October 2023 arXiv papers — page 172

Ali Ebadi, Ebrahim Sahafizadeh

This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa

Josh Collyer, Tim Watson, Iain Phillips

Being able to identify functions of interest in cross-architecture software is useful whether you are analysing for malware, securing the software supply chain or conducting vulnerability research. Cross-Architecture Binary Code Similarity Search has been explored in numerous studies and has used a wide range of different data sources to achieve its goals. T

Carlo Bellavita, Eugenio Alberto Dellepiane

In this paper we study embeddings between de Branges-Rovnyak spaces $H(b)$ and harmonically weighted Dirichlet spaces $\mathcal{D}(\mu)$ in terms of the boundary spectrum of $b$ and the support of the measure $\mu$, by using elementary reproducing kernel estimates. We completely characterize the embedding between the model spaces $K_u$ and the local Dirichle

E. Trentin, V. Ripepi, R. Molinaro, G. Catanzaro

Classical Cepheids (DCEPs) play a fundamental role in the calibration of the extra-galactic distance ladder which eventually leads to the determination of the Hubble constant($H_0$) thanks to the period-luminosity ($PL$) and period-Wesenheit ($PW$) relations exhibited by these pulsating variables. Therefore, it is of great importance to establish the depende

Chuan Fang, Yuan Dong, Kunming Luo, Xiaotao Hu

Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR applications. However, existing methods cannot faithfully capture the room layout, nor do they allow flexible editing of individual objects in the room. To address these problems, we present Ctrl-Room, which can generate convincing 3D rooms with designer-style layouts a

Amena Assem, Marcel Koloschin, Max Pitz

Nash-Williams proved in 1960 that a finite graph admits a $k$-arc-connected orientation if and only if it is $2k$-edge-connected, and conjectured that the same result should hold for all infinite graphs, too. Progress on Nash-Williams's problem was made by C. Thomassen, who proved in 2016 that all $8k$-edge-connected infinite graphs admit a $k$-arc connected

Prashanta Garain, Erik Lindgren

We study the fractional $p$-Laplace equation $$ (-\Delta_p)^s u = 0 $$ for $0<s<1$ and in the subquadratic case $1<p<2$. We provide H\"older estimates with an explicit H\"older exponent. The inhomogeneous equation is also treated and there the exponent obtained is almost sharp. Our results complement the previous results for the superquadratic case when $p\g

Eleni Zavrakli, Andrew Parnell, Subhrakanti Dey

With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant role in the efficiency, accuracy and repeatability of the process, with temperature regulation being one of the most important ones. In this work, we solve the problem of optimal

Helena Mihaljević, Elisabeth Steffen

The Perspective API, a popular text toxicity assessment service by Google and Jigsaw, has found wide adoption in several application areas, notably content moderation, monitoring, and social media research. We examine its potentials and limitations for the detection of antisemitic online content that, by definition, falls under the toxicity umbrella term. Us

Christopher Scherb, Luc Bryan Heitz, Hermann Grieder, Olivier Mattmann

Symbolic Execution is a formal method that can be used to verify the behavior of computer programs and detect software vulnerabilities. Compared to other testing methods such as fuzzing, Symbolic Execution has the advantage of providing formal guarantees about the program. However, despite advances in performance in recent years, Symbolic Execution is too sl

Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich

Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algorithms derived by considering gradient flows in the space of probability measures open up new avenues for algorithm development. This paper makes three contributions to this sampling

Thomas Unfer, Anthony Collé, Jérôme Limido

A novel parallelization paradigm has been developed for multi-GPU architectures. Classical multi-GPU parallelization for SPH rely on domain decomposition. In our approach each particle can be assigned to a GPU independently of its position in space. This ensures a kind of natural load balancing because the number of particles per GPU remains constant. The da

Aritra Ghosh, Jasleen Kaur, Malay Bandyopadhyay

In this paper, we discuss some aspects of the energetics of a quantum Brownian particle placed in a harmonic trap, also known as the dissipative quantum oscillator. Based on the fluctuation-dissipation theorem, we analyze two distinct notions of thermally-averaged energy that can be ascribed to the oscillator. These energy functions, respectively dubbed here

Kaloian D. Lozanov, Shi Pi, Misao Sasaki, Volodymyr Takhistov

Formation of cosmological solitons is generically accompanied by production of gravitational waves (GWs), with a universal GW background expected at frequency scales below that of non-linear dynamics. Beginning with a general phenomenological description of GWs associated with soliton formation, we demonstrate that universal GW background from axion-like par

Aritra Chakrabarty, Gijs D. Mulders

Planet formation models suggest that the small exoplanets that migrate from beyond the snowline of the protoplanetary disk likely contain water-ice-rich cores ($\sim 50\%$ by mass), also known as the water worlds. While the observed radius valley of the Kepler planets is well explained with the atmospheric dichotomy of the rocky planets, precise measurements

Shubham Sharma, Harleen Dahiya

By integrating across the light-cone energy, totally unintegrated, off-diagonal quark-quark generalized parton correlation functions (GPCFs) can yield generalized transverse-momentum dependent parton distributions (GTMDs). We have obtained the twist-4 GTMDs for the case of proton in the light-front quark-diquark model (LFQDM). We have decoded the quark-quark

Nwosu Obinnaya Chikezie Victor

Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual

G. Pérez-Callejo, T. Gawne, T. R. Preston, P. Hollebon

We report on experiments where solid-density Mg plasmas are created by heating with the focused output of the Linac Coherent Light Source x-ray free-electron-laser. We study the K-shell emission from the Helium and Lithium-like ions using Bragg crystal spectroscopy. Observation of the dielectronic satellites in Lithium-like ions confirms that the M-shell ele

Azul Garza, Cristian Challu, Max Mergenthaler-Canseco

In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evaluate our pre-trained model against established statistical, machine learning, and deep learning methods, demonstrating that TimeGPT zero-shot inference excels in performance, efficie

Doyee Byun, Nahum Arav, Maryam Dehghanian, Gwen Walker

We have identified a broad absorption line (BAL) outflow in the HST/STIS spectrum of the quasar QSO B0254-3327B at velocity $v=-3200\text{ km s$^{-1}$}$. The outflow has absorption troughs from ions such as Ne VIII, Na IX, Si XII, and Ne V. We also report the first detection of S XIV absorption troughs, implying very high ionization. Via measurement of the i

Romuald Kilianski, Robert Bennett

We expand the theoretical toolbox for controllable quantum reflection by departing from a simple planar reflector. We introduce a circular hole (a micropore) of variable size, for which the electrostatic image potential can be exactly calculated. We combine this with two-dimensional simulations of wavepacket propagation at arbitrary angle of incidence to sho

Fanyi Kong, Grazia Zambella, Simone Monteleone, Giorgio Grioli

This paper presents an aerial platform capable of performing physically interactive tasks in unstructured environments with human-like dexterity under human supervision. This aerial platform consists of a humanoid torso attached to a hexacopter. A two-degree-of-freedom head and two five-degree-of-freedom arms equipped with softhands provide the requisite dex

Justin N. Kreikemeyer, Philipp Andelfinger

Programs involving discontinuities introduced by control flow constructs such as conditional branches pose challenges to mathematical optimization methods that assume a degree of smoothness in the objective function's response surface. Smooth interpretation (SI) is a form of abstract interpretation that approximates the convolution of a program's output with

Lucas Picasarri-Arrieta, Michael Stiebitz

The dichromatic number $\vec{\chi}(D)$ of a digraph $D$ is the least integer $k$ for which $D$ has a coloring with $k$ colors such that there is no monochromatic directed cycle in $D$. The digraphs considered here are finite and may have antiparallel arcs, but no parallel arcs. A digraph $D$ is called $k$-critical if each proper subdigraph $D'$ of $D$ satisf

Christopher Scherb, Adrian Hadayah, Luc Bryan Heitz

Connected Medical Devices (CMDs) have a large impact on patients as they allow them to lead a more normal life. Any malfunction could not only remove the health benefits the CMDs provide, they could also cause further harm to the patient. Due to this, there are many safety regulations which must be adhered to prior to a CMD entering the market. However, whil

Andrés Franco Grisales

We study the asymptotics of solutions to a particular class of systems of linear wave equations, namely, of silent equations. We obtain asymptotic estimates of all orders for the solutions, and show that solutions are uniquely determined by the asymptotic data contained in the estimates. As an application, we then study solutions to the source free Maxwell's

Jin Jin, Chong Zhang, Jonas Frey, Nikita Rudin

Autonomous robots must navigate reliably in unknown environments even under compromised exteroceptive perception, or perception failures. Such failures often occur when harsh environments lead to degraded sensing, or when the perception algorithm misinterprets the scene due to limited generalization. In this paper, we model perception failures as invisible o

Adnan Aijaz, Sajida Gufran, Tim Farnham, Sita Chintalapati

Open RAN brings multi-vendor diversity and interoperability to mobile/cellular networks. It is becoming part of governmental strategies for diversifying telecoms supply chains. This paper describes the approach and key achievements of the BEACON-5G project, jointly funded by the UK government and industry. The BEACON-5G project aims at developing a competiti

Trang Nguyen, Alexander Tong, Kanika Madan, Yoshua Bengio

Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular processes. However, causal discovery in GRNs is a challenging problem for multiple reasons including the existence of cyclic feedback loops and uncertainty that yields diverse possible causal structures. Previous works in th

Andras Horvath, Csaba M. Jozsa

Neural Radiance Fields (NeRFs) have recently emerged as a powerful tool for 3D scene representation and rendering. These data-driven models can learn to synthesize high-quality images from sparse 2D observations, enabling realistic and interactive scene reconstructions. However, the growing usage of NeRFs in critical applications such as augmented reality, r

Hooman Taghavi, Ahmad El Shafei, Adel Nasiri

Power electronics systems, widely used in various applications such as industrial automation, electric cars, and renewable energy, have the primary function of converting and controlling electrical power to the desired type of load. Despite their reliability and efficiency, power losses in these systems generate significant heat that must be dissipated to ma

Ryunosuke Terasawa, Hiroaki Ishizuka

Long-range magnetic textures, such as magnetic skyrmion, give rise to rich transport properties in magnetic metals, such as the anomalous Hall effect related to spin chirality, a.k.a. topological Hall effect. In addition to the topological Hall effect, recent studies on non-centrosymmetric magnets find that the spin-orbit interaction of itinerant electrons g

Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová

We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture. We provide a sharp end-to-end analysis of the problem. First, we provide a tight closed-form characterization of the learnt velocity field, when parametrized by a shallow denoising auto-encoder trained o

Sarit Maitra, Vivek Mishra, Goutam Kr. Kundu, Kapil Arora

This study enhances option pricing by presenting unique pricing model fractional order Black-Scholes-Merton (FOBSM) which is based on the Black-Scholes-Merton (BSM) model. The main goal is to improve the precision and authenticity of option pricing, matching them more closely with the financial landscape. The approach integrates the strengths of both the BSM

Junliang Luo, Yi Tian Xu, Di Wu, Michael Jenkin

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the

Anders Bjært Sørensen

The note clarifies a gap in the proof of the minimum distance for Projective Reed-Muller Codes. The gap was identified by S.Ghorpade and R.Ludhani in a recent article. Here the original thoughts are explained and the gap closed.

Dario Klingenberg, Hrvoje Jasak, Holger Marschall, Florian Kummer

In this article, we present the foam-dg project, which provides a bridge between OpenFOAM(R) and the high-order DG (discontinuous Galerkin) framework BoSSS. Thanks to the flexibility of the coupling approach, mixed calculations where some parts of the equation system are solved in OpenFOAM(R) and others are solved in BoSSS are easily possible. This is showca

Owen Davis, Mohammad Motamed, Raul Tempone

In this work, we consider the general problem of constructing a neural network surrogate model using multi-fidelity information. Motivated by error-complexity estimates for ReLU neural networks, we formulate the correlation between an inexpensive low-fidelity model and an expensive high-fidelity model as a possibly non-linear residual function. This function

Fei Kong

Let $\mathfrak g$ be a finite dimensional simple Lie algebra over $\mathbb C$, and let $\ell$ be a positive integer. In this paper, we construct the quantization $K_{\hat{\mathfrak g},\hbar}^\ell$ of the parafermion vertex algebra $K_{\hat{\mathfrak g}}^\ell$ as an $\hbar$-adic quantum vertex subalgebra inside the simple quantum affine vertex algebra $L_{\ha

Alexander F. Goncharov, Iskander G. Batyrev, Elena Bykova, Lukas Brüning

Nitrogen represents an archetypal example of material exhibiting a pressure driven transformation from molecular to polymeric state. Detailed investigations of such transformations are challenging because of a large kinetic barrier between molecular and polymeric structures, making the transformation largely dependent on kinetic stimuli. In the case of nitro

August K. Roos, Ermes Scarano, Elisabet K. Arvidsson, Erik Holmgren

We describe a transducer for low-temperature atomic force microscopy based on electromechanical coupling due to a strain-dependent kinetic inductance of a superconducting nanowire. The force sensor is a bending triangular plate (cantilever) whose deflection is measured via a shift in the resonant frequency of a high-Q superconducting microwave resonator at 4

Lotte Maria Bruun, Ulrik Stuhr Larsen, Nikolaj Hinnerskov, Cosmin Oancea

We present and evaluate the Futhark implementation of reverse-mode automatic differentiation (AD) for the basic blocks of parallel programming: reduce, prefix sum (scan), and reduce by index. We first present derivations of general-case algorithms and then discuss several specializations that result in efficient differentiation of most cases of practical int

Markus Schütz, Lukas Herzberger, Michael Wimmer

About: We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and shar

Levi Gagnon-Ririe, Matthew B. Young

We construct a two dimensional unoriented open/closed topological field theory from a finite graded group $\pi:\hat{G} \twoheadrightarrow \{1,-1\}$, a $\pi$-twisted $2$-cocycle $\hat{\theta}$ on $B \hat{G}$ and a character $\lambda: \hat{G} \rightarrow U(1)$. The underlying oriented theory is a twisted Dijkgraaf-Witten theory. The construction is based in th

Gerhard Jung

We investigate two different types of non-Markovian coarse-grained models extracted from a linear, non-equilibrium microscopic system, featuring a tagged particle coupled to underdamped oscillators. The first model is obtained by analytically ``integrating out'' the oscillators and the second is derived using projection operator techniques. We observe that t

Jeet Sampat, Orr Shalit

We study algebras of bounded noncommutative (nc) functions on unit balls of operator spaces (nc operator balls) and on their subvarieties. Considering the example of the nc unit polydisk we show that these algebras, while having a natural operator algebra structure, might not be the multiplier algebra of any reasonable nc reproducing kernel Hilbert space (RK

Ágoston István Csehi, Csaba Máté Józsa

We aim to improve the Inverted Neural Radiance Fields (iNeRF) algorithm which defines the image pose estimation problem as a NeRF based iterative linear optimization. NeRFs are novel neural space representation models that can synthesize photorealistic novel views of real-world scenes or objects. Our contributions are as follows: we extend the localization o

D. L. Harrison, F. van Leeuwen, P. J. Osborne, P. W. Burgess

Aims. To describe two potential options for the Source Environment Analysis pipeline, SEAPipe, for the Gaia mission. This pipeline will enable the discovery of sources which are new to Gaia, in the sense that they were not found by the on-board detection algorithm. These additional sources (secondaries) are discoverable in the vicinity of those Gaia sources

Sunanda, Reetanjali Moharana

On 28 January 2018, the High Energy Stereoscopic System (H.E.S.S.) reported a significant very-high-energy (VHE) gamma-ray activity, occurring nearly 11 days after the high-energy (HE) gamma-ray flare observed by \textit{Fermi}-LAT from the blazar 3C 279. It has long been considered a candidate site for accelerating particles to ultra-high energies (UHE) and

Fergus Imrie, Paulius Rauba, Mihaela van der Schaar

Digital health tools have the potential to significantly improve the delivery of healthcare services. However, their adoption remains comparatively limited due, in part, to challenges surrounding usability and trust. Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information and produce human-quality t

Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports

Limin Song, Domenico Bongiovanni, Zhichan Hu, Ziteng Wang

Kagome lattices represent an archetype of intriguing physics, attracting a great deal of interest in different branches of natural sciences, recently in the context of topological crystalline insulators. Here, we demonstrate two distinct classes of corner states in breathing Kagome lattices (BKLs) with "bearded" edge truncation, unveiling their topological o

Rafiazka Millanida Hilman, Manuel García-Herranz, Vedran Sekara, Márton Karsai

External shocks embody an unexpected and disruptive impact on the regular life of people. This was the case during the COVID-19 outbreak that rapidly led to changes in the typical mobility patterns in urban areas. In response, people reorganised their daily errands throughout space. However, these changes might not have been the same across socioeconomic cla

Kutay Bölat, Simon H. Tindemans, Peter Palensky

Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimat

Anna Puecher, Anuradha Samajdar, Gregory Ashton, Chris Van Den Broeck

The inference of source parameters from gravitational-wave signals relies on theoretical models that describe the emitted waveform. Different model assumptions on which the computation of these models is based could lead to biases in the analysis of gravitational-wave data. In this work, we sample directly on four state-of-the-art binary black hole waveform

Yagmur Yigit, Christos Chrysoulas, Gokhan Yurdakul, Leandros Maglaras

As global Internet of Things (IoT) devices connectivity surges, a significant portion gravitates towards the Edge of Things (EoT) network. This shift prompts businesses to deploy infrastructure closer to end-users, enhancing accessibility. However, the growing EoT network expands the attack surface, necessitating robust and proactive security measures. Tradi

Babatunde Akinsanmi, Monika Lendl, Gwenael Boue, Susana C. C. Barros

With the continuous improvement in the precision of exoplanet observations, it has become feasible to probe for subtle effects that can enable a more comprehensive characterization of exoplanets. A notable example is the tidal deformation of ultra-hot Jupiters by their host stars, whose detection can provide valuable insights into the planetary interior stru

M. J. McCaughrean, S. G. Pearson

We present a near-IR survey of the Trapezium Cluster and inner Orion Nebula using the NASA/ESA/CSA James Webb Space Telescope. The survey with the NIRCam instrument covers 10.9 x 7.5 arcminutes (~1.25 x 0.85 pc) in twelve wide-, medium-, and narrow-band filters from 1-5 microns and is diffraction-limited at all wavelengths, providing a maximum spatial resolu

Tomohiro Fujita, Tomoaki Murata, Ippei Obata, Maresuke Shiraishi

We study a mechanism of generating the trispectrum (4-point correlation) of curvature perturbation through the dynamics of a spectator axion field and U(1) gauge field during inflation. Owing to the Chern-Simons coupling, only one helicity mode of gauge field experiences a tachyonic instability and sources scalar perturbations. Sourced curvature perturbation

Panu Lahti

We study a class of non-local functionals that was introduced by Brezis-Seeger-Van Schaftingen-Yung (2022), and can be used to characterize functions of bounded variation. We give a new lower bound for the liminf of these functionals, involving the three different parts of the total variation, with sharp coefficients.

Serban T. Belinschi, Eli Shamovich

This note aims to study the iteration theory of noncommutative self-maps of bounded matrix convex domains. We prove a version of the Denjoy-Wolff theorem for the row ball and the maximal quantization of the unit ball of $\mathbb{C}^d$. For more general bounded matrix convex sets, we prove a version of Wolff's theorem inspired by the results of Abate. Lastly,

Emma Ayçoberry, Alexandre Barthelemy, Sandrine Codis

The classification of the cosmic web into different environments is both a tool to study in more detail the formation of halos and galaxies via the link between their properties and the large-scale environment and as a class of objects whose statistics contain cosmological information. In this paper, we present an analytical framework to compute the probabil

Daan Haver, Daniel Acuña, Shahram Janbaz, Edan Lerner

The flowing, jamming and avalanche behavior of granular materials is satisfyingly universal and vexingly hard to tune: a granular flow is typically intermittent and will irremediably jam if too confined. Here, we show that granular metamaterials made from particles with a negative Poisson's ratio yield more easily and flow more smoothly than ordinary granula

Marien Renaud, Jiaming Liu, Valentin de Bortoli, Andrés Almansa

Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emerged as a promising method for Monte Carlo sampling and minimum mean squared error (MMSE) estimation by combining physical measurement models with deep-learning priors specified usi

Sukrita Singh, Neeraj Sarna, Yuanyuan Li, Yang Li

Machine learning algorithms have grown in sophistication over the years and are increasingly deployed for real-life applications. However, when using machine learning techniques in practical settings, particularly in high-risk applications such as medicine and engineering, obtaining the failure probability of the predictive model is critical. We refer to thi

Hyeonwoo Kim, Wonsung Lee

The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the probability of the app being installed. This paper describes the lightweight solution to this challenge. We formulate the task as a user response prediction task. For rapid prototyping for the task, we propose a lightweight solution including the following steps: 1) using adversarial

Michal Zajaček, Swayamtrupta Panda, Ashwani Pandey, Raj Prince

Fe II emission is a well-known contributor to the UV spectra of active galactic nuclei and the modeling of this part may affect the results obtained for the MgII$\lambda2800$ emission, which is one of the lines used for black hole mass measurements and cosmological applications. We use the 11-year monitoring of the selected quasar HE 0413-4031 with the South

H Laxmi, Anupam Saikia

Let $K= \mathbb{Q}(\sqrt{d})$ be a real quadratic field with $d$ having three distinct prime factors. We show that the $2$-class group of each layer in the $\mathbb{Z}_2$-extension of $K$ is $\mathbb{Z}/2\mathbb{Z}$ under certain elementary assumptions on the prime factors of $d$. In particular, it validates Greenberg's conjecture on the vanishing of the Iwa

György Baranka, Matteo Giordano

We investigate the connection between localization of low-lying Dirac modes and Polyakov-loop ordering in the lattice $\mathrm{SU}(2)$ Higgs model at finite temperature, probed with the staggered Dirac operator. After mapping out the phase diagram of the model at a fixed temporal extension in lattice units, we study the localization properties of the low-lyi

Sachin Gupta

The progress in materials science has always been associated with the development of functional materials systems, which enables us to design proof-of-concept devices. To advance further, theoretical predictions of new novel materials and their experimental realization is very important. This chapter reviews the intriguing properties of rare earth-based mate

Jorge J. Betancor, Estefanía D. Dalmasso, Pablo Quijano

We represent by $\{W_{\lambda, t}^\alpha\}_{t>0}$ the semigroup generated by $-\mathbb L^{\alpha}_\lambda$, where $\mathbb L^{\alpha}_\lambda$ is a Hardy operator on a half space. The operator $\mathbb L^{\alpha}_\lambda$ includes a fractional Laplacian and it is defined by \[\mathbb L^{\alpha}_\lambda=(-\Delta)^{\alpha/2}_{\mathbb{R}^d_+}+\lambda x_d^{-\alp

Debojyoti Garain, Tapobrata Sarkar

Fallback rate of debris after a partial tidal disruption event of a star with an intermediate mass black hole (IMBH) might provide important signatures of such black holes, compared to supermassive ones. Here using smoothed particle hydrodynamics methods, we provide a comprehensive numerical analysis of this phenomenon. We perform numerical simulations of si

Jae-Sung Bae, Joun Yeop Lee, Ji-Hyun Lee, Seongkyu Mun

Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhance its systems by enlarging the training data through crowd-sourcing or augmenting existing speech data. However, the use of low-quality data has led to a decline in the overall system performance. To avoid such degradation, instead of directly augmenting the input data, we propose a

Diogo E. P. Pinto, Nuno A. M. Araújo, Petr Šulc, John Russo

Self-folding is an emerging paradigm for the inverse design of three-dimensional structures. While most efforts have concentrated on the shape of the net, our approach introduces a new design dimension-bond specificity between the edges. We transform this design process into a Boolean Satisfiability problem to derive solutions for various target structures.

William Samuelson, Viktor Svensson, Martin Leijnse

The possibility to engineer a Kitaev chain in quantum dots coupled via superconductors has recently emerged as a promising path toward topological superconductivity and possibly nonabelian physics. Here, we show that it is possible to avoid some of the main experimental hurdles on this path by using only local proximity effect on each quantum dot in a geomet

Xiang Chen, Jinshan Pan, Jiangxin Dong, Jinhui Tang

Recent years have witnessed significant advances in image deraining due to the kinds of effective image priors and deep learning models. As each deraining approach has individual settings (e.g., training and test datasets, evaluation criteria), how to fairly evaluate existing approaches comprehensively is not a trivial task. Although existing surveys aim to

Chen Zhao, Tong Zhang, Mathieu Salzmann

Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where only a single reference view of the object is available. Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in

Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy

This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering act

Jianfei Xu

The warped conformal symmetries have been found in the covariant phase space of a set of non-trivial diffeomorphism near the Kerr black hole horizon. In this paper, we consider the retarded Green's function and the absorption probability for the scalar and higher spin perturbations on a generic non-extreme Kerr black hole background, and perform their hologr

Manasa Manasa, Mohammad Azam, Tatiana Zajarniuk, Ryszard Diduszko

We have prepared a series of (FeSe0.5Te0.5 + xGd) bulk samples, with x = 0, 0.03, 0.05, 0.07, 0.1 and 0.2, through the convenient solid-state reaction method at ambient pressure (CSP). High gas pressure and high-temperature synthesis methods (HP-HTS) are also applied to grow the parent compound (x = 0) and 5-wt% of Gd-added bulks. Structural, microstructural

Sho Sonoda, Hideyuki Ishi, Isao Ishikawa, Masahiro Ikeda

The symmetry and geometry of input data are considered to be encoded in the internal data representation inside the neural network, but the specific encoding rule has been less investigated. In this study, we present a systematic method to induce a generalized neural network and its right inverse operator, called the ridgelet transform, from a joint group in

Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda

We identify hidden layers inside a deep neural network (DNN) with group actions on the data domain, and formulate a formal deep network as a dual voice transform with respect to the Koopman operator, a linear representation of the group action. Based on the group theoretic arguments, particularly by using Schur's lemma, we show a simple proof of the universa

Abheek Ghosh

We study the convergence of best-response dynamics in Tullock contests with convex cost functions (these games always have a unique pure-strategy Nash equilibrium). We show that best-response dynamics rapidly converges to the equilibrium for homogeneous agents. For two homogeneous agents, we show convergence to an $\epsilon$-approximate equilibrium in $\Thet

Jimmy He, Michael Wheeler

We study the periodic $q$-Whittaker and Hall-Littlewood processes, two probability measures on sequences of partitions. We prove that a certain observable of the periodic $q$-Whittaker process exhibits a $(q,u)$ symmetry after a random shift, generalizing a previous result of Imamura, Mucciconi, and Sasamoto who showed a matching between the periodic Schur a

Ayana Sarkar, Ashutosh Dheer, Santosh Kumar

Multifractal analysis is a powerful approach for characterizing ergodic or localized nature of eigenstates in complex quantum systems. In this context, the eigenvectors of random matrices belonging to invariant ensembles naturally serve as models for ergodic states. However, it has been found that the finite-size versions of multifractal dimensions for these

Tao Huang, Jianan Liu, Xi Zhou, Dinh C. Nguyen

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything cooperative perception, which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. Vehicle-to-everything cooperative perception plays a crucial role

Giovani Dalla Valle Garcia, Felix Kahlhoefer, Maksym Ovchynnikov, Andrii Zaporozhchenko

Axion-like particles (ALPs) emerge in many extensions of the Standard Model as pseudo-Goldstone bosons of a spontaneously broken global symmetry. Understanding their phenomenology in high-energy collisions is crucial for optimizing experimental searches and understanding the exploration potential of future experiments. In this paper, we revise the phenomenol

S. Maji, K. Sowa, M. P. Nowak

We theoretically study scanning gate microscopy (SGM) of electron and hole trajectories in a quantum point contact (QPC) embedded in a normal-superconductor (NS) junction. At zero voltage bias, the electrons and holes transported through the QPC form angular lobes and are subject to self-interference, which marks the SGM conductance maps with interference fr

Melker Veltman, Alexandra Parkegren, Victor Morel

When software services use cloud providers to run their workloads, they place implicit trust in the cloud provider, without an explicit trust relationship. One way to achieve such explicit trust in a computer system is to use a hardware Trusted Platform Module (TPM), a coprocessor for trusted computing. However, in the case of managed platform-as-a-service (

Michael Stanley Smith, Weichang Yu, David J. Nott, David Frazier

In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by "cutting feedback". Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of

Chang-Qing Ye, Hui-Min Fan, Alejandro Torres-Orjuela, Jian-dong Zhang

Space-based gravitational wave detectors like TianQin or LISA could observe extreme-mass-ratio-inspirals (EMRIs) at millihertz frequencies. The accurate identification of these EMRI signals from the data plays a crucial role in enabling in-depth study of astronomy and physics. We aim at the identification stage of the data analysis, with the aim to extract k

Safa Snoun

In this paper, we study Bergman projection $\mathbb{P}_{\alpha,\beta}$ and Toeplitz operators $T^{\alpha,\beta}_\varphi$ on the $\beta$-modified Bergman space $\mathcal{A}_{\alpha,\beta}^p$. We give some properties of $\mathbb{P}_{\alpha,\beta}$ and a necessary and sufficient condition for $T^{\alpha,\beta}_\varphi$ to be compact. We end with a characterizat

Jiachi Liu, Liwen Wang, Guanting Dong, Xiaoshuai Song

In real dialogue scenarios, as there are unknown input noises in the utterances, existing supervised slot filling models often perform poorly in practical applications. Even though there are some studies on noise-robust models, these works are only evaluated on rule-based synthetic datasets, which is limiting, making it difficult to promote the research of n

Meijuan Su, Feihong He, Fanzhang Li

Few-shot image classification has received considerable attention for overcoming the challenge of limited classification performance with limited samples in novel classes. Most existing works employ sophisticated learning strategies and feature learning modules to alleviate this challenge. In this paper, we propose a novel method called PrototypeFormer, expl

Haizhong Li, Yao Wan, Botong Xu

In \cite{LX}, the first author and the third author introduced and studied the horospherical $p$-Minkowski problem for smooth horospherically convex domains in hyperbolic space. In this paper, we introduce and solve the discrete horospherical $p$-Minkowski problem in hyperbolic space for all $p\in(-\infty,+\infty)$ when the given measure is even on the unit

Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier, Luigi Nardi

Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box functions. High-dimensional problems are particularly challenging as the surrogate model of the objective suffers from the curse of dimensionality, which makes accurate modeling difficult. We propose a group testing approach to identify active variables to facilitate

Daniel Zieger, Christoph Näger, Stefan Becker, Tobias Günther

The three-dimensional reconstruction of vocal folds in medicine usually involves endoscopy and an approach to extract depth information like structured light or stereo matching of images. The resulting mesh can accurately represent the superior area of the vocal folds, while new approaches also try to reconstruct the inferior area. We propose a novel approac

Joseph A. Gallego-Mejia, Anna Jungbluth, Laura Martínez-Ferrer, Matt Allen

Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm and its application to Synthetic Aperture Radar (SAR) imagery. We pre-train a vision transformer (ViT)-based DI

Meng Shang, Lenore Dedeyne, Jolan Dupont, Laura Vercauteren

Otago Exercise Program (OEP) is a rehabilitation program for older adults to improve frailty, sarcopenia, and balance. Accurate monitoring of patient involvement in OEP is challenging, as self-reports (diaries) are often unreliable. With the development of wearable sensors, Human Activity Recognition (HAR) systems using wearable sensors have revolutionized h

Mouli Roy-Chowdhury, Mohindar S. Seehra, Subhash Thota

In spin-glasses (SG), the relaxation time $\tau$ ($= 1/2{\pi}f$) vs. $T_f$ data at the peak position $T_f$ in the temperature variation of the ac magnetic susceptibilities at different frequencies f is often fit to the Vogel-Fulcher Law (VFL): $\tau=\tau_0\exp[E_a/k_b(T_f-T_0)]$ and to the Power Law (PL): $\tau = \tau_0^*[(T_f-T_{SG}/T_{SG}]^{-z\nu}$. Both t