March 2023 arXiv papers — page 18

Sofia Ponomareva, Alexey A. Shcherbakov

Light scattering in disordered media plays an important role in various areas of applied science from biophysics to astronomy. In this paper we study two approaches to calculate scattering properties of semi-infinite densely packed media with high contrast and wavelength scale inhomogeneities by combining the Fourier Modal Method and the super-cell approach.

Trifon I. Missov, Silvio C. Patricio, Francisco Villavicencio

Age-specific mortality improvements are non-uniform, neither across ages nor across time. We propose a two-step procedure to estimate the rates of mortality improvement (RMI) in age-specific death rates (ASDR) at ages 85 and above for ten European countries from 1950 to 2019. In the first step, we smooth the raw death counts and estimate ASDR using four diff

Charalampos Tzerefos, Theodoros Papanikolaou, Emmanuel N. Saridakis, Spyros Basilakos

Primordial black holes (PBHs) forming out of the collapse of enhanced cosmological perturbations provide access to the early Universe through their associated observational signatures. In particular, enhanced cosmological perturbations collapsing to form PBHs are responsible for the generation of a stochastic gravitational-wave background (SGWB) induced by s

Tristan J. B. Cann, Ben Dennes, Travis Coan, Saffron O'Neill

Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic simi

Anastasia Hadjievangelou, Gunnar Traustason

In this paper we prove a group theoretic analogue of the well known local nilpotence theorem for sandwich Lie algebras due to Kostrikin and Zel'manov. We introduce the notion of a strong left 3-Engel element of a group G and show that these are always in the locally nilpotent radical of G. This generalises a previous result of Jabara and Traustason that show

Dejia Zhang, Aiguo Xu, Yanbiao Gan, Yudong Zhang

A two-fluid discrete Boltzmann model with a flexible Prandtl number is formulated to study the shock-bubble interaction (SBI). This paper mainly focuses on the viscous effects on morphological and thermodynamic non-equilibrium (TNE) characterizations during the SBI process. Due to the rapid and brief nature of the SBI process, viscosity has a relatively limi

Žiga Krajnik, Johannes Schmidt, Enej Ilievski, Tomaž Prosen

Recent studies have found that fluctuations of magnetization transfer in integrable spin chains violate the central limit property. Here we revisit the problem of anomalous counting statistics in the Landau-Lifshitz field theory by specializing to two distinct anomalous regimes featuring a dynamical critical point. By performing optimized numerical simulatio

Lilas Alrahis, Ozgur Sinanoglu

The participation of third-party entities in the globalized semiconductor supply chain introduces potential security vulnerabilities, such as intellectual property piracy and hardware Trojan (HT) insertion. Graph neural networks (GNNs) have been employed to address various hardware security threats, owing to their superior performance on graph-structured dat

George K. Leontaris, Pramod Shukla

In this report, we present a concise review on the various moduli stabilisation schemes proposed in the context of type IIB superstring compactifications using Calabi-Yau orientifolds. We discuss the details of the known schemes by classifying them into two categories; the first one includes non-perturbative superpotential contributions leading to the well-k

Antonios Gouglidis, Anna Kagia, Vincent C. Hu

Authoring access control policies is challenging and prone to misconfigurations. Access control policies must be conflict-free. Hence, administrators should identify discrepancies between policy specifications and their intended function to avoid violating security principles. This paper aims to demonstrate how to formally verify access control policies. Mod

Sizhong Zhou, Yuli Zhang

Let $k$ and $n$ be two nonnegative integers with $n\equiv0$ (mod 2), and let $G$ be a graph of order $n$ with a 1-factor. Then $G$ is said to be $k$-extendable for $0\leq k\leq\frac{n-2}{2}$ if every matching in $G$ of size $k$ can be extended to a 1-factor. In this paper, we first establish a lower bound on the signless Laplacian spectral radius of $G$ to e

Yuki Yasuda, Ryo Onishi, Keigo Matsuda

Atmospheric simulations for urban cities can be computationally intensive because of the need for high spatial resolution, such as a few meters, to accurately represent buildings and streets. Deep learning has recently gained attention across various physical sciences for its potential to reduce computational cost. Super-resolution is one such technique that

Francesco Parisen Toldin

We study the critical behavior at the ordinary surface universality class of the three-dimensional O($N$) model, bounded by a two-dimensional surface. Using high-precision Monte Carlo simulations of an improved lattice model, where the leading bulk scaling correction is suppressed, and finite-size scaling analysis of the fourth cumulant of the surface magnet

Mitja Briscik, Marie-Agnès Dillies, Sébastien Déjean

Kernel methods have been proven to be a powerful tool for the integration and analysis of highthroughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of Principal Component Analysis is a valid nonlinear alternative to tackle the nonlinearity of biological sample sp

Janik-Vasily Benzin, Gyunam Park, Stefanie Rinderle-Ma

Object-centric process discovery (OCPD) constitutes a paradigm shift in process mining. Instead of assuming a single case notion present in the event log, OCPD can handle events without a single case notion, but that are instead related to a collection of objects each having a certain type. The object types constitute multiple, interacting case notions. The

Kostas Glampedakis, David Ian Jones

The flatness of galaxy rotation curves at large radii is generally considered to be a significant piece of evidence in support of the existence of dark matter. Several studies have claimed that post-Newtonian corrections to the Newtonian equations of galaxy dynamics may remove (at least to some degree) the need for dark matter. A few recent studies have exam

Swaraj Pratim Sarmah, Umananda Dev Goswami

We study the effect of diffusion of ultra-high energy cosmic ray (UHECR) protons in the presence of turbulent magnetic fields in the light of the $f(R)$ theory of gravity. The $f(R)$ theory of gravity is a successful modified theory of gravity in explaining the various aspects of the observable Universe including its current state of expansion. Here, we cons

Frédéric Bayart

For every fixed $\epsilon$ $\in$ (0, 1), we construct an operator on the separable Hilbert space which is $\delta$-hypercyclic for all $\delta$ $\in$ ($\epsilon$, 1) and which is not $\delta$-hypercyclic for all $\delta$ $\in$ (0, $\epsilon$).

Pharnam Bakhshinezhad, Beniamin R. Jablonski, Felix C. Binder, Nicolai Friis

Within quantum thermodynamics, many tasks are modelled by processes that require work sources represented by out-of-equilibrium quantum systems, often dubbed quantum batteries, in which work can be deposited or from which work can be extracted. Here we consider quantum batteries modelled as finite-dimensional quantum systems initially in thermal equilibrium

Alexandre F Boyer, Christophe Haen, Federico Stagni, David R C Hill

Delivering a reproducible environment along with complex and up-to-date software stacks on thousands of distributed and heterogeneous worker nodes is a critical task. The CernVM-File System (CVMFS) has been designed to help various communities to deploy software on worldwide distributed computing infrastructures by decoupling the software from the Operating

Kexin Gu Baugh, Nuri Cingillioglu, Alessandra Russo

Neuro-symbolic rule learning has attracted lots of attention as it offers better interpretability than pure neural models and scales better than symbolic rule learning. A recent approach named pix2rule proposes a neural Disjunctive Normal Form (neural DNF) module to learn symbolic rules with feed-forward layers. Although proved to be effective in synthetic b

Yves Benoist, Dominique Hulin

The classical Fatou theorem identifies bounded harmonic functions on the unit disk with bounded measurable functions on the boundary circle. We extend this theorem to bounded harmonic maps.

Mauritz Kop, Mateo Aboy, Eline De Jong, Urs Gasser

The expected societal impact of quantum technologies (QT) urges us to proceed and innovate responsibly. This article proposes a conceptual framework for Responsible QT that seeks to integrate considerations about ethical, legal, social, and policy implications (ELSPI) into quantum R&D, while responding to the Responsible Research and Innovation dimensions of

Miroslav Ploscica, Friedrich Wehrung

Denote by Id$_c G$ the lattice of all principal $\ell$-ideals of an Abelian $\ell$-group $G$. Our main result is the following. Theorem. For every countable Abelian $\ell$-group $G$, every countable completely normal distributive 0-lattice $L,$ and every closed 0-lattice homomorphism $\varphi : {\rm Id}_c G \to L$, there are a countable Abelian $\ell$-group

Balint Varga, Dongxu Yang, Soeren Hohmann

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner tha

Maximilian Stadelmaier, Jakub Vícha, Vladimír Novotný

Ultrahigh-energy cosmic rays are almost exclusively detected through extensive air showers, which they initiate upon interaction with the atmosphere. The longitudinal development of these air showers can be directly observed using fluorescence detector telescopes, such as those employed at the Pierre Auger Observatory or the Telescope Array. In this article,

Mihaï Bostan, Anh-Tuan Vu

We consider the long time evolution of a population of charged particles, under strong magnetic fields and collision mechanisms. We derive a fluid model and justify the asymptotic behavior toward smooth solutions of this regime. In three space dimensions, a constraint ocurs along the parallel direction. For eliminating the corresponding Lagrange multiplier,

Edoardo Gabrielli, Dimitri Belli, Zoe Matrullo, Vittorio Miori

Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients. In this work, we introduce FLANDERS, a novel pre-aggregation filter for FL resilient to large-scale model poisoning attacks, i.e., when malicious clients far exceed legitimate participants. FLANDE

Angelica Ginnante, Stéphane Caro, Enrico Simetti, François Leborne

<jats:title>Abstract</jats:title> <jats:p>In manufacturing industry, Computer Numerical Control (CNC) machines are often preferred over Industrial Serial Robots (ISR) for machining tasks. Indeed, CNC machines offer high positioning accuracy, which leads to slight dimensional deviation on the final product. However, these machines have a restricted workspace

G. P. Teja, Chanchal

Fock states are quantized states of electromagnetic waves with diverse applications in quantum optics and quantum communication. However, generation of arbitrary optical Fock states still remains elusive. Majority of Fock state generation proposals rely on precisely controlling the atom-cavity interactions and are experimentally challenging. We propose a sch

Anders Enqvist, Özlem Tuğfe Demir, Cicek Cavdar, Emil Björnson

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy

Pan Xiao, Peijie Qiu, Sungmin Ha, Abdalla Bani

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space. Two main classes of VAEs methods may be distinguis

Raphaël V Alexandre

According to the Markus conjecture, closed flat affine manifolds with parallel volume should be complete. We show it is the case for three-manifolds when the holonomy centralizes an affine transformation preserving the volume. It is notably the case when the holonomy group has non trivial center or when the automorphism group is non discrete.

Arend Hintze

The development of advanced generative chat models, such as ChatGPT, has raised questions about the potential consciousness of these tools and the extent of their general artificial intelligence. ChatGPT consistent avoidance of passing the test is here overcome by asking ChatGPT to apply the Turing test to itself. This explores the possibility of the model r

Hamza Boumaza

In the present work, we study the radial and non-radial perturbative stability of neutron stars in which the matter is disformally coupled to the metric. First, we derive the gravitational and the fluid equations of the neutron star in a static and spherically symmetric background. Then, we calculate the second-order expansion of the action that describes th

Julia Kucharek, Rafał Bożek, Wojciech Pacuski

Magnetic doping of 2D materials such as Transition Metal Dichalcogenides is promising for the enhancement of magneto-optical properties, as it was previously observed for 3D diluted magnetic semiconductors. To maximize the effect of magnetic ions, they should be incorporated into the crystal lattice of 2D material rather than form separated precipitates. Thi

Y. Chen, C. Y. Zheng, Z. J. Liu, L. H. Cao

The effects of a frequency-modulated pump on stimulated Brillouin scattering (SBS) in a flowing plasma are investigated by theoretical analysis, three-wave simulations, and kinetic simulations. The resonance point of SBS oscillates in a certain spatial region with time when frequency modulations are applied. There exists a certain frequency modulation that c

Fabian Key, Max von Danwitz, Francesco Ballarin, Gianluigi Rozza

In the context of simulation-based methods, multiple challenges arise, two of which are considered in this work. As a first challenge, problems including time-dependent phenomena with complex domain deformations, potentially even with changes in the domain topology, need to be tackled appropriately. The second challenge arises when computational resources an

Steve T. Wilson, Chris Cotsford, James Armitage, Niamh Holland

Antineutrinos from nuclear reactors have the potential to be used for reactor monitoring in the mid- to far-field under certain conditions. Antineutrinos are an unshieldable signal and carry information about the reactor core and the distance they travel. Using gadolinium-doped water Cherenkov detectors for this purpose has been previously proposed alongside

Lauri Valkonen, Santtu Tikka, Jouni Helske, Juha Karvanen

Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase history (sales data) and conjoint studies where a group of customers is asked to make imaginary purchases in an

Baiwei Guo, Yuning Jiang, Giancarlo Ferrari-Trecate, Maryam Kamgarpour

This paper addresses black-box smooth optimization problems, where the objective and constraint functions are not explicitly known but can be queried. The main goal of this work is to generate a sequence of feasible points converging towards a KKT primal-dual pair. Assuming to have prior knowledge on the smoothness of the unknown objective and constraints, w

Antonino Del Popolo

In this paper, we use SPARC's HSBs, and LSBs galaxies to verify two issues. The first one is related to one claim of \citep{Donato} D09, namely: is the DM surface density (DMsd) a constant universal quantity, equal to $\log{(\rm \Sigma/M_\odot pc^{-2})}=2.15 \pm 0.2$, or does it depend on the baryon surface density of the system? The second one, is based on

Dhaval Taunk, Vasudeva Varma

With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days. But still the number of datasets is low in number. In this work, we (Team HakunaMatata) study how these multilingual models perform on the datasets which have Indian languages as source and target te

Miguel Aguiar, Amritam Das, Karl H. Johansson

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time

Walter Grimus

We consider a quantum field theory (QFT) model of neutrino oscillations in vacuum that attempts to take into account that the neutrino source particle and the neutrino detection particle both interact with their respective environments by collisions. Our model is minimal in a twofold sense. Firstly we simply assume that neutrino production and detection take

Abhish Khanal, Gregory J. Stein

We present a novel approach for efficient and reliable goal-directed long-horizon navigation for a multi-robot team in a structured, unknown environment by predicting statistics of unknown space. Building on recent work in learning-augmented model based planning under uncertainty, we introduce a high-level state and action abstraction that lets us approximat

Zihan Liu, Yun Luo, Lirong Wu, Zicheng Liu

It has become cognitive inertia to employ cross-entropy loss function in classification related tasks. In the untargeted attacks on graph structure, the gradients derived from the attack objective are the attacker's basis for evaluating a perturbation scheme. Previous methods use negative cross-entropy loss as the attack objective in attacking node-level cla

Gianira N. Alfarano, Alessandro Neri, Ferdinando Zullo

In this paper we extend the study of linear spaces of upper triangular matrices endowed with the flag-rank metric. Such metric spaces are isometric to certain spaces of degenerate flags and have been suggested as suitable framework for network coding. In this setting we provide a Singleton-like bound which relates the parameters of a flag-rank-metric code. T

Yang Luo, Frank Neubrech, Alberto Martin-Jimenez, Na Liu

Coherent collective oscillations of electrons excited in metallic nanostructures (localized surface plasmons) can confine incident light to atomic scales and enable strong light-matter interactions, which depend nonlinearly on the local field. Direct sampling of such collective electron oscillations in real-time is crucial to performing petahertz scale optic

Jiahao Chang, Jiahuan Yu, Tianzhu Zhang

Local feature matching is challenging due to textureless and repetitive patterns. Existing methods focus on using appearance features and global interaction and matching, while the importance of geometry priors in local feature matching has not been fully exploited. Different from these methods, in this paper, we delve into the importance of geometry prior a

A. S. Gorban, S. V. Molkov, A. A. Lutovinov, A. N. Semena

We present the results of our study of the X-ray pulsar IGR J21343+4738 based on NuSTAR, Swift, and SRG observations in the wide energy range 0.3 - 79 keV. The absence of absorption features in the energy spectra of the source, both averaged and phase-resolved ones, has allowed us to estimate the upper and lower limits on the magnetic field of the neutron st

K. M. Schoeffler, T. Grismayer, D. Uzdensky, L. O. Silva

The time evolution of high-energy synchrotron radiation generated in a relativistic pair plasma energized by reconnection of strong magnetic fields is investigated with two- and three-dimensional (2D and 3D) particle-in-cell (PIC) simulations. The simulations in this 2D/3D comparison study are conducted with the radiative PIC code OSIRIS, which self-consiste

Ana Ventura Barroso

The CMS silicon tracking system measures the trajectories of charged particles with a hit resolution of the order of microns in the pixel detector and tens of microns in the strip detector. One of the most important inputs for track reconstruction is the precision with which the tracker geometry is known. Therefore the position, orientation, and curvature of

Takumi Imamura, Hiroki Masuda, Hayato Tajima

The Gaussian mixed-effects model driven by a stationary integrated Ornstein-Uhlenbeck process has been used for analyzing longitudinal data having an explicit and simple serial-correlation structure in each individual. However, the theoretical aspect of its asymptotic inference is yet to be elucidated. We prove the local asymptotics for the associated log-li

Reinder Meinsma, Evgeny Shinder

We present a detailed study of elliptic fibrations on Fourier-Mukai partners of K3 surfaces, which we call derived elliptic structures. We fully classify derived elliptic structures in terms of Hodge-theoretic data, similar to the Derived Torelli Theorem that describes Fourier-Mukai partners. In Picard rank two, derived elliptic structures are fully determin

Yi-Syuan Liou, Tsung-Han Wu, Jia-Fong Yeh, Wen-Chin Chen

Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the

Alexander Ulrichsen, Paul Murray, Stephen Marshall, Moncef Gabbouj

Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The majority

Yuyang Wang, Jean-Rémy Chardonnet, Frédéric Merienne

It is well known that locomotion-dominated navigation tasks may highly provoke cybersickness effects. Past research has proposed numerous approaches to tackle this issue based on offline considerations. In this work, a novel approach to mitigate cybersickness is presented based on online adaptative navigation. Considering the Proportional-Integral-Derivative

Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang

The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLM

Alexander Sulaiman, Ken Pierce

Search and Rescue (SAR) is searching for and providing help to people in danger. In the UK, SAR teams are typically charities with limited resources, and SAR missions are time critical. Search managers need to objectively decide which search assets (e.g. helicopter vs drone) would be better. A key metric in the SAR community is effective sweep width (W), whi

René Heinrich, Christoph Scholz, Stephan Vogt, Malte Lehna

In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial

Vladislav Popkov, Marko Žnidarič, Xin Zhang

We describe dynamics of transverse spin-helix state (SHS) -- a product state with spatially rotating magnetization -- under anisotropic Heisenberg XXZ spin chain evolution. Due to experimental relevance we especially focus on magnetization dynamics. At long times the $U(1)$ symmetry of the Hamiltonian is restored, leading to the decay of transverse magnetiza

Haiyan Guo, Bo Zhou, Bizhu Lin

For real $\alpha\in [0,1)$ and a hypergraph $G$, the $\alpha$-spectral radius of $G$ is the largest eigenvalue of the matrix $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$, where $A(G)$ is the adjacency matrix of $G$, which is a symmetric matrix with zero diagonal such that for distinct vertices $u,v$ of $G$, the $(u,v)$-entry of $A(G)$ is exactly the number of

Fahimeh Rahimi, Zeinab Rezaei

Proto-neutron stars are born when a highly evolved and massive star collapses under gravity. In this paper, we investigate the spontaneous scalarization in proto-neutron stars. Based on the scalar tensor theory of gravity as well as the physical conditions in proto-neutron star, we examine the structure of proto-neutron star. To describe the fluid in proto-n

Carlos Gaete-Morales, Julius Jöhrens, Florian Heining, Wolf-Peter Schill

Various options are discussed to de-fossilize heavy-duty vehicles (HDV), including battery-electric vehicles (BEV), electric road systems (ERS), and indirect electrification via hydrogen fuel cells or e-fuels. We investigate their power sector implications in future scenarios of Germany with high renewable energy shares, using an open-source capacity expansi

Qing Lian, Tai Wang, Dahua Lin, Jiangmiao Pang

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis to reveal that ignoring the motion of m

Shan Dong, Yuanchang Li

Topological excitonic insulators combine topological edge states and spontaneous exciton condensation, with dual functionality of topological insulators and excitonic insulators. Yet, they are very rare and little is known about their formation. In this work, we find that a mechanism dubbed as parity frustration prevents excitonic instability in usual topolo

Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a

Anders Enqvist, Özlem Tuğfe Demir, Cicek Cavdar, Emil Björnson

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy

Jiahuan Yu, Jiahao Chang, Jianfeng He, Tianzhu Zhang

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-G

Gustavo Araújo, Anderson Barbosa

In 1931, Banach proved that, far from being exceptional objects, the Weierstrass functions form a residual set in the space $\mathcal{C}[0,1]$ of continuous functions. Later on, in 1966, V. I. Gurariy showed that, except for zero, there is an infinite-dimensional linear subspace of Weierstrass functions. This was the first example of \textit{lineability}. Ov

Takaaki Aoki, Taro Kanao, Hayato Goto, Shiro Kawabata

Kerr-cat qubits are a promising candidate for fault-tolerant quantum computers owing to the biased nature of their errors. The $ZZ$ coupling between the qubits can be utilized for a two-qubit entangling gate, but the residual coupling called $ZZ$ crosstalk is detrimental to precise computing. In order to resolve this problem, we propose a tunable $ZZ$-coupli

Mahmoud Salhab, Haidar Harmanani

Spoken keyword spotting (KWS) is the task of identifying a keyword in an audio stream and is widely used in smart devices at the edge in order to activate voice assistants and perform hands-free tasks. The task is daunting as there is a need, on the one hand, to achieve high accuracy while at the same time ensuring that such systems continue to run efficient

Matthew J. Lake, Shi-Dong Liang, Anucha Watcharapasorn

We present a simple gedanken experiment in which a compact object traverses a spacetime with three macroscopic spatial dimensions and $n$ compact dimensions. The compactification radius is allowed to vary, as a function of the object's position in the four-dimensional space, and we show that the conservation of gravitational self-energy implies the dimension

Nati Linial, Elyassaf Loyfer

The asymptotic rate vs. distance problem is a long-standing fundamental problem in coding theory. The best upper bound to date was given in 1977 and has received since then numerous proofs and interpretations. Here we provide a new, elementary proof of this bound based on counting walks in the Hamming cube.

Sebastian Vincent, Alice Dowek, Rowanne Sumner, Charlotte Blundell

Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent rich character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such model

Haoqian Wu, Zhipeng Hu, Lincheng Li, Yongqiang Zhang

Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blur the high-frequency reflection details. In this paper, we p

Dajana Dimitrić, Mitar Simić, Vladimir Risojević

Deep learning models for image classification are typically trained under the "closed-world" assumption with a predefined set of image classes. However, when the models are deployed they may be faced with input images not belonging to the classes encountered during training. This type of scenario is common in remote sensing image classification where images

Vinod Kumar Dwivedi

We report the Griffiths phase (GP)-like state along with cluster-glass-like state in geometrically frustrated antiferromagnetic $Cr$ substituted $Y_2Ir_2O_7$ pyrochlore iridates. The strength of GP-like behaviour increases with substitution. Interestingly, isothermal remanent magnetization suggests the Ising-like interaction of spins in GP region. The GP-lik

Dmitry S. Kaparulin, Nikita A. Sinelnikov

We consider a motion of a weakly relativistic charged particle with an arbitrary spin in central potential $e/r$ in terms of classical mechanics. We show that the spin-orbital interaction causes the precession of the plane of orbit around the vector of total angular momentum. The angular velocity of precession depends on the distance of the particle from the

Paul Donner

This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to predict error-free data. Through direct Monte Carlo simulation - drawing many replicates of predicted data from the estimated

C. Marianowski, C. Traub, M. Pfeiffer, J. Beyer

Utilizing differential atmospheric forces in the Very Low Earth Orbits (VLEO) regime for the control of the relative motion within a satellite formation is a promising option as any thrusting device has tremendous effects on the mission capacity due to the limited weight and size restrictions of small satellites. One possible approach to increase the availab

Kaifeng Zou, Sylvain Faisan, Boyang Yu, Sébastien Valette

Facial expression generation is one of the most challenging and long-sought aspects of character animation, with many interesting applications. The challenging task, traditionally having relied heavily on digital craftspersons, remains yet to be explored. In this paper, we introduce a generative framework for generating 3D facial expression sequences (i.e. 4

Tieguang Zi, Peng-Cheng Li

We develop approximate ``analytic-kludge" waveforms to describe the inspiral of a stellar-mass compact object into a supermassive compact object in an extreme mass ratio inspiral (EMRI) scenario. The deformability of the supermassive compact object is characterized by a dimensionless quantity called the tidal Love number (TLN). Our analysis shows that, up to

Maryam Viqar, Violeta Madjarova, Elena Stoykova

Watershed segmentation method has been used in various applications. But many a times, due to its over-segmentation attributes, it underperforms in several tasks where noise is a dominant source. In this study, Optical Coherence Tomography images have been acquired, and segmentation has been performed to analyse the different regions of fluid filled sacs in

Satoshi Iso, Noriaki Kitazawa, Takao Suyama

We investigate string amplitudes by using the partial modular transformation which we introduced in our previous works. This enables us to extract stringy threshold corrections from the full string amplitudes and interpret them in terms of the Wilsonian effective field theory in a natural way. We calculate mass shifts and wave function renormalizations for m

Seonwoo Kim, Federico Sau

We consider the symmetric inclusion process on a general finite graph. Our main result establishes universal upper and lower bounds for the spectral gap of this interacting particle system in terms of the spectral gap of the random walk on the same graph. In the regime in which the gamma-like reversible measures of the particle systems are log-concave, our b

Arné Schreuder, Anna Bosman, Andries Engelbrecht, Christopher Cleghorn

The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (

Ashutosh Kumar Alok, Neetu Raj Singh Chundawat, Arindam Mandal

In this work we study the potential of the lepton flavor ratios $R_{K}^{\tau \mu} \equiv \Gamma(B \to K \tau^+ \tau^-)/ \Gamma(B \to K \mu^+ \mu^-)$ and $ R_{K^{*}}^{\tau \mu} \equiv \Gamma(B \to K^* \tau^+ \tau^-)/ \Gamma(B \to K^* \mu^+ \mu^-)$ to probe lepton flavor universality (LFU) violation in $\tau-\mu$ sector. We show that these ratios can deviate f

Rui Li, Jing Qian, Weiping Zhang

Rydberg quantum gate serving as an indispensable computing unit for neutral-atom quantum computation, has attracted intense research efforts for the last decade. However the state-of-the-art experiments have not reached the high gate fidelity as predicted by most theories due to the unexpected large loss remaining in Rydberg and intermediate states. In this

Zheyuan Liu, Weixuan Sun, Yicong Hong, Damien Teney

Composed image retrieval searches for a target image based on a multi-modal user query comprised of a reference image and modification text describing the desired changes. Existing approaches to solving this challenging task learn a mapping from the (reference image, modification text)-pair to an image embedding that is then matched against a large image cor

Raphael Pinard, Mitra Hassani, Wayne Lemieux

Federated learning (FL) is a type of distributed machine learning at the wireless edge that preserves the privacy of clients' data from adversaries and even the central server. Existing federated learning approaches either use (i) secure multiparty computation (SMC) which is vulnerable to inference or (ii) differential privacy which may decrease the test acc

Álvaro Serrano Holgado, Luis Manuel Navas Vicente

We consider the series $\sum_{n=1}^{\infty} z^{n} (a_{n} + x)^{-s}$ where $a_{n}$ satisfies a linear recurrence of arbitrary degree with integer coefficients. Under appropriate conditions, we prove that it can be continued to a meromorphic function on the complex $s$-plane. Thus we may associate a Lerch-type zeta function $\varphi(z,s,x)$ to a general recurr

Amin Setayesh, Hamid Hadian, Radu Prodan

Host load prediction is essential for dynamic resource scaling and job scheduling in a cloud computing environment. In this context, workload prediction is challenging because of several issues. First, it must be accurate to enable precise scheduling decisions. Second, it must be fast to schedule at the right time. Third, a model must be able to account for

Carolina M. Bollo, Claudia M. Gariboldi, Domingo A. Tarzia

In this paper, we consider a family of simultaneous distributed-boundary optimal control problems ($P_{\alpha}$) on the internal energy and the heat flux for a system governed by a mixed elliptic variational equality with a parameter $\alpha >0$ and a simultaneous distributed-boundary optimal control problem ($P$) governed also by an elliptic variational equ

Lujia Bai, Weichi Wu

Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt structural breaks and its competitive finite sample performance. However, existing methods mainly focus on estimators f

Mahya Ghandehari, Teddy Mishura

This paper investigates the Robinson graphon completion/recovery problem within the class of $L^p$-graphons, focusing on the range $5<p\leq \infty$. A graphon $w$ is Robinson if it satisfies the Robinson property: if $x\leq y\leq z$, then $w(x,z)\leq \min\{w(x,y),w(y,z)\}$. We demonstrate that if a graphon possesses localized near-Robinson characteristics, i

Michael Minotakis, Hugo Rossignol, Matteo Cobelli, Stefano Sanvito

The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possib

Manish Pandey, Remco van der Hofstad

Centrality measures aim to indicate who is important in a network. Various notions of `being important' give rise to different centrality measures. In this paper, we study how important the central vertices are for the connectivity structure of the network, by investigating how the removal of the most central vertices affects the number of connected componen

George Profitiliotis, Jacob Haqq-Misra

A 'planetary park' system has been suggested as a way for Mars to 'preserve' land for a multitude of purposes. These parks would represent a diverse portion of martian terrain and would be regulated to minimize human contamination and prevent excess human intervention. Such a planetary parks concept could also appeal to corporate interests as a compromise be