March 2023 arXiv papers — page 77

Ema Dimastrogiovanni, Matteo Fasiello, Martino Michelotti, Lucas Pinol

We consider inflation driven by an axion-like particle coupled to an SU(2) gauge sector via a Chern-Simons term. Known as chromo-natural inflation, this scenario is in tension with CMB observations. In order to remedy this fact and preserve both the symmetries and the intriguing gravitational wave phenomenology exhibited by the model, we explore the non-mini

Mihalis Mourgoglou, Thanasis Zacharopoulos

Let $\Omega \subset \mathbb{R}^{n+1}$, $n\geq 1$, be an open set with $s$-Ahlfors regular boundary $\partial \Omega$, for some $s \in(0,n]$, such that either $s=n$ and $\Omega$ is a corkscrew domain with the pointwise John condition, or $s<n$ and $\Omega= \mathbb{R}^{n+1} \setminus E$, for some $s$-Ahlfors regular set $E \subset \mathbb{R}^{n+1}$. In this pa

Alexandros Deltsidis, Laura Simonelli, Georgios Vailakis, Izar Capel Berdiell

Two-dimensional iron-chalcogenide intercalates display a remarkable correlation of the interlayer spacing with the enhancement of the superconducting critical temperature ($T_c$). In this work, synchrotron x-ray absorption ($XAS$, at Fe and Se K edges) and emission ($XES$) spectroscopies, allow to discuss how the important rise of $T_c$ (44 K) in the molecul

Vefa Goksel

In his earlier work, the author introduced a group theory question that arises in the study of iterated Galois groups of post-critically finite quadratic polynomials. In this paper, we prove the first non-trivial results on this question.

Giovanni Amendola, Marco Manna, Aldo Ricioppo

Similarities between entities occur frequently in many real-world scenarios. For over a century, researchers in different fields have proposed a range of approaches to measure the similarity between entities. More recently, inspired by "Google Sets", significant academic and commercial efforts have been devoted to expanding a given set of entities with simil

Dan Ciubotaru, Lucas Mason-Brown, Emile Okada

The wavefront set is a fundamental invariant of an admissible representation arising from the Harish-Chandra-Howe local character expansion. In this paper, we give a precise formula for the wavefront set of an irreducible representation of real infinitesimal character in Lusztig's category of unipotent representations in terms of the Deligne-Langlands-Luszti

Vincent Brault, Émilie Devijver, Charlotte Laclau

In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic p

Athanassios Tzouvaras

In previous work we defined and studied a notion of typicality, originated with B. Russell, for properties and objects in the context of general infinite first-order structures. In this paper we consider this notion in the context of finite structures. In particular we define the typicality degree of a property $\phi(x)$ over finite $L$-structures, for a lan

Claudio Corianò, Stefano Lionetti, Matteo Maria Maglio

We illustrate how the Conformal Ward Identities (CWI) in momentum space for parity-odd correlators determine the structure of a chiral anomaly interaction, taking the example of the VVA (vector/vector/axial-vector) and AAA correlators in momentum space. Only the conservation and the anomalous WIs, together with the Bose symmetry, are imposed from the outset

Junyuan Deng, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun

Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance fields (NeRF) have shown promising advances in implicit reconstru

Mandy Keck, Samuel Huron, Georgia Panagiotidou, Christina Stoiber

This workshop focuses on visualization education, literacy, and activities. It aims to streamline previous efforts and initiatives of the visualization community to provide a format for education and engagement practices in visualization. It intends to bring together junior and senior scholars to share research and experience and to discuss novel activities,

J. P. Hidalgo, P. J. Käpylä, C. A. Ortiz-Rodríguez, F. H. Navarrete

According to our understanding of stellar evolution, early-type stars have radiative envelopes and convective cores due to a steep temperature gradient produced by the CNO cycle. Some of these stars (mainly, the subclasses Ap and Bp) have strong magnetic fields, enough to be directly observed using the Zeeman effect. Here, we present 3D magnetohydrodynamic s

Polina Barabanshchikova, Alexandr Polyanskii

For a graph whose vertices are points in $\mathbb R^d$, consider the closed balls with diameters induced by its edges. The graph is called a Tverberg graph if these closed balls intersect. A max-sum tree of a finite point set $X \subset \mathbb R^d$ is a tree with vertex set $X$ that maximizes the sum of Euclidean distances of its edges among all trees with

Jinyuan Liu, Yong Liang Guan, Yao Ge, Longfei Yin

Energy efficiency (EE) problem has become an important and major issue in satellite communications. In this paper, we study the beamforming design strategy to maximize the EE of rate-splitting multiple access (RSMA) for the multibeam satellite communications by considering imperfect channel state information at the transmitter (CSIT). We propose an expectati

Marianne Akian, Stéphane Gaubert, Shanqing Liu

We introduce a new numerical method to approximate the solutions of a class of stationary Hamilton-Jacobi (HJ) partial differential equations arising from minimum time optimal control problems. We rely on nested grid approximations, and look for the optimal trajectories by using the coarse grid approximations to reduce the search space in fine grids. This pr

Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee

Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained to reconstruct the normal only training data with the expectation that, in test time, it can poorly reconstruct the anomalous data. However, previous studies have shown that, even t

Srikar Yellapragada, Zhenghong Li, Kevin Bhadresh Doshi, Purva Makarand Mhasakar

Gun violence is a critical security problem, and it is imperative for the computer vision community to develop effective gun detection algorithms for real-world scenarios, particularly in Closed Circuit Television (CCTV) surveillance data. Despite significant progress in visual object detection, detecting guns in real-world CCTV images remains a challenging

Baptiste Nguyen, Pierre-Alain Moellic, Sylvain Blayac

Deploying neural networks on constrained hardware platforms such as 32-bit microcontrollers is a challenging task because of the large memory, computing and energy requirements of their inference process. To tackle these issues, several convolution primitives have been proposed to make the standard convolution more computationally efficient. However, few of

Arkaitz Rodas, Jozef J. Dudek, Robert G. Edwards

Using lattice QCD we extract $\pi\pi$ scattering amplitudes with isospin--0,1,2 in low partial-waves at two values of the light quark mass corresponding to $m_\pi \sim 283$ and $330$ MeV. We confirm expectations of weak repulsion in isospin--2, and the presence of a narrow $\rho$ resonance in isospin--1, and study the pion mass dependence of these channels.

Yinsong Wang, Huaqi Qiu, Chen Qin

Deep learning-based image registration approaches have shown competitive performance and run-time advantages compared to conventional image registration methods. However, existing learning-based approaches mostly require to train separate models with respect to different regularization hyperparameters for manual hyperparameter searching and often do not allo

Weizhe Lin, Zhilin Wang, Bill Byrne

The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer. It has been observed that the original dataset is highly imbalanced and concentrated on a small portion of its associated knowledge graph. We introduce FVQA 2.0 which contai

Izar Capel Berdiell, Edyta Pesko, Elijah Lator, Alexandros Deltsidis

A time-resolved synchrotron X-ray total scattering study sheds light on the evolution of the different structural length scales involved during the intercalation of the layered iron-selenide host by organic molecular donors, aiming at the formation of the expanded lattice $Li_x(C_5H_5N)_yFe_{2-z}Se_2$ hybrid superconductor. The intercalates are found to crys

Karolina Ewa Połczyńska, Simon Le Denmat, Takashi Taniguchi, Kenji Watanabe

Using four-wave mixing microscopy, we measure the coherent response and ultrafast dynamics of excitons and trions in MoSe$_2$ monolayers grown by molecular beam epitaxy on thin films of hexagonal boron nitride. We assess inhomogeneous and homogeneous broadenings in the transition spectral lineshape. The impact of phonons on the homogeneous dephasing is infer

Silvia Falletta, Matteo Ferrari, Letizia Scuderi

In this paper, we propose and analyse a numerical method to solve 2D Dirichlet time-harmonic elastic wave equations. The procedure is based on the decoupling of the elastic vector field into scalar Pressure ($P$-) and Shear ($S$-) waves via a suitable Helmholtz-Hodge decomposition. For the approximation of the two scalar potentials we apply a virtual element

Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi, Stanislaw H /. Zak

Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists a vast literature on this topic and most methods centered around the celebrated average-consensus paradigm, less attention has been devoted to scenarios where the communication bet

Subhankar Ghosh, Taha Belkhouja, Yan Yan, Janardhan Rao Doppa

Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with

Niccolò Muttoni, Danny Laghi, Nicola Tamanini, Sylvain Marsat

Third-generation (3G) gravitational wave detectors, in particular Einstein Telescope (ET) and Cosmic Explorer (CE), will explore unprecedented cosmic volumes in search for compact binary mergers, providing us with tens of thousands of detections per year. In this study, we simulate and employ binary black holes detected by 3G interferometers as dark sirens,

Chaofan Ma, Qisen Xu, Xiangfeng Wang, Bo Jin

Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction independently. We here propose to model i

Yuan Zeng, Yi Gong, Jiawei Liu, Shangao Lin

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly.

Xiaoyi Wen, Junhui Wang, Liping Zhu

In this article, we consider the problem of testing whether two latent position random graphs are correlated. We propose a test statistic based on the kernel method and introduce the estimation procedure based on the spectral decomposition of adjacency matrices. Even if no kernel function is specified, the sample graph covariance based on our proposed estima

Chunmeng Liu, Guangyao Li, Yao Shen, Ruiqi Wang

The initial seed based on the convolutional neural network (CNN) for weakly supervised semantic segmentation always highlights the most discriminative regions but fails to identify the global target information. Methods based on transformers have been proposed successively benefiting from the advantage of capturing long-range feature representations. However

Johannes Franke, Sean R. Muleady, Raphael Kaubruegger, Florian Kranzl

The control over quantum states in atomic systems has led to the most precise optical atomic clocks to date. Their sensitivity is currently bounded by the standard quantum limit, a fundamental floor set by quantum mechanics for uncorrelated particles, which can nevertheless be overcome when operated with entangled particles. Yet demonstrating a quantum advan

Anna Kh. Balci, Alex Kaltenbach

In the present paper, we examine a Crouzeix-Raviart approximation of the $p(\cdot)$-Dirichlet problem. We derive a $\textit{medius}$ error estimate, $\textit{i.e.}$, a best-approximation result, which holds for uniformly continuous exponents and implies $\textit{a priori}$ error estimates, which apply for H\"older continuous exponents and are optimal for Lip

Kunle Adegoke, Jaume Oliver Lafont

Let $\alpha=(1+\sqrt 5)/2$, the golden ratio, and $\beta=-1/\alpha=(1 - \sqrt 5)/2$. Let $F_n$ and $L_n$ be the Fibonacci and Lucas numbers, defined by $F_n=(\alpha^n -\beta^n)/\sqrt 5$ and $L_n=\alpha^n + \beta^n$, for all non-negative integers. We derive base~$\alpha$ expansions of $\log F_n$, $\log L_n$, $\arctan\dfrac1{F_n}$ and $\arctan\dfrac1{L_n}$ for

Leonie Becker, Tommy Nilsson, Paul Topf Aguiar de Medeiros, Flavie Rometsch

Future astronauts living and working on the Moon will face extreme environmental conditions impeding their operational safety and performance. While it has been suggested that Augmented Reality (AR) Head-Up Displays (HUDs) could potentially help mitigate some of these adversities, the applicability of AR in the unique lunar context remains underexplored. To

Cougar A. T. Garcia, Nancyjane Bailey, Chris Kirby, Joshua A. Strong

Understanding the origins of power loss in superconducting interconnects is essential for the energy efficiency and scalability of superconducting digital logic. At microwave frequencies, power dissipates in both the dielectrics and superconducting wires, and these losses can be of comparable magnitude. A novel method to accurately disentangle such losses by

Noam Angrist, Matthew C. H. Jukes, Sian Clarke, R. Matthew Chico

There is limited evidence of health interventions impact on cognitive function and educational outcomes. We build on two prior systematic reviews to conduct a meta-analysis, exploring the effects of one of the most consequential health interventions, malaria chemoprevention, on education outcomes. We pool data from nine study treatment groups (N=4,075) and o

Indrakshi Dey, Nicola Marchetti

Multiple photonic degrees of freedom can be explored to generate high-dimensional quantum states; commonly referred to as `qudits'. Qudits offer several advantages for quantum communications, including higher information capacity, noise resilience and data throughput, and lower information loss over different propagation mediums (free space, optical fibre, u

Katherine Ormeño Bastías, Steen Ryom-Hansen

Let ${\mathbb{TL}_n^{\! \mathbb Q}} $ be the rational Temperley-Lieb algebra, with loop parameter $ 2 $. In the first part of the paper we study the seminormal idempotents $ E_{ \mathfrak{t}} $ for ${\mathbb{TL}_n^{\! \mathbb Q}}$ for $ \mathfrak{t} $ running over two-column standard tableaux. Our main result is here a concrete combinatorial construction of

Rozhina Ghanavi, Ben Liang, Ali Tizghadam

Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values. In this work, we are motivated by network traffic classification, where traditional data imputation methods do not perform well. We recognize that no existing method directly accounts for classification accuracy during data imputation. The

CMS Collaboration

The first measurement of the top quark pair ($\mathrm{t\bar{t}}$) production cross section in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV is presented. Data recorded with the CMS detector at the CERN LHC in Summer 2022, corresponding to an integrated luminosity of 1.21 fb$^{-1}$, are analyzed. Events are selected with one or two charged leptons (electr

Boaz Lubotzky, Alexander Nazarov, Hamza Abudayyeh, Lukas Antoniuk

We demonstrate an important step towards on chip integration of single photon sources operating at room temperature fiber coupling of a directional quantum emitter with back-excitation. Directionality is achieved with a hybrid metal-dielectric bullseye antenna, while back-excitation is permitted by placement of the emitter at or in a sub-wavelength hole posi

Joachim Poutaraud

Birds produce multiple types of vocalizations that, together, constitute a vocal repertoire. For some species, the repertoire size is of importance because it informs us about their brain capacity, territory size or social behaviour. Estimating the repertoire size is challenging because it requires large amounts of data which can be difficult to obtain and a

Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak, Ziran Wang

Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perce

Teena Thomas

For a compact Hausdorff space $S$, we prove that the closed unit ball of a closed linear subalgebra of the space of real-valued continuous functions on $S$, denoted by $C(S)$, satisfies property-$(P_1)$ (the set-valued generalization of strong proximinality) for the non-empty closed bounded subsets of the bidual of $C(S)$. Various stability results related t

Nikolaus Vertovec, Kostas Margellos

We propose a distributed algorithm to solve a dynamic programming problem with multiple agents, where each agent has only partial knowledge of the state transition probabilities and costs. We provide consensus proofs for the presented algorithm and derive error bounds of the obtained value function with respect to what is considered as the "true solution" ob

Hongmeng Liu, Jiapeng Zhao, Yixuan Huo, Yuyan Wang

Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on different markets makes it easy to aggregate user information across different platforms, which helps identify anonymous users. Traditional user representation methods mainly rely on

Santanu Pakhira, David C. Johnston

The body-centered-tetragonal antiferromagnet EuGa$_4$ exhibits A-type antiferromagnetic order below its N\'eel temperature $T_{\rm N} = 16.4$ K in magnetic field $H = 0$ where the moments are ferromagnetically aligned in the $ab$-plane with the Eu moments in adjacent Eu planes aligned antiferromagnetically. Previous magnetization versus field $M_{ab}(H)$ mea

Joseph Farrington, Kezhi Li, Wai Keong Wong, Martin Utley

Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to represent the age profile of stock. The parallel processing capabilities of modern GPUs can reduce the wall time required to run value iteration by updating many states simultaneousl

M. Gen, H. Ishikawa, A. Miyake, T. Yajima

We investigate the crystallographic and magnetic properties of a chromium-based thiospinel CuGaCr$_{4}$S$_{8}$. From a synchrotron x-ray diffraction experiment and structural refinement, Cu and Ga atoms are found to occupy the tetrahedral $A$-sites in an alternate way, yielding breathing pyrochlore Cr network. CuGaCr$_{4}$S$_{8}$ undergoes a magnetic transit

Xu Zhou, Daowen Qiu, Le Lou

Distributed quantum computation has gained extensive attention since small-qubit quantum computers seem to be built more practically in the noisy intermediate-scale quantum (NISQ) era. In this paper, we give a distributed Bernstein-Vazirani algorithm (DBVA) with $t$ computing nodes, and a distributed exact Grover's algorithm (DEGA) that solve the search prob

Ravshan Ashurov, Oqila Mukhiddinova

In recent years, much attention has been paid to the study of forward and inverse problems for the Rayleigh-Stokes equation in connection with the importance of this equation for applications. This equation plays an important role, in particular, in the study of the behavior of certain non-Newtonian fluids. The equation includes a fractional derivative of or

Yiling Xie, Hao Sun

We assign $X(4500)$ as a D-wave tetraquark state and study the decay of $X(4500)$ $\to$ $J/\psi \phi$. The mass and the decay constant of $X(4500)$ are calculated by using the SVZ sum rules. For the decay width of $X(4500)$ $\to$ $J/\psi \phi$, we present the calculation within the framework of both the three-point sum rules and the light-cone sum rules. The

Ho-Hsiang Wu, Oriol Nieto, Juan Pablo Bello, Justin Salamon

Multi-modal contrastive learning techniques in the audio-text domain have quickly become a highly active area of research. Most works are evaluated with standard audio retrieval and classification benchmarks assuming that (i) these models are capable of leveraging the rich information contained in natural language, and (ii) current benchmarks are able to cap

Xiang Li, Yu Su, Zi-Hao Chen, Yao Wang

Dissipaton theory had been proposed as an exact and nonperturbative approach to deal with open quantum system dynamics, where the influence of Gaussian environment is characterized by statistical quasi-particles named as dissipatons. In this work, we revisit the dissipaton equation of motion theory and establish an equivalent dissipatons-embedded quantum mas

Kai Cui, Christian Fabian, Anam Tahir, Heinz Koeppl

Multi-agent reinforcement learning (MARL) remains difficult to scale to many agents. Recent MARL using Mean Field Control (MFC) provides a tractable and rigorous approach to otherwise difficult cooperative MARL. However, the strict MFC assumption of many independent, weakly-interacting agents is too inflexible in practice. We generalize MFC to instead simult

Naihuan Jing, Ning Liu

An algebraic iterative formula for the spin Kostka-Foulkes polynomial $K^-_{\xi\mu}(t)$ is given using vertex operator realizations of Hall-Littlewood symmetric functions and Schur's Q-functions. Based on the operational formula, more favorable properties are obtained parallel to the Kostka polynomial. In particular, we obtain some formulae for the number of

Hao Li, Daowen Qiu, Le Luo

Deutsch-Jozsa (DJ) problem is one of the most important problems demonstrating the power of quantum algorithm. DJ problem can be described as a Boolean function $f$: $\{0,1\}^n\rightarrow \{0,1\}$ with promising it is either constant or balanced, and the purpose is to determine which type it is. DJ algorithm can solve it exactly with one query. In this paper

Alisher Aikyn, Yang Liu, Dmitry A. Lyakhov, Florian Rist

In this paper, we introduce and study a remarkable class of mechanisms formed by a $3 \times 3$ arrangement of rigid quadrilateral faces with revolute joints at the common edges. In contrast to the well-studied Kokotsakis meshes with a quadrangular base, we do not assume the planarity of the quadrilateral faces. Our mechanisms are a generalization of Izmesti

Mengfan He, Cheng Zhao, Huanyuan Shan

The {\it Linear Point} (LP), defined as the midpoint between the BAO peak and the associated left dip of the two-point correlation function (2PCF), $\xi(s)$, is proposed as a new standard ruler which is insensitive to nonlinear effects. In this paper, we use a Bayesian sampler to measure the LP and estimate the corresponding statistical uncertainty, and then

Zexiang Liu, Necmiye Ozay

Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments. However, there is a lack of formal analysis of the impact of preview inform

Yuhang Jiang, Ramakanth Kavuluru

As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in

Martin Bråtelund, Felix Rydell

This paper studies the problem of recovering cameras from a set of fundamental matrices. A set of fundamental matrices is said to be compatible if a set of cameras exists for which they are the fundamental matrices. We focus on the complete graph, where fundamental matrices for each pair of cameras are given. Previous work has established necessary and suffi

Chen Zhang, Junhui Gao, Lingxin Kong, Guangshuo cao

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often

Lucas Farndale, Robert Insall, Ke Yuan

Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data. Despite the progress, practical use of advanced imaging technologies for research and diagnosis remains limited by cost and availability, so information-sparse data such as H&E stains are relied on in practice. The study of diseased tissue requires

Rui Zhang, Wenkui Ding, Zhucheng Zhang, Lei Shao

There is a prevalent effort to achieve quantum-enhanced metrology using criticality. However, the extent to which estimation precision is enhanced through criticality still needs further exploration under the constraint of finite time resources. We clarify relations between quantum metrology and criticality through a unitary parametrization process with a Ha

R. James Cotton, Allison DeLillo, Anthony Cimorelli, Kunal Shah

Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biom

Gaojie Jin, Xinping Yi, Dengyu Wu, Ronghui Mu

In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the most successful methods. To deal with both the robustness against adversarial examples and the accuracy over clean examples, many works develop enhanced adversarial training methods

Ravshan Ashurov, Oqila Mukhiddinova, Sabir Umarov

A nonlocal boundary value problem for the fractional version of the well known in fluid dynamics Rayleigh-Stokes equation is studied. Namely, the condition $u(x,T)=\beta u(x,0)+\varphi(x)$, where $\beta $ is an arbitrary real number, is proposed instead of the initial condition. If $\beta=0$, then we get the inverse problem in time, called the backward probl

Nima Tashakor, Jan Kacetl, Davood Keshavarzi, Stefan Goetz

Modular battery integrated converters or so called dynamically reconfigurable battery packs are expanding into emerging applications, including electromobility and grid storage. Although they offer many degrees of freedom, the state of the art focuses on single output systems and mostly neglects potential of such systems in generating multiple controlled out

Natalia Ślusarz, Ekaterina Komendantskaya, Matthew L. Daggitt, Robert Stewart

Differentiable logics (DL) have recently been proposed as a method of training neural networks to satisfy logical specifications. A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions. These loss functions can then be used during training with standard gradien

U. Özdem

To elucidate the internal structure of exotic states is one of the central purposes of hadron physics. Motivated by this, we study the electromagnetic properties of $\bar D^{(*)}\Xi^{\prime}_c$, $\bar D^{(*)}\Lambda_c$, $\bar D_s^{(*)}\Lambda_c$ and $\bar D_s^{(*)}\Xi_c$ pentaquarks without strange, with strange and with double strange through QCD light-cone

Chris Verhoek, Patrick J. W. Koelewijn, Sofie Haesaert, Roland Tóth

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a di

Zhe Wang, Fan Zhang, Wenan Guo

Using Quantum Monte Carlo simulations, we study spin-1/2 diagonal ladders coupled by ferromagnetic Heisenberg interactions. The model can also be viewed as usual ladders with ferromagnetic rung couplings coupled by antiferromagnetic diagonal couplings. We find that the model hosts a striped magnetic ordered phase and two topological nontrivial Haldane phases

Yudhisthira Jamudulia

We consider $cp_{a,b,m}(n)$, the number of $(a,b,m)$-copartitions of $n$. We find many infinitelymany congruencesmodulo 2 and 6 for some particular value of $a$, $b$ and $m$.

Nicolas Bousquet, Quentin Deschamps, Aline Parreau

The metric dimension has been introduced independently by Harary, Melter and Slater in 1975 to identify vertices of a graph G using its distances to a subset of vertices of G. A resolving set X of a graph G is a subset of vertices such that, for every pair (u,v) of vertices of G, there is a vertex x in X such that the distance between x and u and the distanc

Hung Nguyen-Kha, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas

This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and the core network. The mobility of LEO satellites raises the challenge of determining the optimal association between LEO satellites, BSs, and users (UEs). The goal is to satis

Zihan Wang, Siyang Song, Cheng Luo, Yuzhi Zhou

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature

Shantanu Desai

Lorentz invariance is one of the fundamental tenets of Special Relativity, and has been extensively tested with laboratory and astrophysical observations. However, many quantum gravity models and theories beyond the Standard Model of Particle Physics predict a violation of Lorentz invariance at energies close to the Planck scale. This article reviews observa

Yuan Bai, Zengyang Shao, Xiao Zhang, Ruohan Chen

China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave. We estimated the time-varying reproduction numbers of 32 provincial-level administrative divisions from December 2022 to January 2023. We found that the pooled estimate of initial reproduction numbers is 4.74 (95% CI: 4.41, 5.07).

Ping Zhao, Dachuan Chen, Zhaojun Wang

A spatial-sign based test procedure is proposed for high dimensional white noise test in this paper. We establish the limit null distribution and give the asymptotical relative efficient of our test with respect to the test proposed by Feng et al. (2022) under some special alternative hypothesis. Simulation studies also demonstrate the efficiency and robustn

Benedikt Jahnel, Jonas Köppl

By refining Holley's free energy technique, we show that, under quite general assumptions on the dynamics, the attractor of a (possibly non-translation-invariant) interacting particle system in one or two spatial dimensions is contained in the set of Gibbs measures if the dynamics admits a reversible Gibbs measure. In particular, this implies that there can

Alexander Weitzel, Lea Pfaffinger, Ilaria Maccari, Klaus Kronfeldner

We present a comprehensive investigation of the Berezinskii-Kosterlitz-Thouless (BKT) transition in ultrathin strongly disordered NbN films. Measurements of resistance, current-voltage characteristics and kinetic inductance on the very same device reveal a consistent picture of a sharp unbinding transition of vortex-antivortex pairs that fit standard renorma

A. Caranti, Cindy Tsang

We consider the quotient group $T(G)$ of the multiple holomorph by the holomorph of a finite $p$-group $G$ of class two for an odd prime $p$. By work of the first-named author, we know that $T(G)$ contains a cyclic subgroup of order $p^{r-1}(p-1)$, where $p^r$ is the exponent of the quotient of $G$ by its center. In this paper, we shall exhibit examples of $

Jan Krajicek

Given a sound first-order p-time theory $T$ capable of formalizing syntax of first-order logic we define a p-time function $g_T$ that stretches all inputs by one bit and we use its properties to show that $T$ must be incomplete. We leave it as an open problem whether for some $T$ the range of $g_T$ intersects all infinite NP sets (i.e. whether it is a proof

Shaopeng Hong, Sheng Xiao

In this paper, we investigate a class of mean reflected McKean-Vlasov stochastic differential equation, which extends the equation proposed by \cite{briand2020particles} by allowing the solution's distribution to not only constrain its behavior, but also affect the diffusion and drift coefficients. We establish the existence and uniqueness results of this cl

Song Zhao, Shiwei Cheng, Chenshuang Zhu

Conducting collaborative tasks, e.g., multi-user game, in virtual reality (VR) could enable us to explore more immersive and effective experience. However, for current VR systems, users cannot communicate properly with each other via their gaze points, and this would interfere with users' mutual understanding of the intention. In this study, we aimed to find

Laurent Lafleche, Chiara Saffirio

We review some uniqueness criteria for the Vlasov--Poisson system, emerging as corollaries of stability estimates in strong or weak topologies, and show how they serve as a guideline to solve problems arising in semiclassical analysis. Different topologies allow to treat different classes of quantum states.

T. J. Meijer, V. S. Dolk, W. P. M. H. Heemels

In this technical note, we generalize the well-known Lyapunov-based stabilizability and detectability tests for linear time-invariant (LTI) systems to the context of discrete-time (DT) polytopic linear parameter-varying (LPV) systems. To do so, we exploit the mathematical structure of the class of poly-quadratic Lyapunov functions, which enables us to formul

Amelia Carolina Sparavigna

q-Gaussians are probability distributions having their origin in the framework of Tsallis statistics. A continuous real parameter q is characterizing them so that, in the range 1 < q < 3, the q-functions pass from the usual Gaussian form, for q close to 1, to that of a heavy tailed distribution, at q close to 3. The value q=2 corresponds to the Cauchy-Lorent

Muhammed Gouda, Steven Abreu, Alessio Lugnan, Peter Bienstman

Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics. The vast majority of current systems acquire a large amount of images which are used to train deep artificial neural networks. However, this approach increases both the latency and power consumption of the final apparatus. In this

Peter Kostolányi

A finite automaton is called bideterministic if it is both deterministic and codeterministic -- that is, if it is deterministic and its transpose is deterministic as well. The study of such automata in a weighted setting is initiated. All trim bideterministic weighted automata over integral domains and over positive semirings are proved to be minimal. On the

Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni

Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical p

Elena Rubei

For every $n \in \mathbb{N}$ and every field $K$, let $N(n,K)$ be the set of the nilpotent $n \times n$ matrices over $K$ and let $D(n,K) $ be the set of the $n \times n$ matrices over $K$ which are diagonalizable over $K$. Moreover, let $R(n) $ be the set of the normal $n \times n$ matrices. In this short note we prove that the maximal dimension of an affin

M. H. Annaby, M. E. Mahmoud, H. A. Abdusalam, H. A. Ayad

We compute precise estimates for dimensions of 3D-encryption techniques of 3D-point clouds which use permutations and rigid body motion, in which geometric stability is to be guaranteed. Few attempts are made in this direction. An attempt is established using the notions of dimensional and spatial stability by Jolfaei et al. (2015), who also proposed a 3D ob

S. G. Wang, H. F. Feng, Zhi-Xin Guo

Recently, the in-plane thermal transport in van der Waals (vdW) materials such as graphene, hexagonal boron nitride (h-BN), and transition metal dichalcogenides (TMDs) has been widely studied. Whereas, the cross-plane one is far from sufficient. Based on the non-equilibrium molecular dynamics simulations and Boltzmann transport equation, here we reveal the s

Olga Rozanova

We show that many important natural science models in their mathematical formulation can be reduced to non-strictly hyperbolic systems of the same kind. This allows the same methods to be applied to them so that some essential results concerning a particular model can be obtained as corollaries of general theorems. However, in each case, the models have thei

Clarissa Astuto, Daniele Boffi, Fabio Credali

A finite element discretization is developed for the Cai-Hu model, describing the formation of biological networks. The model consists of a non linear elliptic equation for the pressure $p$ and a non linear reaction-diffusion equation for the conductivity tensor $\mathbb{C}$. The problem requires high resolution due to the presence of multiple scales, the st

Manas Wadhwa, Gagan Raj Gupta, Ashutosh Sahu, Rahul Saini

The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset o

George Stamatelis, Nicholas Kalouptsidis

A combination of deep reinforcement learning and supervised learning is proposed for the problem of active sequential hypothesis testing in completely unknown environments. We make no assumptions about the prior probability, the action and observation sets, and the observation generating process. Our method can be used in any environment even if it has conti