April 2023 arXiv papers — page 26

Jason T. Parker, Till Dreier, Daniel Nilsson, Simo A. Mäkiharju

We combine X-ray-specific tracer particles, a photon counting detector, and a liquid metal jet anode X-ray source to achieve $O$(1 kHz) X-ray imaging speeds in the laboratory, 15$\times$ faster than previous comparable studies with $O$(50 $\mu$m) tracers. To examine the limits of this measurement technique we conduct three experiments: 2D and 3D X-ray partic

Urban F. P. Seifert, Leon Balents

Moir\'e heterostructures of transition metal dichalcogenides exhibit Mott-insulating behaviour both at half-filling as well as at fractional fillings, where electronic degrees of freedom form self-organized Wigner crystal states. An open question concerns magnetic states obtained by lifting the pseudospin-1/2 degeneracy of these states at lowest temperatures

Ryan C. Spieler

Fracton phases of matter are gapped phases of matter that, by dint of their sensitivity to UV data, demand non-standard quantum field theories to describe them in the IR. Two such approaches are foliated quantum theory and exotic field theory. In this paper, we explicitly construct a map from one to the other and work out several examples. In particular, we

Dan Hu, Michal Zajaček, Norbert Werner, Romana Grossová

We report LOFAR and VLA detections of extended radio emission in the spiral galaxy HCG 97b, hosted by an X-ray bright galaxy group. The extended radio emission detected at 144 MHz, 1.4 GHz and 4.86 GHz is elongated along the optical disk and has a tail that extends 27 kpc in projection towards the centre of the group at GHz frequencies or 60 kpc at 144 MHz.

Job Feldbrugge

Angular power spectra are central to the study of our Universe. In this paper, I develop a new method for the numeric evaluation and analytic estimation of the angular cross-power spectrum of two random fields using complex analysis and Picard- Lefschetz theory. The proposed continuous deformation of the integration domain resums the highly oscillatory integ

Viola De Renzis, Davide Gerosa, Matthew Mould, Riccardo Buscicchio

Black-hole binary spin precession admits equilibrium solutions corresponding to systems with (anti-) aligned spins. Among these, binaries in the up-down configuration, where the spin of the heavier (lighter) black hole is co- (counter-) aligned with the orbital angular momentum, might be unstable to small perturbations of the spin directions. The occurrence

Ismail Qunbar, Nicholas C. Stone

Extreme mass ratio inspirals (EMRIs) occur when stellar-mass compact objects begin a gravitational wave (GW) driven inspiral into massive black holes. EMRI waveforms can precisely map the surrounding spacetime, making them a key target for future space-based GW interferometers such as {\it LISA}, but their event rates and parameters are massively uncertain.

Toshihiro Ota, Masato Taki

In the last few years, the success of Transformers in computer vision has stimulated the discovery of many alternative models that compete with Transformers, such as the MLP-Mixer. Despite their weak inductive bias, these models have achieved performance comparable to well-studied convolutional neural networks. Recent studies on modern Hopfield networks sugg

Isabel Papadimitriou, Dan Jurafsky

Both humans and large language models are able to learn language without explicit structural supervision. What inductive biases make this learning possible? We address this fundamental cognitive question by leveraging transformer language models: we inject inductive bias into language models by pretraining on formally-structured data, and then evaluate the b

G. S. Vicente, Rudnei O. Ramos, Vitória N. Magalhães

The quantum cosmology of the flat Friedmann-Lema{\^i}tre-Robertson-Walker Universe, filled with a scalar field, is considered in the de Broglie-Bohm (dBB) interpretation framework. A stiff-matter quantum bounce solution is obtained. The bouncing and subsequent pre-inflationary and inflationary dynamics are studied in details. We consider some representative

Alexander Jercher, Sebastian Steinhaus, Johannes Thürigen

It has been shown in [1] that a class of restricted spin foam models can feature a reduced spectral dimension of space-time. However, it is still an open question how curvature affects the flow of the spectral dimension. To answer this question, we consider another class of restricted spin foam models, so called spin foam frusta, which naturally exhibit osci

Axel Runnholm, Matthew J. Hayes, Yu-Heng Lin, Jens Melinder

Lyman $\alpha$ (Ly$\alpha$) is now routinely used as a tool for studying high-redshift galaxies and its resonant nature means it can trace neutral hydrogen around star-forming galaxies. Integral field spectrograph measurements of high-redshift Ly$\alpha$ emitters indicate that significant extended Ly$\alpha$ halo emission is ubiquitous around such objects. W

Matteo Piani, Javier Rubio

We make use of classical lattice simulations in 3 + 1 dimensions to study the preheating stage of Higgs Inflation in Einstein-Cartan gravity. Focusing for concreteness on a simplified scenario involving the seminal Nieh-Yan term, we demonstrate the formation of dense and spatially localized oscillon configurations constituting up to 70% of the total energy d

S Reece Boston, Charles R Evans, J Christopher Clemens

With the precision now afforded by modern space-based photometric observations from the retired K2 and current TESS missions, the effects of general relativity (GR) may be detectable in the light curves of pulsating white dwarfs (WDs). Almost all WD models are calculated using a Newtonian description of gravity and hydrodynamics. To determine if inclusion of

James Hung-Hsu Chan, Sergey Sibiryakov, Wei Xue

Boson stars are gravitationally bound objects that arise in ultralight dark matter models and form in the centers of galactic halos or axion miniclusters. We systematically study the excitations of a boson star, taking into account the mixing between positive and negative frequencies introduced by gravity. We show that the spectrum contains zero-energy modes

Guillem Domènech, Derek Inman, Alexander Kusenko, Misao Sasaki

If long-range attractive forces exist and are stronger than gravity then cosmic halo formation can begin in the radiation-dominated era. We study a simple realization of this effect in a system where dark matter fermions have Yukawa interactions mediated by scalar particles, analogous to the Higgs boson in the standard model. We develop a self-consistent des

Clifford Cheung, Temple He, Allic Sivaramakrishnan

Inspired by the second law of thermodynamics, we study the change in subsystem entropy generated by dynamical unitary evolution of a product state in a bipartite system. Working at leading order in perturbative interactions, we prove that the quantum $n$-Tsallis entropy of a subsystem never decreases, $\Delta S_n \geq 0$, provided that subsystem is initializ

Anastasia A. Ponomareva, Matt J. Jarvis, Hengxing Pan, Natasha Maddox

We present the first measurement of the HI mass function (HIMF) using data from MeerKAT, based on 276 direct detections from the MIGHTEE Survey Early Science data covering a period of approximately a billion years ($0 \leq z \leq 0.084 $). This is the first HIMF measured using interferometric data over non-group or cluster field, i.e. a deep blank field. We

Shashank Shalgar, Irene Tamborra

Recent body of work points out that the mean-field approximation, widely employed to mimic the neutrino field within a neutrino-dense source, might give different results in terms of flavor evolution with respect to the correspondent many-body treatment. In this paper, we investigate whether such conclusions derived within a constrained framework should hold

Nilotpal Chakraborty, Roderich Moessner, Benoit Doucot

We introduce a model to study magnon scattering in skyrmion crystals, sandwiched between ferromagnets which act as the source of magnons. Skyrmions are topological objects while skyrmion crystals break internal and translational symmetries, thus our setup allows us to study the interplay of topology and symmetry breaking. Starting from a basis of holomorphic

Federico Sestito, Joel Roediger, Julio F. Navarro, Jaclyn Jensen

The formation of "stellar halos" in dwarf galaxies have been discussed in terms of early mergers or Galactic tides, although fluctuations in the gravitational potential due to stellar feedback is also a possible candidate mechanism. A Bayesian algorithm is used to find new candidate members in the extreme outskirts of the Sculptor dwarf galaxy. Precise metal

Benson Au

We study additive finite-rank perturbations of random periodic band matrices under the assumption that the nontrivial eigenvalues of the perturbation do not depend on the dimension. We establish the eigenvalue/eigenvector BBP transition in this model for band widths $b_N \gg N^\varepsilon$. Our analysis relies on moment method calculations for general vector

Nicolas Longeard, Pascale Jablonka, Giuseppina Battaglia, Khyati Malhan

We present a new spectroscopic study of the dwarf galaxy Hercules (d ~ 132 kpc) with data from the Anglo-Australian Telescope and its AAOmega spectrograph together with the Two Degree Field multi-object system to solve the conundrum that whether Hercules is tidally disrupting. We combine broadband photometry, proper motions from Gaia, and our Pristine narrow

Daniel Simons, Erik Gustafson, Yannick Meurice

We discuss the self-consistency imposed by the analyticity of regular parts of form factors, appearing in the $z$-expansion for semileptonic $B$-meson decays, when fitted in different kinematic regions. Relying on the uniqueness of functions defined by analytic continuation, we propose four metrics which measure the departure from the ideal analytic self-con

Polina Novikova, Ekaterina Shishkina, Dmitry Blinov

Fermi-LAT observations provide continuous and regularly-sampled measurements of gamma-ray photon flux for hundreds of blazars. Many of these light curves, spanning almost 15 years, have been thoroughly examined for periodicity in multiple studies. However, the possibility that blazars may exhibit irregularly repeating flaring patterns in their gamma-ray ligh

Victor A. Rodriguez

We calculate numerically the torus one-point string diagram in the two-dimensional string cosmology background by decomposing the one-point functions in $c=1$ and $c=25$ Liouville CFT into torus one-point Virasoro conformal blocks and integrating over the fundamental domain of the torus moduli space. We find a remarkably simple result as a function of the ou

William DeRocco, Jeff A. Dror

The stochastic gravitational-wave background is imprinted on the times of arrival of radio pulses from millisecond pulsars. Traditional pulsar timing analyses fit a timing model to each pulsar and search the residuals of the fit for a stationary time correlation. This method breaks down at gravitational-wave frequencies below the inverse observation time of

Huan-ang Gao, Beiwen Tian, Pengfei Li, Hao Zhao

In this paper, we study the problem of semi-supervised 3D object detection, which is of great importance considering the high annotation cost for cluttered 3D indoor scenes. We resort to the robust and principled framework of selfteaching, which has triggered notable progress for semisupervised learning recently. While this paradigm is natural for image-leve

Zhang Youmin, Guo Xianda, Poggi Matteo, Zhu Zheng

Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model

Matthew Middlehurst, Patrick Schäfer, Anthony Bagnall

In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. Th

Olumakinde Ogunnaike, Johannes Feldmeier, Jong Yeon Lee

We identify emergent hydrodynamics governing charge transport in Brownian random circuits with various symmetries, constraints, and ranges of interactions. This is accomplished via a mapping between the averaged dynamics and the low energy spectrum of a Lindblad operator, which acts as an effective Hamiltonian in a doubled Hilbert space. By explicitly constr

Tianhe Ren, Jianwei Yang, Shilong Liu, Ailing Zeng

This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64.6 AP on COCO val2017 and 64.8 AP on COCO test-dev using only 700M parameters without any test time augmentation. It explores the combination of the powerful FocalNet-Huge backbone with the effective Stable-DINO detector. Different from existing SOTA model

Alexander F. Ritter, Filip Živanović

We construct a filtration by ideals on quantum cohomology for symplectic manifolds with a Hamiltonian $S^1$-action that extends to a pseudoholomorphic $\mathbb{C}^*$-action. These spaces include all Conical Symplectic Resolutions, in particular all Quiver Varieties. In particular, we obtain a family of filtrations on singular cohomology for any Conical Sympl

Ayesha Hussain, Youness Lamzouri

Let $p$ be a prime number and $\left(\frac{\cdot}{p}\right)$ be the Legendre symbol modulo $p$. The \emph{Legendre path} attached to $p$ is the polygonal path whose vertices are the normalized character sums $\frac{1}{\sqrt{p}} \sum_{n\leq j} \left(\frac{n}{p}\right)$ for $0\leq j\leq p-1$. In this paper, we investigate the distribution of Legendre paths as

Michelle C. Anderson, Esmae J. Woods, Thomas P. Fay, David J. Wales

Polariton chemistry holds promise for facilitating mode-selective chemical reactions, but the underlying mechanism behind the rate modifications observed under vibrational strong coupling is not well understood. Using the recently developed quantum transition path theory, we have uncovered a mechanism of resonant suppression of a thermal reaction rate in a s

Zeyu Lu, Di Huang, Lei Bai, Jingjing Qu

Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish trust in photographs. This study aims to c

S. F. Sanchez, L. Galbany, C. J. Walcher, R. Garcia-Benito

This paper describes the extended data release of the Calar Alto Legacy Integral Field Area (CALIFA) survey (eDR). It comprises science-grade quality data for 895 galaxies obtained with the PMAS/PPak instrument at the 3.5 m telescope at the Calar Alto Observatory along the last 12 years, using the V500 setup (3700-7500{\AA}, 6{\AA}/FWHM) and the CALIFA obser

Juan Tapia, Christoph Busch

This paper proposes an explainable visualisation of different face feature extraction algorithms that enable the detection of bona fide and morphing images for single morphing attack detection. The feature extraction is based on raw image, shape, texture, frequency and compression. This visualisation may help to develop a Graphical User Interface for border

Jeffrey Cheng, Ian Malcolm Johnson McInnis, Matthew Yee

We study the inertia of distance matrices of weighted graphs. Our novel congruence-based proof of the inertia of weighted trees extends to a proof for the inertia of weighted unicyclic graphs whose cycle is a triangle. Partial results are given on the inertia of other rationally weighted unicylic graphs.

Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr

Improving and guaranteeing the robustness of deep learning models has been a topic of intense research. Ensembling, which combines several classifiers to provide a better model, has shown to be beneficial for generalisation, uncertainty estimation, calibration, and mitigating the effects of concept drift. However, the impact of ensembling on certified robust

Jeffrey Cheng, Ian Malcolm Johnson McInnis, Matthew Yee

A weighted graph $\phi G$ encodes a finite metric space $D_{\phi G}$. When is $D$ totally decomposable? When does it embed in $\ell_1$ space? When does its representing matrix have $\leq 1$ positive eigenvalue? We give useful lemmata and prove that these questions can be answered without examining $\phi$ if and only if $G$ has no $K_{2,3}$ minor. We also pro

Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi

Electronic health records (EHRs) recorded in hospital settings typically contain a wide range of numeric time series data that is characterized by high sparsity and irregular observations. Effective modelling for such data must exploit its time series nature, the semantic relationship between different types of observations, and information in the sparsity s

Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla

We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges to a constant. By analyzing the squared prediction risk of ridge ensembles as a function of the explicit penalty $\lambda$ and the limiting subsample aspect ratio $\phi_s$ (the rat

Diego Pasmino, Carlos Aravena, Juan Tapia, Christoph Busch

Nowadays, Presentation Attack Detection is a very active research area. Several databases are constituted in the state-of-the-art using images extracted from videos. One of the main problems identified is that many databases present a low-quality, small image size and do not represent an operational scenario in a real remote biometric system. Currently, thes

Marius L. Ruh, Darshan Sarojini, Andrew Fletcher, Isaac Asher

The conceptual design of eVTOL aircraft is a high-dimensional optimization problem that involves large numbers of continuous design parameters. Therefore, eVTOL design method would benefit from numerical optimization algorithms capable of systematically searching these high-dimensional parameters spaces, using comprehensive and multidisciplinary models of th

Tobias Rueckert, Daniel Rueckert, Christoph Palm

In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, w

Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos

We introduce new methods for 1) accelerating and 2) stabilizing training for large language-vision models. 1) For acceleration, we introduce SwitchBack, a linear layer for int8 quantized training which provides a speed-up of 13-25% while matching the performance of bfloat16 training within 0.1 percentage points for the 1B parameter CLIP ViT-Huge -- the large

Timo Breuer, Christin Katharina Kreutz, Philipp Schaer, Dirk Tunger

Digital libraries in the scientific domain provide users access to a wide range of information to satisfy their diverse information needs. Here, ranking results play a crucial role in users' satisfaction. Exploiting bibliometric metadata, e.g., publications' citation counts or bibliometric indicators in general, for automatically identifying the most relevan

Najib Idrissi, Eugene Rabinovich

We apply the theory of operadic Koszul duality to provide a cofibrant resolution of the colored operad whose algebras are prefactorization algebras on a fixed space M. his allows us to describe a notion of prefactorization algebra up to homotopy as well as morphisms up to homotopy between such objects. We make explicit these notions for several special M, su

Wang-Chiew Tan

This paper presents an opinion on the potential of using large language models to query on both unstructured and structured data. It also outlines some research challenges related to the topic of building question-answering systems for both types of data.

Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM capabilities with visual computation to enable effective i

G. Fontanesi, F. Ortíz, E. Lagunas, V. Monzon Baeza

This paper surveys the application and development of Artificial Intelligence (AI) in Satellite Communication (SatCom) and Non-Terrestrial Networks (NTN). We first present a comprehensive list of use cases, the relative challenges and the main AI tools capable of addressing those challenges. For each use case, we present the main motivation, a system descrip

Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz

Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through a voting mechanism over the final answers, but the intermediate steps themselves are discarded. While such approaches imp

Zhe Li, Zerong Zheng, Yuxiao Liu, Boyao Zhou

Creating pose-driven human avatars is about modeling the mapping from the low-frequency driving pose to high-frequency dynamic human appearances, so an effective pose encoding method that can encode high-fidelity human details is essential to human avatar modeling. To this end, we present PoseVocab, a novel pose encoding method that encourages the network to

Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan

Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, i

Marianna Ganapini, Enrico Panai

This is an audit framework for AI-nudging. Unlike the static form of nudging usually discussed in the literature, we focus here on a type of nudging that uses large amounts of data to provide personalized, dynamic feedback and interfaces. We call this AI-nudging (Lanzing, 2019, p. 549; Yeung, 2017). The ultimate goal of the audit outlined here is to ensure t

Roger Creus Castanyer

Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple learning agents that coexist in a shared environment. MARL is more challenging than single-agent RL because it involves more complex learning dynamics: the observations and rewards of each agent are functions of all other agents. In the context of MARL, Real-Time Strategy (RTS) games

Xinyi Li, Michael R. Kosorok

Precision medicine seeks to discover an optimal personalized treatment plan and thereby provide informed and principled decision support, based on the characteristics of individual patients. With recent advancements in medical imaging, it is crucial to incorporate patient-specific imaging features in the study of individualized treatment regimes. We propose

L Glaser

This work examines the deformed fuzzy sphere, as an example of a fuzzy space that can be described through a spectral triple, using computer visualisations. We first explore this geometry using an analytic expression for the eigenvalues to examine the spectral dimension and volume of the geometry. In the second part of the paper we extend the code from our p

Andrea Dittadi

Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that explicitly capturing the underlying structure of data should allow connectionist systems to generalize in a more predictable and systematic manner. Indeed, evidence in humans suggests that interpreting

Simiao Ren, Francesco Luzi, Saad Lahrichi, Kaleb Kassaw

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the \textit{zero-shot} image segmentation accuracy of SAM on a l

Boxiang Zhang, Zhiyong Zhang, Chuanhong Jin

Polymer-sorted high-density carbon nanotube (CNT) arrays have shown great potential to extend the silicon-based Moore's law. Imaging the CNT arrays on insulators like SiO2/Si using low-voltage scanning electron microscopy (LVSEM) to acquire array information like the alignment, density, and distribution of residual polymers is necessary. Such a task remains

Sasmita Patnaik, Sanehlata, Gary Weiss

The notion of automatic selfadjointness of all ideals in a multiplicative semigroup of the bounded linear operators on a separable Hilbert space B(H) arose in a 2015 discussion with Heydar Radjavi who pointed out that B(H) and the finite rank operators F(H) possessed this unitary invariant property which category we named SI semigroups (for automatic selfadj

Gustavo A. Vasquez-Montoya, Tadej Emersic, Noe Atzin, Antonio Tavera-Vazquez

The optical properties of liquid crystals serve as the basis for display, diagnostic, and sensing technologies. Such properties are generally controlled by relying on electric fields. In this work, we investigate the effects of microfluidic flows and acoustic fields on the molecular orientation and the corresponding optical response of nematic liquid crystal

Rongjie Huang, Mingze Li, Dongchao Yang, Jiatong Shi

Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI

Jin Guo, Ting Gao, Peng Zhang, Jiequn Han

In many scientific and engineering problems, noise and nonlinearity are unavoidable, which could induce interesting mathematical problem such as transition phenomena. This paper focuses on efficiently discovering the most probable transition pathway for stochastic dynamical systems employing reinforcement learning. With the Onsager-Machlup action functional

Yuanxin Xia, Cheol-Ho Jeong

The inference of the absorption configuration of an existing room solely using acoustic signals can be challenging. This research presents two methods for estimating the room dimensions and frequency-dependent absorption coefficients using room transfer functions. The first method, a knowledge-based approach, calculates the room dimensions through damped res

Jan van den Brand, Daniel Zhang

Since the development of efficient linear program solvers in the 80s, all major improvements for solving multi-commodity flows to high accuracy came from improvements to general linear program solvers. This differs from the single commodity problem (e.g.~maximum flow) where all recent improvements also rely on graph specific techniques such as graph decompos

Clementa Alonso-González, Miguel Ángel Navarro-Pérez

In the network coding framework, given a prime power $q$ and the vector space $\mathbb{F}_q^n$, a constant type flag code is a set of nested sequences of $\mathbb{F}_q$-subspaces (flags) with the same increasing sequence of dimensions (the type of the flag). If a flag code arises as the orbit under the action of a cyclic subgroup of the general linear group

Lorenzo Guerra, Santanil Jana, Arun Maiti

Unordered flag manifolds are the manifolds of unordered $n$-tuple of mutually orthogonal lines in $\mathbb{R}^n$. In this paper, we develop some basic tools to compute the mod-$2$ cohomology groups of these spaces, and apply them for explicit computation for small $n$. We show that this computation improves the known estimate of the number of Auerbach bases

Niklas Galke, Lauritz van Luijk, Henrik Wilming

A set of classical or quantum states is equivalent to another one if there exists a pair of classical or quantum channels mapping either set to the other one. For dichotomies (pairs of states), this is closely connected to (classical or quantum) R\'enyi divergences (RD) and the data-processing inequality: If a RD remains unchanged when a channel is applied t

Xiao Qi, David J. Foran, John L. Nosher, Ilker Hacihaliloglu

Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy an

Gabriel Narvaez, Michael Bressan, Andres Pantoja, Luis Felipe Giraldo

This paper presents the first study of the long-term impact of climate change on photovoltaic potential in South America. This region has great potential for implementing renewable energy, mainly solar energy solutions, due to its high solar irradiance levels. Based on the Coordinated Regional Downscaling Experiment (CORDEX) for the South American region, we

Imran Chowdhury Dipto, Bill Cassidy, Connah Kendrick, Neil D. Reeves

This research conducts an investigation on the effect of visually similar images within a publicly available diabetic foot ulcer dataset when training deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degr

Yingjie Li, Weilu Gao, Cunxi Yu

Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing,

Truong-Dong Do, Nguyen Xuan-Mung, Sung-Kyung Hong

Autonomous Nano Aerial Vehicles have been increasingly popular in surveillance and monitoring operations due to their efficiency and maneuverability. Once a target location has been reached, drones do not have to remain active during the mission. It is possible for the vehicle to perch and stop its motors in such situations to conserve energy, as well as mai

Brennan Undseth, Oriol Pietx-Casas, Eline Raymenants, Mohammad Mehmandoost

As spin-based quantum processors grow in size and complexity, maintaining high fidelities and minimizing crosstalk will be essential for the successful implementation of quantum algorithms and error-correction protocols. In particular, recent experiments have highlighted pernicious transient qubit frequency shifts associated with microwave qubit driving. Wor

Anis Allagui, Ahmed S. Elwakil

This paper is devoted to the study of the $M$-Wright function ($M_{\alpha}(t)$) which is the inverse Laplace transform of the single-parameter Mittag-Leffler (ML) function ($E_{\alpha}(-s)$). Because $E_{\alpha}(-s)$ can be viewed as the fractional-order generalization of the exponential function for $0<\alpha<1$, to which it reduces for $\alpha=1$, i.e. $E_

James Gung, Raphael Shu, Emily Moeng, Wesley Rose

With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states. However, a lack of dedicated benchmarks and standardized evaluation has made progress difficult to track and comparisons between systems diff

D. Candela

Using numerical simulations it is shown that a jammed, random pack of soft frictional grains can store an arbitrary waveform that is applied as a small time-dependent shear while the system is slowly compressed. When the system is decompressed at a later time, an approximation of the input waveform is recalled in time-reversed order as shear stresses on the

Youness Chatibi

The main goal of this paper is to find analytical solutions of a system of nonlinear ordinary differential equations arising in the virus propagation in blockchain networks. The presented method reduces the problem to an Abel differential equation of the first kind and solve it directly.

Md Mahfuz Ibn Alam, Ruoyu Xie, Fahim Faisal, Antonios Anastasopoulos

This report describes GMU's sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also int

Sasmita Patnaik, Sanehlata, Gary Weiss

This extends our new study of the automatic selfadjoint ideal property for B(H)-operator semigroups introduced to us by Heydar Radjavi (SI semigroups for short). Our investigation here of singly generated SI semigroups led to unexpected algebraic and analytic phenomena on the simplicity of SI semigroups and on the spectral density of their generators. In par

Ashutosh Dash, Ankit Kumar Panda

We investigate the space-time dependence of electromagnetic fields produced by charged participants in an expanding fluid. To address this problem, we need to solve the Maxwell's equations coupled to the hydrodynamics conservation equation, specifically the relativistic magnetohydrodynamics (RMHD) equations, since the charged participants move with the flow.

Kexue Zhang, Elena Braverman

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting i

Wlodzimierz Bryc, Yizao Wang

We compute limit fluctuations of random Motzkin paths with arbitrary end-points as the length of the path tends to infinity.

S. Yu. Melnikov, P. A. Boley, N. S. Nikonova, A. Caratti o Garatti

$Context.$ The YSO Th 28 possesses a highly collimated jet, which clearly exhibits an asymmetric brightness of its jet lobes at optical and NIR wavelengths. There may be asymmetry in the jet plasma parameters in opposite jet lobes (e.g. electron density, temperature, and outflow velocity). $Aims.$ We examined the Th 28 jet in a 3"x3" where the jet material i

Kaisheng Song, Silvan Käser, Kai Töpfer, Luis Itza Vazquez-Salazar

Full dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide means for accurate and efficient molecular simulations in the gas- and condensed-phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced int

EC Gabrick, E Sayari, ASM de Castro, J Trobia

We investigate the solutions for a time dependent potential by considering two scenarios for the fractional Schr\"odinger equation. The first scenario analyzes the influence of the time dependent potential in the absence of the kinetic term. We obtain analytical and numerical solutions for this case by considering the Caputo fractional time derivative, which

Joel Hirst, Sergiu Ruta, Jerome Jackson, Thomas Ostler

It is widely known that antiferromagnets (AFMs) display a high frequency response in the terahertz (THz) range, which opens up the possibility for ultrafast control of their magnetization for next generation data storage and processing applications. However, because the magnetization of the different sublattices cancel, their state is notoriously difficult t

Alberto Nardi, Alisa Davydova, Nikolai Kuznetsov, Miles H. Anderson

Photonic crystals, material structures in which the dielectric function varies periodically in one, two, or three dimensions, can provide exquisite control over the propagation and confinement of light. By tailoring their band structure, exceptional optical effects can be achieved, such as slow light propagation or, through the creation of photonic bandgaps,

Federico D'Onofrio, Yuri Faenza, Lingyi Zhang

We study incremental knapsack problems with profits given by a special class of monotone submodular functions, that we dub all-or-nothing. We show that these problems are not harder to approximate than a less general class of modular incremental knapsack problems, that have been investigated in the literature. We also show that certain extensions to more gen

Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli

Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward function justifying the behavior demonstrated by an expert agent. A well-known limitation of IRL is the ambiguity in the choice of the reward function, due to the existence of multiple rewards that explain the observed behavior. This limitation has been recen

Raúl Morral-Yepes, Adam Smith, S. L. Sondhi, Frank Pollmann

Repeated projective measurements in unitary circuits can lead to an entanglement phase transition as the measurement rate is tuned. In this work, we consider a different setting in which the projective measurements are replaced by dynamically chosen unitary gates that minimize the entanglement. This can be seen as a one-dimensional unitary circuit game in wh

Jennifer Pohle, Johannes Signer, Jana A. Eccard, Melanie Dammhahn

Step-selection models are widely used to study animals' fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, can depend on the animal's unobserved behavioural states, such as resting or foraging. This is ignored in standard (integrated) step-selection analyses (SSA, iSSA). While different approaches have e

Laurent Bréhélin

In recent years, several machine learning approaches have been proposed to predict gene expression and epigenetic signals from the DNA sequence alone. These models are often used to deduce, and, to some extent, assess putative new biological insights about gene regulation, and they have led to very interesting advances in regulatory genomics. This article re

Mohsen Yarmohammadi, Marin Bukov, Michael H. Kolodrubetz

We study the direct laser drive of infrared-active phonons that are quadratically coupled to a spinless fermion chain. Feedback is incorporated by phonon dressing of the electronic dispersion, which enables effective non-linearities in the phonon dynamics. We uncover a first-order phase transition in the phononic steady state in which hysteretic effects allo

Yanbo Dai, Songze Li

In a federated learning (FL) system, distributed clients upload their local models to a central server to aggregate into a global model. Malicious clients may plant backdoors into the global model through uploading poisoned local models, causing images with specific patterns to be misclassified into some target labels. Backdoors planted by current attacks ar

Lars Niedorf

We prove restriction type estimates for sub-Laplacians on general two-step stratified Lie groups. The core of our approach is to use spectral cluster estimates to effectively control the eigenvalue distribution of a family of anisotropic twisted Laplacians.

Matt Post, Marcin Junczys-Dowmunt

It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all translation. It is therefore unfortunate that machine translation -- both research and production -- largely remains stuck in a decades-old sentence-level translation paradigm. It is als