem in the context of a simple semiclassical approach that incorporates some essential features of the infrared sector of Yang-Mills theories in the Landau gauge, in particular, the fact that both the running coupling and the gluon propagator remain finite at infrared scales and that the latter shows positivity violation

Louis L. Chen, Bobbie Chern, Eric Eckstrand, Amogh Mahapatra

Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural networks (NNs) can tolerate modest amounts of these errors, their performance degrades substantially once error levels exceed a certain threshold. We propose a new loss reweighting,

Laura Martinez-Buelvas, Andry Rakotonirainy, Deanna Grant-Smith, Oscar Oviedo-Trespalacios

Connected and automated vehicles are poised to transform the transport system. However, significant uncertainties remain about their impact, particularly regarding concerns that this advanced technology might exacerbate injustices, such as safety disparities for vulnerable road users. Therefore, understanding the potential conflicts of this technology with s

Steven Moore, Eamon Costello, Huy A. Nguyen, John Stamper

Evaluating multiple-choice questions (MCQs) involves either labor intensive human assessments or automated methods that prioritize readability, often overlooking deeper question design flaws. To address this issue, we introduce the Scalable Automatic Question Usability Evaluation Toolkit (SAQUET), an open-source tool that leverages the Item-Writing Flaws (IW

Jose Rafael Espinosa Mena

Matrix scaling problems with sparse cost matrices arise frequently in various domains, such as optimal transport, image processing, and machine learning. The Sinkhorn-Knopp algorithm is a popular iterative method for solving these problems, but its convergence properties in the presence of sparsity have not been thoroughly analyzed. This paper presents a the

Francesco Ronzano, Jay Nanavati

Taking advantage of the widespread use of ontologies to organise and harmonize knowledge across several distinct domains, this paper proposes a novel approach to improve an embedding-Large Language Model (embedding-LLM) of interest by infusing the knowledge formalized by a reference ontology: ontological knowledge infusion aims at boosting the ability of the

Steven Moore, Robin Schmucker, Tom Mitchell, John Stamper

Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowledge. To streamline this process for higher-education courses, we employed GPT-4 to generate KCs for multiple-ch

Kyle Henke, Elijah Pelofske, Garrett Kenyon, Georg Hahn

We consider the problem of computing a sparse binary representation of an image. To be precise, given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when added together best reconstruct the given input. We formulate this problem with an $L_2$ loss on the reconstructi

Simeon Ball, Tomàs Ortega

We detail for the first time a complete explicit description of the quasi-cyclic structure of all classical finite generalized quadrangles. Using these descriptions we construct families of quasi-cyclic LDPC codes derived from the point-line incidence matrix of the quadrangles by explicitly calculating quasi-cyclic generator and parity check matrices for the

Glen Pridham, Karthik K. Tennankore, Kenneth Rockwood, George Worthen

We present a systems-level analysis of end-stage kidney disease (ESKD) with a dynamical network analysis of 14 commonly measured blood-based biomarkers in patients undergoing regular haemodialysis. Utilizing a validated pipeline for declining homeostatic systems, our approach learns a dynamical model together with an invertible transformation that simplifies

Alice Da Fonseca, Peter Lake, Ariana Barrenechea

This project is a collaboration between industry and academia to delve into Finance Social Networks, specifically the Board of Directors of public companies. Knowing the connections between Directors and Executives in different companies can generate powerful stories and meaningful insights on investments. A proof of concept in the form of a Data Visualizati

Md Mohaimin Al Barat, Shaoyu Li, Changlai Du, Y. Thomas Hou

Blockchain's decentralization, transparency, and tamper-resistance properties have facilitated the system's use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of Blockchain. Many solutions have been proposed to address these issues, including first-layer solutions (or on-chain solutions

Youssef S. Hassan, Takumi Kobayashi, Tobias Bothwell, Jacob L. Seigel

We demonstrate programmable control over the spatial distribution of ultra-cold atoms confined in an optical lattice. The control is facilitated through a combination of spatial manipulation of the magneto-optical trap and atomic population shelving to a metastable state. We first employ the technique to load an extended (5 mm) atomic sample with uniform den

Shreyas Kapur, Erik Jenner, Stuart Russell

Large language models generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program's output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data. To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free

Mostafa Ali, Naushad A. Kamar, Alireza Seif, Mohammad Maghrebi

Open driven quantum systems have defined a powerful paradigm of nonequilibrium phases and phase transitions; however, quantum phase transitions are generically not expected in this setting due to the decohering effect of dissipation. In this Letter, we consider a quantum Ising model subject to bulk dissipation (at rate $\Gamma$) and show that, although the c

Bernardo Langa, Deepak Sapkota, Ivan Lainez, Richard Haight

Hybrid Superconductor-Semiconductor (S-Sm) materials systems are promising candidates for quantum computing applications. Their integration into superconducting electronics has enabled on-demand voltage tunability at millikelvin temperatures. Ge quantum wells (Ge QWs) have been among the semiconducting platforms interfaced with superconducting Al to realize

Dongwei Lyu, Rie Nakata, Pu Ren, Michael W. Mahoney

We propose a new deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dim

J. H. Yoon

The canonical formalism of the (2+2) formulation of general relativity of 4 spacetime dimensions is studied under no symmetry assumptions, where the spacetime is viewed as a local product of a 2 dimensional base manifold of Lorentzian signature with the vertical space as its complement. The affine null parameter is chosen as the time coordinate whose level s

Adrian Kent

Recently proposed ``table-top tests of quantum gravity'' involve creating, separating and recombining superpositions of masses at non-relativistic speeds. The general expectation is that these generate superpositions of gravitational fields via the Newtonian potential. Analyses suggest that negligible gravitational radiation is generated if the interference

Dylan Neff, Zhongling Ji, Roli Esha, Gang Wang

A primary objective in high-energy heavy-ion collisions is to investigate the phase transition between confined and deconfined color matter. Complementary to the cumulants of conserved charges integrated over the full azimuth, we introduce a novel experimental approach to explore particle fluctuations in azimuthal partitions, which are potentially sensitive

Aastha Acharya, Caleb Lee, Marissa D'Alonzo, Jared Shamwell

Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic state estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density mo

Aquia Richburg, Marine Carpuat

A new paradigm for machine translation has recently emerged: fine-tuning large language models (LLM) on parallel text has been shown to outperform dedicated translation systems trained in a supervised fashion on much larger amounts of parallel data (Xu et al., 2024a; Alves et al., 2024). However, it remains unclear whether this paradigm can enable massively

Radovan Dermisek, Keith Hermanek

We construct the general two-Higgs doublet model effective field theory where the effects of additional new physics are parameterized by operators up to mass dimension-six. We further transform this effective theory to the Higgs basis and provide matching of the Wilson coefficients between the two descriptions. We illustrate the advantages of the Higgs basis

Minghao Guo, Bohan Wang, Pingchuan Ma, Tianyuan Zhang

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming ri

Behnam Moradkhani, Pejman Kheradmand, Harshith Jella, Kent K. Yamamoto

In-vivo tissue stiffness identification can be useful in pulmonary fibrosis diagnostics and minimally invasive tumor identification, among many other applications. In this work, we propose a palpation-based method for tissue stiffness estimation that uses a sensorized beam buckled onto the surface of a tissue. Fiber Bragg Gratings (FBGs) are used in our sens

Unma Desai, Haley Foladare, Katelynn E. Boerner, Tim F. Oberlander

A teenager's experience of chronic pain reverberates through multiple interacting aspects of their lives. To self-manage their symptoms, they need to understand how factors such as their sleep, social interactions, emotions and pain intersect; supporting this capability must underlie an effective personalized healthcare solution. While adult use of personal

Adrian Kent

We reanalyse and extend constraints on mass and charge interferometry identified by Mari et al. (2016). We show that their constraint on the time required for coherent interference can be extended by a factor of two. We extend their analysis to consider experiments in which one interferometer measures gravitational or electric fields generated by another. We

Adrian Kent

Christodoulou and Rovelli (CR) [1] have argued that a Bose et al.-Marletto-Vedral (BMV) experiment that confirmed the quantum nature of gravity would give laboratory evidence for a quantum superposition of spacetime geometries created in the course of the experiment. Hanif et al. [2] have argued that mass interferometers can be used to test whether gravity a

Edouard Yvinec, Gabriel Kasser

The wide acceptance of large language models (LLMs) has unlocked new applications and social risks. Popular countermeasures aim at detecting misinformation, usually involve domain specific models trained to recognize the relevance of any information. Instead of evaluating the validity of the information, we propose to investigate LLM generated text from the

Tanapol Kosolwattana, Huazheng Wang, Raed Al Kontar, Ying Lin

Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of processes yielding high rewards, and the exploration of uncertain processes. However, most online learning algorithms were designed under 1) a centralized setting that requires data

Asmaa Benchama, Khalid Zebbara

Deep learning is currently extensively employed across a range of research domains. The continuous advancements in deep learning techniques contribute to solving intricate challenges. Activation functions (AF) are fundamental components within neural networks, enabling them to capture complex patterns and relationships in the data. By introducing non-lineari

Mohamed Serry, Haocheng Chang, Jun Liu

Reach-avoid specifications are one of the most common tasks in autonomous aerial vehicle (UAV) applications. Despite the intensive research and development associated with control of aerial vehicles, generating feasible trajectories though complex environments and tracking them with formal safety guarantees remain challenging. In this paper, we propose a con

Sylvia Worlali Azumah, Nelly Elsayed, Zag ElSayed, Murat Ozer

Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researche

Shivendra Agrawal, Suresh Nayak, Ashutosh Naik, Bradley Hayes

The ability to shop independently, especially in grocery stores, is important for maintaining a high quality of life. This can be particularly challenging for people with visual impairments (PVI). Stores carry thousands of products, with approximately 30,000 new products introduced each year in the US market alone, presenting a challenge even for modern comp

Haoyan Zhai, Qianli Hu, Jiangning Chen

Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating mixed-variable spaces effectively. To Address these challenges, we introduce a hybrid Reinforcement Learning (RL) framework that synergizes

Shreyas Malpathak, Nandini Ananth

We present an approximate semiclassical (SC) framework for mixed quantized dynamics in Wigner phase space in a two-part series. In the first article, we introduced the Adiabatic Hybrid Wigner Dynamics (AHWD) method that allows for a few important `system' degrees of freedom to be quantized using high-level Double Herman-Kluk SC theory while describing the re

Zachary Selk, Serdar Yüksel

Decentralized stochastic control problems with local information involve problems where multiple agents and subsystems which are coupled via dynamics and/or cost are present. Typically, however, the dynamics of such couplings is complex and difficult to precisely model, leading to questions on robustness in control design. Additionally, when such a coupling

Rubens E. G. Machado, Ricardo C. Volert, Richards P. Albuquerque, Rogério Monteiro-Oliveira

Abell 1758 (z~0.278) is a galaxy cluster composed of two structures: A1758N and A1758S, separated by ~2.2 Mpc. The northern cluster is itself a dissociative merging cluster that has already been modelled by dedicated simulations. Recent radio observations revealed the existence of a previously undetected bridge connecting A1758N and A1758S. New simulations a

Saeed Azad, Daniel R Herber

As uncertainty considerations become increasingly important aspects of concurrent plant and control optimization, it is imperative to identify and compare the impact of uncertain control co-design (UCCD) formulations on their associated solutions. While previous work developed the theory for various UCCD formulations, their implementation, along with an in-d

Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha

Aligning foundation models is essential for their safe and trustworthy deployment. However, traditional fine-tuning methods are computationally intensive and require updating billions of model parameters. A promising alternative, alignment via decoding, adjusts the response distribution directly without model updates to maximize a target reward $r$, thus pro

Hao Chen, Yujin Han, Diganta Misra, Xiang Li

Diffusion models (DMs) have shown remarkable capabilities in generating realistic high-quality images, audios, and videos. They benefit significantly from extensive pre-training on large-scale datasets, including web-crawled data with paired data and conditions, such as image-text and image-class pairs. Despite rigorous filtering, these pre-training datasets

Shreyas Malpathak, Nandini Ananth

The Wigner phase space formulation of quantum mechanics is a complete framework for quantum dynamic calculations that elegantly highlights connections with classical dynamics. In this series of two articles, building upon previous efforts, we derive the full hierarchy of approximate semiclassical (SC) dynamic methods for adiabatic and non-adiabatic problems

Darij Grinberg, Tom Roby, Stephan Wagner, Mei Yin

Motivated by a question and some enumerative conjectures of Richard Stanley, we explore the equivalence classes of words in the Weyl algebra, $\mathbf{k} \left< D,U \mid DU - UD = 1 \right>$. We show that each class is generated by the swapping of adjacent *balanced subwords*, i.e., those which have the same number of $D$'s as $U$'s, and give several other c

Jesus C. Toscano-Figueroa, Daniel Burrow, Victor H. Guarochico-Moreira, Chengkun Xie

We exploit the geometry of magnetic nanowires, which define 1D contacts to an encapsulated graphene channel, to introduce an out-of-plane component in the polarisation of spin carriers. By design, the magnetic nanowires traverse the angled sides of the 2D material heterostructure. Consequently, the easy axis of the nanowires is inclined, and so the local mag

R. Li, D. Verney, G. De Gregorio, R. Mancino

The Gamow-Teller strength distribution covering the entire $\beta$-decay window, up to 10.312(4) MeV, of $^{80g+m}$Ga was measured for the first time in photo fission of UC$_x$ induced by a 50 MeV electron beam. The new data show significant enhancement in the high-energy region with a jump structure. Simultaneously, the $\gamma$ deexciting behavior of $\bet

Jie Feng, Manasa Muralidharan, Rodrigo Henriquez-Auba, Patricia Hidalgo-Gonzalez

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and n

Balaji Arun, Zekun Li, Florian Suri-Payer, Sourav Das

Today's practical partially synchronous Byzantine Fault Tolerant (BFT) consensus protocols trade off low latency and high throughput. On the one end, traditional BFT protocols such as PBFT and its derivatives optimize for latency. They require, in fault-free executions, only 3 message exchanges to commit, the optimum for BFT consensus. However, this class of

Yuta Kawakami, Manabu Kuroki, Jin Tian

Probabilities of causation (PoC) are valuable concepts for explainable artificial intelligence and practical decision-making. PoC are originally defined for scalar binary variables. In this paper, we extend the concept of PoC to continuous treatment and outcome variables, and further generalize PoC to capture causal effects between multiple treatments and mu

Dimitris Bertsimas, Matthew Peroni

As a multitude of capable machine learning (ML) models become widely available in forms such as open-source software and public APIs, central questions remain regarding their use in real-world applications, especially in high-stakes decision-making. Is there always one best model that should be used? When are the models likely to be error-prone? Should a bla

Harsh Chaudhari, Giorgio Severi, John Abascal, Anshuman Suri

Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs), by anchoring, adapting, and personalizing their responses to the most relevant knowledge sources. It is particularly useful in chatbot applications, allowing developers to customize LLM output without expensive retraining. Despite their significant utility i

Petra Tang, Jan Eldridge, Renate Meyer, Astrid Lamberts

Galactic white dwarf binaries (WDBs) and black hole binaries (BHBs) will be gravitational wave (GW) sources for LISA. Their detection will provide insights into binary evolution and the evolution of our Galaxy through cosmic history. Here, we make predictions of the expected WDB and BHB population within our Galaxy. We combine predictions of the compact remn

Shibam Mukherjee, Roman Walch, Fredrik Meisingseth, Elisabeth Lex

Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected, constitute a critical threat to the user privacy. Privacy-aware recommender systems enable protection of such sensitive user d

Elliot Layne, Jason Hartford, Sébastien Lachapelle, Mathieu Blanchette

Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of correctly mapping low-level observations to latent causal variables -- could advance scientific understanding by enabling inference of latent variables such as pathway activation. I

Paweł Przybyłowicz, Michał Sobieraj

In this paper, we investigate the problem of strong approximation of the solutions of stochastic differential equations (SDEs) when the drift coefficient is given in integral form. We investigate its upper error bounds, in terms of the discretization parameter $n$ and the size $M$ of the random sample drawn at each step of the algorithm, in different subclas

Eliana Tolosa-Villarreal

Every simple finite graph $G$ has an associated Lov\'asz-Saks-Schrijver ring $R_G(d)$ that is related to the $d$-dimensional orthogonal representations of $G$. The study of $R_G(d)$ lies at the intersection between algebraic geometry, commutative algebra and combinatorics. We find a link between algebraic properties such as normality, factoriality and strong

Guodong Li, Roberto J. Assef, Chao-Wei Tsai, Jingwen Wu

Hot Dust-Obscured Galaxies (Hot DOGs) are a rare population of hyper-luminous infrared galaxies discovered by the WISE mission. Despite the significant obscuration of the AGN by dust in these systems, pronounced broad and blue-shifted emission lines are often observed. Previous work has shown that 8 Hot DOGs, referred to as Blue-excess Hot DOGs (BHDs), prese

Kristjan Müürsepp

This conference thesis summarizes my presentation at Tartu Tuorla Cosmology meeting based on a collaborative work with E.Nardi and C.Smarra \cite{Muursepp:2024mbb}. It is well known that a pseudo-Nambu Goldstone boson (pNGB) coupled to a confining gauge group obtains a non-zero contribution to its mass through instanton effects. At non-zero temperature, this

Eric Chamoun, Michael Schlichktrull, Andreas Vlachos

Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surface form and style rather than manuscript content. In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance. We present SWIF$

Moritz Wirth, Christian Hölzl, Aaron Götzelmann, Einius Pultinevicius

Divalent atoms provide excellent means for advancing control in Rydberg atom-based quantum simulation and computing, due to the second optically active valence electron available. Particularly promising in this context are circular Rydberg atoms, for which long-lived ionic core excitations can be exploited without suffering from detrimental autoionization. H

Job Feldbrugge, Rien van de Weygaert

Cosmic filaments are the main transport channels of matter in the Megaparsec universe, and represent the most prominent structural feature in the matter and galaxy distribution. Here we describe and define the physical and dynamical nature of cosmic filaments. It is based on the realization that the complex spatial pattern and connectivity of the cosmic web

Siavash Golkar, Alberto Bietti, Mariel Pettee, Michael Eickenberg

Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enhancing our understanding of Transformers in quantitative and scientific contexts. This task requires precise l

Lindsay Munroe, Mariana da Silva, Faezeh Heidari, Irina Grigorescu

Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroimaging due to the complex brain phenotypes and inter-subject heterogeneity often encountered. The challenge can

Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud

Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer vision to predict living conditions in areas with limited data, but recent studies increasingly focus on causal analysis. Des

Andrei D. Polyanin

Using the principle of structural analogy of solutions, approaches have been developed for constructing exact solutions of complex nonlinear PDEs, including PDEs with delay, based on the use of special solutions to auxiliary simpler related equations. It is shown that to obtain exact solutions of nonlinear non-autonomous PDEs, the coefficients of which depen

Mia Schambeck, Dorothea Golze, Jan Wilhelm

The $GW$ approximation is a widely used method for computing electron addition and removal energies of molecules and solids. The computational effort of conventional $GW$ algorithms increases as $O(N^4)$ with the system size $N$, hindering the application of $GW$ to large and complex systems. Low-scaling $GW$ algorithms are currently very actively developed.

Felix Zahner, Soumyajyoti Haldar, Roland Wiesendanger, Stefan Heinze

Diffusion on surfaces is a fundamental process in surface science, governing nanostructure and film growth, molecular self-assembly, and chemical reactions. Atom motion on non-magnetic surfaces has been studied extensively both theoretically and by real-space imaging techniques. For magnetic surfaces density functional theory (DFT) calculations have predicte

Kurt Schab, K. C. Kerby-Patel

The equivalent external noise temperature of time-varying antennas is studied using the concept of cross-frequency effective aperture, which quantifies the intermodulation conversion of external noise across the frequency spectrum into a receiver's operational bandwidth. The theoretical tools for this approach are laid out following the classical method for

Jiuhong Xiao, Ning Zhang, Daniel Tortei, Giuseppe Loianno

Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for outdoor applications including search and rescue operations, power line inspections, and environmental monitoring. The vulnerability of Global Navigation Satellite Systems (GNSS) signals to interference and spoofing necessitates the development of additional robust localization metho

Seyd Teymoor Seydi

This paper presents a comprehensive survey of 18 distinct polynomials and their potential applications in Kolmogorov-Arnold Network (KAN) models as an alternative to traditional spline-based methods. The polynomials are classified into various groups based on their mathematical properties, such as orthogonal polynomials, hypergeometric polynomials, q-polynom

Krishnakant Singh, Thanush Navaratnam, Jannik Holmer, Simone Schaub-Meyer

A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models p

Mathieu Ciancone, Imene Kerboua, Marion Schaeffer, Wissam Siblini

Recently, numerous embedding models have been made available and widely used for various NLP tasks. The Massive Text Embedding Benchmark (MTEB) has primarily simplified the process of choosing a model that performs well for several tasks in English, but extensions to other languages remain challenging. This is why we expand MTEB to propose the first massive

Yashaswini Murthy, Isaac Grosof, Siva Theja Maguluri, R. Srikant

We consider policy optimization methods in reinforcement learning settings where the state space is arbitrarily large, or even countably infinite. The motivation arises from control problems in communication networks, matching markets, and other queueing systems. Specifically, we consider the popular Natural Policy Gradient (NPG) algorithm, which has been st

Dawei Chen, Qile Chen

Multiscale differentials arise as limits of holomorphic differentials with prescribed zero orders on nodal curves. In this paper, we address the conjecture concerning Gorenstein contractions of multiscale differentials, originally proposed by Ranganathan and Wise and further developed by Battistella and Bozlee. Specifically, in the case of a one-parameter de

Serdar Yildiz, Ahmet Nezih Kasim

The growing importance of person reidentification in computer vision has highlighted the need for more extensive and diverse datasets. In response, we introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of doma

Daniel S. Freed

We give a systematic treatment of index theory on Pin manifolds, based on the Clifford linear Dirac operator and differential KO-theory. This expository article is based on joint work with Mike Hopkins.

Ya. A. Korennoy, V. I. Man'ko

Partial symplectic conditional and joint probability representations of quantum mechanics are considered. The correspondence rules for most interesting physical operators are found and the expressions of the dual symbols of operators are derived. Calculations were made by use of general formalism of quantizers and dequantizers determining the star product qu

Jeremias Epperlein, Scott Schmieding

Given a homeomorphism $T \colon X \to X$ of a compact metric space $X$, the stabilized automorphism group $\textrm{Aut}^{\infty}(T)$ of the system $(X,T)$ is the group of self-homeomorphisms of $X$ which commute with some power of $T$. We study the question of spatiality for stabilized automorphism groups of shifts of finite type. We prove that any isomorphi

Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu

Self-supervised pretraining on large-scale satellite data has raised great interest in building Earth observation (EO) foundation models. However, many important resources beyond pure satellite imagery, such as land-cover-land-use products that provide free global semantic information, as well as vision foundation models that hold strong knowledge of the nat