July 2023 arXiv papers — page 70

Philip Arm, Gabriel Waibel, Jan Preisig, Turcan Tuna

The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot approaches on

Henri De Plaen, Johan A. K. Suykens

In this paper, we characterize Probabilistic Principal Component Analysis in Hilbert spaces and demonstrate how the optimal solution admits a representation in dual space. This allows us to develop a generative framework for kernel methods. Furthermore, we show how it englobes Kernel Principal Component Analysis and illustrate its working on a toy and a real

Damian Maingi

In this paper we establish the existence of monads on special Cartesian products of projective spaces. Special in the sense that we mimick monads on instanton bundles. We construct monads on $\mathbb{P}^1\times\cdots\times\mathbb{P}^1\times\mathbb{P}^3\times\cdots\times\mathbb{P}^3\times\mathbb{P}^5\times\cdots\times\mathbb{P}^5$. We proceed to prove stabili

S. J. Blundell, T. Lancaster

The technique of muon spin rotation ({\mu}SR) has emerged in the last few decades as one of the most powerful methods of obtaining local magnetic information. To make the technique fully quantitative, it is necessary to have an accurate estimate of where inside the crystal structure the muon implants. This can be provided by density functional theory calcula

Maria Kuznetsova

Recently, there appeared a significant interest in inverse spectral problems for non-local operators arising in numerous applications. In the present work, we consider the operator with frozen argument $ly = -y''(x) + p(x)y(x) + q(x)y(a),$ which is a non-local perturbation of the non-selfadjoint Sturm--Liouville operator. We study the inverse problem of reco

William J. Wolf, Marco Sanchioni, James Read

Canonically, `classic' tests of general relativity (GR) include perihelion precession, the bending of light around stars, and gravitational redshift; `modern' tests have to do with, inter alia, relativistic time delay, equivalence principle tests, gravitational lensing, strong field gravity, and gravitational waves. The orthodoxy is that both classic and mod

Lei-Ming Zhou, Yaqiang Qin, Yuanjie Yang, Yuqiang Jiang

Particles trapped by optical tweezers, behaving as mechanical oscillators in an optomechanical system, have found tremendous applications in various disciplines and are still arousing research interest in frontier and fundamental physics. These optically trapped oscillators provide compact particle confinement and strong oscillator stiffness. But these featu

Clark Addis, Salvador Rojas, Andres F. Arrieta

Non-Euclidean origami is a promising technique for designing multistable deployable structures folded from nonplanar developable surfaces. The impossibility of flat foldability inherent to non-Euclidean origami results in two disconnected solution branches each with the same angular deficiency but opposite handedness. We show that these regions can be connec

S. de Wet, L. Izzo, P. J. Groot, S. Bisero

GRB 220627A is a rare burst with two distinct gamma-ray emission episodes separated by almost 1000 s that triggered the Fermi Gamma-ray Burst Monitor twice. High-energy GeV emission was detected by the Fermi Large Area Telescope coincident with the first emission episode but not the second. The discovery of the optical afterglow with MeerLICHT led to MUSE ob

Irene Schimperna, Lalo Magni

In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input-output dynamic of the system under control. Robust satisfaction of input and output constraints and recursive feasibility in presence of model uncertainties are achieved using a constraint tightening approach. Moreover, new termina

Wojciech Jamroga, Yan Kim

Formal verification of intelligent agents is often computationally infeasible due to state-space explosion. We present a tool for reducing the impact of the explosion by means of state abstraction that is (a) easy to use and understand by non-experts, and (b) agent-based in the sense that it operates on a modular representation of the system, rather than on

Justin Salez

The cutoff phenomenon is an abrupt transition from out of equilibrium to equilibrium undergone by certain Markov processes in the limit where the size of the state space tends to infinity: instead of decaying gradually over time, their distance to equilibrium remains close to the maximal value for a while and suddenly drops to zero as the time parameter reac

Beatrice Franzolini, Giovanni Rebaudo

Bayesian nonparametric mixture models are widely used to cluster observations. However, one major drawback of the approach is that the estimated partition often presents unbalanced clusters' frequencies with only a few dominating clusters and a large number of sparsely-populated ones. This feature translates into results that are often uninterpretable unless

Sara Ghomi, Pinaka Pani Tummala, Raimondo Cecchini, Carlo S. Casari

The interest in tellurium nanostructures is on the rise due to their outstanding physical properties including high carrier mobility, anisotropic charge conduction, photoconductivity, thermoelectricity, and piezoelectricity. Applications in related technologies require tailoring the synthesis of tellurium from its preferred vertical growth toward the lateral

Nikhil U. Shinde, Jacob Johnson, Sylvia Herbert, Michael C. Yip

Large offline learning-based models have enabled robots to successfully interact with objects for a wide variety of tasks. However, these models rely on fairly consistent structured environments. For more unstructured environments, an online learning component is necessary to gather and estimate information about objects in the environment in order to succes

JoonHo Lee, Jae Oh Woo, Hankyu Moon, Kwonho Lee

Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions. Several approaches leverage labeled source data to estimate target domain accuracy, but accessing labeled source data is often prohibitively difficult due to data confidentiality or resource limitations on serving devices. Our work pro

Isuru R. Ariyarathna

A systematic electronic structure analysis was conducted for M(L)$_n$ molecular electrides and their corresponding alkalide units M(L)$_n$M' (M/M' = Na, K and L = Tren, Azacryptand, TriPip222 and $n$ = 1, 2). All complexes belong to the superalkali category owing to their low ionization potentials. The saturated molecular electrides display M+(L)n-form with

Nils Lommen, Eleanore Meyer, Jürgen Giesl

There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach wh

Rikhi Bose, Arunabha M. Roy

We present two families of sub-grid scale (SGS) turbulence models developed for large-eddy simulation (LES) purposes. Their development required the formulation of physics-informed robust and efficient Deep Learning (DL) algorithms which, unlike state-of-the-art analytical modeling techniques can produce high-order complex non-linear relations between inputs

Sohan Malkaruge Costa, Benjamin Cohen-Stead, Andy Tanjaroon Ly, James Neuhaus

We compare the acoustic Su-Schrieffer-Heeger (SSH) model with two of its optical variants where the phonons are defined on either on the sites or bonds of the system. First, we discuss how to make fair comparisons between these models in any dimension by ensuring their dimensionless coupling $\lambda$ and relevant phonon energies are the same. We then use de

Dmitriy Kunisky, Peiyuan Zhang

We study the operator norm discrepancy of i.i.d. random matrices, initiating the matrix-valued analog of a long line of work on the $\ell^{\infty}$ norm discrepancy of i.i.d. random vectors. First, using repurposed results on vector discrepancy and new first moment method calculations, we give upper and lower bounds on the discrepancy of random matrices. We

Renan Jackson Soares Isneri, Vandenberg Lopes Vieira, Maxwell Aires da Silva

Prime numbers play a key role in number theory and have applications beyond Mathematics. In particular, in the Theory of Codes and also in Cryptography, the properties of prime numbers are relevant, because, from them, it is possible to guarantee the storage of data and the sending of messages in a secure way. And this is evident in e-commerce when personal

Soheil Abbasloo

How do we assess a new Internet congestion control (CC) design? How do we compare it with other existing schemes? Under what scenarios and using what network parameters? These are just a handful of simple questions coming up every time a new CC design is going to be evaluated. Interestingly, the number of specific answers to these questions can be as large a

Nachuan Xiao, Xiaoyin Hu, Kim-Chuan Toh

In this paper, we focus on providing convergence guarantees for stochastic subgradient methods in minimizing nonsmooth nonconvex functions. We first investigate the global stability of a general framework for stochastic subgradient methods, where the corresponding differential inclusion admits a coercive Lyapunov function. We prove that, for any sequence of

M. Mai, J. Hergenrather, M. Döring, T. Mart

Exclusive electroproduction reactions provide an access to the structure of excited baryons. To extract electroproduction multipoles encoding this information, the J\"ulich-Bonn-Washington (JBW) analysis framework is extended to the analysis of differential cross sections in $K\Lambda$ electroproduction. This update enlarges the scope of previous coupled-cha

Carlos P. Herrero, Rafael Ramirez, Gabriela Herrero-Saboya

Silicon carbide is a hard, semiconducting material presenting many polytypes, whose behavior under extreme conditions of pressure and temperature has attracted large interest. Here we study the mechanical properties of 3C-SiC over a wide range of pressures (compressive and tensile) by means of molecular dynamics simulations, using an effective tight-binding

Saswata Das, Mohammad Hossein Samaei, Caterina Scoglio

To simplify mathematical models of disease spread, we often assume equal contact rates among hosts, but real-world scenarios differ. Network-based frameworks help capture these complexities and structural variations in actual systems. We explore two scenarios involving Susceptible-Infected-Recovered (SIR) dynamics in interconnected networks. First, we study

J. J. Alonso, B. Alles, J. G. Malherbe, V. Russier

We study by numerical simulation the magnetic order in ensembles of randomly packed magnetic spherical particles which, induced by their uniaxial anisotropy in the strong coupling limit, behave as Ising dipoles. We explore the role of the frozen disorder in the positions of the particles assuming a common fixed direction for the easy axes of all spheres. We

Mingzhe Guo, Zhipeng Zhang, Liping Jing, Haibin Ling

Multimodal vision-language (VL) learning has noticeably pushed the tendency toward generic intelligence owing to emerging large foundation models. However, tracking, as a fundamental vision problem, surprisingly enjoys less bonus from recent flourishing VL learning. We argue that the reasons are two-fold: the lack of large-scale vision-language annotated vid

Ramana Nagasamudram, Anindya Banerjee, David A. Naumann

In relational verification, judicious alignment of computational steps facilitates proof of relations between programs using simple relational assertions. Relational Hoare logics (RHL) provide compositional rules that embody various alignments of executions. Seemingly more flexible alignments can be expressed in terms of product automata based on program tra

Tim Pesch, Erhard Cramer, Edward Cripps, Adriano Polpo

A load sharing system has several components and the failure of one component can affect the lifetime of the surviving components. Since component failure does not equate to system failure for different system designs, the analysis of the dependency structure between components becomes a meaningful exercise. The Extended Sequential Order Statistics model all

Clément Cardoen, Swann Marx, Anthony Nouy, Nicolas Seguin

We propose a numerical method to solve parameter-dependent hyperbolic partial differential equations (PDEs) with a moment approach, based on a previous work from Marx et al. (2020). This approach relies on a very weak notion of solution of nonlinear equations, namely parametric entropy measure-valued (MV) solutions, satisfying linear equations in the space o

Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan, Pin-Yu Chen

Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous position-navigation-time tasks within stringent onboard weight and power constraints. We observe that reducing onboard operating voltage can benefit the energy efficiency of both the computation and flight mi

R. Verdel, V. Vitale, R. K. Panda, E. D. Donkor

Quantum simulators offer powerful means to investigate strongly correlated quantum matter. However, interpreting measurement outcomes in such systems poses significant challenges. Here, we present a theoretical framework for information extraction in synthetic quantum matter, illustrated for the case of a quantum quench in a spinor Bose-Einstein condensate e

Natasa Krejic, Natasa Krklec Jerinkic, Angeles Martinez, Mahsa Yousefi

In this work, we introduce a novel stochastic second-order method, within the framework of a non-monotone trust-region approach, for solving the unconstrained, nonlinear, and non-convex optimization problems arising in the training of deep neural networks. The proposed algorithm makes use of subsampling strategies which yield noisy approximations of the fini

Jia-Xin Zhuang, Jiabin Cai, Jianguo Zhang, Wei-shi Zheng

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In thi

Alexandr V. Kostochka, Mina Nahvi, Douglas B. West, Dara Zirlin

The $(n-\ell)$-deck of an $n$-vertex graph is the multiset of (unlabeled) subgraphs obtained from it by deleting $\ell$ vertices. An $n$-vertex graph is $\ell$-reconstructible if it is determined by its $(n-\ell)$-deck, meaning that no other graph has the same deck. We prove that every tree with at least $6\ell+11$ vertices is $\ell$-reconstructible.

Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli

JSON Schema is the de-facto standard schema language for JSON data. The language went through many minor revisions, but the most recent versions of the language added two novel features, dynamic references and annotation-dependent validation, that change the evaluation model. Modern JSON Schema is the name used to indicate all versions from Draft 2019-09, wh

Siyu Li, Jin Yang, Kui Zhao

As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like

Armin Wolf, Cristian Grozea

Obtaining Quadratic Unconstrained Binary Optimisation models for various optimisation problems, in order to solve those on physical quantum computers (such as the the DWave annealers) is nowadays a lengthy and tedious process that requires one to remodel all problem variables as binary variables and squeeze the target function and the constraints into a sing

Peimeng Guan, Naveed Iqbal, Mark A. Davenport, Mudassir Masood

Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem with non-unique solutions. Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used. They either require rigo

Spiros Patsourakos, Angelos Vourlidas, Alexander Nindos

In a previous work we analysed the white-light coronal brightness as a function of elongation and time from Wide-Field Imager (WISPR) observations on board the Parker Solar Probe (PSP) mission when PSP reached a minimum heliocentric distance of ~ 28 Rs. We found 4-5 transient outflows per day over a narrow wedge in the PSP orbital plane, which is close to th

Wojciech Cygan, Nikola Sandrić, Stjepan Šebek, Andrew Wade

We establish laws of the iterated logarithm for intrinsic volumes of the convex hull of many-step, multidimensional random walks whose increments have two moments and a non-zero drift. Analogous results in the case of zero drift, where the scaling is different, were obtained by Khoshnevisan. Our starting point is a version of Strassen's functional law of the

Gaurav Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan

Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this is often unrealistic. For example, most of the time we do not want an autonomous car to simply copy the speed of surrounding cars -- we don't want our car to run a red light if a ne

Yulin Zhou

Fertility issues are closely related to population security, in 60 years China's population for the first time in a negative growth trend, the change of fertility policy is of great concern to the community. 2023 "two sessions" proposal "suggests that the country in the form of legislation, the birth of the registration of the cancellation of the marriage re

Hirotaka Ito, Jin Matsumoto, Shigehiro Nagataki, Donald C. Warren

We explore the properties of photospheric emission in the context of long gamma-ray bursts (LGRBs) using three numerical models that combine relativistic hydrodynamical simulations and Monte Carlo radiation transfer calculations in three dimensions. Our simulations confirm that the photospheric emission gives rise to correlations between the spectral peak en

Konstantinos Pitas

I propose an open dataset of country-level historical opinion polling data for the European Union and the UK. The dataset aims to fill a gap in available opinion polling data for the European Union. Some existing datasets are restricted to the past five years, limiting research opportunities. At the same time, some larger proprietary datasets exist but are a

Ian R. Berkman, Alexey Lyasota, Gabriele G. de Boo, John G. Bartholomew

Spins in silicon that are accessible via a telecom-compatible optical transition are a versatile platform for quantum information processing that can leverage the well-established silicon nanofabrication industry. Key to these applications are long coherence times on the optical and spin transitions to provide a robust system for interfacing photonic and spi

A. P. Milone, G. Cordoni, A. F. Marino, F. Muratore

Young and intermediate-age star clusters of both Magellanic Clouds exhibit complex color-magnitude diagrams. In addition to the extended main-sequence turn-offs (eMSTOs), commonly observed in star clusters younger than ~2 Gyr, the clusters younger than ~800 Myr exhibit split main sequences (MSs). These comprise a blue MS, composed of stars with low-rotation

Yann Palu

Extriangulated categories axiomatize extension-closed subcategories of triangulated categories and generalise both exact categories and triangulated categories. This survey article presents three applications of extriangulated categories to homotopical algebra, algebraic combinatorics and representation theory. The first shows that, via some generalised Hove

Aline Lima de Oliveira, Cauê Addae da Silva Gomes, Cecília Virginia Santos da Silva, Charles Matheus de Sousa Alves

Rob\^oCIn has participated in RoboCup Small Size League since 2019, won its first world title in 2022 (Division B), and is currently a three-times Latin-American champion. This paper presents our improvements to defend the Small Size League (SSL) division B title in RoboCup 2023 in Bordeaux, France. This paper aims to share some of the academic research that

Du Nguyen

For a manifold embedded in an inner product space, we express geometric quantities such as {\it Hamilton vector fields, affine and Levi-Civita connections, curvature} in global coordinates. Instead of coordinate indices, the global formulas for most quantities are expressed as {\it operator-valued} expressions, using an {\it affine projection} to the tangent

Thomas Louf, José J. Ramasco, David Sánchez, Márton Karsai

The socioeconomic background of people and how they use standard forms of language are not independent, as demonstrated in various sociolinguistic studies. However, the extent to which these correlations may be influenced by the mixing of people from different socioeconomic classes remains relatively unexplored from a quantitative perspective. In this work w

Wenqing Jiang, Chengqian Li, Jinyue Cao, Sören Schwertfeger

With the increasing application of robots, stable and efficient Visual Odometry (VO) algorithms are becoming more and more important. Based on the Fourier Mellin Transformation (FMT) algorithm, the extended Fourier Mellin Transformation (eFMT) is an image registration approach that can be applied to downward-looking cameras, for example on aerial and underwa

Gregory Patellis, Werner Porod, George Zoupanos

We examine an extension of the Standard Model which results from a $10D$, $\mathcal{N}=1$, $E_8$ gauge theory. The theory is dimensionally reduced over a $M_4 \times B_0/ \mathbf{Z}_3 $ space, where $B_0$ is the nearly-K\"ahler manifold $SU(3)/U(1) \times U(1)$ and $\mathbf{Z}_3$ is a freely acting discrete group on $B_0$ that triggers a Wilson flux breaking

Grazia Salerno, Tomoki Ozawa, Päivi Törmä

We study the effect of quantum geometry on the many-body ground state of one-dimensional interacting bosonic systems. We find that the Drude weight is given by the sum of the kinetic energy and a term proportional to the many-body quantum metric of the ground state. Notably, the many-body quantum metric determines the upper bound of the Drude weight. We vali

Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, Christos Diou

Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years; however, such systems, including widely popular cloud-based solutions, have been found to exhibit significant biases related to race, age, and gender, a problem that requires in-depth exploration and solutions. This paper presents

Luis Morales-Navarro, Michael T. Giang, Deborah A. Fields, Yasmin B. Kafai

Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective: We introduce the CS Interests and Beliefs Inventory (CSIBI), an instrument designed for novice secondary students learn

Wen Hu, Zhuojia Fu, Leevan Ling

This paper introduces the first attempt to employ a localized meshless method to analyze time-harmonic acoustic wave propagation on curved surfaces with periodic holes/inclusions. In particular, the generalized finite difference method is used as a localized meshless technique to discretize the surface gradient and Laplace-Beltrami operators defined extrinsi

Yunfei Liu, Lijian Lin, Fei Yu, Changyin Zhou

Audio-driven portrait animation aims to synthesize portrait videos that are conditioned by given audio. Animating high-fidelity and multimodal video portraits has a variety of applications. Previous methods have attempted to capture different motion modes and generate high-fidelity portrait videos by training different models or sampling signals from given v

Esteban A. Rodríguez-Mena, José Carlos Abadillo-Uriel, Gaëtan Veste, Biel Martinez

We investigate the existence of linear-in-momentum spin-orbit interactions in the valence band of Ge/GeSi heterostructures using an atomistic tight-binding method. We show that symmetry breaking at the Ge/GeSi interfaces gives rise to a linear Dresselhaus-type interaction for heavy-holes. This interaction results from the heavy-hole/light-hole mixings induce

Jonas B. Profe, Sophie Beck, Dante M. Kennes, Antoine Georges

The pairing symmetry of Sr$_2$RuO$_4$ is a long-standing fundamental question in the physics of superconducting materials with strong electronic correlations. We use the functional renormalization group to investigate the behavior of superconductivity under uniaxial strain in a two-dimensional realistic model of Sr$_2$RuO$_4$ obtained with density functional

Vrindha M. K., Geethu V., Anurenjan P. R., Deepak S.

In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer's. Many studies have shown that certain acoustic features can be used to differentiate between normal aging and Alzheimer's disease, and speech analysis has been found to be a cost-effectiv

Ye Ouyang, Yaqin Zhang, Peng Wang, Yunxin Liu

6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, etc. 6G will realize the transition from serving people and people-things communication to supporting the effic

Amirhossein Aminimehr, Pouya Khani, Amirali Molaei, Amirmohammad Kazemeini

The field of Explainable Artificial Intelligence (XAI) aims to improve the interpretability of black-box machine learning models. Building a heatmap based on the importance value of input features is a popular method for explaining the underlying functions of such models in producing their predictions. Heatmaps are almost understandable to humans, yet they a

Jan-Christoph Kassing, Jürgen Giesl

Dependency pairs are one of the most powerful techniques to analyze termination of term rewrite systems (TRSs) automatically. We adapt the dependency pair framework to the probabilistic setting in order to prove almost-sure innermost termination of probabilistic TRSs. To evaluate its power, we implemented the new framework in our tool AProVE.

Ana Cristina Neves Carloni, João Luiz F. Azevedo

The current work is concerned with studying processes for constructing reduced-order models capable of performing transonic aeroelastic stability analyses in the frequency domain based on computational fluid dynamics (CFD) techniques. The CFD calculations are based on the Euler equations, and the code uses a finite volume formulation for general unstructured

Julia Grabinski, Janis Keuper, Margret Keuper

Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context. Whether achieved through large convolution kernels or self-attention, models scale poorly with the increased spatial context, such that the improved model accuracy often comes at significant costs. In this paper, we propose a module for

Ioannis Sarridis, Jochen Spangenberg, Olga Papadopoulou, Symeon Papadopoulos

Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights investigators, that explores the capability of Artificial Intelligence (AI)-based image filters to potentially

Wei Xia, Chen Wang

In this paper, we mainly establish two supercongruences involving truncated hypergeometric series by using some hypergeometric transformation formulas. The first supercongruence confirms a recent conjecture of the second author. The second supercongruence confirms a conjecture of Guo, Liu and Schlosser partially, and gives a parametric extension of a superco

Yilun Wang, Haibo Yuan, Bingqiu Chen, Xinlei Chen

We found 50 new globular cluster (GC) candidates around M\,31 with Gaia Early Data Release 3 (EDR3), with the help from Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and \textit{simbad}, we trained 2 Random Forest (RF) classifiers, the first one to distinguish extended sources

Jordan Meadows, Marco Valentino, Andre Freitas

This paper investigates how hallucination rates in Large Language Models (LLMs) may be controlled via a symbolic data generation framework, exploring a fundamental relationship between the rate of certain mathematical errors and types of input intervention. Specifically, we systematically generate data for a derivation generation task using a symbolic engine

Isabel Funke, Dominik Rivoir, Stefanie Krell, Stefanie Speidel

Objective: To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being performed by the medical team. The primary source of information for surgical phase recognition is typically video, which presents two challenges: extracting meaningful features from the

R. Kaissner, J. X. Li, W. Z. Lu, X. Li

Recently in nanophotonics, a rigorous evolution from passive to active metasurfaces has been witnessed. This advancement not only brings forward interesting physical phenomena but also elicits opportunities for practical applications. However, active metasurfaces operating at visible frequencies often exhibit low performance due to design and fabrication res

Ricardo Euler, Pedro Maristany de las Casas

The landscape of applications and subroutines relying on shortest path computations continues to grow steadily. This growth is driven by the undeniable success of shortest path algorithms in theory and practice. It also introduces new challenges as the models and assessing the optimality of paths become more complicated. Hence, multiple recent publications i

K. Castillo, D. Mbouna

In his monograph [Classical and quantum orthogonal polynomials in one variable, Cambridge University Press, 2005 (paperback edition 2009)], Ismail conjectured that certain structure relations involving the Askey-Wilson operator characterize proper subsets of the set of all $\mathcal{D}_q$-classical orthogonal polynomials, here to be understood as the Askey-W

Peng Zhao

The Cross-resonance (CR) gate architecture that exploits fixed-frequency transmon qubits and fixed couplings is a leading candidate for quantum computing. Nonetheless, without the tunability of qubit parameters such as qubit frequencies and couplings, gate operations can be limited by the presence of quantum crosstalk arising from the always-on couplings. Wh

Carl Shneider, Peyman Rostami, Anis Kacem, Nilotpal Sinha

Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. Disentangled latent representations produced by variational autoencoder (VAE) networks are a promising approach

Laetitia Farinacci, Gael Reecht, Felix von Oppen, Katharina J. Franke

Kagome lattices constitute versatile platforms for studying paradigmatic correlated phases. While molecular self-assembly of kagome structures on metallic substrates is promising, it is challenging to realize pristine kagome properties because of hybridization with the bulk degrees of freedom and modified electron-electron interactions. We suggest that a sup

Stefano Manzoni, Elena Mazzeo, Javier Mazzitelli, Marius Wiesemann

We present a new simulation for Higgs boson production in association with bottom quarks ($b\bar{b}H$) at next-to-leading order (NLO) accuracy matched to parton showers in hadronic collisions. Both contributions, the standard one proportional to the bottom-quark Yukawa coupling and the loop-induced one proportional to the top-quark Yukawa coupling from the g

Daniel Dantchev

On the examples of the quantum-electrodynamical Casimir force, as well as critical Casimir and Helmholtz forces, we present a review of some results available for the class of fluctuation induced forces. In addition, we also concisely present examples of other such fluctuation-induced forces. On the instance of the Ising model we discuss the connection betwe

Qifang Zhao, Tianyu Li, Meng Du, Yu Jiang

When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost. We present a unified user-item matching framework to simultaneously conduct item recommendation and user targeting with just one model. We empirically demonstrate t

Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan

On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due to the constrained memory and compute resources, and the limited availability of labelled user data. Nonetheless, prior works neglect the data scarcity issue, require excessively

Wenyang Qian

Jets provide one of the primary probes of the quark-gluon plasma produced in ultrarelativistic heavy ion collisions and the cold nuclear matter explored in deep inelastic scattering experiments. However, despite important developments in the last years, a description of the real-time evolution of QCD jets inside a medium is still far from complete. In our pr

Erwan Le Merrer, Gilles Tredan, Ali Yesilkanat

Numerous discussions have advocated the presence of a so called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies a

Yu-chen Fan, Yitong Ji, Jie Zhang, Aixin Sun

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g., like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a m

Tzu-Chao Hung, Yokari Godinez-Loyola, Manuel Steinbrecher, Brian Kiraly

Luminescence of open-shell 3d metal complexes is often quenched due to ultrafast intersystem crossing (ISC) and cooling into a dark metal-centered excited state. We demonstrate successful activation of fluorescence from individual nickel phthalocyanine (NiPc) molecules in the junction of a scanning tunneling microscope (STM) by resonant energy transfer from

Zhi-Feng Zhang, Qing-Rui Wang, Peng Ye

Universal topological data of topologically ordered phases can be captured by topological quantum field theory in continuous space time by taking the limit of low energies and long wavelengths. While previous continuum field-theoretical studies of topological orders in $3$D real space focus on either self-statistics, braiding statistics, shrinking rules, fus

Aleks Kleyn

I dedicated the volume $2$ of monograph 'Introduction into Noncommutative Algebra' to studying of module over non-commutative algebra.

Siyan Dong, Shaohui Liu, Hengkai Guo, Baoquan Chen

Visual (re)localization is critical for various applications in computer vision and robotics. Its goal is to estimate the 6 degrees of freedom (DoF) camera pose for each query image, based on a set of posed database images. Currently, all leading solutions are structure-based that either explicitly construct 3D metric maps from the database with structure-fr

Xuemin Liu, Jonathan F. MacArt

We develop neural-network active flow controllers using a deep learning PDE augmentation method (DPM). The sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function. In 1D Burgers' examples with analytic control functions, DPM-based control is comparably ef

Margherita Bertè, Daniela Paolotti, Kyriaki Kalimeri

Highly skilled professionals' forced migration from Ukraine was triggered by the conflict in Ukraine in 2014 and amplified by the Russian invasion in 2022. Here, we utilize LinkedIn estimates and official refugee data from the World Bank and the United Nations Refugee Agency, to understand which are the main pull factors that drive the decision-making proces

Simone Milanesi, Giuseppe De Nicolao

When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrosp

Yulan Gao, Ziqiang Ye, Yue Xiao, Wei Xiang

The paradigm of federated learning (FL) to address data privacy concerns by locally training parameters on resource-constrained clients in a distributed manner has garnered significant attention. Nonetheless, FL is not applicable when not all clients within the coverage of the FL server are registered with the FL network. To bridge this gap, this paper propo

Dhruba Jyoti Gogoi, N. Heidari, J. Kříž, H. Hassanabadi

In this work, we have studied the quasinormal mode, greybody factors, and absorption cross section of de Sitter Reissner-Nordstr\"om black hole surrounded by quintessence field in Rastall gravity. The violation of energy-momentum conservation has a non-linear effect on the quasinormal modes. With an increase in the black hole charge, both real parts of quasi

Mauro Daniel Luigi Bruno, Giuseppe Emanuele Lio, Antonio Ferraro, Sara Nocentini

The development of new anti-counterfeiting solutions is a constant challenge and involves several research fields. Much interest is devoted to systems that are impossible to clone, based on the Physical Unclonable Function (PUF) paradigm. In this work, new strategies based on electrospinning and electrospraying of dye-doped polymeric materials are presented

K. Mori, H. Hotta

In this work, we systematically investigate the scale-dependent angular momentum flux by analysing high-resolution three-dimensional magnetohydrodynamic simulations in which the solar-like differential rotation is reproduced without using any manipulations. More specifically, the magnetic angular momentum transport (AMT) plays a dominant role in the calculat

Shankar Bhamidi, Dhruv Patel, Vladas Pipiras

A dynamic factor model with factor series following a VAR$(p)$ model is shown to have a VARMA$(p,p)$ model representation. Reduced-rank structures are identified for the VAR and VMA components of the resulting VARMA model. It is also shown how the VMA component parameters can be computed numerically from the original model parameters via the innovations algo

Pau Amaro Seoane

In galactic nuclei, stellar densities are so high that stars can physically collide with each other. In this work we focus on the collision of red giants and in particular on the formation of non-thermal processes through collisions and their properties. We analytically address these points by evaluating head-on collisions but also take into account scenario

Tomislav Antic, Andrew Keane, Tomislav Capuder

The share of end-users with installed rooftop photovoltaic (PV) systems is continuously growing. Since most end-users are located at the low voltage (LV) level and due to technical limitations of LV networks, it is necessary to calculate PV hosting capacity. Most approaches in calculating a network's hosting capacity are based on three-phase optimal power fl