July 2023 arXiv papers — page 41

Isabel M. Rayas Fernández

Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specifica

Hanzhi Wang, Zhewei Wei

PageRank is a famous measure of graph centrality that has numerous applications in practice. The problem of computing a single node's PageRank has been the subject of extensive research over a decade. However, existing methods still incur large time complexities despite years of efforts. Even on undirected graphs where several valuable properties held by Pag

William Q. Erickson, Daniel Herden, Jonathan Meddaugh, Mark R. Sepanski

The tableau reconstruction problem, posed by Monks (2009), asks the following. Starting with a standard Young tableau $T$, a 1-minor of $T$ is a tableau obtained by first deleting any cell of $T$, and then performing jeu de taquin slides to fill the resulting gap. This can be iterated to arrive at the set of $k$-minors of $T$. The problem is this: given $k$,

Peixin Wang, Tengshun Yang, Hongfei Fu, Guanyan Li

In Bayesian probabilistic programming, a central problem is to estimate the normalised posterior distribution (NPD) of a probabilistic program with conditioning via score (a.k.a. observe) statements. Most previous approaches address this problem by Markov Chain Monte Carlo and variational inference, and therefore could not generate guaranteed outcomes within

Atharva Dikshit, Alison Bartsch, Abraham George, Amir Barati Farimani

With the goal of developing fully autonomous cooking robots, developing robust systems that can chop a wide variety of objects is important. Existing approaches focus primarily on the low-level dynamics of the cutting action, which overlooks some of the practical real-world challenges of implementing autonomous cutting systems. In this work we propose an aut

Zijiang Yan, Wael Jaafar, Bassant Selim, Hina Tabassum

This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with

Ariadna Fosch, Alberto Aleta, Yamir Moreno

Albeit numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However

Tihana Galinac Grbac, Csaba Szabó, João Paulo Fernandes

This volume exhibits the revised lecture notes of the 2nd teacher training organized as part of the project Promoting Sustainability as a Fundamental Driver in Software Development Training and Education, held at the Juraj Dobrila University of Pula, Croatia, in the week January 23-27, 2023. It is the Erasmus+ project No. 2020-1-PT01-KA203-078646 - Sustraina

Francesco Fournier-Facio, Maria Gerasimova, Pieter Spaas

We introduce a notion of local Hilbert--Schmidt stability, motivated by the recent definition by Bradford of local permutation stability, and give examples of (non-residually finite) groups that are locally Hilbert--Schmidt stable but not Hilbert--Schmidt stable. For amenable groups, we provide a criterion for local Hilbert--Schmidt stability in terms of gro

Carlos Henrique de Lima, Daniel Stolarski

In this work, we present a comprehensive study of the phase diagram of supersymmetric QCD with $N_{f}=N_{c}+1$ flavors perturbed by Anomaly Mediated Supersymmetry Breaking (AMSB). We extend the previous analyses on s-confining ASQCD theories in three different directions. We show that the existence of the QCD-like vacuum is independent of the size of the SUS

Konstantinos Georgiou, Somnath Kundu, Pawel Pralat

We investigate a combinatorial optimization problem that involves patrolling the edges of an acute triangle using a unit-speed agent. The goal is to minimize the maximum (1-gap) idle time of any edge, which is defined as the time gap between consecutive visits to that edge. This problem has roots in a centuries-old optimization problem posed by Fagnano in 17

Suman Rath, Subham Sahoo, Shamik Sengupta

Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner. This paper formulates the interactions between CSAs and microgrid defenders as a non-cooperative, zero-sum game. Additionally, it presents a hybrid Moving Target Defense (MTD) strategy

Shane Cooper, Ilia Kamotski, Valery P. Smyshlyaev

We study an abstract family of asymptotically degenerating variational problems. Those are natural generalisations of families of problems emerging upon application of a rescaled Floquet-Bloch-Gelfand transform to resolvent problems for high-contrast elliptic PDEs with highly oscillatory periodic coefficients. An asymptotic analysis of these models leads us

Jared Town, Zachary Morrison, Rushikesh Kamalapurkar

The focus of this paper is behavior modeling for pilots of unmanned aerial vehicles. The pilot is assumed to make decisions that optimize an unknown cost functional, which is estimated from observed trajectories using a novel inverse reinforcement learning (IRL) framework. The resulting IRL problem often admits multiple solutions. In this paper, a recently d

Bahador Bahmani, Hyoung Suk Suh, WaiChing Sun

Conventional neural network elastoplasticity models are often perceived as lacking interpretability. This paper introduces a two-step machine learning approach that returns mathematical models interpretable by human experts. In particular, we introduce a surrogate model where yield surfaces are expressed in terms of a set of single-variable feature mappings

Christian Pedersen, Michael Eickenberg, Shirley Ho

Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well. However, CNNs require large amounts of training data, which is potentially problematic in the domain of expensive cosmological simulations, and it

Nicholas J. Moore, Jinzi Mac Huang

We introduce a low-order dynamical system to describe thermal convection in an annular domain. The model derives systematically from a Fourier-Laurent truncation of the governing Navier-Stokes Boussinesq equations and accounts for spatial dependence of the flow and temperature fields. Comparison with fully-resolved direct numerical simulations (DNS) shows th

William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann

The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations. In particular, the method learns optimal forecasts given irregularly sampled time series of incomplete past observations. So far the process itself and the coordinate-wise observa

Jinzi Mac Huang, Nicholas J. Moore

We examine thermal convection in a two-dimensional annulus using fully resolved direct numerical simulation (DNS) in conjunction with a low-dimensional model deriving from Galerkin truncation of the governing Navier-Stokes Boussinesq (NSB) equations. The DNS is based on fast and accurate pseudo-spectral discretization of the full NSB system with implicit-exp

Nima Jamalian, Marios Constantinides, Sagar Joglekar, Xueni Pan

To increase mobile user engagement, current apps employ a variety of behavioral nudges, but these engagement techniques are applied in a one-size-fits-all approach. Yet the very same techniques may be perceived differently by different individuals. To test this, we developed HarrySpotter, a location-based AR app that embedded six engagement techniques. We de

Christian Pedersen, Laure Zanna, Joan Bruna, Pavel Perezhogin

Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integrating ML models into the numerical scheme can lead to instabilities. In the context of a 2D, quasi-geostrophic tur

Michael Unterkalmsteiner, Tony Gorschek, A. K. M. Moinul Islam, Chow Kian Cheng

BACKGROUND: Software Process Improvement (SPI) is a systematic approach to increase the efficiency and effectiveness of a software development organization and to enhance software products. OBJECTIVE: This paper aims to identify and characterize evaluation strategies and measurements used to assess the impact of different SPI initiatives. METHOD: The systema

Henric Krawczynski, Yajie Yuan, Alexander Y. Chen, Kun Hu

The Imaging X-ray Polarimetry Explorer (IXPE) observations of the X-ray binary 4U 1630-47 in the high soft state revealed high linear polarization degrees (PDs) rising from 6% at 2 keV to 10% at 8 keV. We discuss in this letter three different mechanisms that impact the polarization of the observed X-rays: the reflection of gravitationally lensed emission by

Gregor Stiglic, Leon Kopitar, Lucija Gosak, Primoz Kocbek

Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve the above-mentioned problem, we employed generative artificial intelligence techniques based on large-scale language models to summarize abstracts of scientific papers. Our objecti

Bo Xue Zheng, Christian Pedersen, Andreas Carlson, Tak Shing Chan

A droplet can deform a soft substrate due to capillary forces when they are in contact. We study the static deformation of a soft solid layer coated on a rigid cylindrical fiber when an axisymmetric barrel-shaped droplet is embracing it. We find that the elastic deformation increases with decreasing rigid fiber radius. Significant disparities of deformation

Tarun Chitra, Peteris Erins, Kshitij Kulkarni

As decentralized money market protocols continue to grow in value locked, there have been a number of optimizations proposed for improving capital efficiency. One set of proposals from Euler Finance and Mars Protocol is to have an interest rate curve that is a proportional-integral-derivative (PID) controller. In this paper, we demonstrate attacks on proport

Zsuzsa Danku, Gergő Pál, Ferenc Kun

We investigate how the macroscopic response and the size scaling of the ultimate strength of materials change when their local strength is sampled from a fat-tailed distribution and the degree of disorder is varied in a broad range. Using equal and localized load sharing in a fiber bundle model, we demonstrate that a transition occurs from a perfectly brittl

Ainamar Clariana-Rodagut, Alessio Cardillo

The research presented here uses the tools of social network analysis to empirically show a socio-cultural phenomenon already addressed by the social sciences and history: the historical marginalisation of women in the field of cinema. The novelty of our approach lies in the use of a large amount of heterogeneous historical data. On the one hand, we built a

Megan Richards, Polina Kirichenko, Diane Bouchacourt, Mark Ibrahim

For more than a decade, researchers have measured progress in object recognition on ImageNet-based generalization benchmarks such as ImageNet-A, -C, and -R. Recent advances in foundation models, trained on orders of magnitude more data, have begun to saturate these standard benchmarks, but remain brittle in practice. This suggests standard benchmarks, which

Shaojun Ma, Mengxue Hou, Xiaojing Ye, Haomin Zhou

We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable agents whose behaviors can be collectively modeled as a time-evolving probability distribution. The goal is to steer the agents from an initial distribution to reach (or approximate)

Luca Mastella

In this article we prove a refinement of a theorem of Longo and Vigni in the anticyclotomic Iwasawa theory for modular forms. More precisely we give a definition for the ($\mathfrak{p}$-part of the) Shafarevich-Tate groups $\widetilde{\mathrm{sha}}_{\mathfrak{p}^\infty}(f/K)$ and $\widetilde{\mathrm{sha}}_{\mathfrak{p}^\infty}(f/K_\infty)$ of a modular form

Maria Bauza, Antonia Bronars, Yifan Hou, Ian Taylor

Existing robotic systems have a clear tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking precise generalization, i.e., the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick-and-p

Ibrahim Abdurrahman, Matthew Kafker, Aurel Bulgac, Ionel Stetcu

Just before a nucleus fissions a neck is formed between the emerging fission fragments. It is widely accepted that this neck undergoes a rather violent rupture, despite no direct experimental evidence, and only a few contentious theoretical treatments of this fission stage were ever performed in the more than eight decades since nuclear fission was experimen

Yi Han, Matthew Chan, Eric Wengrowski, Zhuohuan Li

Camera-based autonomous systems that emulate human perception are increasingly being integrated into safety-critical platforms. Consequently, an established body of literature has emerged that explores adversarial attacks targeting the underlying machine learning models. Adapting adversarial attacks to the physical world is desirable for the attacker, as thi

Nick E. Mavromatos, Joan Solà Peracaula, Adrià Gómez-Valent

In the context of a string-inspired running vacuum model (RVM) of cosmology with anomalies and torsion-induced axion-like fields, we discuss quantum corrections to the corresponding energy density, in approximately de Sitter eras, during which the Hubble parameter $H(t)$ varies very slowly with the cosmic time $t$. Such corrections arise either from graviton

Katrina Honigs, Graham McDonald

We study some combinatorial aspects of the fixed loci of symplectic involutions acting on hyperk\"ahler varieties of Kummer type. Given an abelian surface $A$ with a $(1,d)$-polarization $L$, there is an isomorphism $K_{d-1}A\cong K_{\hat{A}}(0,\hat{l},-1)$ between a hyperk\"ahler of Kummer type that parametrizes length-$d$ subschemes of $A$ and one that par

Abby Newcomb, Jugal Kalita

Automated math word problem solvers based on neural networks have successfully managed to obtain 70-80\% accuracy in solving arithmetic word problems. However, it has been shown that these solvers may rely on superficial patterns to obtain their equations. In order to determine what information math word problem solvers use to generate solutions, we remove p

Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional performance across all evaluation metrics.

Satoru Fujii

Many properties in the real world don't have metrics and can't be numerically observed, making them difficult to learn. To deal with this challenging problem, prior works have primarily focused on estimating those properties by using graded human scores as the target label in the training. Meanwhile, rating algorithms based on the Bradley-Terry model are ext

Sean Grate, Hal Schenck

We study the Artinian reduction $A$ of a configuration of points $X \subset {\mathbb P}^n $, and the relation of the geometry of $X$ to Lefschetz properties of $A$. Migliore initiated the study of this connection, with a particular focus on the Hilbert function of $A$, and further results appear in work of Migliore--Mir\'o-Roig--Nagel. Our specific focus is

Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan

Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but the

IGVF Consortium

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and

Helton Graziadei, Paulo C. Marques F., Eduardo F. L. de Melo, Rodrigo S. Targino

We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric mo

Matthew Du, Agnish Kumar Behera, Suriyanarayanan Vaikuntanathan

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the syste

Alexander J. Elias, John T. Wen

Seven degree-of-freedom (DOF) robot arms have one redundant DOF which does not change the motion of the end effector. The redundant DOF offers greater manipulability of the arm configuration to avoid obstacles and singularities, but it must be parameterized to fully specify the joint angles for a given end effector pose. For 7-DOF revolute (7R) manipulators,

Hossein Faridian

We prove that if $f:R \rightarrow S$ is a local homomorphism of noetherian local rings, and $M$ is a non-zero finitely generated or artinian $S$-module whose injective dimension over $R$ is bounded by the difference of the embedding dimensions of $R$ and $S$, then $M$ is an injective $S$-module and $f$ is an exceptional complete intersection map.

Paraj Mukherjee

The structure of the Euclidean algorithm can be used to generate a very large family of rhythms. In this paper, we explore a very specific family of Euclidean rhythms, the Euclidean rhythms in which the rests have a palindromic structure. We look at the structural and geometric properties of such rhythms; most of the properties have a certain combinatorial i

Marco Bertola, Tamara Grava, Giuseppe Orsatti

We develop the theory of integrable operators $\mathcal{K}$ acting on a domain of the complex plane with smooth boundary in analogy with the theory of integrable operators acting on contours of the complex plane. We show how the resolvent operator is obtained from the solution of a $\overline{\partial}$-problem in the complex plane. When such a $\overline{\p

Olivia P. Dizon-Paradis, Nitin Varshney, M Tanjidur Rahman, Michael Strizich

Hardware assurance of electronics is a challenging task and is of great interest to the government and the electronics industry. Physical inspection-based methods such as reverse engineering (RE) and Trojan scanning (TS) play an important role in hardware assurance. Therefore, there is a growing demand for automation in RE and TS. Many state-of-the-art physi

Carmine Giardino, Michael Unterkalmsteiner, Nicolò Paternoster, Tony Gorschek

An impressive number of new startups are launched every day as a result of growing new markets, accessible technologies, and venture capital. New ventures such as Facebook, Supercell, Linkedin, Spotify, {WhatsApp}, and Dropbox, to name a few, are good examples of startups that evolved into successful businesses. However, despite many successful stories, the

Yuqi Li, Hao-Yu Sun

Revisiting the Coon amplitude, a deformation of the Veneziano amplitude with a logarithmic generalization of linear Regge trajectories, we scrutinize its potential origins in a worldsheet theory by proposing a definition of its $q$-deformation through the integral representation of the $q$-beta function. By utilizing $q$-deformed commutation relations and ve

Lea Boßmann, Nikolai Leopold, David Mitrouskas, Sören Petrat

We consider a gas of N weakly interacting bosons in the ground state. Such gases exhibit Bose-Einstein condensation. The binding energy is defined as the energy it takes to remove one particle from the gas. In this article, we prove an asymptotic expansion for the binding energy, and compute the first orders explicitly for the homogeneous gas. Our result add

Matthias Beckmann, Ayush Bhandari, Meira Iske

Inspired by the multiple-exposure fusion approach in computational photography, recently, several practitioners have explored the idea of high dynamic range (HDR) X-ray imaging and tomography. While establishing promising results, these approaches inherit the limitations of multiple-exposure fusion strategy. To overcome these disadvantages, the modulo Radon

Jennifer E. DeMell, Ivan Naumov, Gregory M. Stephen, Nicholas A. Blumenschein

Mechanical stacking of two dissimilar materials often has surprising consequences for heterostructure behavior. In particular, a two-dimensional electron gas (2DEG) is formed in the heterostructure of the topological crystalline insulator Pb0.24Sn0.76Te and graphene due to contact of a polar with a nonpolar surface and the resulting changes in electronic str

Stefan Trandafir

The vector partition function $p_A$ associated to a $d \times n$ matrix $A$ with integer entries is the function $\mathbb{Z}^d \to \mathbb{N}$ defined by $\mathbf{b} \to \#\{\mathbf{x} \in \mathbb{N}^n : A\mathbf{x} = \mathbf{b}\}$. It is known that vector partition functions are piecewise quasi-polynomials whose domains of quasi-polynomiality are maximal co

Roman R. Dyachenko, Sergey A. Matveev, P. L. Krapivsky

We investigate analytically and numerically a system of clusters evolving via collisions with clusters of minimal mass (monomers). Each collision either leads to the addition of the monomer to the cluster or the chipping of a monomer from the cluster, and emerging behaviors depend on which of the two processes is more probable. If addition prevails, monomers

Andreas Happe, Jürgen Cito

The field of software security testing, more specifically penetration testing, is an activity that requires high levels of expertise and involves many manual testing and analysis steps. This paper explores the potential usage of large-language models, such as GPT3.5, to augment penetration testers with AI sparring partners. We explore the feasibility of supp

Sai Kumar Reddy Manne, Shaotong Zhu, Sarah Ostadabbas, Michael Wan

Respiration is a critical vital sign for infants, and continuous respiratory monitoring is particularly important for newborns. However, neonates are sensitive and contact-based sensors present challenges in comfort, hygiene, and skin health, especially for preterm babies. As a step toward fully automatic, continuous, and contactless respiratory monitoring,

Mohammad Ali Gorji, Misao Sasaki, Teruaki Suyama

Recently, pulsar timing array (PTA) collaborations announced evidence for an isotropic stochastic gravitational wave (GW) background. The origin of the PTA signal can be astrophysical or cosmological. In the latter case, the so-called secondary scalar-induced GW scenario is one of the viable explanations, but it has a potentially serious issue of the overpro

Favour Nerrise, Qingyu Zhao, Kathleen L. Poston, Kilian M. Pohl

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and p

Md Abu Sayed, Ahmed H. Anwar, Christopher Kiekintveld, Branislav Bosansky

Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventional defenses. Honeypots have been used to deceive attackers by misrepresenting the true state of the network. Existing work on cyber deception does not model zero-day attacks. In t

Petter Törnberg

This guide introduces Large Language Models (LLM) as a highly versatile text analysis method within the social sciences. As LLMs are easy-to-use, cheap, fast, and applicable on a broad range of text analysis tasks, ranging from text annotation and classification to sentiment analysis and critical discourse analysis, many scholars believe that LLMs will trans

Olivia P. Dizon-Paradis, Daniel E. Capecci, Nathan T. Jessurun, Damon L. Woodard

A Bill of Materials (BoM) is a list of all components on a printed circuit board (PCB). Since BoMs are useful for hardware assurance, automatic BoM extraction (AutoBoM) is of great interest to the government and electronics industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB text and logos must be utilized. In this study, we discuss

Nicolò Paternoster, Carmine Giardino, Michael Unterkalmsteiner, Tony Gorschek

Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present an unique combination of characteristics which pose several cha

Daniel Odell, Daniel R. Phillips, Ubirajara van Kolck

We show the system of a heavy charged particle and a neutral atom can be described by a low-energy effective field theory where the attractive $1/r^4$ induced dipole potential determines the long-distance/low-energy wave functions. The $1/r^4$ interaction is renormalized by a contact interaction at leading order. Derivative corrections to that contact intera

Giuseppina Simone

Phase modulation has come to be recognized as a fundamental paradigm for optical device design in applications involving the spatiotemporal control of optical wavefronts. Here, asymmetric resonant multilayer cavities based on the inclusion of absorptive and lossless layers combined with the ideal conductive metal allow for reflection and phase shift control

Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu

While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and specifying reward functions is challenging. Motivated by these challenges, prior work has developed data-driven approaches that learn entirely from samples from the transition dyna

William Toner, Amos Storkey

Training neural network classifiers on datasets with label noise poses a risk of overfitting them to the noisy labels. To address this issue, researchers have explored alternative loss functions that aim to be more robust. The `forward-correction' is a popular approach wherein the model outputs are noised before being evaluated against noisy data. When the t

Anarya Ray, Pinchen Fan, Vincent F. He, Malachy Bloom

The speed of gravitational waves $v_g$ can be measured with the time delay between gravitational-wave detectors. Our study provides a more precise measurement of $v_g$ using gravitational-wave signals only, compared with previous studies. We select 52 gravitational-wave events that were detected with high confidence by at least two detectors in the first thr

Nitin Jain, Ulrich Hoff, Marco Gambetta, Jesper Rodenberg

Data centers are nowadays referred to as the digital world's cornerstone. Quantum key distribution (QKD) is a method that solves the problem of distributing cryptographic keys between two entities, with the security rooted in the laws of quantum physics. This document provides an assessment of the need and opportunity for ushering QKD in data centers. Togeth

Frank Hansen

We investigate geometric properties of a class of trace functions expressed in terms of the deformed logarithmic and exponential functions. These trace functions and their properties may be of independent interest. We use them in particular to extend earlier results of Epstein, Hiai, Carlen and Lieb.

Daniel Camarero Muñoz

The production of jets and prompt isolated photons at hadron colliders provides stringent tests of perturbative QCD. The latest measurements performed by the ATLAS Collaboration at the LHC are presented in these proceedings. The inclusive prompt-photon production is measured for two distinct photon isolation cones, $R = 0.2$ and $0.4$, as well as for their r

Paolo D'Alberto, Abhishek Jain, Ismail Bustany, Henri Fraisse

The peak performance of any SpMV depends primarily on the available memory bandwidth and its effective use. GPUs, ASICs, and new FPGAs have higher and higher bandwidth; however, for large scale and highly sparse matrices, SpMV is still a hard problem because of its random access pattern and workload imbalance. Here, we show how to turn randomness to our adva

Yuehao Bai, Hongchang Guo, Azeem M. Shaikh, Max Tabord-Meehan

This paper studies inference for the local average treatment effect in randomized controlled trials with imperfect compliance where treatment status is determined according to "matched pairs." By "matched pairs," we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each p

Ahmet Gungor, Muhammad Saif Ullah Khalid, Arman Hemmati

This study introduces novel physics-based scaling laws to estimate the propulsive performance of synchronously pitching foils in various schooling configurations at Re=4000. These relations are derived from quasi-steady lift-based and added mass forces. Hydrodynamic interactions among the schooling foils are considered through vortex-induced velocities impos

Cristian Hernández Cuevas, Roberto E. González, Nelson D. Padilla

Using data from TNG300-2, we train a neural network (NN) to recreate the stellar mass ($M^*$) and star formation rate (SFR) of central galaxies in a dark-matter-only simulation. We consider 12 input properties from the halo and sub-halo hosting the galaxy and the near environment. $M^*$ predictions are robust, but the machine does not fully reproduce its sca

Giorgos Lysandrou, Roma English Owen, Kirsty Mursec, Grant Le Brun

This study presents a comparative analysis of three Generative Pre-trained Transformer (GPT) solutions in a question and answer (Q&A) setting: Drug-GPT 3, Drug-GPT 4, and ChatGPT, in the context of healthcare applications. The objective is to determine which model delivers the most accurate and relevant information in response to prompts related to patient e

Nicolò G. Di Novo, Alvise Bagolini, Nicola M. Pugno

Surfaces capable of delaying the frosting passively and facilitating its removal are highly desirable in fields where ice introduces inefficiencies and risks. Coalescence jumping, enabled by highly hydrophobic surfaces, is already exploited to slow down the frosting but it is insufficient to completely eliminate the propagation by ice-bridging. We show how t

Emanuel Hubenschmid, Thiago L. M. Guedes, Guido Burkard

Following recent progress in the experimental application of electro-optic sampling to the detection of the quantum fluctuations of the electromagnetic-field ground state and ultrabroadband squeezed states on a subcycle scale, we propose an approach to elevate broadband electro-optic sampling from a spectroscopic method to a full quantum tomography scheme, a

Michael Unterkalmsteiner, Tony Gorschek, A. K. M. Moinul Islam, Chow Kian Cheng

Software Process Improvement (SPI) encompasses the analysis and modification of the processes within software development, aimed at improving key areas that contribute to the organizations' goals. The task of evaluating whether the selected improvement path meets these goals is challenging. On the basis of the results of a systematic literature review on SPI

Sho Onoe, Stéphane Virally, Denis V. Seletskiy

We develop the theoretical tools necessary to promote electro-optic sampling to a time-domain quantum tomography technique. Our proposed framework implements detection of the time evolution of both the electric-field of a propagating electromagnetic wave and its Hilbert transform (quadrature). Direct detection of either quadrature is not strictly possible in

Vladislav V. Kravchenko

An approach for solving a variety of inverse coefficient problems for the Sturm-Liouville equation -y''+q(x)y={\lambda}y with a complex valued potential q(x) is presented. It is based on Neumann series of Bessel functions representations for solutions. With their aid the problem is reduced to a system of linear algebraic equations for the coefficients of the

Sowmya S. Sundaram, Mark A. Musen

With the global increase in experimental data artifacts, harnessing them in a unified fashion leads to a major stumbling block - bad metadata. To bridge this gap, this work presents a Natural Language Processing (NLP) informed application, called FAIRMetaText, that compares metadata. Specifically, FAIRMetaText analyzes the natural language descriptions of me

Andrei G. Vladimirov, Daria A. Dolinina

A neutral delay differential equation (NDDE) model of a Kerr cavity with external coherent injection is developed that can be considered as a generalization of the Ikeda map with second and higher order dispersion being taken into account. It is shown that this model has solutions in the form of dissipative solitons both in the limit, where the model can be

Pablo Lemos, Paul Shah

The cosmic microwave background (CMB) offers a unique window into the early universe, providing insights into cosmological parameters like the Hubble constant. Recent precise measurements of the CMB by experiments like Planck seem to point to a lower value for the Hubble constant compared to some other measurements like those from Type Ia supernovae. This di

Xinyu Jiang, Haofan Sun, Kamal Choudhary, Houlong Zhuang

Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the preprocess to transfer a crystal structure into the input of ML, called descriptor, needs to be designed carefully. To

Bingkang Huang, Václav Mácha, Šárka Nečasová

We study the dynamics of a coupled system, formed by a rigid body with a cavity entirely filled with magnetohydrodynamic compressible fluid. Our aim is to derive the global existence of the unique classical solutions and weak solutions to this system. Moreover, we show the weak-strong uniqueness principle which means that a weak solution coincides with a str

Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji

Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available because a record was not maintained throughout data collection or for privacy reasons. This setting is known as demographic scarce regime. Prior research has shown that training an at

Arya Tanmay Gupta, Sandeep S Kulkarni

In this paper, we study the problem of gathering distance-1 myopic robots on an infinite triangular grid. We show that the algorithm developed by Goswami et al. (SSS, 2022) is lattice-linear (cf. Gupta and Kulkarni, SRDS 2023). This implies that a distributed scheduler, assumed therein, is not required for this algorithm: it runs correctly in asynchrony. It

Brian T. Le, Katherine M. Auer, David A. Lopez, Justin P. Shum

Microscopes are essential for biomechanics and hydrodynamical investigation of small aquatic organisms. We report a DIY microscope (GLUBscope) that enables the visualization of organisms from two orthogonal imaging planes (top and side views). Compared to conventional imaging systems, this approach provides a comprehensive visualization strategy of organisms

Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard

As deep learning models continue to advance and are increasingly utilized in real-world systems, the issue of robustness remains a major challenge. Existing certified training methods produce models that achieve high provable robustness guarantees at certain perturbation levels. However, the main problem of such models is a dramatically low standard accuracy

Marco Castrillón, María Eugenia Rosado, Alberto Soria

In this work, ruled surfaces in 3-dimensional Riemannian manifolds are studied. We determine the expression for the extrinsic and sectional curvature of a parametrized ruled surface, where the former one is shown to be non-positive. We also quantify the set of ruling vector fields along a given base curve which allows to define a relevant reference frame tha

Kaori Fuyuto, Emanuele Mereghetti

We study the $\mu\to e$ conversion process in nuclear targets arising in models of axion-like particles (ALPs) with hadronic and charged lepton flavor violating (CLFV) interactions. Contributions to this process generally fall into two categories: spin-independent (SI) and spin-dependent (SD). While the SI contribution can be generated by a dipole operator t

Ruskin Patel, Kaloyan Penev, Joshua Schussler

Tidal dissipation in binary systems is the primary source for synchronization and circularization of the objects in the system. The efficiency of the dissipation of tidal energy inside stars or planets results in significant changes in observed properties of the binary system and is often studied empirically using a parameter, commonly known as the modified

Francisco Albergaria, Gustavo C. Branco

We study, in a systematic way, the $V_{\text{CKM}}$ unitarity relations which arise in extensions of the three generations Standard Model (3gSM) involving the addition of vector-like quarks (VLQ). In particular, we emphasize the effect of the presence of VLQ on $V_{\text{CKM}}$ moduli differences, as well on the size of the imaginary parts of rephasing invar

F. D. Priestley, P. C. Clark, S. C. O. Glover, S. E. Ragan

Much of what we know about molecular clouds, and by extension star formation, comes from molecular line observations. Interpreting these correctly requires knowledge of the underlying molecular abundances. Simulations of molecular clouds typically only model species that are important for the gas thermodynamics, which tend to be poor tracers of the denser ma

Yves Brihaye, Betti Hartmann

We study the transition of charged boson stars from sub- to super-criticality. This transition is defined as that choice of coupling constants for which the Coulomb repulsion of two individual bosons (that make up the star) exactly cancels their gravitational attraction. It was recently shown that without self-interaction super-critical boson stars are unsta

Justin Barhite

The familiar trace of a square matrix generalizes to a trace of an endomorphism of a dualizable object in a symmetric monoidal category. To extend these ideas to other settings, such as modules over non-commutative rings, the trace can be generalized to a bicategory equipped with additional structure called a shadow. We propose a notion of bicategorical cotr

Viet Duong, Qiong Wu, Zhengyi Zhou, Eric Zavesky

Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) systems. While a plethora of methods have been developed to detect uni-modal OOD samples, only a few have focused on multi-modal OOD detection. Current contrastive learning-based met

George S. Manyali

The African School Series on Electronic Structure Methods and Applications (ASESMA) has had a positive impact on growth of computational material science in Kenya, visibility of Kenyan universities and strong collaboration ties between Kenyan scientist and the rest of the world. Data retrieved from Scopus indicate that computational materials scientists in K