July 2022 arXiv papers — page 32

Frédérique Bassino, Mathilde Bouvel, Valentin Féray, Lucas Gerin

We prove that Aldous' Brownian CRT is the scaling limit, with respect to the Gromov--Prokhorov topology, of uniform random graphs in each of the three following families of graphs: distance-hereditary graphs, $2$-connected distance-hereditary graphs and $3$-leaf power graphs. Our approach is based on the split decomposition and on analytic combinatorics.

Tom Westermann, Nemanja Hranisavljevic, Alexander Fay

The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach

Dimosthenis Theofilopoulos

In the first part, we concentrate on CFTs in coordinate space. We lay the foundations of Conformal Field Theory and we also demonstrate a method where by using the embedding formalism we can derive up to n-point scalar conformal correlators. We proceed with our analysis in momentum space and we illustrate the theory of the conformal anomalies. We move on to

Dmitry Chicherin, Johannes Henn, Simone Zoia

Conformal symmetry underlies many massless quantum field theories, but little is known about the consequences of this powerful symmetry for on-shell scattering amplitudes. Working in a dimensionally-regularised $\phi^3$ model at the conformal fixed point, we show that the on-shell renormalised amplitudes satisfy anomalous conformal Ward identities. Each exte

Federico Camia, Jianping Jiang, Charles M. Newman

In this paper, we consider Ising models with ferromagnetic pair interactions. We prove that the Ursell functions $u_{2k}$ satisfy: $(-1)^{k-1}u_{2k}$ is increasing in each interaction. As an application, we prove a 1983 conjecture by Nishimori and Griffiths about the partition function of the Ising model with complex external field $h$: its closest zero to t

ATLAS Collaboration

In a special run of the LHC with $\beta^\star = 2.5~$km, proton-proton elastic-scattering events were recorded at $\sqrt{s} = 13~$TeV with an integrated luminosity of $340~\mu \textrm{b}^{-1}$ using the ALFA subdetector of ATLAS in 2016. The elastic cross section was measured differentially in the Mandelstam $t$ variable in the range from $-t = 2.5 \cdot 10^

Tristan Laidlow, Jan Czarnowski, Stefan Leutenegger

While the keypoint-based maps created by sparse monocular simultaneous localisation and mapping (SLAM) systems are useful for camera tracking, dense 3D reconstructions may be desired for many robotic tasks. Solutions involving depth cameras are limited in range and to indoor spaces, and dense reconstruction systems based on minimising the photometric error b

Munesh Kumari, Kalika Prasad, Hrishikesh Mahato

This paper aims to introduce the $k$-Mersenne and $k$-Mersenne-Lucas octonions. We give the closed form formulae for these octonions and obtain some well-known identities like Cassini's identity, d'Ocagne's identity, Catalan identity, Vajda's identity and generating functions of them. As a consequence k=1 yields all the above properties for Mersenne and Mers

Dorje C. Brody, Lane P. Hughston

Recently there has been much progress in the development of stochastic models for state reduction in quantum mechanics. In such models, the collapse of the wave function is a physical process, governed by a nonlinear stochastic differential equation that generalizes the Schr\"odinger equation. The present paper considers energy-based stochastic extensions of

Si Tiep Dinh, Tien Son Pham

We provide necessary and sufficient conditions for a set-valued mapping between finite dimensional spaces to be directionally open by relating this property with directional regularity, H\"older continuity of the inverse mapping, coderivatives and variations. These generalize and refine some previously known results.

Karl Battams, Angel J. Gutarra-Leon, Brendan M. Gallagher, Matthew M. Knight

We present an update to the first white-light detections of a dust trail observed closely following the orbit of asteroid (3200) Phaethon, as seen by the Wide-field Imager for Parker Solar Probe (WISPR) instrument on the NASA Parker Solar Probe (PSP) mission. Here we provide a summary and analysis of observations of the dust trail over nine separate mission

Amrest Chinkamol, Vetit Kanjaras, Phattarapong Sawangjai, Yitian Zhao

While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem. In this wo

Andrew Zic, George Hobbs, R. M. Shannon, Daniel Reardon

Pulsar timing array collaborations have recently reported evidence for a noise process with a common spectrum among the millisecond pulsars in the arrays. The spectral properties of this common-noise process are consistent with expectations for an isotropic gravitational-wave background (GWB) from inspiralling supermassive black-hole binaries. However, recen

Qi Yang, Sergey Nikolenko, Alfred Huang, Aleksandr Farseev

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing effici

Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang

Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state TOD models, variational learning is often used, but suffers from the annoying high-variance of the gradients propagated through discrete latent variables and the drawback of indirec

M. T. DiMario, F. E. Becerra

Quantum state discrimination is a central problem in quantum measurement theory, with applications spanning from quantum communication to computation. Typical measurement paradigms for state discrimination involve a minimum probability of error or unambiguous discrimination with a minimum probability of inconclusive results. Alternatively, an optimal inconcl

Graziella Branduardi-Raymont, Chi Wang

This chapter describes the scientific motivations that led to the development of the SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) mission. The solar wind coupling with the terrestrial magnetosphere is a key link in Sun-Earth interactions. In-situ missions can provide detailed observations of plasma and magnetic field conditions in the solar wind

Daegyu Lee, Chanyoung Jung, Andrea Finazzi, Hyunki Seong

This paper describes a resilient navigation and planning system used in the Indy Autonomous Challenge (IAC) competition. The IAC is a competition where full-scale race cars run autonomously on Indianapolis Motor Speedway(IMS) up to 290 km/h (180 mph). Race cars will experience severe vibrations. Especially at high speeds. These vibrations can degrade standar

Kazi Abu Zubair, Sumit Kumar Jha, David Mohaisen, Clayton Hughes

Processing In Memory (PIM) accelerators are promising architecture that can provide massive parallelization and high efficiency in various applications. Such architectures can instantaneously provide ultra-fast operation over extensive data, allowing real-time performance in data-intensive workloads. For instance, Resistive Memory (ReRAM) based PIM architect

Benedikt Zerulla, Ramakrishna Venkitakrishnan, Dominik Beutel, Marjan Krstić

The accurate and efficient computation of the electromagnetic response of objects made from artificial materials is crucial for designing photonic functionalities and interpreting experiments. Advanced fabrication techniques can nowadays produce new materials as three-dimensional lattices of scattering unit cells. Computing the response of objects of arbitra

Siddhartha Mukherjee, Rahul K. Singh, Martin James, Samriddhi Sankar Ray

A hydrodynamic model of active, low Reynolds number suspensions, shows the emergence of an asymptotic state with a universal spectral scaling and non-Gaussian (intermittent) fluctuations in the velocity field. Such states arise when these systems are pushed beyond a critical level of activity and show features akin to high Reynolds number, inertial turbulenc

Everett Patterson, Robert B. Mann

Relativistic quantum metrology is the study of optimal measurement procedures within systems that have both quantum and relativistic components. Here we use Unruh-DeWitt detectors coupled to a massless scalar field as probes of thermal parameters in different spacetimes via a relativistic quantum metrology analysis. We consider both (2+1)-dimensional anti-de

Florian Huber, Luca Onorante, Michael Pfarrhofer

In this paper, we forecast euro area inflation and its main components using an econometric model which exploits a massive number of time series on survey expectations for the European Commission's Business and Consumer Survey. To make estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulat

Natalia Quiroga, Alex Mitrevski, Paul G. Plöger

Robots applied in therapeutic scenarios, for instance in the therapy of individuals with Autism Spectrum Disorder, are sometimes used for imitation learning activities in which a person needs to repeat motions by the robot. To simplify the task of incorporating new types of motions that a robot can perform, it is desirable that the robot has the ability to l

Yuri Kondratiev, José Luís da Silva

In this paper we study the existence of Green measures for Markov processes with a nonlocal jump generator. The jump generator has no second moment and satisfies a suitable condition on its Fourier transform. We also study the same problem for certain classes of random time changes Markov processes with jump generator.

Luca Bisconti, Matteo Caggio

We consider the compressible Navier-Stokes system describing the motion of a barotropic fluid with density dependent viscosity confined in a three-dimensional bounded domain $\Omega$. We show the convergence of the weak solution to the compressible Navier-Stokes system to the strong solution to the compressible Euler system when the viscosity and the damping

Alexander E. Black, Raman Sanyal

We describe a natural geometric relationship between matroids and underlying flag matroids by relating the geometry of the greedy algorithm to monotone path polytopes. This perspective allows us to generalize the construction of underlying flag matroids to polymatroids. We show that the polytopes associated to underlying flag polymatroid are simple by provin

M. Hvozd, T. Patsahan, O. Patsahan, M. Holovko

We study a fluid-fluid phase transition of the explicit solvent model represented as a mixture of the restricted primitive model (RPM) of ionic fluid and neutral hard spherocylinders (HSC). To this end, we combine two theoretical approaches, i.e., the scale particle theory (SPT) and the associative mean spherical approximation (AMSA). Whereas the SPT is suff

Robert F. Allen, Flavia Colonna, Andrew Prudhom

In this article, we characterize the bounded and the compact multiplication operators between distinct iterated logarithmic Lipschitz spaces, and between the Lipschitz space and an iterated logarithmic Lipschitz space of an infinite tree. In addition, we provide operator norm estimates and show that there are no isometries among such operators. %We obtain a

Han-Dong Lim, Donghwan Lee

Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades. Although researches on asymptotic convergence analysis of Q-learning have a long tradition, non-asymptotic convergence has only recently come under active study. The main goal of this paper

Robert F. Allen, Katherine Heller, Matthew A. Pons

In this paper, we study the multiplication operators on $S^2$, the space of analytic functions on the open unit disk $\mathbb D$ whose first derivative is in $H^2$. Specifically, we characterize the bounded and the compact multiplication operators, establish estimates on the operator norm, and determine the spectrum. Finally, we prove that the isometric mult

S. B. Rutkevich

The infinite Heisenberg XXZ spin-(1/2) chain in the gapped antiferromagnetic regime has two degenerate vacua and kink topological excitations (which are also called spinons) interpolating between these vacua as elementary excitations. Application of an arbitrary weak staggered longitudinal magnetic field h induces a long-range attractive potential between tw

Yan Sun, Yi Han, Jicong Fan

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data. However, few of them take into consideration the potential cluster information contained implicitly in the high-dimensional data. In this paper, we

Robert F. Allen, Flavia Colonna, Glenn R. Easley

We introduce a class of iterated logarithmic Lipschitz spaces $\mathcal{L}^{(k)}$, $k\in\mathbb{N}$, on an infinite tree which arise naturally in the context of operator theory. We characterize boundedness and compactness of the multiplication operators on $\mathcal{L}^{(k)}$ and provide estimates on their operator norm and their essential norm. In addition,

Ed Darnbrough

With the invent of nanoindentation technology capable of greater frequency of oscillation the full resonant behaviour can be observed. Here we lay out a proposed mathematical basis to interpret the measured dynamic compliance of a system to discover the viscoelastic properties of test samples.

Paul Boniol, Themis Palpanas

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches proposed so far in the literature have severe limitations: they either require prior domain knowledge used to design the anomaly discovery algorithms, or become cumbersome and expensive to use in situations with recurr

Simone Biondini, Philipp Schicho, Tuomas V. I. Tenkanen

Beyond the Standard Model physics is required to explain both dark matter and the baryon asymmetry of the universe, the latter possibly generated during a strong first-order electroweak phase transition. While many proposed models tackle these problems independently, it is interesting to inquire whether the same model can explain both. In this context, we li

Jun-Xing Chin, Gabriela Hug

With the increasing penetration of distributed energy resources (DERs) in distribution grids, their impact on grid operations can no longer be ignored. However, the individual control of these increasingly ubiquitous devices remains a challenge due to their numbers. One solution is to control them in groups via virtual power plants. Previous work has typical

Claire Guépin, Kumiko Kotera, Foteini Oikonomou

The recent discovery of high-energy astrophysical neutrinos and first hints of coincident electromagnetic and neutrino emission herald the beginning of the era of multi-messenger astronomy. Due to their high power, transient sources are expected to supply a significant fraction of the observed energetic astroparticles, through enhanced particle acceleration

L. V. Kotova, A. V. Platonov, R. André, H. Mariette

Photoluminescence and polarized reflection spectra of quantum well structures with symmetric Cd$_{0.9}$Zn$_{0.1}$Te/CdTe/Cd$_{0.9}$Zn$_{0.1}$Te and asymmetric Cd$_{0.9}$Zn$_{0.1}$Te/CdTe/Cd$_{0.4}$Mg$_{0.6}$Te barriers were studied. The Stokes parameters of the reflected light from these structures were measured. In the structures with symmetric barriers, ex

Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han

Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an ad

Feng Yang, Xingle Zhang, Bo Liu

Multiple object tracking (MOT) is an important technology in the field of computer vision, which is widely used in automatic driving, intelligent monitoring, behavior recognition and other directions. Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in industry, and the performance of them d

Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misl

Pedro Rito, Ana Almeida, Andreia Figueiredo, Christian Gomes

This article presents the deployment and experimentation architecture of the Aveiro Tech City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of Internet-of-Things devices with communication, sensing and computing capabilities. The communication infrastructure, built on fiber and Millimeter-wave (mmWave) links, integrates a com

Richard S. J. Tol

Earlier meta-analyses of the economic impact of climate change are updated with more data, with three new results: (1) The central estimate of the economic impact of global warming is always negative. (2) The confidence interval about the estimates is much wider. (3) Elicitation methods are most pessimistic, econometric studies most optimistic. Two previous

Hubert Szolc, Tomasz Kryjak

This paper presents a system for hardware-in-the-loop (HiL) simulation of unmanned aerial vehicle (UAV) control algorithms implemented on a heterogeneous SoC FPGA computing platforms. The AirSim simulator running on a PC and an Arty Z7 development board with a Zynq SoC chip from AMD Xilinx were used. Communication was carried out via a serial USB link. An ap

Himanshu Goyal, Krishna Kodali, Sudipta Saha

In the modern digital world, a user of a smart system remains surrounded with as well as observed by a number of tiny IoT devices round the clock almost everywhere. Unfortunately, the ability of these devices to sense and share various physical parameters, although play a key role in these smart systems but also causes the threat of breach of the privacy of

Flavio Petruzzellis, Corrado Monti, Gianmarco De Francisci Morales, Francesco Bonchi

While much attention has been devoted to the causes of opinion change, little is known about its consequences. Our study sheds a light on the relationship between one user's opinion change episode and subsequent behavioral change on an online social media, Reddit. In particular, we look at r/ChangeMyView, an online community dedicated to debating one's own o

Robert F. Allen, Cassandra Jens, Theodore J. Wendt

In this paper, we investigate the existence of stability-changing bifurcations in epidemiological models used to study the spread of zombiism through a human population. These bifurcations show that although linear instability of disease-free equilibria may exist in a model, perturbations of model parameters may result in stability. Thus, we show that humans

Sen Pei, Jiaxi Sun, Richard Yi Da Xu, Shiming Xiang

Machine learning systems, especially the methods based on deep learning, enjoy great success in modern computer vision tasks under experimental settings. Generally, these classic deep learning methods are built on the \emph{i.i.d.} assumption, supposing the training and test data are drawn from a similar distribution independently and identically. However, t

Hamidreza Ramezani

I show that a single embedded non-Hermitian defect in a one-dimensional topological system at certain degrees of non-Hermiticity can remove the topological mode from the edge and restore it inside the lattice at the same place where the non-Hermitian defect is placed. I relate this unexpected phenomenon to the wave matching condition and continuity of the wa

Christophe Profeta

We consider a branching Markov process in continuous time in which the particles evolve independently as spectrally negative L\'evy processes. When the branching mechanism is critical or subcritical, the process will eventually die and we may define its overall maximum, i.e. the maximum location ever reached by a particule. The purpose of this paper is to gi

Fabrice Baudoin

We present developments in the theory of Korevaar-Schoen-Sobolev spaces on metric measure spaces. While this theory coincides with those of Cheeger and Shanmugalingam if the space is doubling and satisfies a Poincar\'e inequality, it offers new perspectives in the context of fractals for which the approach by weak upper gradients is inadequate.

Eric Cancès, Geneviève Dusson, Gaspard Kemlin, Laurent Vidal

In this article, we propose general criteria to construct optimal atomic centered basis sets in quantum chemistry. We focus in particular on two criteria, one based on the ground-state one-body density matrix of the system and the other based on the ground-state energy. The performance of these two criteria are then numerically tested and compared on a param

Alastair Kay

In a recent work (arXiv:2207.01954), we showed that a uniformly coupled chain could be symmetrically extended by engineered spin chains in such a way that we could choose part of the spectrum of the overall system. When combined with an encoding that avoids the uncontrolled eigenvalues, this resulted in the possibility of achieving a range of tasks such as p

Che-Kai Liu, Haobang Chen, Mohsen Imani, Kai Ni

In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, performing the cosine similarities between the vectors in Von-Neumann machines involves a large number of multiplications, Euclidean normalizations and division operations, thus inc

Martino Lovisetto, Didier Clamond, Bruno Marcos

The nonlinear Schr\"odinger and the Schr\"odinger-Newton equations model many phenomena in various fields. Here, we perform an extensive numerical comparison between splitting methods (often employed to numerically solve these equations) and the integrating factor technique, also called Lawson method. Indeed, the latter is known to perform very well for the

Atsumoto Ohashi, Ryuichiro Higashinaka

Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based methods. To solve this problem, we propos

Jens Bossaert, Tom De Medts

In 2000, Marc Burger and Shahar Mozes introduced universal groups acting on trees. Such groups provide interesting examples of totally disconnected locally compact groups. Intuitively, these are the largest groups for which all local actions satisfy a prescribed behavior. Since then, their study has evolved in various directions. In particular, Adrien Le Bou

Joseph T. Meyer

We establish optimal convergence rates up to a log-factor for a class of deep neural networks in a classification setting under a restraint sometimes referred to as the Tsybakov noise condition. We construct classifiers in a general setting where the boundary of the bayes-rule can be approximated well by neural networks. Corresponding rates of convergence ar

Li Chen, Juan S. Pereyra, Min Zhu

Recent literature shows that dynamic matching mechanisms may outperform their static counterparts. The college admissions procedure used in Inner Mongolia motivates an underexplored design dimension of such mechanisms: the time constraint that students face. We study the time-constrained dynamic mechanism (TCDM) theoretically and show that, under straightfor

Jia-Ming Xie, Ming-Zhu Liu, Li-Sheng Geng

Motivated by the recent discovery of a charmonium $X(3960)$ in $B$ decays by the LHCb Collaboration, the likely existence of two bound/virtual states (denoted by $ X_{s\bar{s}}$ and $ X_{q\bar{q}}$) below the $D_{s}^{+}D_{s}^{-}$ and $\bar{D}D$ mass thresholds has been re-examined recently. In this work, we employ the effective Lagrangian approach to calcula

Zhan Li, Xiaohu Sun, Yaquan Fang, Gang Li

We present a study of top quark mass measurements at the $t\bar{t}$ threshold based on CEPC. A centre-of-mass energy scan near two times of the top mass is performed and the measurement precision of top quark mass, width and $\alpha_S$ are evaluated using the $t\bar{t}$ production rates. Realistic scan strategies at the threshold are discussed to maximise th

Suzanne Lafon, Alexis Chennevière, Frédéric Restagno, Samy Merabia

The effect of temperature on friction and slip at the liquid-solid interface has attracted attention over the last twenty years, both numerically and experimentally. However, the role of temperature on slip close to the glass transition has been less explored. Here, we use molecular dynamics to simulate a bi-disperse atomic fluid, which can remain liquid bel

Russell A. Howard, Guillermo Stenborg, Angelos Vourlidas, Brendan M. Gallagher

The closest perihelion pass of Parker Solar Probe (PSP), so far, occurred between 16 and 26 of November 2021 and reached ~13.29 Rsun from Sun center. This pass resulted in very unique observations of the solar corona by the Wide-field Instrument for Solar PRobe (WISPR). WISPR observed at least ten CMEs, some of which were so close that the structures appear

Siyana Pavlova, Maxime Amblard, Bruno Guillaume

In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR)a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA)-an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter

Arunabha Majumdar, Bogdan Pasaniuc

Polygnicity refers to the phenomenon that multiple genetic variants have a non-zero effect on a complex trait. It is defined as the proportion of genetic variants that have a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygen

Vaclav Rechtberger, Miroslav Bures, Bestoun S. Ahmed, Hynek Schvach

Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths can be automatically generated from the system model to test such systems using a suitable algorithm. This paper presents a strategy that generates test paths and allows to start and end test paths only in defined states of the finite state

John Çamkıran, Fabian Parsch, Glenn D. Hibbard

Coordination geometries describe how the neighbours of a central particle are arranged around it. Such geometries can be thought to lie in an abstract topological space; a model of this space could provide a mathematical basis for understanding physical transformations in crystals, liquids, and glasses. With this motivation, the present work proposes a metri

Elisa Gorla, Flavio Salizzoni

In 1997 Rosenthal and York defined generalized Hamming weights for convolutional codes, by regarding a convolutional code as an infinite dimensional linear code endowed with the Hamming metric. In this paper, we propose a new definition of generalized weights of convolutional codes, that takes into account the underlying module structure of the code. We deri

Benjamin Martin

The notion of a \emph{$G$-completely reducible} subgroup is important in the study of algebraic groups and their subgroup structure. It generalizes the usual idea of complete reducibility from representation theory: a subgroup $H$ of a general linear group $G= {\rm GL}_n(k)$ is $G$-completely reducible if and only if the inclusion map $i\colon H\rightarrow {

Panagiotis Mavrogiannis

Magnetic fields are a very special form of elastic medium. Within astrophysical environments (magnetised stars and protogalaxies) they counteract shear and rotational distortions as well as gravitational collapse. Their vector nature allows for their extraordinary coupling with spacetime curvature in the framework of general relativity. This particular coupl

Diana L. van der Ven, Davide Morrone, Miguel A. Quetzeri-Santiago, David Fernandez Rivas

Injecting with needles causes fear, pain and contamination risks. Billions of injections every year also cause environmental burden in terms of material consumption and waste. Controlled microfluidic-jet injection systems offer a needle-free alternative. However, understanding the relation between jet parameters and resulting injection depth are needed to en

Maxime Amblard, Bruno Guillaume, Siyana Pavlova, Guy Perrier

This paper presents how the online tool GREW-MATCH can be used to make queries and visualise data from existing semantically annotated corpora. A dedicated syntax is available to construct simple to complex queries and execute them against a corpus. Such queries give transverse views of the annotated data, these views can help for checking the consistency of

Paul Boniol, Mohammed Meftah, Emmanuel Remy, Themis Palpanas

Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decisions is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though, the explanations pr

Belinda A. Nicholson, Suzanne Aigrain

In recent years, Gaussian Process (GP) regression has become widely used to analyse stellar and exoplanet time-series data sets. For spotted stars, the most popular GP covariance function is the quasi-periodic (QP) kernel, whose the hyperparameters of the GP have a plausible interpretation in terms of physical properties of the star and spots. In this paper,

Azin Jahedi, Lukas Mehl, Marc Rivinius, Andrés Bruhn

Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches -- RAFT -- hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-sca

Maria Boritchev, Maxime Amblard

We present Dialogues in Games (DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialog

Navin Kumar Chandra, Kaustuv Lahiri, Aloke Kumar

The present work investigates the role of contact angle hysteresis at the liquid-liquid-solid interface (LLS) on the rod climbing effect of two immiscible Newtonian liquids using experimental and numerical approaches. Experiments revealed that the final steady state contact angle, $\theta_{w}$ at the LLS interface varies with the rod rotation speed, $\omega$

Stuart Shepard, Ramón Lorenzo Panadés-Barrueta, Saverio Moroni, Anthony Scemama

We show that recently developed quantum Monte Carlo methods, which provide accurate vertical transition energies for single excitations, also successfully treat double excitations. We study the double excitations in medium-sized molecules, some of which are challenging for high level coupled-cluster calculations to model accurately. Our fixed-node diffusion

Eduardo Martini

Periodogram methods are widely used for the estimation of power- and cross-spectra, of which Welch's method is the most popular. Previous studies have analyzed the variance of the power spectra estimates and developed analytical probability functions, showing that the approach is unbiased when applied to white-noise signals or in the limit of infinite window

Ladislav Šamaj

Electric double layers (EDL) with counterions only, say electrons with the elementary charge $-e$, in thermal equilibrium at the inverse temperature $\beta$ are considered. In particular, we study the effect of the surface charge modulation on the particle number density profile and the pressure. The mobile particles are constrained to the surface of a 2D cy

Jiangdong Ai, Stefanie Gerke, Gregory Gutin, Anders Yeo

A {\em quasi-kernel} of a digraph $D$ is an independent set $Q\subseteq V(D)$ such that for every vertex $v\in V(D)\backslash Q$, there exists a directed path with one or two arcs from $v$ to a vertex $u\in Q$. In 1974, Chv\'{a}tal and Lov\'{a}sz proved that every digraph has a quasi-kernel. In 1976, Erd\H{o}s and S\'zekely conjectured that every sink-free d

Ye-Hong Chen, Yuan Qiu, Adam Miranowicz, Neill Lambert

The experimental observation of quantum phase transitions predicted by the quantum Rabi model in quantum critical systems is usually challenging due to the lack of signature experimental observables associated with them. Here, we describe a method to identify the dynamical critical phenomenon in the quantum Rabi model consisting of a three-level atom and a c

Alessandro Pesci

In the vast amount of results linking gravity with thermodynamics, statistics, information, a path is described which tries to explore this connection from the point of view of (non)locality of the gravitational field. First the emphasis is put on that well-known thermodynamic results related to null hypersurfaces (i.e. to lightsheets and to generalized cova

Abtin Shahkarami, Mansoor Yousefi, Yves Jaouen

Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computat

David Damanik, Daniel Lenz

We consider continuous $\mathrm{SL}(2,\mathbb{R})$ valued cocycles over general dynamical systems and discuss a variety of uniformity notions. In particular, we provide a description of uniform one-parameter families of continuous $\mathrm{SL}(2,\mathbb{R})$ cocycles as $G_\delta$-sets. These results are then applied to Schr\"odinger operators with dynamical

Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Tom Drummond

The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use of the transformer for the problem of stereo matching remains relatively unexplored. In this paper, we comprehensively investigate the use of the transf

Haonan Yang, Chichuan Jin, Weimin Yuan

Active Galactic Nuclei (AGN) are generally considered as the scaled-up counterparts of X-ray binaries (XRBs). It is known that the power spectral density (PSD) of the X-ray emission of XRBs shows significant evolution with spectral states. It is not clear whether AGN follow a similar evolutionary trend, however, though their X-ray emission and the PSD are bo

Milos Katanic, John Lygeros, Gabriela Hug

Power network and generators state estimation are usually tackled as separate problems. We propose a dynamic scheme for the simultaneous estimation of the network and the generator states. The estimation is formulated as an optimization problem on a moving-horizon of past observations. The framework is a generalization of static state estimation; it can hand

Dionne Ibarra

We survey, complete, and modify a proof, involving knot theory, of Stiefel's theorem that all orientable $3$-manifolds are parallelizable. The completion of the proof is done by using the relationship between the tangent bundle and normal bundle of manifolds with non-trivial boundary and on stably parallelizable and parallelizable manifolds. We end with a re

Sylvia Frühwirth-Schnatter, Peter Knaus

In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using a non-cent

Hong-Ming Liu, Jing Zhang, Zeng-Hua Li, Jin-Biao Wei

A microscopic nuclear equation of state compatible with all current astrophysical constraints constructed within the Brueckner-Hartree-Fock formalism is presented and extended in a consistent way to finite temperature. The effects of finite temperature on the properties of neutron stars are studied in detail and a universal relation regarding stellar stabili

Rufei Ren

The ghost conjecture is first provided by Bergdall and Pollack in [BP-1,BP-2] to study the Up-slopes of spaces of modular forms, which, so far, has already brought plenty of important results. The local version of this conjecture under genericity condition has been solved by Liu-Truong-Xiao-Zhao in [LTXZ-1, LTXZ-2]. In the current paper, we prove a necessary

Michał Stolarz, Alex Mitrevski, Mohammad Wasil, Paul G. Plöger

In robot-assisted therapy for individuals with Autism Spectrum Disorder, the workload of therapists during a therapeutic session is increased if they have to control the robot manually. To allow therapists to focus on the interaction with the person instead, the robot should be more autonomous, namely it should be able to interpret the person's state and con

Mojtaba Alaei, Homa Karimi

In this study, we use Hubbard-Corrected density functional theory (DFT+$U$) to derive spin model Hamiltonians consisting of Heisenberg exchange interactions up to the fourth nearest neighbors and bi-quadratic interactions. We map the DFT+$U$ results of several magnetic configurations to the Heisenberg spin model Hamiltonian to estimate Heisenberg exchanges.

Zhendong Jin, Biaoyan Hu, Yiran Liu, Yangmu Li

Topological invariants in the band structure, such as Chern numbers, are crucial for the classification of topological matters and dictate the occurrence of exotic properties, yet their direct spectroscopic determination has been largely limited to electronic bands. Here, we use inelastic neutron scattering in conjunction with ab initio calculations to ident

Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang

Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning. Previous works learn a dynamics model that fits under the empirical state-action visitation distribution for all historical policies, i.e., the sample replay buffer. However, in t

Dmitry Kuznichov

During the last decade, Neural Networks (NNs) have proved to be extremely effective tools in many fields of engineering, including autonomous vehicles, medical diagnosis and search engines, and even in art creation. Indeed, NNs often decisively outperform traditional algorithms. One area that is only recently attracting significant interest is using NNs for

Martin Georgiev, Simon Eberz, Ivan Martinovic

The field of touch-based authentication has been rapidly developing over the last decade, creating a fragmented and difficult-to-navigate area for researchers and application developers alike due to the variety of methods investigated. In this study, we perform a systematic literature analysis of 30 studies on the techniques used for feature extraction, clas