March 2023 arXiv papers — page 4

Reyhaneh Hosseini, Ziming Chen, Ewan Goligher, Eddy Fan

Background: We aimed to design a Bayesian adaption trial through extensive simulations to determine values for key design parameters, demonstrate error rates, and establish the expected sample size. The complexity of the proposed outcome and analysis meant that Markov Chain Monte Carlo methods were required, resulting in an infeasible computational burden. T

Armin Danner, Niels Geerits, Hartmut Lemmel, Richard Wagner

The paradoxical phenomenon of the quantum Cheshire Cat (qCC) refers to situations where different properties of a particle appear to be localised in different paths of an interferometer and therefore spatially separated. This observation is obtained by implementing a pre- and postselection procedure. The localisations are determined qualitatively through con

Johan Kolvik, Paul Burger, Joey Frey, Raphaël Van Laer

Optomechanical crystals (OMCs) are a promising and versatile platform for transduction between mechanical and optical fields. However, the release from the substrate used in conventional suspended OMCs also prevents heat-carrying noise phonons from rapidly leaking away. Thermal anchoring may be improved by attaching the OMCs directly to the substrate. Previo

Khadija El Anouz, Abderrahim El Allati, Farhan Saif

We show the quantum correlation between two coupled hybrid optomechanical cavities by quantifying the non-classical correlation using Gaussian quantum discord. This involves analyzing and solving Heisenberg Langevin equations to obtain the (12*12)dimensional covariance matrix of this system. Based on the experimentalist conditions, we simulate quantum correl

Gui-Long Jiang, Wen-Qiang Liu, Hai-Rui Wei

Entanglement concentration is a critical technique to prevent degraded fidelity and security in long-distance quantum communication. We propose novel practical entanglement concentration protocols (ECPs) for less-entangled Bell and Greenberger-Horne-Zeilinger states with unknown parameters by solely using simple linear optics. We avoid the need for the post-

Miquel Montero

We introduce a technique to merge two biased Brownian motions into a single regular process. The outcome follows a stochastic differential equation with a constant diffusion coefficient and a non-linear drift. The emerging stochastic process has outstanding properties, such as spatial and temporal translational invariance of its mean squared displacement, an

Esther Rolf

As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine

Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke

We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-SQLP) -- is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google. Towards designing DP-SQLP

Matthew R. Ballard, Srikanth B. Iyengar, Pat Lank, Alapan Mukhopadhyay

This work concerns generators for the bounded derived category of coherent sheaves over a noetherian scheme $X$ of prime characteristic. The main result is that when the Frobenius map on $X$ is finite, for any compact generator $G$ of $\mathsf{D}(X)$ the Frobenius pushforward $F ^e_*G$ generates the bounded derived category whenever $p^e$ is larger than the

Chenghao Shi, Xieyuanli Chen, Huimin Lu, Wenbang Deng

Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus re

Abdoulaye Koroko, Ani Anciaux-Sedrakian, Ibtihel Ben Gharbia, Valérie Garès

As a second-order method, the Natural Gradient Descent (NGD) has the ability to accelerate training of neural networks. However, due to the prohibitive computational and memory costs of computing and inverting the Fisher Information Matrix (FIM), efficient approximations are necessary to make NGD scalable to Deep Neural Networks (DNNs). Many such approximati

Jing Guo, Zhenxin Liu

This paper is devoted to proving the polynomial mixing for a weakly damped stochastic nonlinear Schr\"{o}dinger equation with additive noise on a 1D bounded domain. The noise is white in time and smooth in space. We consider both focusing and defocusing nonlinearities, respectively, with exponents of the nonlinearity $\sigma\in[0,2)$ and $\sigma\in[0,\infty)

N. J. Ayres, G. Bison, K. Bodek, V. Bondar

We report on a search for a new, short-range, spin-dependent interaction using a modified version of the experimental apparatus used to measure the permanent neutron electric dipole moment at the Paul Scherrer Institute. This interaction, which could be mediated by axion-like particles, concerned the unpolarized nucleons (protons and neutrons) near the mater

Yasser Benigmim, Subhankar Roy, Slim Essid, Vicky Kalogeiton

Adapting a segmentation model from a labeled source domain to a target domain, where a single unlabeled datum is available, is one the most challenging problems in domain adaptation and is otherwise known as one-shot unsupervised domain adaptation (OSUDA). Most of the prior works have addressed the problem by relying on style transfer techniques, where the s

John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero

We introduce a novel method, called Dispersion Entropy for Graph Signals, $DE_G$, as a powerful tool for analysing the irregularity of signals defined on graphs. We demonstrate the effectiveness of $DE_G$ in detecting changes in the dynamics of signals defined on synthetic and real-world graphs, by defining mixed processing on random geometric graphs or thos

Isabelle Schneider, Jia-Yuan Dai

Reaction-diffusion equations are ubiquitous in various scientific domains and their patterns represent a fascinating area of investigation. However, many of these patterns are unstable and therefore challenging to observe. To overcome this limitation, we present new noninvasive feedback controls based on symmetry groupoids. As a concrete example, we employ t

Mireille Bousquet-Mélou, Frédéric Chapoton

We consider a greedy version of the $m$-Tamari order defined on $m$-Dyck paths, recently introduced by Dermenjian. Inspired by intriguing connections between intervals in the ordinary 1-Tamari order and planar triangulations, and more generally by the existence of simple formulas counting intervals in the ordinary $m$-Tamari orders, we investigate the number

Sarah Mechbal, Markus Ackermann, Marek Kowalski

We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools. We show that the redshift, $z$, bolometric luminosity, $L_{\rm Bol}$, central mass of the supermassive black hole (SMBH), $M_{\rm BH}$, Eddington ratio, $\lambda_{\rm Edd}$, and AGN class (obscured or unobscured) can be reconstructed through multi-wavelengt

Nathaniel Dene Hoffman

The "interior goat problem" is a numerical puzzle which has haunted mathematicians for nearly three centuries. Closed-form solutions have only been found for the two-dimensional case, and although approximations have been established for higher-dimensional versions, the mathematical literature lacks a solution to the one-dimensional interior goat problem. To

Sonika, Sunil Gagwar, Nikhlesh Singh Mehta, G. Sharma

The Planar Hall effect (PHE) in topological materials has been a subject of great interest in recent years. Generally, it is understood to originate from the chiral-anomaly (CA) induced charge pumping between doubly degenerate Weyl nodes. However, the occurrence of PHE in the materials with positive and anisotropic orbital magnetoresistance has raised questi

Antonio Ferreiro, Silvia Pla

We construct the instantaneous vacuum state for a quantum scalar field coupled to another classical scalar field as described in [1]. We then compare it with the state of low energy constructed for a particular solution. We show that under physically motivated conditions they become very similar.

J. P. Velasquez-Rodriguez

In this note we extend to metrizable profinite groups the classical theorems of Titchmarsh on the Fourier transform of H\"older-Lipschitz functions. This generalizes the results of Younis on compact zero-dimensional abelian groups to the noncommutative case, and proves a relation between the H\"older-Lipschitz-continuity of functions and their Sobolev regula

Robin Herkert, Patrick Buchfink, Bernard Haasdonk

Classical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems with slowly decaying Kolmogorov n-widths. Additionally, Hamiltonian structure of dynamical systems may be available and should be preserved during the reduction. In the current prese

Ben Kane, Luhao Xue

In this paper, we first investigate the relationship between the number of primitive representations of $n$ by quadratic forms and the number of non-primitive ones. We hence obtain a theorem to deal with the Eisenstein series part with quadratic Dirichlet character when deriving the formula for the number of primitive representations of an integer $n$ by eve

Shinji Ejiri, Yuya Horikoshi

In the gradient flow method of lattice gauge theory, coarse graining is performed so as to reduce the action, and as the coarse graining progresses, the field strength becomes very small. However, the confinement property that particles interact strongly is not lost by the gradient flow. It is seemingly mysterious, and something stable against coarse grainin

Athar Abdul-Quader, Mateusz Łełyk

We study subsets of countable recursively saturated models of $\mathsf{PA}$ which can be defined using pathologies in satisfaction classes. More precisely, we characterize those subsets $X$ such that there is a satisfaction class $S$ where $S$ behaves correctly on an idempotent disjunction of length $c$ if and only if $c \in X$. We generalize this result to

S. S. Manna

The notion of Self-organized criticality (SOC) had been conceived to interpret the spontaneous emergence of long range correlations in nature. Since then many different models had been introduced to study SOC. All of them have few common features: externally driven dynamical systems self-organize themselves to non-equilibrium stationary states exhibiting flu

Armin Nurkanović, Jonathan Frey, Anton Pozharskiy, Moritz Diehl

This paper extends the Finite Elements with Switch Detection (FESD) method [Nurkanovi\'c et al., 2022] to optimal control problems with nonsmooth systems involving set-valued step functions. Logical relations and common nonsmooth functions within a dynamical system can be expressed using linear and nonlinear expressions involving step functions. A prominent

Rita Fioresi, Bin Shu

We investigate pairs $(G,Y)$, where $G$ is a reductive algebraic group and $Y$ a purely-odd $G$-superscheme, asking when a pair corresponds to a quasi-reductive algebraic supergroup $\mathbb{G}$, that is, $\mathbb{G}_{\text{ev}}$ is isomorphic to $G$, and the quotient $\mathbb{G}/\mathbb{G}_{\text{ev}}$ is $G$-equivariantly isomorphic to $Y$. We prove that,

Moataz Alghamdi, Fleurianne Bertrand, Daniele Boffi, Abdul Halim

We use a Gaussian Process Regression (GPR) strategy that was recently developed [3,16,17] to analyze different types of curves that are commonly encountered in parametric eigenvalue problems. We employ an offline-online decomposition method. In the offline phase, we generate the basis of the reduced space by applying the proper orthogonal decomposition (POD)

Domenico Cantone, Simone Faro

We present a new variable-length computation-friendly encoding scheme, named SFDC (Succinct Format with Direct aCcesibility), that supports direct and fast accessibility to any element of the compressed sequence and achieves compression ratios often higher than those offered by other solutions in the literature. The SFDC scheme provides a flexible and simple

Amin Radbord, Italo Atzeni, Antti Tölli

We provide new analytical results on the uplink data detection in massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The statistical properties of the soft-estimated symbols (i.e., after linear combining and prior to the data detection process) have been previously characterized only for a single user equipment (UE) and u

Ruiyin Li, Peng Liang, Paris Avgeriou

Code review is a common practice in software development and often conducted before code changes are merged into the code repository. A number of approaches for automatically recommending appropriate reviewers have been proposed to match such code changes to pertinent reviewers. However, such approaches are generic, i.e., they do not focus on specific types

Riccardo Aiudi, Raffaella Burioni, Alessandro Vezzani

We investigate critical equilibrium and out of equilibrium properties of a ferromagnetic Ising model in one and two dimension in the presence of long range interactions, $J_{ij}\propto r^{-(d+\sigma)}$. We implement a novel local dynamics on a dynamical L\'evy lattice, that correctly reproduces the static critical exponents known in the literature, as a func

Snehal Mandal, Sandip Halder, Biswarup Satpati, Kalpataru Pradhan

Interface engineering in complex oxide heterostructures has developed into a flourishing field as various intriguing physical phenomena can be demonstrated which are otherwise absent in their constituent bulk compounds. Here we present La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) / Sm$_{0.5}$Ca$_{0.5}$MnO$_3$ (SCMO) based heterostructures showcasing the dominance of a

Rose A. Finn, Benedetta Vulcani, Gregory Rudnick, Michael L. Balogh

We investigate the role of dense environments in suppressing star formation by studying $\rm \log_{10}(M_\star/M_\odot) > 9.7$ star-forming galaxies in nine clusters from the Local Cluster Survey ($0.0137 < z < 0.0433$) and a large comparison field sample drawn from the Sloan Digital Sky Survey. We compare the star-formation rate (SFR) versus stellar mass re

Cencheng Shen, Carey E. Priebe, Jonathan Larson, Ha Trinh

In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect: for sufficiently large vertex size, the accuracy of vertex classification consistently benefits from the incorpora

Jie Lin, Xiaofeng Wang, Jun Mo, Gaobo Xi

Over the past few years, wide-field time-domain surveys like ZTF and OGLE have led to discoveries of various types of interesting short-period stellar variables, such as ultracompact eclipsing binary white dwarfs, rapidly rotating magnetised white dwarfs (WDs), transitional cataclysmic variables between hydrogen-rich and helium accretion, and blue large-ampl

Biwei Cao, Lulu Hua, Jiuxin Cao, Jie Gui

Online Social Network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to ta

Eric V. Woods, Mahander P. Singh, Se-Ho Kim, Tim M. Schwarz

Repeatable and reliable site-specific preparation of specimens for atom probe tomography (APT) at cryogenic temperatures has proven challenging. A generalized workflow is required for cryogenic-specimen preparation including lift-out via focused-ion beam and in-situ deposition of capping layers, to strengthen specimens that will be exposed to high electric f

Jinyan Su, Changhong Zhao, Di Wang

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in $\ell_p^d$ space; (3) DP-SC

Théophile Buffière, Philippe Marchal

Following Barany et al., who proved that large random lattice zonotopes converge to a deterministic shape in any dimension after rescaling, we establish a central limit theorem for finite-dimensional marginals of the boundary of the zonotope. In dimension 2, for large random convex lattice polygons contained in a square, we prove a Donsker-type theorem for t

Shengyang Sun, Xiaojin Gong

Weakly supervised video anomaly detection (WS-VAD) is a challenging problem that aims to learn VAD models only with video-level annotations. In this work, we propose a Long-Short Temporal Co-teaching (LSTC) method to address the WS-VAD problem. It constructs two tubelet-based spatio-temporal transformer networks to learn from short- and long-term video clips

Sarlota Birnsteinova, Danilo E. Ferreira de Lima, Egor Sobolev, Henry J. Kirkwood

The X-ray microscopy technique at the European X-ray free-electron laser (EuXFEL), operating at a MHz repetition rate, provides superior contrast and spatial-temporal resolution compared to typical microscopy techniques at other X-ray sources. In both online visualization and offline data analysis for microscopy experiments, baseline normalization is essenti

Ryuichi Ito, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applications of cardinality estimation, the query optimizer, to speed up query processing. However, none of the existing methods do not precisely estimate cardinalities when relational sc

Sebastian Bischof, Bernhard Mühlherr

We introduce the notion of a wall-connected twin building and show that the local-to-global principle holds for these twin buildings. As each twin building satisfying Condition (co) (introduced in [7]) is wall-connected, we obtain a strengthening of the main result of [7] that covers also the thick irreducible affne twin buildings of rank at least 3.

S. Alinder, P. J. McMillan, T. Bensby

Context: With the data releases from the astrometric space mission Gaia, the exploration of the structure of the Milky Way has developed in unprecedented detail and unveiled many previously unknown structures in the Galactic disc and halo. One such feature is the phase spiral where the stars in the Galactic disc form a spiral density pattern in the $Z-V_Z$ p

Abdallah El Ali

In what capacity are affective augmentations helpful to humans, and what risks (if any) do they pose? In this position paper, we outline three works on affective augmentation systems, where our studies suggest these systems have the ability to influence our cognitive, affective, and (social) bodily perceptions in perhaps unusual ways. We provide consideratio

Jingzhan Ge

Although there are many datasets for traffic sign classification, there are few datasets collected for traffic sign recognition and few of them obtain enough instances especially for training a model with the deep learning method. The deep learning method is almost the only way to train a model for real-world usage that covers various highly similar classes

Daniele Cassese, Nicola Dimitri, Gianluca Breghi, Tiziana Spadafina

Invasive non-Typhoidal Salmonella (iNTS) is one of the leading causes of blood stream infections in Sub-Saharan Africa, especially among children. iNTS can be difficult to diagnose, particularly in areas where malaria is endemic and difficult to treat, partly because of the emergence of antibiotic resistance. We developed a mathematical model to evaluate the

Sebastian Bischof, Anton Chosson, Bernhard Mühlherr

A twin building consists of two buildings that are twinned by a codistance function. We prove that the local structure of a twin building uniquely determines the two buildings up to isomorphism. This has been known for twin buildings satisfying a technical condition (co).

Xuyang Wu, Changxin Liu, Sindri Magnusson, Mikael Johansson

Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops async

Christian Kipp

Motivated by first-order conditions for extremal bodies of geometric functionals, we study a functional analytic notion of infinitesimal perturbations of convex bodies and give a full characterization of the set of realizable perturbations if the perturbed body is a polytope. As an application, we derive a necessary condition for polytopal maximizers of the

Fabrizio Colombo, Rolf Soeren Krausshar, Irene Sabadini, Yilmaz Simsek

The aim of this paper is to study generating functions for the coefficients of the classical superoscillatory function associated with weak measurements. We also establish some new relations between the superoscillatory coefficients and many well-known families of special polynomials, numbers, and functions such as Bernstein basis functions, the Hermite poly

Jack Keeler, Darren Crowdy

New exact solutions are presented to the problem of steadily-travelling water waves with vorticity wherein a submerged von K\'arm\'an point vortex street cotravels with a wave on a linear shear current. Surface tension and gravity are ignored. The work generalizes an earlier study by Crowdy & Nelson [Phys. Fluids, 22, 096601, (2010)] who found analytical sol

Fábio Protti, João V. C. Thompson

Let $\P$ be any collection of paths of a graph $G=(V,E)$. For $S\subseteq V$, define $I(S)=S\cup\{v\mid v \ \mbox{lies in a path of} \ \P \ \mbox{with endpoints in} \ S\}$. Let $\C$ be the collection of fixed points of the function $I$, that is, $\C=\{S\subseteq V\mid I(S)=S\}$. It is well known that $(V,\C)$ is a finite convexity space, where the members of

Ryota Takaki, Louise Jawerth, Marko Popović, Frank Jülicher

Biological condensates are assemblies of proteins and nucleic acids that form membraneless compartments in cells and play essential roles in cellular functions. In many cases they exhibit the physical properties of liquid droplets that coexist in a surrounding fluid. Recently, quantitative studies on the material properties of biological condensates have bec

Jungo Kasai, Yuhei Kasai, Keisuke Sakaguchi, Yutaro Yamada

As large language models (LLMs) gain popularity among speakers of diverse languages, we believe that it is crucial to benchmark them to better understand model behaviors, failures, and limitations in languages beyond English. In this work, we evaluate LLM APIs (ChatGPT, GPT-3, and GPT-4) on the Japanese national medical licensing examinations from the past f

Alejandro Jimeno-Pozo, Zachary A. H. Goodwin, Pierre A. Pantaleón, Valerio Vitale

We discuss the effect of long-range interactions within the self-consistent Hartree-Fock (HF) approximation in comparison to short-range atomic Hubbard interactions on the band structure of twisted bilayer graphene (TBG) at charge neutrality for various twist angles. Starting from atomistic calculations, we determine the quasi-particle band structure of TBG

Christian Krieg

This volume represents the proceedings of the 3rd Workshop on Open-Source Design Automation (OSDA) 2023, co-hosted with Design, Automation, and Test in Europe (DATE) conference in Antwerp, Belgium, April 17, 2023.

Pavel Záviška, Pavel Rajmic, Ondřej Mokrý

Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structure matrices constructed from time-domain signal blocks. We adapt their approach to solving the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modifications, some of them leading to an improv

Angel Victor Juanco Muller, Joao F. C. Mota, Keith A. Goatman, Corne Hoogendoorn

Retinal fundus images can be an invaluable diagnosis tool for screening epidemic diseases like hypertension or diabetes. And they become especially useful when the arterioles and venules they depict are clearly identified and annotated. However, manual annotation of these vessels is extremely time demanding and taxing, which calls for automatic segmentation.

Huu-Thinh Do, Franco Blanchini, Ionela Prodan

Using the properties of differential flatness, a controllable system, such as a quadcoper model, may be transformed into a linear equivalent system via a coordinate change and an input mapping. This is a straightforward advantage for the quadcopter's controller design and its real-time implementation. However, one significant hindrance is that, while the dyn

Ángel L. Corps, Armando Relaño

Spontaneous symmetry-breaking in phase transitions occurs when the system Hamiltonian is symmetric under a certain transformation, but the equilibrium states observed in nature are not. Here, we prove that when a discrete symmetry is spontaneously broken in a quantum system, then the time evolution necessarily conserves two additional and non-commuting quant

Gary Sarwin, Alessandro Carretta, Victor Staartjes, Matteo Zoli

Advanced minimally invasive neurosurgery navigation relies mainly on Magnetic Resonance Imaging (MRI) guidance. MRI guidance, however, only provides pre-operative information in the majority of the cases. Once the surgery begins, the value of this guidance diminishes to some extent because of the anatomical changes due to surgery. Guidance with live image fe

Yue Li, Lei Wang, Junjie Li, Jincheng Wang

This article presents a new phase-field formulation for non-equilibrium interface conditions in rapid phase transformations. With a particular way of defining concentration fields, the classical sharp and diffuse (thick) interface theories are unified into the present phase-field model. Each point inside the diffuse interface is comparable to the classical s

Allison E. Andrews, Hugh Dickinson, James P. Hague

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould des

H. Y. Huang, P. R. Tsai, C. Y. Lu, H. Hau

To simulate the heated exterior of a meteorite, we impact a granular bed with melted tin. The morphology of tin remnant and crater is found to be sensitive to the temperature and solidification of tin. By employing deep learning and convolutional neural network, we can quantify and map the complex impact patterns onto network systems based on feature maps an

Daniel Q. L. Nguyen, Irina Heinz, Guido Burkard

Two-qubit gates between spin qubits are often performed using a rectangular or an adiabatic exchange interaction pulse resulting in a CZ gate. An oscillating exchange pulse not only performs a CZ gate, but also enables the iSWAP gate, which offers more flexibility to perform quantum algorithms. We provide a detailed description for two-qubit gates using reso

Chayan Das, Dibyajyoti Saikia, Atanu Betal, Satyajit Sahu

We investigated the thermoelectric properties of the 2D monolayer of WX2N4 using Density Functional Theory combined with Boltzmann Transport Equation. We obtained an outstanding thermoelectric figure of merit of 0.91 at 400K for p-type WGe2N4, whether it showed a ZT value of 0.56 for n-type at the same temperature. On the other hand, the WSi2N4 showed signif

Zijun Long, Zaiqiao Meng, Gerardo Aragon Camarasa, Richard McCreadie

Vision Transformers (ViTs) have emerged as popular models in computer vision, demonstrating state-of-the-art performance across various tasks. This success typically follows a two-stage strategy involving pre-training on large-scale datasets using self-supervised signals, such as masked random patches, followed by fine-tuning on task-specific labeled dataset

Marco Biroli, Satya N. Majumdar, Gregory Schehr

We consider $N$ Brownian motions diffusing independently on a line, starting at $x_0>0$, in the presence of an absorbing target at the origin. The walkers undergo stochastic resetting under two protocols: (A) each walker resets independently to $x_0$ with rate $r$ and (B) all walkers reset simultaneously to $x_0$ with rate $r$. We compute analytically the me

Amit Kumar, Ajay Pratap, Anil Kumar Singh

Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT). However, feeding multiple morphologically languages into a single model during training reduces the NMT's performance. In GAN, similar to bilingual models, multilingual NMT only considers one reference translation for each sentence during model training. Th

Emily Dardaman, Abhishek Gupta

AI systems may be better thought of as peers than as tools. This paper explores applications of augmented collective intelligence (ACI) beneficial to collaborative ideation. Design considerations are offered for an experiment that evaluates the performance of hybrid human- AI collectives. The investigation described combines humans and large language models

Raghuveer Garani, Nirmal Raj, Javier Reynoso-Cordova

The dark matter content of globular clusters, highly compact gravity-bound stellar systems, is unknown. It is also generally unknow*able*, due to their mass-to-light ratios typically ranging between 1$-$3 in solar units, accommodating a dynamical mass of dark matter at best comparable to the stellar mass. That said, recent claims in the literature assume den

Bashar Huleihel, Oron Sabag, Haim H. Permuter, Victoria Kostina

In this paper, we investigate the capacity of finite-state channels (FSCs) in presence of delayed feedback. We show that the capacity of a FSC with delayed feedback can be computed as that of a new FSC with instantaneous feedback and an extended state. Consequently, graph-based methods to obtain computable upper and lower bounds on the delayed feedback capac

Lutz Bornmann, Robin Haunschild

In this study, we propose a new index for measuring excellence in science which is based on collaborations (co-authorship distances) in science. The index is based on the Erd\H{o}s number - a number that was introduced several years ago. We propose to focus with the new index on laureates of prestigious prizes in a certain field and to measure co-authorship

Emily Dardaman, Abhishek Gupta

Without the ability to estimate and benchmark AI capability advancements, organizations are left to respond to each change reactively, impeding their ability to build viable mid and long-term strategies. This paper explores the recent growth of forecasting, a political science tool that uses explicit assumptions and quantitative estimation that leads to impr

Jack Breen, Katie Allen, Kieran Zucker, Pratik Adusumilli

Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria required that research evaluated AI on h

Gan Jin, ongsheng Pang, Yuyang Ji, Zujian Dai

We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital (NAO) bases, such as ABACUS. The package comprises three m

Yiao Ju, Shenwei Huang

In this paper, we initiate a systematic study on a new notion called near optimal colourability which is closely related to perfect graphs and the Lov{\'a}sz theta function. A graph family $\mathcal{G}$ is {\em near optimal colourable} if there is a constant number $c$ such that every graph $G\in\mathcal{G}$ satisfies $\chi(G)\leq\max\{c, \omega(G)\}$, where

Rui-Xiang Shi, Li-Sheng Geng

The BESIII experiment at the Beijing Electron Positron collider, run as a ``hyperon factory'', recently reported the first measurement of the asymmetry parameter of the $\Lambda\to n\gamma$ decay and updated its branching fraction, which differs from the PDG average by 5.6 sigma. We highlight the impact of this new measurement on our understanding of the puz

Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling

In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspec

Tadashi Kawanago

We prove a Hopf bifurcation theorem in general Banach spaces, which improves a classical result by Crandall and Rabinowitz. Actually, our theorem does not need any compactness conditions, which leads to wider applications. In particular, our theorem can be applied to semilinear and quasi-linear partial differential equations in unbounded domains of $\mathbb{

Rami Masri, Marius Zeinhofer, Miroslav Kuchta, Marie E. Rognes

Starting from full-dimensional models of solute transport, we derive and analyze multi-dimensional models of time-dependent convection, diffusion, and exchange in and around pulsating vascular and perivascular networks. These models are widely applicable for modelling transport in vascularized tissue, brain perivascular spaces, vascular plants and similar en

Daniele Bufalo, Michele Bufalo, Felice Iavernaro

We address the question of the infinitude of twin and cousin prime pairs from a probabilistic perspective. Our approach partitions the set of integer numbers greater than $2$ in finite intervals of the form $[p_{n-1}^2,p_n^2)$, $p_{n-1}$ and $p_n$ being two consecutive primes, and evaluates the probability $q_n$ that such an interval contains a twin prime an

Yonglin Xiang, Yunlan Zuo, Xun-Wei Xu, Ran Huang

We study how to achieve, manipulate, and switch classical or quantum nonreciprocal effects of light with a spinning Kerr resonator. In particular, we show that even when there is no classical nonreciprocity (i.e., with the same mean number of photons for both clockwise and counterclockwise propagating modes), it is still possible to realize nonreciprocity of

Barbara Dalena, Tatiana Da Silva, Antoine Chance, Adnan Ghribi

After the discovery of the Higgs boson at the LHC, particle physics community is exploring and proposing next accelerators, to address the remaining open questions on the underlying mechanisms and constituents of the present universe. One of the studied possibilities is FCC (Future Circular Collider), a 100 km long collider at CERN. The feasibility study of

Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Murugappan Murugappan

Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation methods are required to characterize the chaotic dynamic of the system. Our concept of Neural Network Entrop

Apoorva Singh, Niteesh Sahni

A Helson-Lowdenslager type result has been proved by Chen in the context of Lebesgue spaces of the unit circle equipped with a continuous rotationally symmetric norm by studying the simply invariant subspaces of the operator of multiplication by the coordinate function $z$. In this paper, we generalize Chen's result by obtaining a description of simply invar

Patricia D. Beites, Alejandra S. Córdova-Martínez, Isabel Cunha, Alberto Elduque

S-structures on Lie algebras, introduced by Vinberg, represent a broad generalization of the notion of gradings by abelian groups. Gradings by, not necessarily reduced, root systems provide many examples of natural S-structures. Here we deal with a situation not covered by these gradings: the short (SL2xSL2)-structures, where the reductive group is the simpl

Mai-Quyen Pham, Jérémy Cohen, Thierry Chonavel

Nonnegative Matrix Factorization (NMF) is a fundamental tool in unsupervised learning, widely used for tasks such as dimensionality reduction, feature extraction, representation learning, and topic modeling. Many algorithms have been developed for NMF, including the well-known Multiplicative Updates (MU) algorithm, which belongs to a broader class of majoriz

Jan Frahm, Quentin Labriet

The goal of this article is to study the branching problem for a holomorphic discrete series representation of the conformal group of a simple Euclidean Jordan algebra $V$ restricted to the subgroup $\operatorname{PSL}_2(\mathbb{R})\times\operatorname{Aut}(V)$ where $\operatorname{Aut}(V)$ denotes the compact group of automorphisms of $V$. We use a realizati

Xavier Marjou, Arnaud Braud, Gaël Fromentoux

In scenarios where a single player cannot control other players, cooperative AI is a recent technology that takes advantage of deep learning to assess whether cooperation might occur. One main difficulty of this approach is that it requires a certain level of consensus on the protocol (actions and rules), at least from a majority of players. In our work, we

Panagiotis Agrafiotis, Anastastios Doulamis, Andreas Georgopoulos

Computer vision for detecting building pathologies has interested researchers for quite some time. Vision-based crack detection is a non-destructive assessment technique, which can be useful especially for Cultural Heritage (CH) where strict regulations apply and, even simple, interventions are not permitted. Recently, shallow and deep machine learning archi

Piet Groeneboom, Geurt Jongbloed

We construct bootstrap confidence intervals for a monotone regression function. It has been shown that the ordinary nonparametric bootstrap, based on the nonparametric least squares estimator (LSE) $\hat f_n$ is inconsistent in this situation. We show, however, that a consistent bootstrap can be based on the smoothed $\hat f_n$, to be called the SLSE (Smooth

Kimikazu Taniguchi

We propose a new boson expansion method using a norm operator. The small parameter expansion, in which the boson approximation becomes the zeroth-order approximation, requires the double commutation relations between phonon operators that are not closed between the phonon excitation modes adopted as boson excitations. This results in an infinite expansion re

Niko Jokela, K. Kajantie, M. Laine, Sami Nurmi

The free propagator of a massless mode in an expanding universe can be written as a sum of two terms, a lightcone and a tail part. The latter describes a subluminal (time-like) signal. We show that the inflationary gravitational wave background, influencing cosmic microwave background polarization, and routinely used for constraining inflationary models thro

Zifan Wu, Chao Yu, Chen Chen, Jianye Hao

Research in model-based reinforcement learning has made significant progress in recent years. Compared to single-agent settings, the exponential dimension growth of the joint state-action space in multi-agent systems dramatically increases the complexity of the environment dynamics, which makes it infeasible to learn an accurate global model and thus necessi

A. Kent, S. L. Waters, J. Oliver, S. J. Chapman

Asymptotic homogenisation is considered for problems with integral constraints imposed on a slowly-varying microstructure; an insulator with an array of perfectly dielectric inclusions of slowly varying size serves as a paradigm. Although it is well-known how to handle each of these effects (integral constraints, slowly-varying microstructure) independently