December 2024 arXiv papers — page 6

Noor Aboueleneen, Yahuza Bello, Abdullatif Albaseer, Ahmed Refaey Hussein

Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs' systems still get overwhelmed when they encounter a complex dynamic change in t

Xu Jiawei, Huang Ran, Tao Lei, Zhang Jiaming

The gold nanocone array, fabricated with the ion track method, exhibits exceptionally high light absorptivity, making it a wide angle light absorber. The finite difference time domain (FDTD) method was employed to study the influence of the geometric parameters on total absorptivity and promote strategies to enhance the absorptivity of the gold nanocone arra

Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho

This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communicat

Qile Su, Rodrigo G. Cortiñas, Jayameenakshi Venkatraman, Shruti Puri

The spontaneous switching of a quantum particle between the wells of a double-well potential is a phenomenon of general interest to physics and chemistry. It was broadly believed that the switching rate decreases steadily with the size of the energy barrier. This view was challenged by a recent experiment on a driven superconducting Kerr nonlinear oscillator

Moaiz Abrar, Yusuf Sermet, Ibrahim Demir

This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. While LLMs have shown proficiency in clinical question answering (QA) benchmarks, their effectiveness on real-world, consumer-based, medical questions rem

Anastasiia Tkachenko, Haitao Wang

Given a set $P$ of $n$ points in the plane, its unit-disk graph $G(P)$ is a graph with $P$ as its vertex set such that two points of $P$ are connected by an edge if their (Euclidean) distance is at most $1$. We consider several classical problems on $G(P)$ in a special setting when points of $P$ are in convex position. These problems are all NP-hard in the g

Juan Carlos Rocha, Anne-Sophie Crépin

Abrupt transitions in ecosystems can be interconnected, raising challenges for science and management in identifying sufficient interventions to prevent them or recover from undesirable shifts. Here we use principles of network controllability to explore how difficult it is to manage coupled regime shifts. We find that coupled regime shifts are easier to man

Patrick Concha, Evelyn Rodríguez, Sebastián Salgado

Carroll symmetry arises from Poincar\'e symmetry when the speed of light is sent to zero. In this work, we apply the Lie algebra expansion method to find the Carroll versions of different gravity models in three space-time dimensions. Our starting point is the 2D Euclidean AdS algebra along with its flat version. Novel and already known Carrollian algebras,

Tingxuan Wu, Zhaorui Ma, Yanjun Cui, Ziyi Zhou

Although social bots can be engineered for constructive applications, their potential for misuse in manipulative schemes and malware distribution cannot be overlooked. This dichotomy underscores the critical need to detect social bots on social media platforms. Advances in artificial intelligence have improved the abilities of social bots, allowing them to g

Hammad Ather, Jean Luca Bez, Chen Wang, Hank Childs

Driven by artificial intelligence, data science, and high-resolution simulations, I/O workloads and hardware on high-performance computing (HPC) systems have become increasingly complex. This complexity can lead to large I/O overheads and overall performance degradation. These inefficiencies are often mitigated using tools and techniques for characterizing,

Imin Chen, Joshua Swidinsky

Let $E$ be an elliptic curve over the rationals which does not have complex multiplication. Serre showed that the adelic representation attached to $E/\mathbb{Q}$ has open image, and in particular there is a minimal natural number $C_E$ such that the mod $\ell$ representation $\bar{\rho}_{E,\ell}$ is surjective for any prime $\ell > C_E$. Assuming the Genera

Luis F. Abanto-Leon, Setareh Maghsudi

We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and communications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining

Duo Zhou, Christopher Brix, Grani A Hanasusanto, Huan Zhang

Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on generic cutting planes (cuts) generated from external mixed integer programming (MIP) solvers. Due to the poor scalability of MIP solvers, large neural networks cannot benefit from thes

Giuliano Lorenzoni, Pedro Elkind Velmovitsky, Paulo Alencar, Donald Cowan

Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public health agencies and avoid the onset of other major comorbidities. Additionally, the shortage of specialized personnel is a critical issue because clinical depression diagnosis is h

Lele Du, Minbo Yang

We investigate the positive solutions of the semilinear elliptic equation \begin{align*} \sum^{N}_{i=1}\left(-\partial_{ii}\right)^{s}u=u^{p} \end{align*} with one-dimensional symmetric $2s$-stable operators. Firstly, in the whole space $\R^{N}$, we establish the nonexistence of positive supersolutions for $1<p\leq\frac{N}{N-2s}$. Furthermore, the symmetry o

Maria Gillespie, Eugene Gorsky, Sean T. Griffin

We introduce a variety $Y_{n,k}$, which we call the \textit{affine $\Delta$-Springer fiber}, generalizing the affine Springer fiber studied by Hikita, whose Borel-Moore homology has an $S_n$ action and a bigrading that corresponds to the Delta Conjecture symmetric function $\mathrm{rev}_q\,\omega \Delta'_{e_{k-1}}e_n$ under the Frobenius character map. We si

Ali Rabeh, Ethan Herron, Aditya Balu, Soumik Sarkar

Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications, including aerodynamics and biomedical flows. However, while scientific machine learning (SciML) has shown considerable promise, most studies in this field are limited to simple geometries, and complex, rea

Arnbjörg Soffía Árnadóttir, David E. Roberson

A quantum Latin square is an $n \times n$ array of unit vectors where each row and column forms an orthonormal basis of a fixed complex vector space. We introduce the notion of $(G,G')$-invariant quantum Latin squares for finite groups $G$ and $G'$. These are quantum Latin squares with rows and columns indexed by $G$ and $G'$ respectively such that the inner

Akshansh Mishra

This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge

Qiaoyi Fang, Weiyu Du, Hang Wang, Junshan Zhang

World models have recently emerged as a promising approach to reinforcement learning (RL), achieving state-of-the-art performance across a wide range of visual control tasks. This work aims to obtain a deep understanding of the robustness and generalization capabilities of world models. Thus motivated, we develop a stochastic differential equation formulatio

D. Thirumalai, Naoto Hori, Hung T. Nguyen

The increasing importance of RNA as a prime player in biology can hardly be overstated. Problems in RNA, such as folding and RNA-RNA interactions that drive phase separation, require cations. Because experiments alone cannot reveal the dynamics of cation-RNA interactions, well calibrated theory and computations are needed to predict how ions control the beha

Jianan Wu, Ahmet Yusuf Salim, Eslam Elmitwalli, Selçuk Köse

Pseudo-random number generators (PRNGs) are essential in a wide range of applications, from cryptography to statistical simulations and optimization algorithms. While uniform randomness is crucial for security-critical areas like cryptography, many domains, such as simulated annealing and CMOS-based Ising Machines, benefit from controlled or non-uniform rand

Zhenting Wang, Shuming Hu, Shiyu Zhao, Xiaowen Lin

Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on establish

Giacomo Como, Fabio Fagnani, Leonardo Massai, Martina Vanelli

We study a networked economic system composed of $n$ producers supplying a single homogeneous good to a number of geographically separated markets and of a centralized authority, called the market maker. Producers compete \`a la Cournot, by choosing the quantities of good to supply to each market they have access to in order to maximize their profit. Every m

Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA) play a vital role in sepsis identification within clinicians' workflow, providing evi

Martin A Erinin, Cole R. Sagan, Ilian Ahmed, Gwenore F. Pokrifka

We present a new experimental facility to investigate the nucleation and growth of liquid droplets and ice particles under controlled conditions and characterize processes relevant to cloud microphysics: the rapid expansion aerosol chamber (REACh). REACh is an intermediate size chamber (~0.14 m$^3$) combining the principle of an expansion chamber with the ab

Xiu-Hua Zhao, Yu-Han Ma

A famous paper [Am. J. Phys. 43, 22 (1975)] unveiled the efficiency at maximum power (EMP) of the endo-reversible Carnot heat engine, now commonly referred to as the Curzon-Ahlborn (CA) engine, pioneering finite-time thermodynamics. Historically, despite the significance of the CA engine, similar findings had emerged at an earlier time, such as the Yvon engi

Yi-Hsuan Lin, Tae Hun Kim, Suman Das, Tanmoy Pal

Liquid-liquid phase separation (LLPS) involving intrinsically disordered protein regions (IDRs) is a major physical mechanism for biological membraneless compartmentalization. The multifaceted electrostatic effects in these biomolecular condensates are exemplified here by experimental and theoretical investigations of the different salt- and ATP-dependent LL

Wei Huang, Shi-Ting Cao, Shi-Jun Liang, Shan Yin

Most recently, a brand new optical phenomenon, collectively induced transparency (CIT) has already been proposed in the cavity quantum electrodynamics system, which comes from the coupling between the cavity and ions and the quantum interference of collective ions. In this paper, we propose the CIT in terahertz (THz) metamaterial device by employing the coup

Amirreza Payandeh, Daeun Song, Mohammad Nazeri, Jing Liang

Most existing social robot navigation techniques either leverage hand-crafted rules or human demonstrations to connect robot perception to socially compliant actions. However, there remains a significant gap in effectively translating perception into socially compliant actions, much like how human reasoning naturally occurs in dynamic environments. Consideri

Francisco Riberi, Lorenza Viola

Interactions among sensors can provide, in addition to entanglement, an important resource for boosting the precision in quantum estimation protocols. Dephasing noise, however, remains a leading source of decoherence in state-of-the-art quantum sensing platforms. We analyze the impact of classical {\em collective dephasing with arbitrary temporal correlation

Rezwan Ahmed, Izumi Mochizuki, Toshio Hyodo, Tetsuroh Shirasawa

We report on the development of a low-energy positron diffraction (LEPD) experimental station for surface structure analysis using a linac-based slow-positron beam. LEPD, the positron counterpart of low-energy electron diffraction (LEED), offers higher accuracy in surface structure determination. The station enables acquisition of LEPD I-V curves within a fe

Josephine K. Wairimu, Andrew Gothard, Grzegorz A. Rempala

We extend the classical Susceptible-Infected-Recovered (SIR) model to a network-based framework where the degree distribution of nodes follows a Poisson distribution. This extension incorporates an additional parameter representing the mean node degree, allowing for the inclusion of heterogeneity in contact patterns. Using this enhanced model, we analyze epi

Dmytro Tymoshchuk, Vasyl Yatskiv, Vitaliy Tymoshchuk, Nataliya Yatskiv

Rapid progress in the development of information technology has led to a significant increase in the number and complexity of cyber threats. Traditional methods of cybersecurity training based on theoretical knowledge do not provide a sufficient level of practical skills to effectively counter real threats. The article explores the possibilities of integrati

June-Young Kim, Kirill M. Semenov-Tian-Shansky, Ho-Yeon Won, Sangyeong Son

We revisit the definition of the leading-twist chiral-even generalized parton distributions (GPDs) for $N \to \Delta$ baryon transitions. We identify and address deficiencies in previous definitions of the transition GPDs inspired by the transition form factors of the vector and axial-vector currents. Through systematic analysis of all possible covariant str

Amirhossein Nadiri, Jing Li, Ali Faraji, Ghadeer Abuoda

Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. Deep learning approaches have become key in this area, utilizing large-scale trajectory datasets to model movement patterns, but face challenges in managi

Bogi Kim, Jehan Oh, Abhrojyoti Sen

This article is devoted to exploring the Lipschitz truncation method for parabolic multi-phase problems. The method is based on Whitney decomposition and covering lemmas with a delicate comparison scheme of appropriate alternatives to distinguish phases, as introduced by the first and the second author in [24].

Irina Nizhnik

The existence of normal deterministic diffusion in dynamical systems with a two-dimensional phase space tiled by regular triangles (or their unions into regular hexagons) is proven.

Philippe-A. Bourdin, Yasuhito Narita

The concept of electromotive field appears in various applications in space and astrophysical plasmas. A review is given on the electromotive field highlighting our current understanding of the theoretical picture and the spacecraft observations in interplanetary space. The electromotive field is a key concept to successfully close the set of turbulent magne

Anton Pershin, András Tárkányi, Vladimir Verkhovlyuk, Viktor Ivády

Recent advances in the engineering of diamond surfaces make it possible to stabilize the charge state of 7-30 nanometers deep nitrogen-vacancy (NV) quantum sensors in diamond and to remove the charge noise at the surface principally. However, it is still a challenge to simultaneously increase the action volume of the quantum sensor by placing NV centers 0.5-

Zlatan Ajanović, Hamza Merzić, Suad Krilasević, Eldar Kurtić

In this paper, we analyze examples of research institutes that stand out in scientific excellence and social impact. We define key practices for evaluating research results, economic conditions, and the selection of specific research topics. Special focus is placed on small countries and the field of artificial intelligence. The aim is to identify components

Chang-Sheng Mei, Shenyan Zong, Bruno Madore, Garth R. Cosgrove

Purpose: To adjust the location of the ablation focus by correcting the spatial discrepancy induced by temperature changes between the target coordinate and the hotspot as seen in MR thermography. Methods: A two-step correction procedure was used to account for the chemical and k-space shifts. The first step corrected the spatial shift pixel by pixel using t

Maria Vittoria Garzelli

Measurements of the products of UHECR interactions with the Earth's atmosphere, as obtained in Extended Air Shower experiments, offer important information concerning hadronic interactions, which for some aspects overlaps and for many others complements the information extracted by measurements of collisions at human-made accelerators. In this contribution I

Aditya Bahulikar, Steven L. Richardson, Rodrick Kuate Defo

The elucidation of the mechanism of Sn$V^-$ formation in diamond is especially important as the Sn$V^-$ color center has the potential to be a superior single-photon emitter when compared to the N$V$ and to other Group IV color centers. The typical formation of the Sn$V$ involves placing Sn in diamond by ion implantation, but the formation of a charged Sn$V$

Hisashi Hayakawa, Edward W. Cliver, Frédéric Clette, Yusuke Ebihara

We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman-Silverman storm) apparently originated from a complex active region of moderate area (\approx 500 {\mu}sh) that was favorably situated near disk center (S19{\deg} E05{

Céline Vanini, Jan Gruber, Christopher Hargreaves, Zinaida Benenson

Timestamps play a pivotal role in digital forensic event reconstruction, but due to their non-essential nature, tampering or manipulation of timestamps is possible by users in multiple ways, even on running systems. This has a significant effect on the reliability of the results from applying a timeline analysis as part of an investigation. In this paper, we

Marco Siino, Ilenia Tinnirello, Marco La Cascia

Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly lite

Lewis Bowen, Michael Chapman, Thomas Vidick

This paper, and its companion [BCLV24], are devoted to a negative resolution of the Aldous--Lyons Conjecture [AL07, Ald07]. In this part we study tailored non-local games. This is a subclass of non-local games -- combinatorial objects which model certain experiments in quantum mechanics, as well as interactive proofs in complexity theory. Our main result is

Burak Kürkçü, Masayoshi Tomizuka

In this paper, we establish necessary and sufficient conditions for stable inversion, addressing challenges in non-minimum phase, non-square, and singular systems. An H-Infinity based algebraic approximation is introduced for near-perfect tracking without preview. Additionally, we propose a novel robust control strategy combining the nominal model with dual

Noy Soffer Aranov

We study the minimal denominator problem in function fields. In particular, we compute the probability distribution function of the the random variable which returns the degree of the smallest denominator $Q$, for which the ball of a fixed radius around a point contains a rational function of the form $\frac{P}{Q}$. Moreover, we discuss the distribution of t

Hongrui Shi, Valentin Radu, Po Yang

With the rapid expansion of edge devices, such as IoT devices, where crucial data needed for machine learning applications is generated, it becomes essential to promote their participation in privacy-preserving Federated Learning (FL) systems. The best way to achieve this desiderate is by reducing their training workload to match their constrained computatio

Nick Papoulias

Deep Learning experiments have critical requirements regarding the careful handling of their datasets as well as the efficient and correct usage of APIs that interact with hardware accelerators. On the one hand, software mistakes during data handling can contaminate experiments and lead to incorrect results. On the other hand, poorly coded APIs that interact

Mykola Maslych, Christian Pumarada, Amirpouya Ghasemaghaei, Joseph J. LaViola

We present a virtual reality (VR) environment featuring conversational avatars powered by a locally-deployed LLM, integrated with automatic speech recognition (ASR), text-to-speech (TTS), and lip-syncing. Through a pilot study, we explored the effects of three types of avatar status indicators during response generation. Our findings reveal design considerat

Costas Kravaris

This paper proposes a novel approach for designing functional observers for nonlinear systems, with linear error dynamics and assignable poles. Sufficient conditions for functional observability are first derived, leading to functional relationships between the Lie derivatives of the output to be estimated and the ones of the measured output. These are direc

Valentin Deaconu, Marius Ionescu

We introduce a cochain complex for ample groupoids $\mathcal G$ using a flat resolution defining their homology with coefficients in $\mathbb Z$. We prove that the cohomology of this cochain complex with values in a $\mathcal G$-module $M$ coincides with the previously introduced continuous cocycle cohomology of $\mathcal G$. In particular, this groupoid coh

Ben McClusky

This work presents a novel communication framework for decentralized multi-agent systems operating in dynamic network environments. Integrated into a multi-agent reinforcement learning system, the framework is designed to enhance decision-making by optimizing the network's collective knowledge through efficient communication. Key contributions include adapti

Subramaniam Vincent, Phoebe Wang, Zhan Shi, Sahas Koka

Since the launch of ChatGPT in late 2022, the capacities of Large Language Models and their evaluation have been in constant discussion and evaluation both in academic research and in the industry. Scenarios and benchmarks have been developed in several areas such as law, medicine and math (Bommasani et al., 2023) and there is continuous evaluation of model

D. G. Steyn, Troy Mithrush, Chris Koentges, Susan Andrews

We define and investigate a successful third shot drop in pickleball using a numerical model of pickleball ball aerodynamics. Our overall objective is to investigate the ranges of initial speeds, angles and spins that result in a successful third shot drop. We conclude that the initial speed must be in the range $10.9~ms^{-1}$ to $13~ms^{-1}$ for down-the-li

Akul Goyal, Carl Edwards

Existing work within transfer learning often follows a two-step process -- pre-training over a large-scale source domain and then finetuning over limited samples from the target domain. Yet, despite its popularity, this methodology has been shown to suffer in the presence of distributional shift -- specifically when the output spaces diverge. Previous work h

Clément Dallard, Maël Dumas, Claire Hilaire, Anthony Perez

The $H$-Induced Minor Containment problem ($H$-IMC) consists in deciding if a fixed graph $H$ is an induced minor of a graph $G$ given as input, that is, whether $H$ can be obtained from $G$ by deleting vertices and contracting edges. Equivalently, the problem asks if there exists an induced minor model of $H$ in $G$, that is, a collection of disjoint subset

David Goll, Jobst Heitzig, Wolfram Barfuss

Multi-Agent Reinforcement Learning involves agents that learn together in a shared environment, leading to emergent dynamics sensitive to initial conditions and parameter variations. A Dynamical Systems approach, which studies the evolution of multi-component systems over time, has uncovered some of the underlying dynamics by constructing deterministic appro

Azeddine Zaidni, Philip J. Morrison

A unified thermodynamic algorithm (UTA) is presented for constructing thermodynamically consistent dynamical systems, i.e., systems that have Hamiltonian and dissipative parts that conserve energy while producing entropy. The algorithm is based on the metriplectic 4-bracket given in Morrison and Updike [Phys.\ Rev.\ E 109, 045202 (2024)]. A feature of the UT

Kleanthis Malialis, Nefeli Mavri, Stelios G. Vrachimis, Marios S. Kyriakou

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses

Marcin Anholcer, Bartłomiej Bosek, Grzegorz Gutowski, Michał Lasoń

Given a hypergraph $H=(V,E)$, define for every edge $e\in E$ a linear expression with arguments corresponding to the vertices. Next, let the polynomial $p_H$ be the product of such linear expressions for all edges. Our main goal is to find a relationship between the Alon-Tarsi number of $p_H$ and the edge density of $H$. We prove that $AT(p_H)=\lceil \mathrm

Oliver Daisey, Yue Ren, Yuvraj Singh

Vertically parametrised polynomial systems are a particular nice class of parametrised polynomial systems for which a lot of interesting algebraic information is encoded in its combinatorics. Given a fixed polynomial system, we empirically study what constitutes a good vertically parametrised polynomial system that gives rise to it and how to construct said

Ouknine Anas

This study uses Lie's theory of symmetries to compute the symmetry group of a class of partial differential equations parameterized by four constants: $u_{t}=-\left((a-bx)u_{x}+(d-ey)u_{y}+\frac{x}{2}u_{xx}+\frac{y}{2}u_{yy}\right)$; under the various conditions on the constants $a,b,d$ and $e$, we deduce the largest and smallest Lie algebra of symmetries, a

Bernardo Subercaseaux, Marcelo Arenas, Kuldeep S Meel

Formal XAI is an emerging field that focuses on providing explanations with mathematical guarantees for the decisions made by machine learning models. A significant amount of work in this area is centered on the computation of "sufficient reasons". Given a model $M$ and an input instance $\vec{x}$, a sufficient reason for the decision $M(\vec{x})$ is a subse

Somayeh Khakpash, Federica Bianco, Georgios Vernardos, Gregory Dobler

Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extra-galactic microlensing rely on computationally expensive magnification map generation. With large datasets expected from wide-field surveys

Jiayu Song, Mahmud Elahi Akhter, Dana Atzil Slonim, Maria Liakata

This paper explores whether enhancing temporal reasoning capabilities in Large Language Models (LLMs) can improve the quality of timeline summarisation, the task of summarising long texts containing sequences of events, such as social media threads. We first introduce NarrativeReason, a novel dataset focused on temporal relationships among sequential events

M. Selch, M. A. Zubkov

We consider macroscopic motion of the normal component of superfluid $^3$He - A in global thermodynamic equilibrium within the context of the Zubarev statistical operator method. We formulate the corresponding effective theory in the language of the functional integral. The effective Lagrangian comprising macroscopic motion of fermionic excitations is calcul

Art B. Owen

Quasi-Monte Carlo sampling can attain far better accuracy than plain Monte Carlo sampling. However, with plain Monte Carlo sampling it is much easier to estimate the attained accuracy. This article describes methods old and new to quantify the error in quasi-Monte Carlo estimates. An important challenge in this setting is that the goal of getting accuracy co

Grace Jiang, Jiangchao Qiu, Sai Ravela

Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic simulations. Here, we show that surrogate models are promising from accuracy, recall, and precision perspectives, and they "g

Natig Atakishiyev

A simple analytic approach to the evaluation of the eigenvalues and eigenvectors f_n of the 5D discrete number operator N_5 is formulated. This approach is essentially based on the symmetry of the intertwining operators with respect to the discrete reflection operator. A procedure for the sparsealization of the intertwining operators has been developed, whic

Michael Reh

Tokai to Kamioka (T2K) is a long-baseline neutrino oscillation experiment that measures oscillation parameters related to both $\nu_\mu(\bar{\nu}_\mu)$ disappearance and $\nu_e(\bar{\nu}_e)$ appearance in a $\nu_\mu(\bar{\nu}_\mu)$ beam. T2K uses Super-Kamiokande (SK) as its far detector, and SK detector systematic errors are currently among the leading sour

Xin Yue Yu, Zishen Guan, Ushak Rahaman, Nikolina Ilic

In this paper, we have tested the non-unitary mixing hypothesis with the latest data from NO$\nu$A and T2K experiments. We have also analysed their combined data. We have provided the best-fit values of the standard and non standard parameters after the analysis. $90\%$ limits on the non-unitary mixing parameters have also been provided. The constraints on u

Catarina Botelho, David Gimeno-Gómez, Francisco Teixeira, John Mendonça

This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach, encompassing both knowledge-based acoustic and text-based feature sets, as well as LLM-based macrolinguistic descriptors, pause-ba

Arturo Merino, Bernardo Subercaseaux

It is well-known by now that any state of the $3\times 3 \times 3$ Rubik's Cube can be solved in at most 20 moves, a result often referred to as "God's Number". However, this result took Rokicki et al. around 35 CPU years to prove and is therefore very challenging to reproduce. We provide a novel approach to obtain a worse bound of 36 moves with high confide

Cheng-Hau Yang, Guglielmo Scovazzi, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

This paper presents an incomplete Octree mesh implementation of the Shifted Boundary Method (Octree-SBM) for multiphysics simulations of coupled flow and heat transfer. Specifically, a semi-implicit formulation of the thermal Navier-Stokes equations is used to accelerate the simulations while maintaining accuracy. The SBM enables precise enforcement of field

Jeremy Klotz, Shree K. Nayar

A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can be modeled as the first layer of a neur

Mark van den Bosch

The purpose of these notes is to distribute, mostly without proofs, fundamental definitions and results concerning the theory of semimartingales and stochastic integration. The material serves as a foundational guide for those interested in applying these concepts, particularly in the study of stochastic (functional) differential equations driven by L\'evy p

Maya Nunez

Light and air pollution are the two main forms of pollution, containing the highest concentration in urban areas. I set out to investigate the effects of anthropogenic air and light pollution on the night sky, how this affects the astronomical data-collecting process, and how the general public perceives both forms of pollution. This paper utilizes primary a

Fay Dowker, Roger Liu, Daniel Lloyd-Jones

The causal set action of dimension $d$ is investigated for causal sets that are Poisson sprinklings into submanifolds of $d$-dimensional Minkowski space. Evidence, both analytic and numerical, is provided for the conjecture that the mean of the causal set action over sprinklings into a manifold with a timelike boundary, diverges like $l^{-1}$ in the continuu

Uroš Mlakar, Iztok Fister, Iztok Fister

The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that form an entire pipeline, e.g., preprocess

Nicholas Mankowski, Hassan Mushtaq, Hanliang Guo

This paper develops a computational framework based on a car-following model to study traffic instability and lane changes. Building upon Newell's classical first-order car-following model, we show that, both analytically and numerically, there exists a vehicle-density-dependent critical reaction time that determines the stability of single-lane traffic. Spe

Yu. B. Chernyakov, G. I. Sharygin

In this paper we discuss the relation between the functions that give first integrals of full symmetric Toda system (an important Hamilton system on the space of traceless real symmetric matrices) and the vector fields on the group of orthogonal matrices: it is known that this system is equivalent to an ordinary differential equation on the orthogonal group,

Taniya Das, Louis Mahon, Thomas Lukasiewicz

One of the challenging tasks in the field of video understanding is extracting semantic content from video inputs. Most existing systems use language models to describe videos in natural language sentences, but this has several major shortcomings. Such systems can rely too heavily on the language model component and base their output on statistical regularit

Maryna Kapitonova, Tonio Ball

Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have been demonstrated for computer science

Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng

Quantum computing is an exciting non-Von Neumann paradigm, offering provable speedups over classical computing for specific problems. However, the practical limits of classical simulatability for quantum circuits remain unclear, especially with current noisy quantum devices. In this work, we explore the potential of leveraging Large Language Models (LLMs) to

S. I. Ipatov

The evolution of the orbits of bodies ejected from the Earth, Moon, Mercury and Mars was studied. At ejection velocities about 12-14 km/s, the fraction of bodies ejected from the Earth that fall back onto the Earth was about 0.15-0.25. The total number of bodies ejected from the Earth and delivered to the Earth and Venus probably did not differ much. The pro

Mario C. Bertin, Ronaldo Thibes

We consider the null-plane dynamics for a reduced-order version of the higher-derivatives Bopp-Podoslky generalized electrodynamics model. By introducing an auxiliary vector field, we achieve a simpler equivalent version with lower derivatives. The massive and massless modes for the Podolsky gauge field get split into two sectors. We describe the model in te

Adedamola D. Aladese, Xiao Shen

Phase stabilization continues to be a critical issue in hafnium oxide (HfO$_2$) due to the interdependence of various contributing factors. Using first-principles calculations, we analyze the effects of strain and doping on stabilizing the ferroelectric phase. We found that combining Y-doping, O-vacancy, and compressive biaxial strain, particularly in the (1

Nabarun Chakrabarty, Indrani Chakraborty, Himadri Roy

It has been known that under-abundant dark matter density of an inert doublet can be replenished by an additional dark matter component, say, a fermion. We find that such a scenario can lead to the formation of stable Fermi-balls through coexisting minima of the finite temperature scalar potential. More importantly, we demonstrate that the Fermi-balls contri

Matthew R. Ballard, Christine Berkesch, Michael K. Brown, Lauren Cranton Heller

We state and prove a realization of King's Conjecture for a category glued from the derived categories of all of the toric varieties arising from a given Cox ring. Our perspective extends ideas of Beilinson and Bondal to all semiprojective toric varieties.

Julia Ive, Paulina Bondaronek, Vishal Yadav, Daniel Santel

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatr

M. Borysova, D. Zavazieva, N. Kakati, E. Gross

In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle Identification (PID) in future collider experiments. For PID, we achieved classification efficiencies exceeding 50% for neutrons and pions, with notably higher efficiencies for kaon

Surya T. Bijjala, Susan R. Atlas, Pankaj Kumar

The elastic tensor provides valuable insight into the mechanical behavior of a material with lattice strain, such as disordered binary alloys. Traditional stress-strain methods have made it possible to compute elastic constants for ordered structures and individually tailored alloy compositions. However, this approach depends on predetermined or iteratively-

Francisco Pedroche

In this paper we analyze the FIA formula one world championships from 2012 to 2022 taking into account the drivers classifications and the constructors teams classifications of each Grand Prix. The needed data consisted of 22 matrices of sizes ranging from $25 \times 20$ to $10 \times 19$ that have been elaborated from the GP classifications extracted from t

Lohith Senthilkumar, Tim Menzies

When SE data is scarce, "active learners" use models learned from tiny samples of the data to find the next most informative example to label. In this way, effective models can be generated using very little data. For multi-objective software engineering (SE) tasks, active learning can benefit from an effective set of initial guesses (also known as "warm sta

Jiaojiao Ye, Zhen Wang, Linnan Jiang

Diffusionmodels(DMs)havedemonstratedremarkableachievements in synthesizing images of high fidelity and diversity. However, the extensive computational requirements and slow generative speed of diffusion models have limited their widespread adoption. In this paper, we propose a novel post-training quantization for diffusion models (PQD), which is a time-aware

Adam Chapman, Ilan Levin, Uzi Vishne, Marco Zaninelli

We develop a theory of loops with involution. On this basis we define a Cayley-Dickson doubling on loops, and use it to investigate the lattice of varieties of loops with involution, focusing on properties that remain valid in the Cayley-Dickson double. Specializing to central-by-abelian loops with elementary abelian $2$-group quotients, we find conditions u