April 2023 arXiv papers — page 101

Shifan Zhao, Tianshi Xu, Hua Huang, Edmond Chow

The spectrum of a kernel matrix significantly depends on the parameter values of the kernel function used to define the kernel matrix. This makes it challenging to design a preconditioner for a regularized kernel matrix that is robust across different parameter values. This paper proposes the Adaptive Factorized Nystr\"om (AFN) preconditioner. The preconditi

Efthymia Tsamoura, Jaehun Lee, Jacopo Urbani

The role of uncertainty in data management has become more prominent than ever before, especially because of the growing importance of machine learning-driven applications that produce large uncertain databases. A well-known approach to querying such databases is to blend rule-based reasoning with uncertainty. However, techniques proposed so far struggle wit

Matthew Palmer, Andreas Strömbergsson

The Lorentz gas describes an ensemble of noninteracting point particles in an infinite array of spherical scatterers. In the present paper we consider the case when the scatterer configuration P is a fixed union of (translated) lattices in R^d, and prove that in the limit of low scatterer density, the particle dynamics converges to a random flight process. I

Tomáš Plšek, Norbert Werner, Martin Topinka, Aurora Simionescu

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, howev

Jonathan A. Jensen

Marketers routinely allocate a significant portion of their budget to sponsorship. However, isolating the return on investment from such efforts has remained a challenge. Thus, a dataset of more than 5,800 sponsorships is analyzed using survival analysis approaches that utilizes the sponsor's renewal of the sponsorship as a proxy for positive ROI. In additio

Daniel Carmon, Amir Yehudayoff

We define a family of graphs we call dual systolic graphs. This definition comes from graphs that are duals of systolic simplicial complexes. Our main result is a sharp (up to constants) isoperimetric inequality for dual systolic graphs. The first step in the proof is an extension of the classical isoperimetric inequality of the boolean cube. The isoperimetr

Lorenzo Gavassino

It is well known that a mixture of two chemical components undergoing one chemical reaction is a bulk viscous fluid, where the bulk stress evolves according to the Israel-Stewart theory. Here, we show that a mixture of three independent chemical components undergoing two distinct chemical reactions can also be viewed as a bulk viscous fluid, whose bulk stres

Chenhan Yuan, Qianqian Xie, Sophia Ananiadou

The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT's ability on zero-shot temporal relation extraction. We designed three different prompt techniques to break down the task and evaluate ChatGPT. Our experiments show th

Eivind Jørstad, Robert C. Myers, Shan-Ming Ruan

We investigate how the complexity=anything observables proposed by [arXiv:2111.02429, arXiv:2210.09647] can be used to investigate the interior geometry of AdS black holes. In particular, we illustrate how the flexibility of the complexity=anything approach allows us to systematically probe the geometric properties of black hole singularities. We contrast ou

Victor Porée, Han Yan, Félix Desrochers, Sylvain Petit

Electronic spins can form long-range entangled phases of condensed matter named quantum spin liquids. Their existence is conceptualized in models of two- or three-dimensional frustrated magnets that evade symmetry-breaking order down to zero temperature. Quantum spin ice (QSI) is a theoretically well-established example described by an emergent quantum elect

Eduardo Noboro Tominaga, Onel Luiz Alcaraz López, Hirley Alves, Richard Demo Souza

Massive Multiple-Input Multiple-Output (mMIMO) is one of the essential technologies introduced by the Fifth Generation (5G) of wireless communication systems. However, although mMIMO provides many benefits for wireless communications, it cannot ensure uniform wireless coverage and suffers from inter-cell interference inherent to the traditional cellular netw

Bastien F. Grosso, Daniel W. Davies, Bonan Zhu, Aron Walsh

Building on the extensive exploration of metal oxide and metal halide perovskites, metal nitride perovskites represent a largely unexplored class of materials. We report a multi-tier computational screening of this chemical space. From a pool of 3660 ABN$_3$ compositions covering I-VIII, II-VII, III-VI and IV-V oxidation state combinations, 279 are predicted

Daniel G. Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi

We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional dat

Arpita Chatterjee

We successively pass two $V$-type three-level atoms through a single-mode cavity field. Considering the field to be initially in a classical state, we evaluate various statistical properties such as the quasiprobability $Q$ function, Wigner distribution, Mandel's $Q$ parameter and normal squeezing of the resulted field. We notice that the sequential crossing

Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca

Convolutional Neural Networks (CNNs) are the predominant model used for a variety of medical image analysis tasks. At inference time, these models are computationally intensive, especially with volumetric data. In principle, it is possible to trade accuracy for computational efficiency by manipulating the rescaling factor in the downsample and upsample layer

Jacques Giacomoni, Sushmita Rawat, K. Sreenadh

In this article, we study the Brezis-Nirenberg type problem of nonlinear Choquard equation with Neumann boundary condition \begin{equation*} \begin{aligned} -\Delta u &= \lambda \alpha(x)u + \left(\int\limits_{\Omega}\frac{u(y)^{2^*_{\mu}}}{|x-y|^{\mu}}\;dy\right)u^{2^*_{\mu}-1}, \;\;\text{in} \; \Omega,\\ \frac{\partial u}{\partial \nu} &= 0\;\; \text{on} \

Vedran Krčadinac, Mario Osvin Pavčević, Kristijan Tabak

We study $n$-dimensional matrices with $\{0,1\}$-entries ($n$-cubes) such that all their $2$-dimensional slices are incidence matrices of symmetric designs. A known construction of these objects obtained from difference sets is generalized so that the resulting $n$-cubes may have inequivalent slices. For suitable parameters, they can be transformed into $n$-

Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes en

Apoorv Jindal, Amartyajyoti Saha, Zizhong Li, Takashi Taniguchi

Achieving electrostatic control of quantum phases is at the frontier of condensed matter research. Recent investigations have revealed superconductivity tunable by electrostatic doping in twisted graphene heterostructures and in two-dimensional (2D) semimetals such as WTe$_2$. Some of these systems have a polar crystal structure that gives rise to ferroelect

Murali Dadi, Shubhang Pandey, Aparna Behera, T G Venkatesh

The current workloads and applications are highly diversified, facing critical challenges such as the Power Wall and the Memory Wall Problem. Different strategies over the multiple levels of Caches have evolved to mitigate these problems. Also, to work with such diversified applications, the Asymmetric Multi-Core Processor (AMP) presents itself as a viable s

Marc Salinas, Jorge Piekarewicz

The confluence of major theoretical, experimental, and observational advances are providing a unique perspective on the equation of state of dense neutron-rich matter -- particularly its symmetry energy -- and its imprint on the mass-radius relation for neutron stars. In this contribution we organize these developments in an equation of state density ladder.

Haley M. So, Laurie Bose, Piotr Dudek, Gordon Wetzstein

Conventional image sensors digitize high-resolution images at fast frame rates, producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices, because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor

Ilaria Castellani, Alceste Scalas

This volume contains the proceedings of PLACES 2023, the 14th edition of the Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software. The PLACES workshop series offers a forum for researchers from different fields to exchange new ideas about the challenges of modern and future programming, where concurrency and distribut

Brian Lehmann, Eric Riedl, Sho Tanimoto

Let $X$ be a smooth Fano variety over $\mathbb{C}$ and let $B$ be a smooth projective curve over $\mathbb{C}$. Geometric Manin's Conjecture predicts the structure of the irreducible components $M \subset \mathrm{Mor}(B, X)$ parametrizing curves which are non-free and have large anticanonical degree. Following ideas of our previous work, we prove the first pr

Akira Harada, Yudai Suwa, Masayuki Harada, Yusuke Koshio

Supernova neutrinos are invaluable signals that offer information about the interior of supernovae. Because a nearby supernova can occur at any time, preparing for future supernova neutrino observation is an urgent task. For the prompt analysis of supernova neutrinos, we have developed a new analysis code, ``Supernova Parameter Estimation Code based on Insig

Hamed Khosravi, Mohammad Reza Shafie, Morteza Hajiabadi, Ahmed Shoyeb Raihan

This paper presents a bibliometric analysis of the scientific literature related to chatbots, focusing specifically on ChatGPT. Chatbots have gained increasing attention recently, with an annual growth rate of 19.16% and 27.19% on the Web of Sciences (WoS) and Scopus, respectively. In this study, we have explored the structure, conceptual evolution, and tren

Jia Liu, Yan Luo, Muyuan Song

Axion-like particles (ALPs) have been studied in numerous experiments to search for their interactions, but most studies have focused on deriving bounds for the single coupling. However, in ultraviolet (UV) models, these couplings can appear simultaneously, and their interplay could have important implications for collider and beam dump searches. In this stu

Jin-Ah Kim, Terry Jay Jones, C. Darren Dowell

SOFIA/HAWC+ 154 $\mu$m Far-Infrared polarimetry observations of the well-studied edge-on galaxy NGC 891 are analyzed and compared to simple disk models with ordered (planar) and turbulent magnetic fields. The overall low magnitude and the narrow dispersion of fractional polarization observed in the disk require significant turbulence and a large number of tu

Shilin Liu, Clark McGrew

Data analysis in HEP experiments often uses binned likelihood from data and finite Monte Carlo sample. Statistical uncertainty of Monte Carlo sample has been introduced in Frequentist Inference in some literatures, but they are not suitable for Bayesian Inference. This technical note introduces the binned likelihood with Monte Carlo statistical uncertainty i

Woojae Kim, Naitik A. Panjwani, K. C. Krishnapriya, Kanad Majumder

Molecular dimers are widely utilized as a tool to investigate the structure-property relationships behind the complex photophysical processes of condensed-phase systems, where structural tuning remains a challenge. This approach often implicitly treats the dimers as static, with their relevant state energies and couplings determined by their optimized geomet

Giacomo Cacciapaglia, Aldo Deandrea, Wanda Isnard

The attractive feature of supersymmetry is predictive power, due to the large number of calculable properties and to coupling non-renormalisation. This power can be fully expressed in hidden sectors where supersymmetry may be exact, as these sectors are secluded from the visible one where instead supersymmetry must be broken. This suggests a new paradigm for

Gaurav Verma, Siddhisanket Raskar, Zhen Xie, Abid M Malik

Tuning tensor program generation involves searching for various possible program transformation combinations for a given program on target hardware to optimize the tensor program execution. It is already a complex process because of the massive search space and exponential combinations of transformations make auto-tuning tensor program generation more challe

Bram Bekker, Olga Kuryatnikova, Fernando Mário de Oliveira Filho, Juan C. Vera

Witsenhausen's problem asks for the maximum fraction $\alpha_n$ of the $n$-dimensional unit sphere that can be covered by a measurable set containing no pairs of orthogonal points. The best upper bounds for $\alpha_n$ are given by extensions of the Lov\'asz theta number. In this paper, optimization hierarchies based on the Lov\'asz theta number, like the Las

Yuanhang Shao, Tonmoy Dey, Nikola Vuckovic, Luke Van Popering

Combinatorial optimization (CO) aims to efficiently find the best solution to NP-hard problems ranging from statistical physics to social media marketing. A wide range of CO applications can benefit from local search methods because they allow reversible action over greedy policies. Deep Q-learning (DQN) using message-passing neural networks (MPNN) has shown

XENON Collaboration, E. Aprile, K. Abe, S. Ahmed Maouloud

We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is

Juuso Österman, Philipp Schicho, Aleksi Vuorinen

Both nonzero temperature and chemical potentials break the Lorentz symmetry present in vacuum quantum field theory by singling out the rest frame of the heat bath. This leads to complications in the application of thermal perturbation theory, including the appearance of novel infrared divergences in loop integrals and an apparent absence of four-dimensional

Bao-Fei Li, Parampreet Singh

The occurrence of singularities where spacetime curvature becomes infinite and geodesic evolution breaks down are inevitable events in classical general relativity (GR) unless one chooses an exotic matter violating weak energy condition. These singularities show up in various physical processes, such as the gravitational collapse, the birth of the universe i

James H. H. Chan, Kenneth C. Wong, Xuheng Ding, Dani Chao

We report the discovery of new lensed quasar candidates in the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) DR4, covering $1\,310~{\rm deg}^2$ of the sky with seeing of $\approx0.6''$. In addition to two catalogs of MILLIQUAS and AllWISEAGN, which contain confirmed and candidate quasars, we preselect quasar sources using color cut

Alexander C. Tyner, Pallab Goswami

Recent years have seen multiple high-throughput studies reveal an immense number of topological materials through use of symmetry indicators. Despite this success, three-dimensional topological insulators (TI) admitting a band-gap larger than Bi$_{2}$Se$_{3}$ and two-dimensional TIs admitting a band gap larger than $\beta$-bismuthene, two of the originally p

Adam J. McRoberts, Federico Balducci, Roderich Moessner, Antonello Scardicchio

We study the transport and equilibration properties of a classical Heisenberg chain, whose couplings are random variables drawn from a one-parameter family of power-law distributions. The absence of a scale in the couplings makes the system deviate substantially from the usual paradigm of diffusive spin hydrodynamics, and exhibit a regime of subdiffusive tra

Dominik Dold, Derek Aranguren van Egmond

Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspira

Sebastian Murk, Ioannis Soranidis

Singularity-free regular black holes are a popular alternative to the singular mathematical black holes predicted by general relativity. Here, we derive a generic condition that spherically symmetric dynamical regular black holes must satisfy to be compatible with the first law of black hole mechanics based on an expression for the surface gravity at the out

Simon J. Evered, Dolev Bluvstein, Marcin Kalinowski, Sepehr Ebadi

The ability to perform entangling quantum operations with low error rates in a scalable fashion is a central element of useful quantum information processing. Neutral atom arrays have recently emerged as a promising quantum computing platform, featuring coherent control over hundreds of qubits and any-to-any gate connectivity in a flexible, dynamically recon

M. S. Mirakhor, S. A. Walker, J. Runge

Cosmological simulations of structure formation predict that galaxy clusters continue to grow and evolve through ongoing mergers with group-scale systems. During these merging events, the ram pressure applied by the intracluster medium acts to strip the gas from the infalling groups, forming large tails of stripped gas, which eventually become part of the ma

Jack Franklin, Yuber F. Perez-Gonzalez, Jessica Turner

It remains a possibility that neutrinos are pseudo-Dirac states, such that a generation is composed of two maximally mixed Majorana neutrinos separated by a very small mass difference. We explore the physics potential of the JUNO experiment in constraining this possibility using the measurement of solar neutrinos. In particular, we investigate cases where on

Luigi Riz, Andrea Caraffa, Matteo Bortolon, Mohamed Lamine Mekhalfi

We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Tar

Jelena V. Rakonjac, Samuele Grandi, Sören Wengerowsky, Dario Lago-Rivera

We report on the transmission of telecom photons entangled with a multimode solid-state quantum memory over a deployed optical fiber in a metropolitan area. Photon pairs were generated through spontaneous parametric down-conversion, with one photon stored in a rare earth-based quantum memory, and the other, at telecommunication wavelengths, traveling through

Oliver Scholer, Jordy de Vries, Lukáš Gráf

We present $\nu$DoBe, a Python tool for the computation of neutrinoless double beta decay ($0\nu\beta\beta$) rates in terms of lepton-number-violating operators in the Standard Model Effective Field Theory (SMEFT). The tool can be used for automated calculations of $0\nu\beta\beta$ rates, electron spectra and angular correlations for all isotopes of experime

Niklas Garner, Wenjun Niu

We analyze the non-semisimple category of line operators in Chern-Simons gauge theories based off the Lie superalgebra $\mathfrak{gl}(1|1)$. Our proposal is that the category of line operators $\mathcal{C}$ can be identified with the derived category of modules for a boundary vertex operator algebra $\mathcal{V}$ realized as a certain infinite-order simple c

F. Gittins, T. Celora, A. Beri, N. Andersson

We re-visit the calculation of mode oscillations in the ocean of a rotating neutron star, which may be excited during thermonuclear X-ray bursts. Our present theoretical understanding of ocean modes relies heavily on the traditional approximation, commonly employed in geophysics. The approximation elegantly decouples the radial and angular sectors of the per

Joey Neilsen, Nathalie Degenaar

X-ray binaries, as bright local sources with short variability timescales for a wide range of accretion processes, represent ideal targets for high-resolution X-ray spectroscopy. In this chapter, we present a high-resolution X-ray spectral perspective on X-ray binaries, focusing on black holes and neutron stars. The majority of the chapter is devoted to obse

Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas

Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of inequity. Addressing such performance gaps remains a challenge, as little is understood about why performance degrades across incomes or geographies. We take a step in this direct

Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan

In recent years, Text-to-Image (T2I) models have been extensively studied, especially with the emergence of diffusion models that achieve state-of-the-art results on T2I synthesis tasks. However, existing benchmarks heavily rely on subjective human evaluation, limiting their ability to holistically assess the model's capabilities. Furthermore, there is a sig

Juliana Jansen Ferreira, Vinícius Segura, Joana G. R. Souza, Gabriel D. J. Barbosa

Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replacements for the ``forever chemicals''-- chemicals that enable our modern lives, but are harmful to the environment and the human health. Our approach combines AI capabilities with th

M. E. Shirokov

We discuss the interconnections between basic correlation measures of a bipartite quantum state and basic information characteristics of a quantum channel, focusing on the benefits of these interconnections for solving specific problems concerning the characteristics of both types. We describe properties of the (unoptimized and optimized) quantum discord in

Sai Saketh Rambhatla, Ishan Misra, Rama Chellappa, Abhinav Shrivastava

We tackle the challenging task of unsupervised object localization in this work. Recently, transformers trained with self-supervised learning have been shown to exhibit object localization properties without being trained for this task. In this work, we present Multiple Object localization with Self-supervised Transformers (MOST) that uses features of transf

Jorge Medina, Andrew D White

Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions o

Intae Jung, Steven L. Finkelstein, Pablo Arrabal Haro, Mark Dickinson

We analyze rest-frame ultraviolet to optical spectra of three $z\simeq7.47$ - $7.75$ galaxies whose Ly$\alpha$-emission lines were previously detected with Keck/MOSFIRE observations, using the JWST/NIRSpec observations from the Cosmic Evolution Early Release Science (CEERS) survey. From NIRSpec data, we confirm the systemic redshifts of these Ly$\alpha$ emit

Alex Vañó-Viñuales

Gravitational radiation and some global properties of spacetimes can only be unambiguously measured at future null infinity . This motivates the interest in reaching it within simulations of coalescing compact objects, whose waveforms are extracted for gravitational wave modelling purposes. One promising method to include future null infinity in the numerica

Franziska Scheibel, Christian Lauhoff, Philipp Krooß, Stefan Riegg

Ni-Mn-based Heusler alloys like Ni-Mn-Sn show an elastocaloric as well as magnetocaloric effect during the magneto-structural phase transition, making this material interesting for solid-state cooling application. Material processing by additive manufacturing can overcome difficulties related to machinability of the alloys, caused by their intrinsic brittlen

Azadeh Maleknejad

This work introduces the chiral memory effect on the celestial sphere that measures the permanent change of electromagnetic fields by spin-dependent processes in bulk. Unlike the conventional memory effect based on the permanent soft shift in the gauge field itself, it is a permanent change in its spin angular momentum. The concept underlying the chiral memo

Alexey Moiseevskiy

Advanced Encryption Standard is one of the most widely used and important symmetric ciphers for today. It well known, that it can be subjected to the quantum Grover's attack that twice reduces its key strength. But full AES attack requires hundreds of qubits and circuit depth of thousands, that makes impossible not only experimental research but also numeric

Sai Pavan Deekshitula, B. Sundar Rajan

Index coding (IC) is a source coding technique employed to improve spectral utilisation, where the source node aims to satisfy users' demands by making minimum transmissions. Non-orthogonal multiple access (NOMA) is integral to the radio access technique used in 5G networks. Index-coded NOMA (IC-NOMA) transmission scheme in Vehicular Adhoc Networks (VANETs)

Pablo Arrabal Haro, Mark Dickinson, Steven L. Finkelstein, Seiji Fujimoto

We present JWST/NIRSpec prism spectroscopy of seven galaxies selected from the Cosmic Evolution Early Release Science Survey (CEERS) NIRCam imaging with photometric redshifts z_phot>8. We measure emission line redshifts of z=7.65 and 8.64 for two galaxies, and z=9.77(+0.37,-0.29) and 10.01(+0.14,-0.19) for two others via the detection of continuum breaks con

Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun, Frauke Liers

Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. As the latter is often unknown, Distributionally Robust Optimization (DRO) provides a strong alternative that determines the best guaranteed solution over a set of distributions

Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari

Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related

Shashank Srikant, Anna A. Ivanova, Yotaro Sueoka, Hope H. Kean

Our goal is to identify brain regions involved in comprehending computer programs. We use functional magnetic resonance imaging (fMRI) to investigate two candidate systems of brain regions which may support this -- the Multiple Demand (MD) system, known to respond to a range of cognitively demanding tasks, and the Language system (LS), known to primarily res

Nate Harman, Andrew Snowden

Pre-Tannakian categories are a natural class of tensor categories that can be viewed as generalizations of algebraic groups. We define a pre-Tannkian category to be discrete if it is generated by an \'etale commutative algebra; these categories generalize finite groups. The main theorem of this paper establishes a rough classification of these categories: we

Tarek M. Elgindi, Kyle Liss, Jonathan C. Mattingly

We consider the advection-diffusion equation on $\mathbb{T}^2$ with a Lipschitz and time-periodic velocity field that alternates between two piecewise linear shear flows. We prove enhanced dissipation on the timescale $|\log \nu|$, where $\nu$ is the diffusivity parameter. This is the optimal decay rate as $\nu \to 0$ for uniformly-in-time Lipschitz velocity

Levi Lopes de Lima

We study the deformation theory of Einstein-Yang-Mills fields over conformally compact, asymptotically locally hyperbolic manifolds. We prove that if an Einstein-Yang-Mills field $(g_0,\omega_0)$ is trivial (which means that $g_0$ is Poincar\'e-Einstein and $\omega_0$ is a flat connection on a principal bundle over the underlying manifold) and non-degenerate

Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung, Che-rung Lee

Nowadays, the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services. In this paper, we investigate latency attacks on deep learning applications. Unlike common adversarial attacks for misclassification, the goal of latency attacks is to increase the inference time, which may stop applicati

Florian Bordes, Samuel Lavoie, Randall Balestriero, Nicolas Ballas

Self-Supervised Learning (SSL) models rely on a pretext task to learn representations. Because this pretext task differs from the downstream tasks used to evaluate the performance of these models, there is an inherent misalignment or pretraining bias. A commonly used trick in SSL, shown to make deep networks more robust to such bias, is the addition of a sma

Paria Ghaheri, Ahmadreza Shateri, Hamid Nasiri

Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's. Movement symptoms and imaging techniques are the most popular ways to diagnose this disease. However, they are not accurate and fast and may only be accessible to a few people. This study provides an autonomous syst

Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson

No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in support of the notion that individual problems require specially tailored inductive biases. While vi

Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Raaz Dwivedi

There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g., prior activity level, location, etc.). Online RL is a pro

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu

Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-image tasks. Building on this success, diffusion models have recently been extended to the Riemannian manifold setting, broadening their applicability to a range of problems from the

Adrián Gómez Pueyo, Alaska Subedi

KTaO${}_{3}$ presents a rich hyper-Raman spectrum originating from two-phonon processes at the Brillouin zone boundary, indicating the possibility of driving these phonon modes using intense midinfrared laser sources. We obtained the coupling of light to the highest-frequency longitudinal optic phonon mode $Q_{\rm{HY}}$ at the $X$ $(0,0, \frac{1}{2})$ point

Mario Berta, Lampros Gavalakis, Ioannis Kontoyiannis

A new finite form of de Finetti's representation theorem is established using elementary information-theoretic tools. The distribution of the first $k$ random variables in an exchangeable vector of $n\geq k$ random variables is close to a mixture of product distributions. Closeness is measured in terms of the relative entropy and an explicit bound is provide

Francesco Di Feola, Lorenzo Tronchin, Paolo Soda

The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latter relaxes this constraint. The large availability of unpaired datasets has raised the interest in deepening unpaired denoising strategies that, in turn, need for robust evaluation

Lukas Schulte, Anamaria Mojica-Hanke, Mario Linares-Vásquez, Steffen Herbold

Context: Many studies consider the relation between individual aspects of the software engineering process and bug-introduction, e.g., software testing and code review. These studies typically only identify correlations between their set of variables without accounting for interactions with external variables, such as confounding factors. Objective: Within t

Nadir Möller, Lukas Hecht, Ran Niu, Benno Liebchen

Writing is an ancient communication technique dating back at least 30,000 years. While even sophisticated contemporary writing techniques hinge on solid surfaces for engraving or the deposition of ink, writing within a liquid medium requires a fundamentally different approach. We here demonstrate writing of lines, letters, and complex patterns in water by as

Shuhei Ohyama, Shinsei Ryu

For a parameterized family of invertible states (short-range-entangled states) in $(1+1)$ dimensions, we discuss a generalization of the Berry phase. Using translationally-invariant, infinite matrix product states (MPSs), we introduce a gerbe structure, a higher generalization of complex line bundles, as an underlying mathematical structure describing topolo

Saad Kriouile, Dimitrios Tsilimantos, Theodoros Giannakas

To accommodate low latency and computation-intensive services, such as the Internet-of-Things (IoT), 5G networks are expected to have cloud and edge computing capabilities. To this end, we consider a generic network setup where devices, performing analytics-related tasks, can partially process a task and offload its remainder to base stations, which can then

Manish Kumar

Cancer is a complex genetic disease involving uncontrolled cell growth and proliferation, and necessitates effective targeting of dysregulated cellular pathways underlying cancer progression. Multiple genetic and epigenetic alterations characterize tumor progression and define hallmarks of cancer. Importantly, patients with the same cancer type respond diffe

Dinamo Djounvouna, Derek Krepski

Let $M$ be a smooth manifold and let $\chi\in \Omega^3(M)$ be closed differential form with integral periods. We show the Lie 2-algebra of sections of the $\chi$-twisted Courant algebroid on $M$ is quasi-isomorphic to the Lie 2-algebra of connection-preserving multiplicative vector fields on an $S^1$-bundle gerbe with connection (over $M$) whose 3-curvature

Oleksiy Klurman, Alexander P. Mangerel, Joni Teräväinen

Let $f:\mathbb{N}\to \mathbb{D}$ be a multiplicative function. Under the merely necessary assumption that $f$ is non-pretentious (in the sense of Granville and Soundararajan), we show that for any pair of distinct integer shifts $h_1,h_2$ the two-point correlation $$\frac{1}{x}\sum_{n\leq x}{f(n+h_1)\overline{f}(n+h_2)}$$ tends to $0$ along a set of $x\in\ma

Yangyiwei Yang, Patrick Kühn, Mozhdeh Fathidoost, Bai-Xiang Xu

Confronting the unveiled sophisticated multiscale structural and physical characteristics of hysteresis simulation of permanent magnets, notably samarium-cobalt (Sm-Co) alloy, a novel scheme is proposed linking physics-based micromagnetics on the nanostructure level and magnetostatic homogenization on the mesoscale polycrystal level. Thereby the micromagneti

Alexey I. Boyko, Anastasiia Kornilova, Rahim Tariverdizadeh, Mirfarid Musavian

This paper addresses the following research question: ``can one compress a detailed 3D representation and use it directly for point cloud registration?''. Map compression of the scene can be achieved by the tensor train (TT) decomposition of the signed distance function (SDF) representation. It regulates the amount of data reduced by the so-called TT-ranks.

Mayk Caldas Ramos, Shane S. Michtavy, Marc D. Porosoff, Andrew D. White

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-3.5, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to

Yue Zhang, Chengtao Peng, Qiuli Wang, Dan Song

Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-modal images, thus limiting the usage of multi-modal data for clinical purposes. To address this issue, in this paper,

Suprim Nakarmi, Sanam Pudasaini, Safal Thapaliya, Pratima Upretee

The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisi

Clemens Berger, Victor Iwaniack

We show that finite (i.e. locally finite and decomposition-finite) objects of a connected Grothendieck topos span a Boolean pretopos with an essentially unique Galois point. The automorphism group of this point carries a profinite topology whose classifying topos is equivalent to the given Grothendieck topos if the latter is finitely generated. This leads to

Andrés Chirre, Dimitar K. Dimitrov, Emily Quesada-Herrera, Mateus Sousa

We study two variations of the classical one-delta problem for entire functions of exponential type, known also as the Carath\'eodory--Fej\'er--Tur\'an problem. The first variation imposes the additional requirement that the function is radially decreasing while the second one is a generalization which involves derivatives of the entire function. Various int

Gabriela Pałka, Artur Nowakowski

This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER. The task involves the identification, categorization, and lemmatization of named entities in Slavic languages. Our approach involved exploring the use of foundation models for these tasks. In particular, we used models based on the popular BERT and T5 model a

Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan

Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with critical information needs like students or patients engaging with chatbots, the safety of these syst

Haoran Yun, Jose Luis Ponton, Carlos Andujar, Nuria Pelechano

The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have shown that the Sense of Embodiment, and in particular the Sense of Agency, depends on the extent to which the avatar's movements mimic the user's m

Naga Mallika Gunturu

Corporate Cloud transformation is expected to continue to grow double-digit each of the next few years. This growth is augmented by digital transformation, which itself is gaining huge momentum due to the recent consumer behavior trends and especially the COVID pandemic. It is also estimated that globally billions of dollars are wasted due to efficiencies in

Sauro Succi, Wael Itani, Katepalli R. Sreenivasan, Rene Steijl

We discuss the viability of ensemble simulations of fluid flows on quantum computers. The basic idea is to formulate a functional Liouville equation for the probability distribution of the flow field configuration and recognize that, due to its linearity, such an equation is in principle more amenable to quantum computing than the equations of fluid motion.

Daniil A. Boiko, Robert MacKnight, Gabe Gomes

Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and computer programming. Extreme scaling and reinforcement learning from human feedback have significantly improved the quality of generated text, enabling these models to perform various t

Harry Ramanantoanina, Anastasia Borschevsky, Michael Block, Larry Viehland

Ion mobilities of Lr^+ (Z = 103) and of its lighter chemical homolog Lu^+ (Z = 71) in helium were calculated for the ground state ^1S_0 and the lowest metastable state ^3D_1. To this end we applied the multi-reference configuration interaction (MRCI) method to calculate the ion-atom interaction potentials in the different states. The Gram-Charlier approach t