February 2024 arXiv papers — page 56

Shanker Ram, Chen Qian

ChatGPT is a chatbot that can answer text prompts fairly accurately, even performing very well on postgraduate-level questions. Many educators have found that their take-home or remote tests and exams are vulnerable to ChatGPT-based cheating because students may directly use answers provided by tools like ChatGPT. In this paper, we try to provide an answer t

Crystal Qian, Emily Reif, Minsuk Kahng

As large language models (LLMs) become more advanced and impactful, it is increasingly important to scrutinize the data that they rely upon and produce. What is it to be a dataset practitioner doing this work? We approach this in two parts: first, we define the role of "dataset practitioners" by performing a retrospective analysis on the responsibilities of

Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

Recent research has demonstrated that transformers, particularly linear attention models, implicitly execute gradient-descent-like algorithms on data provided in-context during their forward inference step. However, their capability in handling more complex problems remains unexplored. In this paper, we prove that each layer of a linear transformer maintains

MD Ashraful Goni, Fahad Mostafa, Kerk F. Kee

Ethnic media, which caters to diaspora communities in host nations, serves as a vital platform for these communities to both produce content and access information. Rather than utilizing the language of the host nation, ethnic media delivers news in the language of the immigrant community. For instance, in the USA, Bangla ethnic media presents news in Bangla

Cemal Tugrul Yilmaz, Mamadou Diagne, Miroslav Krstic

This paper introduces extremum seeking (ES) algorithms designed to achieve perfect tracking of arbitrary time-varying extremum. In contrast to classical ES approaches that employ constant frequencies and controller gains, our algorithms leverage time-varying parameters, growing either asymptotically or exponentially, to achieve desired convergence behaviors.

Nadav Borenstein, Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein

Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To study the dynamics of communities online, we propose a method to computationally analyse values present on Reddit. Our method allows analysis at scale, complementing survey based appro

Nikhilesh Maity, Milan Haddad, Nazanin Bassiri-Gharb, Amit Kumar

Size-driven transition of an antiferroelectric into a polar ferroelectric or ferrielectric state is a strongly debated issue from both experimental and theoretical perspectives. While critical thickness limits for such transitions have been explored, a bottom-up approach in the ultrathin limit considering few atomic layers could provide insight into the mech

Priyanka Rao, Oren Salzman, Jessica Burgner-Kahrs

Tendon-driven continuum robots (TDCRs), with their flexible backbones, offer the advantage of being used for navigating complex, cluttered environments. However, to do so, they typically require multiple segments, often leading to complex actuation and control challenges. To this end, we propose a novel approach to navigate cluttered spaces effectively for a

Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang

As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question. Existing dynamic game formulations assume all agents are task-driven and

Sean P. Goggins, Kevin Lumbard, Matt Germonprez, Caifan Du

Sociotechnical research increasingly includes the social sub-networks that emerge from large-scale sociotechnical infrastructure, including the infrastructure for building open source software. This paper addresses these numerous sub-networks as advantageous for researchers. It provides a methodological synthesis focusing on how researchers can best span adj

Christiane Klein, Mojgan Soltani, Marc Casals, Stefan Hollands

We present a numerical calculation of the expectation value of the quantum angular-momentum current flux density for a scalar field in the Unruh state near the inner horizon of a Kerr-de Sitter black hole. Our results indicate that this flux diverges as $V_-^{-1}$ in a suitable Kruskal coordinate such that $V_-=0$ at the inner horizon. Depending on the param

Yue Cao, Naihuan Jing, Yiling Wang

This research offers a comprehensive approach to strengthening both monogamous and polygamous relationships within the context of quantum correlations in multipartite quantum systems. We present the most stringent bounds for both monogamy and polygamy in multipartite systems compared to recently established relations. We show that whenever a bound is given (

Omer Nivron, Damon J. Wischik, Mathieu Vrac, Emily Shuckburgh

Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly disregard the dependence between consecutiv

J. Wijnen, J. P. M. Hoefnagels, M. G. D. Geers, R. H. J. Peerlings

The slip system activity in microtensile tests of ferrite single crystals is compared with predictions made by the discrete slip plane model proposed by Wijnen et al. (International Journal of Solids and Structures 228, 111094, 2021). This is an extension of conventional crystal plasticity in which the stochastics and physics of dislocation sources are taken

Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie

Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we introduce sampling Trajectory Stitching T-Stitch, a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using

Francisco Barranco, Gregory Potel, Enrico Vigezzi

We present a theoretical framework for treating the full excitation spectrum of J{\pi} = 0+ pair addition modes, including the well-known low-lying and bound Pairing Vibration on par with the predicted Giant Pairing Vibration lying in the continuum. Our formalism includes the coupling to low-energy collective quadrupole modes of the core, in such a way that

Aleksandr M. Skliarevskii, Eduard I. Vorobyov

FU Orionis type objects (fuors) are characterized by rapid (tens to hundreds years) episodic outbursts, during which the luminosity increases by orders of magnitude. One of the possible causes of such events is a close encounter between stars and protoplanetary disks. Numerical simulations show that the fuor-like outburst ignition requires a very close encou

Nikita Misuna

We put forward a novel method of constructing unfolded formulations of field theories, which is based on initial fixation of the form of an unfolded field and subsequent looking for the corresponding unfolded equation as an identity that this field satisfies. Making use of this method, we find an unfolded formulation for 4d scalar electrodynamics. By conside

Boris Širola

We introduce an interesting and rather large class of monoid homomorphisms, on arbitrary integral domain $R$, that we call Dumas valuations. Then we formulate a conjecture addressing the question asking when a polynomial $f\in R[X]$ cannot be written as a product $f=gh$ for some nonconstant polynomials $g,h\in R[X]$. The statement of the conjecture presents

Minh-Hao Van, Prateek Verma, Xintao Wu

Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilities with multimodal data. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks. Consequently, t

Dan Pirjol, Lingjiong Zhu

We derive the short-maturity asymptotics for option prices in the local volatility model in a new short-maturity limit $T\to 0$ at fixed $\rho = (r-q) T$, where $r$ is the interest rate and $q$ is the dividend yield. In cases of practical relevance $\rho$ is small, however our result holds for any fixed $\rho$. The result is a generalization of the Berestyck

Wonseok Jeon, Mukul Gagrani, Raghavv Goel, Junyoung Park

Speculative decoding is an inference-acceleration method for large language models (LLMs) where a small language model generates a draft-token sequence which is further verified by the target LLM in parallel. Recent works have advanced this method by establishing a draft-token tree, achieving superior performance over a single-sequence speculative decoding.

Adil Rasheed, Florian Stadtmann, Eivind Fonn, Mandar Tabib

NorthWind, a collaborative research initiative supported by the Research Council of Norway, industry stakeholders, and research partners, aims to advance cutting-edge research and innovation in wind energy. The core mission is to reduce wind power costs and foster sustainable growth, with a key focus on the development of digital twins. A digital twin is a v

Yuhan Liu, Amna Liaqat, Owen Xingjian Zhang, Mariana Consuelo Fernández Espinosa

Beyond the well-known giants like Uber Eats and DoorDash, there are hundreds of independent food delivery platforms in the United States. However, little is known about the sociotechnical landscape of these ``indie'' platforms. In this paper, we analyzed these platforms to understand why they were created, how they operate, and what technologies they use. We

Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu

Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learning to use specific tools via fine-tuning, language models still struggle with learning how to robustly use new tools from only a few demonstrations. In this work we introduce a se

Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian

Reconfigurable intelligent surface (RIS) have introduced unprecedented flexibility and adaptability toward smart wireless channels. Recent research on integrated sensing and communication (ISAC) systems has demonstrated that RIS platforms enable enhanced signal quality, coverage, and link capacity. In this paper, we explore the application of fully-connected

Nam Nguyen, Richard Thompson

Maxwells equations are fundamental to our understanding of electromagnetic fields, but their solution can be computationally demanding, even for high-performance computing clusters. Quantum computers offer a promising alternative for solving these equations, as they can simulate larger and more complex systems more efficiently both in time and resources. In

Amogh Mannekote, Xiaoyi Tian, Kristy Elizabeth Boyer, Bonnie J. Dorr

Task-oriented dialogue systems are expected to handle a constantly expanding set of intents and domains even after they have been deployed to support more and more functionalities. To live up to this expectation, it becomes critical to mitigate the catastrophic forgetting problem (CF) that occurs in continual learning (CL) settings for a task such as intent

Emily Reif, Crystal Qian, James Wexler, Minsuk Kahng

Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific: instruments and

Yiqiao Jin, Minje Choi, Gaurav Verma, Jindong Wang

Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. Multimodal Large Language Models (MLLMs) have emerged as a promising solution to these challenges, yet they struggle to accurate

Avner Ash, Paul E. Gunnells, Mark McConnell

Denote the virtual cohomological dimension of SL_n(Z) by t=n(n-1)/2. Let St denote the Steinberg module of SL_n(Q) tensored with Q. Let Sh_* denote the sharbly resolution of the Steinberg module St. By Borel-Serre duality, the one-dimensional Q-vector space H^0(SL_n(Z), Q) is isomorphic to H_t(SL_n(Z),St). We find an explicit generator of H_t(SL_n(Z),St) in

Jonathan Ku, Junyao Zhang, Haoxuan Shan, Saichand Samudrala

Elliptic curve cryptography (ECC) is widely used in security applications such as public key cryptography (PKC) and zero-knowledge proofs (ZKP). ECC is composed of modular arithmetic, where modular multiplication takes most of the processing time. Computational complexity and memory constraints of ECC limit the performance. Therefore, hardware acceleration o

Xiaoyue Wang, Jianyou Wang, Weili Cao, Kaicheng Wang

We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO). BIRCO evaluates the ability of IR systems to retrieve documents given multi-faceted user objectives. The benchmark's complexity and compact size make it suitable for evaluating large language model (LLM)-based information retrieval systems. We present a modular fram

Robert Warren, Stephen Warren, Ekaterini Protopapa

The giant Chinguetti meteorite that Gaston Ripert reported seeing in 1916 has never been found. A radionuclide analysis by Welten et al (2001) of the 4.5kg mesosiderite that Ripert recovered, supposedly sitting on the larger object, has convinced many that Ripert was mistaken, and interest in the giant meteorite has subsequently faded. Aspects of Ripert's ac

Likai Jiao

We study properties of the \textit{continuum seed-bank coalescent} proposed by \cite{jiao2023wright} such as \textit{not coming down from infinity} and \textit{bounds on the expected time to the most recent common ancestor}. We also provide results on the limiting distributions and propose some interesting open problems.

Gaëtan Cortes, Joaquin Garcia-Suarez

In this concise contribution, it is demonstrated that Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) architecture, possess the capability to learn the complex dynamics of rate-and-state friction (RSF) laws from synthetic data. The data employed for training the network is generated through the application of traditional RSF equations co

Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng, Jiwoo Kim

AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of evaluation datasets for AI that impacts them

Karin de Langis, Ryan Koo, Dongyeop Kang

Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust). An open question is how language models can be explicitly controlled so that they weave together target styles when generating text: for example, to produce text that is both negative and non-toxic.

Steven Wilkins-Reeves, Xu Chen, Qi Ma, Christine Agarwal

Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We focus on scenarios where data distributions vary across multiple segments of the entire population and only make local assumptions about the differences between training and test (d

Eduardo Mapurunga, Michel Gevers, Alexandre S. Bazanella

This paper deals with the design of Excitation and Measurement Patterns (EMPs) for the identification of dynamical networks, when the objective is to identify only a subnetwork embedded in a larger network. Recent results have shown how to construct EMPs that guarantee identifiability for a range of networks with specific graph topologies, such as trees, loo

Rishabh Bajpai, Bhooma Aravamuthan

Movement disorder diagnosis often relies on expert evaluation of patient videos, but sharing these videos poses privacy risks. Current methods for de-identifying videos, such as blurring faces, are often manual, inconsistent, or inaccurate. Furthermore, these methods can compromise objective kinematic analysis - a crucial component of diagnosis. To address t

B. Wojtsekhowski, E. J. Brash, G. B. Franklin, A. Rosso

A light guide is an essential part of many scintillator counters and light collection systems. Our main interest is a light guide for a thin wide scintillator which has high light transmission while converting the area of the light source to the shape of a photo-detector. We propose a variation of the light guide which avoids a 90$\rm ^o$ twist of the strips

Matthieu Mambrini, Didier Poilblanc

In condensed matter, Chiral Spin Liquids (CSL) are quantum spin analogs of electronic Fractional Quantum Hall states (in the continuum) or Fractional Chern Insulators (on the lattice). As the latter, CSL are remarquable states of matter, exhibiting topological order and chiral edge modes. Preparing CSL on quantum simulators like cold atom platforms is still

Jan von der Assen, Muriel F. Franco, Muyao Dong, Burkhard Stiller

Threat modeling has emerged as a key process for understanding relevant threats within businesses. However, understanding the importance of threat events is rarely driven by the business incorporating the system. Furthermore, prioritization of threat events often occurs based on abstract and qualitative scoring. While such scores enable prioritization, they

Dhananjay Saikumar, Blesson Varghese

Efficient on-device Convolutional Neural Network (CNN) training in resource-constrained mobile and edge environments is an open challenge. Backpropagation is the standard approach adopted, but it is GPU memory intensive due to its strong inter-layer dependencies that demand intermediate activations across the entire CNN model to be retained in GPU memory. Th

Ioannis Argyrokastritis, Konstantinos Kalimeris, Leonidas Mindrinos

In this study, we derive an analytical solution to address the problem of one-dimensional vertical infiltration within bounded profiles. We consider the Richards equation together with various boundary conditions, simulating different scenarios of water application onto the surface of a homogeneous and bounded medium. To solve the corresponding initial bound

Alfredo Raya, Cristian Villavicencio

In this article we explore the effect of chemical potential at zero temperature in the implementation of in-medium effects in the perturbative sector in finite energy sum rules. For this purpose, we explore the axial, axial-pseudoscalar and pseudoscalar current correlators involving charged pions. The inclusion of non-normal ordered condensates with chemical

Lawrence J. Klinkert, Stephanie Buongiorno, Corey Clark

To enhance immersion and engagement in video games, the design of Affective Non-Player Characters (ANPCs) is a key focus for researchers and practitioners. Affective Computing frameworks improve Non-player characters (NPC) by providing personalities, emotions, and social relations. Large Language Models (LLMs) bring the promise to dynamically enhance charact

Ho Lyun Jeong, Ziqi Wang, Colin Samplawski, Jason Wu

Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, w

K. Iwasawa, T. Liu, Th. Boller, J. Buchner

We selected sources with a steep soft-X-ray-band spectrum with a photon index larger than 2.5 -- measured by eROSITA on board the Spectrum-Roentgen-Gamma (SRG) -- from the eFEDS AGN catalogue as candidates of highly accreting supermassive black holes, and investigated their multi-wavelength properties. Among 601 bright AGN with 0.2-5 keV counts of greater th

M. A. Figueroa, Vladimir Juricic, P. A. Orellana

Whispering gallery resonators offer a versatile platform for manipulating the photonic transmission. Here, we study such a system, including periodically distributed pointlike impurities along the resonator with ring geometry. Based on an exact expression for the transmission probability we obtain here, we demonstrate that the bound states in the continuum (

Maryam Saem, Ralph Brinks

We use the illness-death model (IDM) for chronic conditions to derive a new analytical relation between the transition rates between the states of the IDM. The transition rates are the incidence rate (i) and the mortality rates of people without disease (m0) and with disease (m1). For the most generic case, the rates depend on age, calendar time and in case

Khalfa Douak

We investigate the symmetric Dunkl-classical orthogonal polynomials by using a new approach applied in connection with the Dunkl operator. The main aim of this technique is to determine the recurrence coefficients first and foremost. We establish the existence and uniqueness of symmetric Dunkl-classical orthogonal polynomials, and also confirm that only two

Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai

It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a pos

Zihao Chen, Faiek Ahsan, Johannes Leugering, Gert Cauwenberghs

Neuromorphic or neurally-inspired optimizers rely on local but parallel parameter updates to solve problems that range from quadratic programming to Ising machines. An ideal realization of such an optimizer not only uses a compute-in-memory (CIM) paradigm to address the so-called memory-wall (i.e. energy dissipated due to repeated memory read access), but al

Andrei Jaikin-Zapirain, Henrique Souza

Let $G$ be a finitely generated torsion-free pro-$p$ group containing an open free-by-$\mathbb{Z}_p$ pro-$p$ subgroup. We show that the completed group algebra of $G$ over $\mathbb{F}_p$ is a Sylvester domain. Moreover the inner rank of a matrix $A$ over this completed group algebra can be calculated by approximation by ranks corresponding to finite quotient

Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi, Zheng-Meng Zhai

Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state nature of the dynamics about a tipping point makes its prediction significantly more challenging than predicting other types of critical transitions from oscillatory or c

Zhi Hong, Kyle Chard, Ian Foster

Relation extraction is an efficient way of mining the extraordinary wealth of human knowledge on the Web. Existing methods rely on domain-specific training data or produce noisy outputs. We focus here on extracting targeted relations from semi-structured web pages given only a short description of the relation. We present GraphScholarBERT, an open-domain inf

Blake Fernandino, Moein Samak Bisheh

Timely detection of illnesses is vital to prevent severe infections and ensure effective treatment, as it's always better to prevent diseases than to cure them. Sadly, many patients remain undiagnosed until their conditions worsen, resulting in high death rates. Expert systems offer a solution by automating early-stage diagnoses using a fuzzy rule-based appr

Ziyad AlSharawi, Jose S. Cánovas, Sadok Kallel

We consider $k$-dimensional discrete-time systems of the form $x_{n+1}=F(x_n,\ldots,x_{n-k+1})$ in which the map $F$ is continuous and monotonic in each one of its arguments. We define a partial order on $\mathbb{R}^{2k}_+$, compatible with the monotonicity of $F$, and then use it to embed the $k$-dimensional system into a $2k$-dimensional system that is mon

Rasool Hafezi, Abdolnaser Bahlekeh

Let $\Lambda$ be an Artin algebra and ${\mathsf{mod}}\mbox{-} ({\underline{\mathsf{Gprj}}}\mbox{-}\Lambda)$ the category of finitely presented functors over the stable category ${\underline{\mathsf{Gprj}}}\mbox{-}\Lambda$ of finitely generated Gorenstein projective $\Lambda$-modules. This paper deals with those algebras $\Lambda$ in which ${\mathsf{mod}}\mbo

Joel E. Restrepo, Michael Ruzhansky, Berikbol T. Torebek

We first study the existence, uniqueness and well-posedness of a general class of integro-differential diffusion equation on $L^p(\mathbb{G})$ $(1<p<+\infty$, $\mathbb{G}$ is a graded Lie group). We show the explicit solution of the considered equation by using the Fourier analysis of the group. The equation involves a nonlocal in time operator (with a gener

Michiharu Yamashita, Thanh Tran, Dongwon Lee

While recent studies have exposed various vulnerabilities incurred from data poisoning attacks in many web services, little is known about the vulnerability on online professional job platforms (e.g., LinkedIn and Indeed). In this work, first time, we demonstrate the critical vulnerabilities found in the common Human Resources (HR) task of matching job seeke

Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami

Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex scenes, humans instinctively rely on deictic descriptions in natural language, i.e., referring to something depending on the context such as "The object that is on the desk and behind

Noah A. W. Walton, Oleksii Zivenko, William Fritsch, Jacob Forbes

Global and national efforts to deliver high-quality nuclear data to users have a broad impact across applications such as national security, reactor operation, basic science, medical fields, and more. Cross section evaluation is a large part this effort as it combines theory and experiment to produce suggested values and uncertainty for reaction probabilitie

Aman Kar, Todd J. Henry, Andrew A. Couperus, Eliot Halley Vrijmoet

We present the optical photometric variability of 32 planet-hosting M dwarfs within 25 parsecs over timescales of months to decades. The primary goal of this project, ATLAS -- A Trail to Life Around Stars, is to follow the trail to life by revealing nearby M dwarfs with planets that are also "quiet", which may make them more amiable to habitability. There ar

Colette Johnen, Adnane Khattabi, Alessia Milani, Jennifer L. Welch

Relaxing the sequential specification of a shared object is a way to obtain an implementation with better performance compared to implementing the original specification. We apply this approach to the Counter object, under the assumption that the number of times the Counter is incremented in any execution is at most a known bound $m$. We consider the $k$-mul

Stefan Meinecke, Benjamin Lingnau, André Röhm, Kathy Lüdge

Simultaneous two-state lasing is a unique property of semiconductor quantum-dot (QD) lasers. This not only changes steady-state characteristics of the laser device but also its dynamic response to perturbations. In this paper we investigate the dynamic stability of QD lasers in an external optical injection setup. Compared to conventional single-state laser

Brian Wheatman, Meghana Madhyastha, Randal Burns

Artificial intelligence workloads, especially transformer models, exhibit emergent sparsity in which computations perform selective sparse access to dense data. The workloads are inefficient on hardware designed for dense computations and do not map well onto sparse data representations. We build a vectorized and parallel matrix-multiplication system A X B =

Rui Xu, Sven Bestmann, Bradley E. Treeby, Eleanor Martin

Focused ultrasound spinal cord neuromodulation studies have demonstrated spinal cord neuromodulation in small animals. The safe and efficacious translation of these approaches to human scale requires an understanding of ultrasound propagation and heat deposition within the human spine. To address this, combined acoustic and thermal modelling was used to asse

Andrew Zhu, Alyssa Hwang, Liam Dugan, Chris Callison-Burch

One type of question that is commonly found in day-to-day scenarios is ``fan-out'' questions, complex multi-hop, multi-document reasoning questions that require finding information about a large number of entities. However, there exist few resources to evaluate this type of question-answering capability among large language models. To evaluate complex reason

Oleksandr Tomalak, Minerba Betancourt, Kaushik Borah, Richard J. Hill

New physics contributions to the (anti)neutrino-nucleon elastic scattering process can be constrained by precision measurements, with controlled Standard Model uncertainties. In a large class of new physics models, interactions involving charged leptons of different flavor can be related, and the large muon flavor component of accelerator neutrino beams can

Hugo Figueiras, Helena Aidos, Nuno Cruz Garcia

Self-supervised learning has proven to be an effective way to learn representations in domains where annotated labels are scarce, such as medical imaging. A widely adopted framework for this purpose is contrastive learning and it has been applied to different scenarios. This paper seeks to advance our understanding of the contrastive learning framework by ex

Ryan R. Martin, Nick Veldt

Given a set $X$, a collection $\mathcal{F} \subset \mathcal{P}(X)$ is said to be $k$-Sperner if it does not contain a chain of length $k+1$ under set inclusion and it is saturated if it is maximal with respect to this probability. Gerbner et al. proved that the smallest saturated $k$-Sperner system contains at least $2^{k/2-1}$ elements, and later, Morrison,

Robin Durand, Louis-Thomas Gendron, Théo Nathaniel Dionne, Ion Garate

The miniaturization of integrated circuits is facing an obstruction due to the escalating electrical resistivity of conventional copper interconnects. The underlying reason for this problem was unveiled by Fuchs and Sondheimer, who showed that thinner wires are more resistive because current-carrying electrons encounter the rough surfaces of the wire more fr

Giuseppe Pipitò, Emanuele Macca

Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often involving advanced studies on past campaigns to extract insights that aiding companies in optimizing their crowdfunding project

Shaimaa K. El-Baklish, Anastasios Kouvelas, Michail A. Makridis

Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today's roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic

Aatif Kaisar Khan, Yasir Hassan Dar, Elias C. Vagenas, Salman Sajad Wani

In this work, we show that quantum state discrimination can be modified due to a change in the underlying topology of a system. In particular, we explicitly demonstrate that the quantum state discrimination of systems with underlying discrete topology differs from that of systems with underlying continuous topology. Such changes in the topology of a spacetim

Juan P. Beltrán Almeida, Alejandro Guarnizo, Thiago S. Pereira, César A. Valenzuela-Toledo

We introduce a multifield dark energy model with a nonflat field-space metric, in which one field is dynamical while the others have constant spatial gradients. The model is predictive at the background level, leading to an early dark energy component at high redshifts and a suppressed fraction of late-time anisotropy. Both features have simple expressions i

J. Nagl, D. Flavián, S. Hayashida, K. Yu. Povarov

We present a thorough experimental investigation on single crystals of the rare-earth based frustrated quantum antiferromagnet Pr$_3$BWO$_9$, a purported spin-liquid candidate on the breathing kagome lattice. This material possesses a disordered ground state with an unusual excitation spectrum involving a coexistence of sharp spin-waves and broad continuum e

Daniel G. Boettger, Shane R. Keating, Michael L. Banner, Russel P. Morison

The breaking of surface gravity waves is a key process contributing to air-sea fluxes and turbulent ocean mixing. The highly nonlinear nature of wave breaking, combined with the challenges of observing this process in a laboratory or field setting, leaves our understanding of the energetic processes underpinning wave breaking incomplete. Progress towards ref

Dimitris Dermanis, Panagiotis Rizomiliotis, Adonis Bogris, Charis Mesaritakis

In this work we provide numerical results concerning a silicon-on-insulator photonic neuromorphic circuit configured as a physical unclonable function. The proposed scheme is enhanced with the capability to be operated as an unconventional deterministic pseudo-random number generator, suitable for cryptographic applications that alleviates the need for key s

Chen Wang, Kathryn Mohror, Marc Snir

The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes a performance penalty that becomes more significant as the

Xiang Li, J. Huang, J. E. Thomas

We measure universal temperature-independent density shifts for the thermal conductivity $\kappa_T$ and shear viscosity $\eta$, relative to the high temperature limits, for a normal phase unitary Fermi gas confined in a box potential. We show that a time-dependent kinetic theory model enables extraction of the hydrodynamic transport times $\tau_\eta$ and $\t

Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel

We study computational-statistical gaps for improper learning in sparse linear regression. More specifically, given $n$ samples from a $k$-sparse linear model in dimension $d$, we ask what is the minimum sample complexity to efficiently (in time polynomial in $d$, $k$, and $n$) find a potentially dense estimate for the regression vector that achieves non-tri

Luciano Dyballa, Samuel Lang, Alexandra Haslund-Gourley, Eviatar Yemini

The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, beha

Preni Golazizian, Alireza S. Ziabari, Ali Omrani, Morteza Dehghani

In subjective NLP tasks, where a single ground truth does not exist, the inclusion of diverse annotators becomes crucial as their unique perspectives significantly influence the annotations. In realistic scenarios, the annotation budget often becomes the main determinant of the number of perspectives (i.e., annotators) included in the data and subsequent mod

Yan Dolinsky, Doron Greenstein

In this note we consider the maximization of the expected terminal wealth for the setup of quadratic transaction costs. First, we provide a very simple probabilistic solution to the problem. Although the problem was largely studied, as far as we know up to date this simple and probabilistic form of the solution has not appeared in the literature. Next, we ap

Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Rui Zhang

Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which accounts for 87% of diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient's survival rate and reduces the mortality rate. Accurate tumor segmentation is a critic

Roy Friedman, Yair Weiss

Modern GANs achieve remarkable performance in terms of generating realistic and diverse samples. This has led many to believe that ``GANs capture the training data manifold''. In this work we show that this interpretation is wrong. We empirically show that the manifold learned by modern GANs does not fit the training distribution: specifically the manifold d

Francesco Giacosa

In these lectures, we present the behavior of conventional $\bar{q}q$ mesons, glueballs, and hybrids in the large-$N_{c}$ limit of QCD. To this end, we use an approach based on rather simple NJL-like bound-state equations. The obtained large-$N_{c}$ scaling laws are general and coincide with the known results. A series of consequences, such as the narrowness

Yifan Zhang, Jiliang Li, Zachary Karas, Aakash Bansal

Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural repre

Evan Gerritz, Luciano Dyballa, Steven W. Zucker

Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and a

Shimeles Terefe Mengistue, Paola Marziani, Ascensión del Olmo, Mirjana Pović

Context. An optically thick, geometrically thin accretion disk (AD) around a supermassive black hole might contribute to broad-line emission in type-1 active galactic nuclei (AGN). However, emission line profiles are most often not immediately consistent with the profiles expected from a rotating disk. The extent to which an AD in AGN contributes to the broa

Sean Dewar, Anthony Nixon, Andrew Sainsbury

We consider the rigidity and global rigidity of bar-joint frameworks in Euclidean $d$-space under additional dilation constraints in specified coordinate directions. In this setting we obtain a complete characterisation of generic rigidity. We then consider generic global rigidity. In particular, we provide an algebraic sufficient condition and a weak necess

Virgile Dandoy, Toni Bertólez-Martínez, Francesco Costa

High-Frequency Gravitational Waves (HFGWs) constitute a unique window on the early Universe as well as exotic astrophysical objects. If the current gravitational wave experiments are more dedicated to the low frequency regime, the graviton conversion into photons in a strong magnetic field constitutes a powerful tool to probe HFGWs. In this paper, we show th

Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall

The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the various side effects can trigger opioid relapse. In addition to MAT, the dietary nutrition intervention has been demonstrated its importa

B. U. Felderhof

A theoretical expression is derived for the mechanical contribution to the mean swimming speed of a vibrating two-sphere immersed in a viscous incompressible fluid. The two spheres are connected by an elastic spring which provides a harmonic potential for oscillations about a mean distance between centers. The system is made to oscillate at a chosen frequenc

Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain

Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI for automated policy-making that connects with the Reinforcement Learning, EconCS, and Computat

Kaushik Satapathy, Dimitrios Psaltis, Feryal Özel

The partition of turbulent heating between ions and electrons in radiatively inefficient accretion flows plays a crucial role in determining the observational appearance of accreting black holes. Modeling this partition is, however, a challenging problem because of the large scale separation between the macroscopic scales at which energy is injected by turbu