arXiv papers from January 2025

Yuanyuan Yang, Ruimin Zhang, Jamie Morgenstern, Haifeng Xu

In this paper, we study the Markovian Pandora's Box Problem, where decisions are governed by both order constraints and Markovianly correlated rewards, structured within a shared directed acyclic graph. To the best of our knowledge, previous work has not incorporated Markovian dependencies in this setting. This framework is particularly relevant to applicati

Hadi Keramati, Feridun Hamdullahpur

This paper presents a single-life reinforcement learning (SLRL) approach to adaptively select the dimension of the Krylov subspace during the generalized minimal residual (GMRES) iteration. GMRES is an iterative algorithm for solving large and sparse linear systems of equations in the form of \(Ax = b\) which are mainly derived from partial differential equa

Jun Xing, Mayur Bhatia, Sahil Phulwani, Darshan Suresh

Evaluating the real-world applicability of large language models (LLMs) provides valuable insights for their development and use in software development tasks. Existing benchmarks often focus on standalone coding problems or specific libraries, overlooking multi-file, project-based scenarios and lacking a rigorous evaluation of consistency. The HackerRank-AS

Paul Wild, Lutz Schröder

Behavioural conformances -- e.g. behavioural equivalences, distances, preorders -- on a wide range of system types (non-deterministic, probabilistic, weighted etc.) can be dealt with uniformly in the paradigm of universal coalgebra. One of the most commonly used constructions for defining behavioural distances on coalgebras arises as a generalization of the

Neil R. Sheeley

In a previous paper, I described a new way of determining the high-latitude solar rotation rate statistically from space-time maps of polar faculae observed in the 6767 \r{A} continuum by the Michelson Doppler Interferometer (MDI) on the Solar and Heliospheric Observatory (SOHO) Sheeley (2024). Now, I have tested the technique by applying it to simulated ima

Conor Power, Paraschos Koutris, Joseph M Hellerstein

Building on prior work on distributed databases and the CALM Theorem, we define and study the question of free termination: in the absence of distributed coordination, what query properties allow nodes in a distributed (database) system to unilaterally terminate execution even though they may receive additional data or messages in the future? This completene

Jonah Botvinick-Greenhouse

We propose a novel approach for performing dynamical system identification, based upon the comparison of simulated and observed physical invariant measures. While standard methods adopt a Lagrangian perspective by directly treating time-trajectories as inference data, we take on an Eulerian perspective and instead seek models fitting the observed global time

Jocelyn Shen, Audrey Lee, Sharifa Alghowinem, River Adkins

Despite living in an increasingly connected world, social isolation is a prevalent issue today. While social robots have been explored as tools to enhance social connection through companionship, their potential as asynchronous social platforms for fostering connection towards humanity has received less attention. In this work, we introduce the design of a s

Guillermo Sarasa, Ana Granados, Francisco B Rodríguez

P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression Distance (NCD) to extract the P300 structure, ensuring robustness against variability. We propose a novel signal-to-ASCII tra

Yiling Lin, Linzhuo Li, Lingfei Wu

As science transitions from the age of lone geniuses to an era of collaborative teams, the question of whether large teams can sustain the creativity of individuals and continue driving innovation has become increasingly important. Our previous research first revealed a negative relationship between team size and the Disruption Index-a network-based metric o

Savvas Germanis, Xuchao Chen, René Dost, Dominic J. Hallett

We report on an integrated semiconductor chip where a single quantum dot (QD) is excited in-plane via a photonic-crystal waveguide through its nearest p-shell optical transition. The chirality of the waveguide mode is exploited to achieve both directional absorption and directional emission, resulting in a substantial enhancement in directional contrast, as

Negar Hassanpour, Muhammad Kamran Janjua, Kunlin Zhang, Sepehr Lavasani

Balancing competing objectives remains a fundamental challenge in multi-task learning (MTL), primarily due to conflicting gradients across individual tasks. A common solution relies on computing a dynamic gradient update vector that balances competing tasks as optimization progresses. Building on this idea, we propose ConicGrad, a principled, scalable, and r

Urs Frauenfelder, Joa Weber

In this article we introduce the notion of Floer function which has the property that the Hessian is a Fredholm operator of index zero in a scale of Hilbert spaces. Since the Hessian has a complicated transformation under chart transition, in general this is not an intrinsic condition. Therefore we introduce the concept of Floerfolds for which we show that t

Fabio Spada, Purnanand Elango, Behçet Açıkmeşe

Recent investments in cislunar applications open new frontiers for space missions within highly nonlinear dynamical regimes. In this paper, we propose a method based on Sequential Convex Programming (SCP) to loiter around a given target with impulsive actuation while satisfying path constraints continuously over the finite time-horizon, i.e., independently o

Michael C. Donohue, Philip S. Insel, Oliver Langford

Nonlinear longitudinal proportional effect models have been proposed to improve power and provide direct estimates of the proportional treatment effect in randomized clinical trials. These models assume a fixed proportional treatment effect over time, which can lead to bias and Type I error inflation when the assumption is violated. Even when the proportiona

Akiyoshi Tomihari, Issei Sato

Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite their empirical success, the reasons behind Adam's superior performance over SGD remain poorly understood. In this study, we analyze the optimization of Transformer models through the lens of \emph{gradient heterogeneity

Kefan Dong, Tengyu Ma

A fundamental challenge in formal theorem proving by LLMs is the lack of high-quality training data. Although reinforcement learning or expert iteration partially mitigates this issue by alternating between LLM generating proofs and finetuning them on correctly generated ones, performance quickly plateaus due to the scarcity of correct proofs (sparse rewards

Sophie Middleton

Since its discovery, the muon has proven to be an invaluable probe of the Standard Model (SM). Muons are readily available in tertiary beams in facilities around the world. They do not decay hadronically and have a lifetime of a few $\mu$ s; consequently, muon experiments offer clean, high-statistics environments to make precision measurements and search for

Yannis Angelopoulos, Ryan Unger

We revisit global existence and decay for small-data solutions of semilinear wave equations on extremal Reissner-Nordstr\"om black hole backgrounds satisfying the classical null condition, a problem which was previously addressed by the first author in joint work with Aretakis and Gajic (Ann. of PDE, 2020). In this paper, we develop a new approach based on p

Paul H. Y. Cheung, Yusufcan Masatlioglu

We explore the influence of framing on decision-making, where some products are framed (e.g., displayed, recommended, endorsed, or labeled). We introduce a novel choice function that captures observed variations in framed alternatives. Building on this, we conduct a comprehensive revealed preference analysis, employing the concept of frame-dependent utility

Ana Granados, Kostadin Koroutchev, Francisco de Borja Rodríguez

Text datasets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the dataset nature, there can be advantages using a model that preserves text structure over one that does not, and viceversa. The key is to determine the best way of representing a particular da

Shi Chen, Evgenii Ievlev, Mikhail Shifman

In supersymmetric Yang-Mills theories (SYM) tension-degenerate domain walls are typical. Adding matter fields in fundamental representation we arrive at supersymmetric QCD (SQCD) supporting similar walls. We demonstrate that the degenerate domain walls can belong to one of two classes: (i) locally distinguishable, i.e. those which differ from each other loca

Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Nir Weinberger

We address the prominent communication bottleneck in federated learning (FL). We specifically consider stochastic FL, in which models or compressed model updates are specified by distributions rather than deterministic parameters. Stochastic FL offers a principled approach to compression, and has been shown to reduce the communication load under perfect down

Omar H. Khater, Abdul Jabbar Siddiqui, M. Shamim Hossain, Aiman El-Maleh

Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds compete for essential resources with crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The adoption of automated computer vision technologies a

Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni, Andrea Celli

We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual information and commits to a mixed strategy, after which the follower best-responds. We provide learning algorithms for the leader which achieve $O(T^{1/2})$ regret under bandit feedbac

Shengyang Sun, Yian Zhang, Alexander Bukharin, David Mosallanezhad

The rapid development of large language model (LLM) alignment algorithms has resulted in a complex and fragmented landscape, with limited clarity on the effectiveness of different methods and their inter-connections. This paper introduces Reward-Aware Preference Optimization (RPO), a mathematical framework that unifies popular preference optimization techniq

Koushik Chowdhury

This paper examines the vulnerabilities of convolutional neural networks (CNNs) to adversarial attacks and explores a method for their safeguarding. In this study, CNNs were implemented on four of the most common image datasets, namely CIFAR-10, ImageNet, MNIST, and Fashion-MNIST, and achieved high baseline accuracy. To assess the strength of these models, t

Hyeok Kim, Mingyoung J. Jeng, Kaitlin N. Smith

By leveraging quantum-mechanical properties like superposition, entanglement, and interference, quantum computing (QC) offers promising solutions for problems that classical computing has not been able to solve efficiently, such as drug discovery, cryptography, and physical simulation. Unfortunately, adopting QC remains difficult for potential users like QC

Yisong Chen, Chuqing Zhao, Yixin Xu, Chuanhao Nie

This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convoluti

Noel Pimentel, Alejandro Schuler, Mark van der Laan

Targeted maximum likelihood estimators (TMLEs) are asymptotically optimal among regular, asymptotically linear estimators. In small samples, however, we may be far from "asymptopia" and not reap the benefits of optimality. Here we propose a variant (score-preserving TMLE; SP-TMLE) that leverages an initial estimator defined as the solution of a large number

Xiuzhen Ye, Wentao Tang

In this paper, we study the resilience of process systems in an {\it information-theoretic framework}, from the perspective of an attacker capable of optimally constructing data injection attacks. The attack aims to distract the stationary distributions of process variables and stay stealthy, simultaneously. The problem is formulated as designing a multivari

Luyang Zhang, Cathy Jiao, Beibei Li, Chenyan Xiong

Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition. This paper introduces a theoretical framework for LLM data markets, modeling the strategic interactions between buyers (LLM builders) and sellers (human anno

Yingshan Chang, Yonatan Bisk

The notion of generalization has moved away from the classical one defined in statistical learning theory towards an emphasis on out-of-domain generalization (OODG). There has been a growing focus on generalization from easy to hard, where a progression of difficulty implicitly governs the direction of domain shifts. This emerging regime has appeared in the

Abdurrahim Yilmaz, Furkan Yuceyalcin, Ece Gokyayla, Donghee Choi

A major barrier to developing vision large language models (LLMs) in dermatology is the lack of large image--text pairs dataset. We introduce DermaSynth, a dataset comprising of 92,020 synthetic image--text pairs curated from 45,205 images (13,568 clinical and 35,561 dermatoscopic) for dermatology-related clinical tasks. Leveraging state-of-the-art LLMs, usi

Arman Gorji, Ali Fathi Jouzdani, Nima Sanati, Ren Yuan

Objectives: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) framework that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET a

Hao Liu, Yong Ge

Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high turnover rates. Effective prediction of potential employee turnovers benefits multiple stakeholders such as firms and onlin

Andrew Caplin, Daniel Martin, Philip Marx, Anastasiia Morozova

We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). We show that choice data are consistent with

Althaf Shajihan, Kirill Mechitov, Girish Chowdhary, Billie F. Spencer

Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, av

Maximilian Egger, Mayank Bakshi, Rawad Bitar

We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink communication costs. We introduce transformed robust aggregation to give convergence guarantees for general non-convex objectives under client data heterogeneity. Empirical evaluat

Ruyu Zhou, Fang Liu

Differential Privacy (DP) is a mathematical framework for releasing information with formal privacy guarantees. While numerous DP procedures have been developed for statistical analysis and machine learning, valid statistical inference methods offering high utility under DP constraints remain limited. We formalize this gap by introducing the notion of valid

Armando Castañeda, Gilde Valeria Rodríguez

Implementing correct distributed systems is an error-prone task. Runtime Verification (RV) offers a lightweight formal method to improve reliability by monitoring system executions against correctness properties. However, applying RV in distributed settings - where no process has global knowledge - poses fundamental challenges, particularly under full asynch

Thu Bui, Carola-Bibiane Schönlieb, Bruno Ribeiro, Beatrice Bevilacqua

Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers. In this paper, we demonstrate that random weights can be surprisingly effective, achieving performance comparable to end-to-end training counterparts, across various tasks and dat

Ho Chit Siu, Jaime D. Peña, Yutai Zhou, Ross E. Allen

We seek measurable properties of AI agents that make them better or worse teammates from the subjective perspective of human collaborators. Our experiments use the cooperative card game Hanabi -- a common benchmark for AI-teaming research. We first evaluate AI agents on a set of objective metrics based on task performance, information theory, and game theory

A. Ashtari Esfahani, S. Bhagvati, S. Böser, M. J. Brandsema

The most sensitive direct neutrino mass searches today are based on measurement of the endpoint of the beta spectrum of tritium to infer limits on the mass of the unobserved neutrino. To avoid the smearing associated with the distribution of molecular final states in the T-He molecule, the next generation of these experiments will need to employ atomic (T) r

Marco Muraca, Pablo Rodriguez-Fernandez, Nathaniel T. Howard, Joe Hall

In this paper an extensive database of SPARC H-modes confinement predictions has been provided, to assess its variability with respect to few input assumptions. The simulations have been performed within the ASTRA framework, using the quasi-linear model TGLF SAT2, including electromagnetic effects, for the core transport, and a neural network trained on EPED

Nathaniel Tomczak, Sanmukh Kuppannagari

Transformers have demonstrated great success in numerous domains including natural language processing and bioinformatics. This success stems from the use of the attention mechanism by these models in order to represent and propagate pairwise interactions between individual tokens of sequential data. However, the primary limitation of this operation is its q

Vibhu Dalal

This work introduces a framework for quantifying the information content of logical propositions through the use of implication hypergraphs. We posit that a proposition's informativeness is primarily determined by its relationships with other propositions; specifically, the extent to which it implies or derives other propositions. To formalize this notion, w

Prakash Poudel, Raghvendra V. Cowlagi

We address path-planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a mobile sensor network. For this problem, we present a new information gain measure for optimal sensor placement that quantifies redu

Andrea Colesanti, Lei Qin, Paolo Salani

We prove a Brunn-Minkowski type inequality for the first (nontrivial) Dirichlet eigenvalue of the weighted $p$-operator \[ -\Delta_{p,\gamma}u=-\text{div}(|\nabla u|^{p-2} \nabla u)+(x,\nabla u)|\nabla u|^{p-2}, \] where $p>1$, in the class of bounded Lipschitz domains in $\mathbb{R}^n$. We also prove that any corresponding positive eigenfunction is log-conc

Cristian Bontoiu, Alexandre Bonatto, Öznur Apsimon, Laura Bandiera

Wakefield wavelengths associated with solid-state plasmas greatly limit the accelerating length. An alternative approach employs 2D carbon-based nanomaterials, like graphene or carbon nanotubes (CNTs), configured into structured targets. These nanostructures are designed with voids or low-density regions to effectively reduce the overall plasma density. This

Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong

As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources without sharing raw data. However, a significant challenge in FL is that client data are often non-IID (non-independent and identically distributed), leading to reduced performance c

Roi Benita, Michael Elad, Joseph Keshet

Diffusion models (DMs) have emerged as powerful tools for modeling complex data distributions and generating realistic new samples. Over the years, advanced architectures and sampling methods have been developed to make these models practically usable. However, certain synthesis process decisions still rely on heuristics without a solid theoretical foundatio

Darian McLaren, Matthew A. Graydon, Ali Assem Mahmoud, Joel J. Wallman

Quantum measurements with feed-forward are crucial components of fault-tolerant quantum computers. We show how the error rate of such a measurement can be directly estimated by fitting the probability that successive randomly compiled measurements all return the ideal outcome. Unlike conventional randomized benchmarking experiments and alternative measuremen

Gabriela Ciuperca

This article considers the automatic selection problem of the relevant explanatory variables in a right-censored model on a massive database. We propose and study four aggregated censored adaptive LASSO estimators constructed by dividing the observations in such a way as to keep the consistency of the estimator of the survival curve. We show that these estim

Eirini Schoinas, Adyah Rastogi, Anissa Carter, Jacob Granley

Human-in-the-loop optimization (HILO) is a promising approach for personalizing visual prostheses by iteratively refining stimulus parameters based on user feedback. Previous work demonstrated HILO's efficacy in simulation, but its performance with human participants remains untested. Here we evaluate HILO using sighted participants viewing simulated prosthe

Benedek Kovács, Zoltán Lóránt Nagy, Dávid R. Szabó

What is the maximum number of points that can be selected from an $n \times n$ square lattice such that no $k+1$ of them are in a line? This has been asked more than $100$ years ago for $k=2$ and it remained wide open ever since. In this paper, we prove the precise answer is $kn$, provided that $k>C\sqrt{n\log{n}}$ for an absolute constant $C$. The proof rel

Talwinder Singh, Dinesha V. Hegde, Tae K. Kim, Nikolai V. Pogorelov

Interplanetary Coronal Mass Ejections (ICMEs) are the primary sources of geomagnetic storms at Earth. Negative out-of-ecliptic component (Bz) of magnetic field in the ICME or its associated sheath region is necessary for it to be geo-effective. For this reason, magnetohydrodynamic simulations of CMEs containing data-constrained flux ropes are more suitable f

Guilherme H. Bandeira Costa, Miguel Freire, Arlindo L. Oliveira

The Abstraction and Reasoning Corpus challenges AI systems to perform abstract reasoning with minimal training data, a task intuitive for humans but demanding for machine learning models. Using CodeT5+ as a case study, we demonstrate how limitations in positional encoding hinder reasoning and impact performance. This work further examines the role of positio

Rohan Chacko, Nicolai Haeni, Eldar Khaliullin, Lin Sun

We introduce Lifting By Gaussians (LBG), a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently, 3DGS Fields have emerged as a highly efficient and explicit alternative to Neural Field-based methods for high-quality Novel View Synthesis. Our 3D instance segmentation method directly lifts 2D segmentation

Jizhou Huang, Brendan Juba

We study ``selective'' or ``conditional'' classification problems under an agnostic setting. Classification tasks commonly focus on modeling the relationship between features and categories that captures the vast majority of data. In contrast to common machine learning frameworks, conditional classification intends to model such relationships only on a subse

Yu Zhu, Zehang Richard Li

Cause-of-death data is fundamental for understanding population health trends and inequalities as well as designing and evaluating public health interventions. A significant proportion of global deaths, particularly in low- and middle-income countries (LMICs), do not have medically certified causes assigned. In such settings, verbal autopsy (VA) is a widely

Alapan Bera, Soumik Mukhopadhyay

Iron-based van der Waals (vdW) ferromagnets with relatively high ordering temperatures are a current research focus due to their significance in fundamental physics and potential applications in spintronics. Competing magnetic interactions and anisotropies can give rise to nontrivial spin textures in these materials, resulting in novel topological features.

Omur Sahin, Man Zhang, Andrea Arcuri

Search-Based Software Testing (SBST) has seen several success stories in academia and industry. The effectiveness of a search algorithm at solving a software engineering problem strongly depends on how such algorithm can navigate the fitness landscape of the addressed problem. The fitness landscape depends on the used fitness function. Understanding the prop

Aidan Patterson

The goal of this paper is to develop the theory of Courant algebroids with integrable para-Hermitian vector bundle structures by invoking the theory of Lie bialgebroids. We consider the case where the underlying manifold has an almost para-complex structure, and use this to define a notion of para-holomorphic algebroid. We investigate connections on para-hol

Jan Dereziński

Hypergeometric class equations are given by second order differential operators in one variable whose coefficient at the second derivative is a polynomial of degree $\leq2$, at the first derivative of degree $\leq1$ and the free term is a number. Their solutions, called hypergeometric class functions, include the Gauss hypergeometric function and its various

Debosmita Pathak, Adam Leroy, Todd Thompson, Laura Lopez

Radiation pressure is a key mechanism by which stellar feedback disrupts molecular clouds and drives HII region expansion. This includes direct radiation pressure exerted by UV photons on dust grains, pressure associated with photoionization, and infrared (IR) radiation pressure on grains due to dust-reprocessed IR photons. We present a new method that combi

Onur Ağırseven, M. A. Ollis

A Hamiltonian path in the complete graph $K_v$ whose vertices are labeled with the integers $0,1,\ldots,v-1$ is a linear realization for the multiset $L$ of the linear edge-lengths (given by $|x-y|$ for the edge between vertices $x$ and $y$) of the edges in the path. A linear realization is standard if an end-vertex is 0 and perfect if the end-vertices are 0

Ibrahim Yazici, Ivan Yotov

We develop multipoint stress mixed finite element methods for linear elasticity with weak stress symmetry on cuboid grids, which can be reduced to a symmetric and positive definite cell-centered system. The methods employ the lowest-order enhanced Raviart-Thomas finite element space for the stress and piecewise constant displacement. The vertex quadrature ru

Byron Droguett, Pablo Martin, Eduardo Rojas, Jorge Olivares

A new analytical approximation function is proposed to accurately fit the solution of a fractional differential equation of order one-half, whose nonhomogeneous term is defined by a modified Bessel function of the first kind. The exact analytical solution of this equation is expressed as the product of two modified Bessel functions. The approximation is cons

Eron Ristich, Lei Zhang, Yi Ren, Jiefeng Sun

Koopman operator theory provides a powerful data-driven technique for modeling nonlinear dynamical systems in a linear framework, in comparison to computationally expensive and highly nonlinear physics-based simulations. However, Koopman operator-based models for soft robots are very high dimensional and require considerable amounts of data to properly resol

Kundan Kadam, Eduard Vorobyov, Peter Woitke, Shantanu Basu

Canonically, a protoplanetary disk is thought to undergo (gravito-)viscous evolution, wherein the angular momentum of the accreting material is transported outwards. However, several lines of reasoning suggest that the turbulent viscosity in a typical protoplanetary disk is insufficient to drive the observed accretion rates. An emerging paradigm suggests tha

Charles Bricout, Kang Ik K. Cho, Michael Harms, Ofer Pasternak

MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To address these issues, we introduce an approach that pretrains a model on synthetically generated motion artifacts before applying transfer learning for QC classification. This method

Schuyler Arn, Pablo Illing, Joshua Mendez Harper, Justin C. Burton

Fluid triboelectrification, also known as flow electrification, remains an under-explored yet ubiquitous phenomenon with potential applications from material science to planetary evolution. Building upon previous efforts to position water within the triboelectric series, we investigate the charge on individual, millimetric water drops falling through air. Ou

Binchi Zhang, Zhengzhang Chen, Zaiyi Zheng, Jundong Li

Large Language Models (LLMs) excel in natural language processing by encoding extensive human knowledge, but their utility relies on timely updates as knowledge evolves. Updating LLMs involves two key tasks simultaneously: unlearning to remove unwanted knowledge and editing to incorporate new information. Existing methods face two major challenges: ineffecti

Mitchell Lozier, Rahul Deshpande, Ahmad Zarei, Luka Lindić

A new statistical definition for the mean turbulent boundary layer thickness is introduced, based on identification of the point where the streamwise velocity skewness changes sign, from negative to positive, in the outermost region of the boundary layer. Importantly, this definition is independent of arbitrary thresholds, and broadly applicable, including t

Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah

Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition. Action recognition models often suffer from background bias (i.e., inferring actions based on background cues) and foreground bias (i.e., relying on subject appearance), which ca

Thiago Holleben, Lisa Nicklasson

Inspired by the Roller Coaster Theorem from graph theory, we prove the existence of artinian Gorenstein algebras with unconstrained Hilbert series, which we call Roller Coaster algebras. Our construction relies on Nagata idealization of quadratic monomial algebras defined by whiskered graphs. The monomial algebras are interesting in their own right, as our r

Yi-Hsiang Chen, Charles H. Baldwin

Leakage errors are unwanted transfer of population outside of a defined computational subspace and they occur in almost every platform for quantum computing. While prevalent, leakage is often overlooked when measuring and reporting the fidelity of quantum gates with standard methods. In fact, when leakage is substantial it can cause a large overestimation of

Ran Ginosar

When a single core is scaled up to m cores occupying the same chip area and executing the same (parallelizable) task, achievable speedup is square-root m, power is reduced by square-root m and energy is reduced by m. Thus, many-core architectures can efficiently outperform architectures of a single core and a small-count multi-core.

Leon Hostetler, M. A. Clark, Carleton DeTar, Steven Gottlieb

Typically, the conjugate gradient (CG) algorithm employs mixed precision and even-odd preconditioning to compute propagators for highly improved staggered quarks (HISQ). This approach suffers from critical slowing down as the light quark mass is decreased to its physical value. Multigrid is one alternative to combat critical slowing down; however, it involve

Kwanho Kim, Soojong Kim

Sentiment analysis of alternative tobacco products on social media is important for tobacco control research. Large Language Models (LLMs) can help streamline the labor-intensive human sentiment analysis process. This study examined the accuracy of LLMs in replicating human sentiment evaluation of social media messages about heated tobacco products (HTPs). T

Varun Dhanraj, Chris Eliasmith

Large language models (LLMs) continue to face challenges in reliably solving reasoning tasks, particularly those that require precise rule following, as often found in mathematical reasoning. This paper introduces a novel neurosymbolic method that improves LLM reasoning by encoding hidden states into neurosymbolic vectors, enabling problem-solving within a n

Viet Trinh

Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in deci

Alen Alexanderian, Hugo Díaz, Vishwas Rao, Arvind K. Saibaba

In data assimilation, the model may be subject to uncertainties and errors. The weak-constraint data assimilation framework enables incorporating model uncertainty in the dynamics of the governing equations. We propose a new framework for near-optimal sensor placement in the weak-constrained setting. This is achieved by first deriving a design criterion base

Aris Filos-Ratsikas, Vasilis Gkatzelis, Mohamad Latifian, Emma Rewinski

We study the distortion of one-sided and two-sided matching problems on the line. In the one-sided case, $n$ agents need to be matched to $n$ items, and each agent's cost in a matching is their distance from the item they were matched to. We propose an algorithm that is provided only with ordinal information regarding the agents' preferences (each agent's ra

Johannes K. Fichte, Markus Hecher

Modern society is full of computational challenges that rely on probabilistic reasoning, statistics, and combinatorics. Interestingly, many of these questions can be formulated by encoding them into propositional formulas and then asking for its number of models. With a growing interest in practical problem-solving for tasks that involve model counting, the

L. F. Melo, O. Jiménez, L. Neves

The optimal measurement that discriminates nonorthogonal quantum states with fixed rates of inconclusive outcomes (FRIO) can be decomposed into an assisted separation of the inputs, yielding conclusive and inconclusive outputs, followed by a minimum-error (ME) measurement for the conclusive ones (standard FRIO) or both ones (concatenated FRIO). The implement

Thomas Eiter, Johannes K. Fichte, Markus Hecher, Stefan Woltran

Answer Set Programming (ASP) is a prominent problem-modeling and solving framework, whose solutions are called answer sets. Epistemic logic programs (ELP) extend ASP to reason about all or some answer sets. Solutions to an ELP can be seen as consequences over multiple collections of answer sets, known as world views. While the complexity of propositional pro

Denis Los

The physical register file supports increasing the execution width and depth of a superscalar microprocessor to exploit more instruction-level parallelism. The efficient design of the physical register file is critical since its resources, such as the number of read and write ports, have a significant impact on CPU power consumption. Reducing the number of p

Hassan Jahanandish, Shengtian Sang, Cynthia Xinran Li, Sulaiman Vesal

Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound (TRUS) images during biopsy, which resul

David Speck, Markus Hecher, Daniel Gnad, Johannes K. Fichte

Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probability on the plan space. Indeed, qualita

Mehdi El Bouhaddouti, Muhsin Aljaf, Ilias Cholis

Primordial black holes (PBH) may constitute a considerable fraction of dark matter. In this work we use the recent observations by the LIGO-Virgo-KAGRA (LVK) collaborations to set direct limits on stellar-mass range PBHs. We evaluate the merger rates of PBH binaries by accounting for the binaries formed by two-body captures inside dark matter halos and by st

Ambre Chabert

Given a compact surface of revolution with Laplace-beltrami operator $\Delta$, we consider the spectral projector $P_{\lambda,\delta}$ on a polynomially narrow frequency interval $[\lambda-\delta,\lambda + \delta]$, which is associated to the self-adjoint operator $\sqrt{-\Delta}$. For a large class of surfaces of revolution, and after excluding small disks

Patatchona Keyela, Soumaya Cherkaoui

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient res

John Cremona, Kalani Thalagoda, Dan Yasaki

Let $K$ be an imaginary quadratic field and let $\mathcal{O}_K$ be its ring of integers. For an integral ideal $\mathfrak{n}$ of $\mathcal{O}_K$, let $\Gamma_0({\mathfrak{n}})$ be the congruence subgroup of level ${\mathfrak{n}}$ consisting of matrices in $\operatorname{GL}_2{\mathcal{O}_K}$ that are upper triangular mod ${\mathfrak{n}}$. In this paper, we d

Qin Jiang, Chengjia Wang, Michael Lones, Wei Pang

While Graph Neural Networks (GNNs) have achieved remarkable success, their design largely relies on empirical intuition rather than theoretical understanding. In this paper, we present a comprehensive analysis of GNN behavior through three fundamental aspects: (1) we establish that \textbf{$k$-layer} Message Passing Neural Networks efficiently aggregate \tex

Jianhua Mo, Ahmad AlAmmouri, Shenggang Dong, Younghan Nam

Joint phase-time arrays (JPTA) is a new mmWave radio frequency front-end architecture constructed with appending time-delay elements to phase shifters for analog beamforming. JPTA allows the mmWave base station (BS) to form multiple frequency-dependent beams with a single RF chain, exploiting the extra degrees of freedom the time-delay elements offer. Withou

Azminul Jaman, Lorenzo Fratino, Majid Ahmadi, Rodolfo Rocco

The precipitous rise of consumer network applications reiterates the urgency to redefine computing hardware with low power footprint. Neuromorphic computing utilizing correlated oxides offers an energy-efficient solution. By designing anisotropic functional properties in LSMO on a twinned LAO substrate and driving it out of thermodynamic equilibrium, we demo

Leonardo Berti, Bardh Prenkaj, Paola Velardi

Financial markets are complex systems characterized by high statistical noise, nonlinearity, volatility, and constant evolution. Thus, modeling them is extremely hard. Here, we address the task of generating realistic and responsive Limit Order Book (LOB) market simulations, which are fundamental for calibrating and testing trading strategies, performing mar

Edward Y. Chang

This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial