April 2024 arXiv papers — page 91

David Samuel, Lucas Georges Gabriel Charpentier, Sondre Wold

Retrieval-augmented language models pose a promising alternative to standard language modeling. During pretraining, these models search in a corpus of documents for contextually relevant information that could aid the language modeling objective. We introduce an 'ideal retrieval' methodology to study these models in a fully controllable setting. We conduct a

Jaemin Lee, Jeeseop Kim, Aaron D. Ames

This paper proposes a comprehensive framework designed for the autonomous inspection of complex environments, with a specific focus on multi-tiered settings such as distillation column trays. Leveraging quadruped robots equipped with roller arms, and through the use of onboard perception, we integrate essential motion components including: locomotion, safe a

William Bernardoni

Arithmetic valuations are intimately connected with the structure of the ideals of a commutative ring. We show how the generalized idempotent semiring valuations of Jeffrey and Noah Giansiracusa can be used to make this connection explicit. Through this generalized valuation theory sufficiently complete positive valuations give rise to Galois correspondences

Ibrahim Kilinc, Ryan M. Dreifuerst, Junghoon Kim, Robert W. Heath

Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems. Location information coupled with machine learning (ML) beam recommendation is one way to reduce the overhead of beam p

Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima

Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibr

J. Pablo Muñoz, Jinjie Yuan, Nilesh Jain

Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, we introduce a novel approach called Shears, demonstrating how the

Taeho Kim, Yanming Wang, Vatshank Chaturvedi, Lokesh Gupta

Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given env

Rodrigo Saavedra, Fernando Peruani

We study systems of active particles, whose perception is constrained by a vision cone, that are attracted to other particles and repelled from static obstacles. We report a novel self-trapping mechanism: active particles with non-reciprocal attraction form particle chains, which eventually become closed loops that shrink around one or many obstacles. These

Yuhki Hosoya

To study the assumption that the utility maximization hypothesis implicitly adds to consumer theory, we consider a mathematical representation of pre-marginal revolution consumer theory based on subjective exchange ratios. We introduce two axioms on subjective exchange ratio, and show that both axioms hold if and only if consumer behavior is consistent with

Catalina J. Villalba, Aurelio R. L. Oliveira

The Interior-Point Methods are a class for solving linear programming problems that rely upon the solution of linear systems. At each iteration, it becomes important to determine how to solve these linear systems when the constraint matrix of the linear programming problem includes dense columns. In this paper, we propose a preconditioner to handle linear pr

Pravesh Koirala, Forrest Laine

Gig economy consists of two market groups connected via an intermediary. Popular examples are rideshares where passengers and drivers are mediated via platforms such as Uber and Lyft. In a duopoly market, the platforms must compete to attract not only the passengers by providing a lower rate but also the drivers by providing better wages. While this should i

Xinyao Yi, Yuxin Qiao

Recent years have witnessed a rapid advancement in GPU technology, establishing it as a formidable high-performance parallel computing technology with superior floating-point computational capabilities compared to traditional CPUs. This paper explores the application of this technology in the field of photoacoustic imaging, an emerging non-destructive testin

Ellianna Abrahams, Tasha Snow, Matthew R. Siegfried, Fernando Pérez

We propose a new tiling strategy, Flip-n-Slide, which has been developed for specific use with large Earth observation satellite images when the location of objects-of-interest (OoI) is unknown and spatial context can be necessary for class disambiguation. Flip-n-Slide is a concise and minimalistic approach that allows OoI to be represented at multiple tile

Carlos A. Argüelles, Kareem Farrag, Teppei Katori

Ultra-light dark matter is a class of dark matter models where the mass of the dark matter particle is very small and the dark matter behaves as a classical field pervading our galaxy. If astrophysical neutrinos interact with ultra-light dark matter, these interactions would produce a matter potential in our galaxy which may cause anomalous flavor conversion

Murat Can Aşkaroğulları, Atabey Kaygun

We prove that the Leibniz PROP is isomorphic (as $\Bbbk$-linear categories) to the symmetric crossed presimplicial algebra $\Bbbk[(\Delta^+)^{op} \mathbb{S}]$ where $\Delta^+$ is the skeletal category of finite well-ordered sets with surjections, but the distributive law between $(\Delta^+)^{op}$ and the symmetric groups $\mathbb{S} = \bigsqcup_{n\geq 1} S_n

Croix Gyurek, Niloy Talukder, Mohammad Al Hasan

For natural language understanding and generation, embedding concepts using an order-based representation is an essential task. Unlike traditional point vector based representation, an order-based representation imposes geometric constraints on the representation vectors for explicitly capturing various semantic relationships that may exist between a pair of

Mamoru Ueda

We construct a homomorphism from the affine Yangian $Y_{\hbar,\varepsilon+\hbar}(\widehat{\mathfrak{sl}}(n))$ to the affine Yangian $Y_{\hbar,\varepsilon}(\widehat{\mathfrak{sl}}(n+1))$ which is different from the one in arXiv:2312.09933. By using this homomorphism, we give a homomorphism from $Y_{\hbar,\varepsilon}(\widehat{\mathfrak{sl}}(n))\otimes Y_{\hba

Pavel Denisov, Ngoc Thang Vu

Recent advancements in language modeling have led to the emergence of Large Language Models (LLMs) capable of various natural language processing tasks. Despite their success in text-based tasks, applying LLMs to the speech domain remains limited and challenging. This paper presents BLOOMZMMS, a novel model that integrates a multilingual LLM with a multiling

Santosh Pandey, Amir Yazdanbakhsh, Hang Liu

Microarchitecture simulators are indispensable tools for microarchitecture designers to validate, estimate, and optimize new hardware that meets specific design requirements. While the quest for a fast, accurate and detailed microarchitecture simulation has been ongoing for decades, existing simulators excel and fall short at different aspects: (i) Although

Gürdal Arslan, Serdar Yüksel

We consider a stochastic dynamic game where players have their own linear state dynamics and quadratic cost functions. Players are coupled through some environment variables, generated by another linear system driven by the states and decisions of all players. Each player observes his own states realized up to the current time as well as the past realization

Alexander Altland, Joaquim Telles de Miranda, Tobias Micklitz

The evolution of complex correlated quantum systems such as random circuit networks is governed by the dynamical buildup of both entanglement and entropy. We here introduce a real-time field theory approach -- essentially a fusion of the $G \Sigma$-functional of the SYK-model and the field theory of disordered systems -- enigneered to microscopically describ

William J. Leigh

An earlier scheme [arXiv:2404.03360], where torsion plays an essential part in a flat spacetime account of fermion spin, is extended to spacetimes with non-zero Riemann curvature. It is found that further essential features of the fermion, in particular its electromagnetic field, are determined by the curvature and torsion of spacetime. A natural model for t

Yating Wu, Ritika Mangla, Alexandros G. Dimakis, Greg Durrett

Inquisitive questions -- open-ended, curiosity-driven questions people ask as they read -- are an integral part of discourse processing (Kehler and Rohde, 2017; Onea, 2016) and comprehension (Prince, 2004). Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. But the space of inquisitive

Gennadiy Feldman

By the well-known I.Kotlarski lemma, if $\xi_1$, $\xi_2$, and $\xi_3$ are independent real-valued random variables with nonvanishing characteristic functions, $L_1=\xi_1-\xi_3$ and $L_2=\xi_2-\xi_3$, then the distribution of the random vector $(L_1, L_2)$ determines the distributions of the random variables $\xi_j$ up to shift. Siran Li and Xunjie Zheng gene

Sara Amato, Andrea Arnold

Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as the penumbra. Microglial cells can be activated into two distinct phenotypes; however, the dynamics between the detrimental M1 phenotype and be

Brian Lai, Dennis S. Bernstein

Recursive least squares (RLS) is derived as the recursive minimizer of the least-squares cost function. Moreover, it is well known that RLS is a special case of the Kalman filter. This work presents the Kalman filter least squares (KFLS) cost function, whose recursive minimizer gives the Kalman filter. KFLS is an extension of generalized forgetting recursive

Piotr Mitosek

The ZH calculus is a graphical language for quantum computation reasoning. The phase-free variant offers a simple set of generators that guarantee universality. ZH calculus is effective in MBQC and analysis of quantum circuits constructed with the universal gate set Toffoli+H. While circuits naturally translate to ZH diagrams, finding an ancilla-free circuit

Elaine Venson, Reem Alfayez

Curricula recommendation for undergraduate Software Engineering courses underscore the importance of transcending from traditional lecture format to actively involving students in time-limited, iterative development practices. This paper presents a teaching approach for a software testing course that integrates theory and practical experience through the uti

Brian Lai, Dennis S. Bernstein

Traditionally, batch least squares (BLS) and recursive least squares (RLS) are used for identification of a vector of parameters that form a linear model. In some situations, however, it is of interest to identify parameters in a matrix structure. In this case, a common approach is to transform the problem into standard vector form using the vectorization (v

Nakul Aggarwal, Keshav Das Agarwal, Tanoy Kanti Konar, Leela Ganesh Chandra Lakkaraju

Entanglement in quantum many-body systems is required for a variety of quantum information tasks, making it crucial to identify the parameter space in which the ground state is fully separable, known as the factorization surface (FS). Nonetheless, the tuning parameters indicating FS for several quantum spin models remain unknown. We employ symbolic regressio

Gaurab Rimal, Tanzila Tasnim, Gabriel Calderon Ortiz, George E. Sterbinsky

The large spin-orbit coupling in iridium oxides plays a significant role in driving novel physical behaviors, including emergent phenomena in the films and heterostructures of perovskite and Ruddlesden-Popper iridates. In this work, we study the role of epitaxial strain on the electronic behavior of thin SrIrO$_3$ films. We find that compressive epitaxial st

Martin Bucher, Daniel Molnar

We analyse a lossy transmission line and the Johnson-Nyquist noise generated therein. A representation as a noisy two-port with a voltage and a current noise sources on one end of a noiseless two-port is given. An expression for the noise properties is given for an arbitrary temperature profile along the transmission line. Agreement is demonstrated between t

Patricio Arrué, Kaveh Laksari, Nancy Sweitzer, Mindy Fain

Background: Aortic stenosis (AS) is the most common acquired valvar disease and is associated with increased risk for frailty. Frailty as a geriatric syndrome is associated with muscle weakness and a compromised autonomic nervous system (ANS) performance in older adults. The purpose of the current work was to assess differences in both motor and ANS performa

Abhishek Dalvi, Neil Ashtekar, Vasant Honavar

Matching is one of the simplest approaches for estimating causal effects from observational data. Matching techniques compare the observed outcomes across pairs of individuals with similar covariate values but different treatment statuses in order to estimate causal effects. However, traditional matching techniques are unreliable given high-dimensional covar

Hector Kohler, Quentin Delfosse, Paul Festor, Philippe Preux

Embracing the pursuit of intrinsically explainable reinforcement learning raises crucial questions: what distinguishes explainability from interpretability? Should explainable and interpretable agents be developed outside of domains where transparency is imperative? What advantages do interpretable policies offer over neural networks? How can we rigorously d

Fabio Ancona, Alberto Bressan, Elio Marconi, Luca Talamini

For a scalar conservation law with strictly convex flux, by Oleinik's estimates the total variation of a solution with initial data $\overline{u}\in \bf{L}^\infty(\mathbb R)$ decays like $t^{-1}$. This paper introduces a class of intermediate domains $\mathcal P_\alpha$, $0<\alpha<1$, such that for $\overline u\in \mathcal P_\alpha$ a faster decay rate is ac

Marah Halawa, Florian Blume, Pia Bideau, Martin Maier

Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that captur

Ayse Cakmak, Erik Reinertsen, Shamim Nemati, Gari D. Clifford

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance,

Jiaxin Xu, Agboola Suleiman, Gang Liu, Michael Perez

Gas separation using polymer membranes promises to dramatically drive down the energy, carbon, and water intensity of traditional thermally driven separation, but developing the membrane materials is challenging. Here, we demonstrate a novel graph machine learning (ML) strategy to guide the experimental discovery of synthesizable polymer membranes with perfo

Ilaria Petralia, Dante Minniti, José G. Fernández-Trincado, Richard R. Lane

Context. Traditionally, Globular Clusters (GCs) have been assumed to be quasi-relaxed non-rotating systems, characterized by spherical symmetry and orbital isotropy. However, in recent years, a growing set of observational evidence is unveiling an unexpected dynamical complexity in Galactic GCs. Indeed, kinematic studies show that a measurable amount of inte

Ziyi Zhou, Ming Cheng, Yanjun Cui, Xingjian Diao

The increasing number of diabetic patients is a serious issue in society today, which has significant negative impacts on people's health and the country's financial expenditures. Because diabetes may develop into potential serious complications, early glucose prediction for diabetic patients is necessary for timely medical treatment. Existing glucose predic

Mario Ghossoub, Giulio Principi, Ruodu Wang

The classical theory of efficient allocations of an aggregate endowment in a pure-exchange economy has hitherto primarily focused on the Pareto-efficiency of allocations, under the implicit assumption that transfers between agents are frictionless, and hence costless to the economy. In this paper, we argue that certain transfers cause frictions that result i

Elliot Maceda, Emily C. Hector, Amanda Lenzi, Brian J. Reich

Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate Bayesian Computing (ABC), calculate posteriors without accessing a likelihood function by leveraging the fact that data can be quickly simulated from the model, but converge slowly and/or poorly in h

Sergey A. Matveev, Ilya Tretyak

We propose an efficient implementation of the numerical tensor-train (TT) based algorithm solving the multicomponent coagulation equation preserving the nonnegativeness of solution. Unnatural negative elements in the constructed approximation arise due to the errors of the low-rank decomposition and discretization scheme. In this work, we propose to apply th

Motahhare Eslami, Eric Gilbert, Sarita Schoenebeck, Eric P. S. Baumer

Social technologies are the systems, interfaces, features, infrastructures, and architectures that allow people to interact with each other online. These technologies dramatically shape the fabric of our everyday lives, from the information we consume to the people we interact with to the foundations of our culture and politics. While the benefits of social

Vincenzo Liguori

This paper starts with a simple lossless ~1.5:1 compression algorithm for the weights of the Large Language Model (LLM) Llama2 7B [1] that can be implemented in ~200 LUTs in AMD FPGAs, processing over 800 million bfloat16 numbers per second. This framework is then extended to variable precision, variable range, compressed numerical data types that are a user

Dariusz Chruściński, Bihalan Bhattacharya

A class of unital qubit maps displaying diagonal unitary and orthogonal symmetries is analyzed. Such maps already found a lot applications in quantum information theory. We provide a complete characterization of this class of maps showing intricate relation between positivity, operator Schwarz inequality, and complete positivity. Finally, it is shown how to

Kechun Liu, Wenjun Wu, Joann G. Elmore, Linda G. Shapiro

Accurate cancer diagnosis remains a critical challenge in digital pathology, largely due to the gigapixel size and complex spatial relationships present in whole slide images. Traditional multiple instance learning (MIL) methods often struggle with these intricacies, especially in preserving the necessary context for accurate diagnosis. In response, we intro

Kali Krishna Kota, Praful D. Mankar, Harpreet S. Dhillon

This letter presents optimal beamforming and outage analysis for a Reconfigurable Intelligent Surface (RIS)-aided multiple input single output downlink system under Rician fading on both the direct and the RIS-assisted indirect links. We focus on maximizing the capacity for two transmitter architectures: fully digital (FD) and fully analog (FA). This capacit

Deepa Krishnaswamy, Bálint Kovács, Stefan Denner, Steve Pieper

With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cance

Tomas Klinavičius, Nadzeya Khinevich, Asta Tamulevičienė, Loic Vidal

Electron microscopy, while reliable, is an expensive, slow, and inefficient technique for thorough size distribution characterization of both mono- and polydisperse colloidal nanoparticles. If rapid in-situ characterization of colloid samples is to be achieved, a different approach, based on fast, widely accessible, and inexpensive optical measurements such

Rafael Arias Gonzalez, Steve DiPaola

Large language models (LLMs) hold potential for innovative HCI research, including the creation of synthetic personae. However, their black-box nature and propensity for hallucinations pose challenges. To address these limitations, this position paper advocates for using LLMs as data augmentation systems rather than zero-shot generators. We further propose t

Erim Yanik, Xavier Intes, Suvranu De

Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes. This study introduces a novel approach by leveraging deep neural networks (DNNs) to analyze and integrate both cognitive dec

Kathie Cameron, Aristotelis Chaniotis, Celina M. H. de Figueiredo, Sophie Spirkl

For a property $\mathcal{P}$ of graphs, the $\mathcal{P}$-\textsc{Sandwich-Problem}, introduced by Golumbic and Shamir (1993), is the following: Given a pair of graphs $(G_1, G_2)$ on the same vertex set $V$, does there exist a graph $G$ such that $V(G)=V$, $E(G_{1})\subseteq E(G) \subseteq E(G_{2})$, and $G$ satisfies $\mathcal{P}$? A {\em hole} in a graph

Moghis Fereidouni, Adib Mosharrof, A. B. Siddique

The development of agents powered by large language models (LLMs) to accomplish complex high-level user intents, has attracted significant attention recently. However, employing LLMs with billions of parameters (e.g., GPT-4) may incur substantial costs on top of handcrafting extensive prompts. To address this, we introduce a Grounded Language Agent for Intel

Lukas Bundrock

This paper addresses the geometric optimization problem of the first Robin eigenvalue in exterior domains, specifically the lowest point of the spectrum of the Laplace operator under Robin boundary conditions in the complement of a bounded domain. In contrast to the Laplace operator on bounded domains, the spectrum of this operator is not purely discrete. Th

Emília Halušková, Danica Jakubíková-Studenovská

A retract variety is defined as a class of algebras closed under isomorphisms, retracts and products. Let a principal retract variety be generated by one algebra and a set-principal retract variety be generated by some set of algebras. It is shown that (a) not each set-principal retract variety is principal, and (b) not each retract variety is set-principal.

Yifan Yang, Chixiang Chen, Hwiyoung Lee, Ming Wang

Alcohol consumption has been shown to influence cardiovascular mechanisms in humans, leading to observable alterations in the plasma metabolomic profile. Regression models are commonly employed to investigate these effects, treating metabolomics features as the outcomes and alcohol intake as the exposure. Given the latent dependence structure among the numer

Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang

Reinforcement learning (RL) algorithms often struggle to learn policies that generalize to novel situations due to issues such as causal confusion, overfitting to irrelevant factors, and failure to isolate control of state factors. These issues stem from a common source: a failure to accurately identify and exploit state-specific causal relationships in the

Jens Gerlach Christensen, Christopher Benjamin Deng

We classify self-adjoint first-order differential operators on weighted Bergman spaces on the $N$-dimensional unit ball $\mathbb{B}^N$ and $\mathbb{D}^2$ of $2\times2$ complex matrices satisfying $I-ZZ^*>0$.Our main tools are the discrete series representations of $\mathrm{SU}(N,1)$ and $\mathrm{SU}(2,2)$. We believe that our observations extend to general b

Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar

Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of individual gradients. We start with new near-optimal bounds for the classic mean estimation problem but with sparse data, improving upon existing algorithms particularly for the high-dimensional regime. Building on this, we obtain

Arnab Kumar Mondal, Stefano Alletto, Denis Tome

Understanding human motion from video is essential for a range of applications, including pose estimation, mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures, these approaches have limitations in practical scenarios. Transformers are slower when sequentially predicting on a continuous st

Maximilian Leitenstern, Florian Sauerbeck, Dominik Kulmer, Johannes Betz

Today's software stacks for autonomous vehicles rely on HD maps to enable sufficient localization, accurate path planning, and reliable motion prediction. Recent developments have resulted in pipelines for the automated generation of HD maps to reduce manual efforts for creating and updating these HD maps. We present FlexMap Fusion, a methodology to automati

Johannes Lawen

To integrate wave and sediment transport modeling, a computationally extensive wave-resolving Voronoi mesh-based simulation has been developed to improve upon heretofore separate sediment and spectral wave modeling. Orbital wave motion-dependent sediment transport and fine structures of the dynamic Rouse number distribution across the seabed were brought int

Letian Peng, Jingbo Shang

In this paper, we aim to generate text classification data given arbitrary class definitions (i.e., user instruction), so one can train a small text classifier without any human annotation or raw corpus. Compared with pioneer attempts, our proposed Incubator is the first framework that can handle complicated and even mutually dependent classes (e.g., "TED Ta

Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these systems often overlook a critical aspect: the evolving skill demand of the job market. This paper focuses on t

Deepak Vungarala, Mahmoud Nazzal, Mehrdad Morsali, Chao Zhang

In the ever-evolving landscape of Deep Neural Networks (DNN) hardware acceleration, unlocking the true potential of systolic array accelerators has long been hindered by the daunting challenges of expertise and time investment. Large Language Models (LLMs) offer a promising solution for automating code generation which is key to unlocking unprecedented effic

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Aihara

We measure the branching fraction of the decay $B^- \to D^0 \rho(770)^-$ using data collected with the Belle II detector. The data contain 387 million $B\overline{B}$ pairs produced in $e^+e^-$ collisions at the $\Upsilon(4S)$ resonance. We reconstruct $8360\pm 180$ decays from an analysis of the distributions of the $B^-$ energy and the $\rho(770)^-$ helici

Alireza S Golsefidy, Keivan Mallahi-Karai, Amir Mohammadi

We prove the spectral gap property for random walks on the product of two non-locally isomorphic analytic real or p-adic compact groups with simple Lie algebras, under the necessary condition that the marginals posses a spectral gap. Furthermore, we give additional control on the spectral gap depending on certain specific properties of the given groups and m

Sheridan J. Lloyd, Paula M. Chadwick, Anthony M. Brown

We analyse 13 yrs of $\textit{Fermi}$-LAT PASS 8 events from 127 gamma-ray emitting millisecond pulsars (MSPs) in the energy range 0.1$-$100 GeV and significantly detect 118 MSPs. We fit the stacked emission with a log parabola (LP) spectral model which we show is preferred to two previously published models. We consider the influence of pulsar properties an

Ijaz Ahmed, Sidra Swalheen, Mansoor Ur Rehman, Rimsha Tariq

In the grand tapestry of Physics, the magnetic monopole is a holy grail. Therefore, numerous efforts are underway in search of this hypothetical particle at CMS, ATLAS and MoEDAL experiments of LHC by employing different production mechanisms. The cornerstone of our comprehension of monopoles lies in Dirac's theory which outlines their characteristics and dy

Martin Kassabov, Igor Pak

We construct a new large family of finitely generated groups with continuum many values of the following monotone parameters: spectral radius, critical percolation, and asymptotic entropy. We also present several open problems on other monotone parameters.

Vytene Janiukstyte, Csaba Kozma, Thomas W. Owen, Umair J Chaudhury

EEG slowing is reported in various neurological disorders including Alzheimer's, Parkinson's and Epilepsy. Here, we investigate alpha rhythm slowing in individuals with refractory temporal lobe epilepsy (TLE), compared to healthy controls, using scalp electroencephalography (EEG) and magnetoencephalography (MEG). We retrospectively analysed data from 17,(46)

Rudi Gaelzer, Horst Fichtner, Klaus Scherer

In this work we define the plasma dispersion function for a suprathermal plasma described with a regularized kappa distribution. As is known from Maxwellian as well as (standard) kappa plasmas, the respective Fried-Conte and the modified plasma dispersion functions are valuable tools for various analytical studies. For the latter is has been proven very usef

A. E. Calderón, E. Villar-Sepúlveda

We generalize the definition of topological entropy given by Adler, Konheim, and McAndrew (AKM) for piecewise continuous self-maps defined on a compact interval (pc-maps). For this notion of entropy, we prove that the properties of the AKM-entropy in the compact-continuous setting get naturally extended, including that it can be estimated using Bowen's formu

Joseph W. Fowler, Paul Szypryt, Raymond Bunker, Ellen R. Edwards

Naturally occurring background radiation is a source of correlated decoherence events in superconducting qubits that will challenge error-correction schemes. To characterize the radiation environment in an unshielded laboratory, we performed broadband, spectroscopic measurements of background events in silicon substrates located inside a millikelvin refriger

Matthew Inkawhich, Nathan Inkawhich, Hao Yang, Jingyang Zhang

An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work in open object detection today is disjointed and fails to adequately address applications that prioritize unknown object recall \textit{in addition to} known-class accuracy. To clos

Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota

Large vision-language models revolutionized image classification and semantic segmentation paradigms. However, they typically assume a pre-defined set of categories, or vocabulary, at test time for composing textual prompts. This assumption is impractical in scenarios with unknown or evolving semantic context. Here, we address this issue and introduce the Vo

Jiayin Lu, Chris H. Rycroft

Hypo-elastoplasticity is a framework suitable for modeling the mechanics of many hard materials that have small elastic deformation and large plastic deformation. In most laboratory tests for these materials the Cauchy stress is in quasi-static equilibrium. Rycroft et al. discovered a mathematical correspondence between this physical system and the incompres

Daniel Chabeda, Stephen Gee, Eran Rabani

Recent experiments have probed the chiral properties of semiconductor nanocrystal (NC) quantum dots (QDs), but understanding the circular dichroism lineshape, excitonic features, and ligand induction mechanism remains a challenge. We propose an atomistic pseudopotential method to model chiral ligand passivated QDs, computing circular dichroism (CD) spectra f

Matthew Andres Moreno, Connor Yang, Emily Dolson, Luis Zaman

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 8

Daebeom Choi

Using representations of vertex operator algebras, we describe the line bundles on a wide range of contractions of $\overline{\rm{M}}_{0,n}$, the moduli space of stable $n$-pointed rational curves, by proving a stronger version of the contraction theorem for these morphisms. These include the celebrated constructions of Kapranov, Keel, and Knudsen. Our main

Yiming Zhang, Avi Schwarzschild, Nicholas Carlini, Zico Kolter

Despite being trained specifically to follow user instructions, today's instructiontuned language models perform poorly when instructed to produce random outputs. For example, when prompted to pick a number uniformly between one and ten Llama-2-13B-chat disproportionately favors the number five, and when tasked with picking a first name at random, Mistral-7B

Ayah Almousa, Victor Reiner, Sheila Sundaram

Supersolvable hyperplane arrangements and matroids are known to give rise to certain Koszul algebras, namely their Orlik-Solomon algebras and graded Varchenko-Gel'fand algebras. We explore how this interacts with group actions, particularly for the braid arrangement and the action of the symmetric group, where the Hilbert functions of the algebras and their

Aida Mostafazadeh Davani, Mark Díaz, Dylan Baker, Vinodkumar Prabhakaran

While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that have critically examined this issue are often situated in the Western context, and solely document differences across age, gender, or racial groups. As a result, NLP research on subjectivity have overlo

Henry Marichal, Diego Passarella, Christine Lucas, Ludmila Profumo

The automatic detection of tree-ring boundaries and other anatomical features using image analysis has progressed substantially over the past decade with advances in machine learning and imagery technology, as well as increasing demands from the dendrochronology community. This paper presents a publicly available database of 64 scanned images of transverse s

Christopher Helmerich, Jared Fuchs, Alexey Bobrick, Brandon Melcher

The last few decades of warp drive research have focused on analytic methods to explore warp solutions to Einstein's field equations. These analytic solutions tend to favor simple metric forms which are easier to analyze but limit the space of exploration. In addition, all solutions to date have involved unphysical qualities, such as negative energy, violati

Matthew Andres Moreno

Complexity is a signature quality of interest in artificial life systems. Alongside other dimensions of assessment, it is common to quantify genome sites that contribute to fitness as a complexity measure. However, limitations to the sensitivity of fitness assays in models with implicit replication criteria involving rich biotic interactions introduce the po

Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Auralee Edelen, Eric Wisniewski

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic technique

E. Chalbaud, Rui M. Silva, António Onofre, Ricardo Gonçalo

In this paper, we propose two sets of different CP-sensitive observables inspired by the Higgs production in association with the top quark. We employ a Dark Matter simplified model that couples a scalar mediator with top quarks. The reconstruction of the kinematic variables is presented at NLO accuracy for events associated with this massive scalar particle

Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard

We provide the first known algorithm that provably achieves $\varepsilon$-optimality within $\widetilde{\mathcal{O}}(1/\varepsilon)$ function evaluations for the discounted discrete-time LQR problem with unknown parameters, without relying on two-point gradient estimates. These estimates are known to be unrealistic in many settings, as they depend on using t

Brian Lai, Dennis S. Bernstein

Discrete-time input/output models, also called infinite impulse response (IIR) models or autoregressive moving average (ARMA) models, are useful for online identification as they can be efficiently updated using recursive least squares (RLS) as new data is collected. Several works have studied the convergence of the input/output model coefficients identified

Satya R. Jaladi, Zhimin Chen, Narahari R. Malayanur, Raja M. Macherla

The current autonomous stack is well modularized and consists of perception, decision making and control in a handcrafted framework. With the advances in artificial intelligence (AI) and computing resources, researchers have been pushing the development of end-to-end AI for autonomous driving, at least in problems of small searching space such as in highway

Wiam Adnan, Joel Tang, Yassine Bel Khayat Zouggari, Seif Edinne Laatiri

Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and hence, several multimodal models were developed in the field of Visual Document Understanding (VDU). However, while research is mainly focused on Key Information Extraction (KIE), Re

R. F. Haack, A. V. Smith Castelli, C. Mendes de Oliveira, F. Almeida-Fernandes

All-sky multi-band photometric surveys represent a unique opportunity of exploring rich nearby galaxy clusters up to several virial radii, reaching the filament regions where pre-processing is expected to occur. These projects aim to tackle a large number of astrophysical topics, encompassing both the galactic and extragalactic fields. In that sense, generat

Ósmar Rodríguez, Ehud Nakar, Dan Maoz

The luminosity of ``stripped-envelope supernovae'', a common type of stellar explosions, has been generally thought to be driven by the radioactive decay of the nickel synthesized in the explosion and carried in its ejecta. Additional possible energy sources have been previously suggested, but these claims have been statistically inconclusive or model-depend

Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas

This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary a

Brian Lai, Dennis S. Bernstein

This paper presents subspace of information forgetting recursive least squares (SIFt-RLS), a directional forgetting algorithm which, at each step, forgets only in row space of the regressor matrix, or the \textit{information subspace}. As a result, SIFt-RLS tracks parameters that are in excited directions while not changing parameter estimation in unexcited

Blaine Quackenbush, Paul J. Atzberger

We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators. We show how GNPs can be used (i) to estimate geometric properties, such as the metric and curvatures, (ii) to approximate Partial Differential Equations (PDEs) on manifolds, (iii) learn solution maps for Laplace-Beltrami (LB) o

Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas

This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. The work proposes a Federated Learning model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Fede