July 2022 arXiv papers — page 26

Aayush Mishra, Anqi Liu

We study the problem of invariant learning when the environment labels are unknown. We focus on the invariant representation notion when the Bayes optimal conditional label distribution is the same across different environments. Previous work conducts Environment Inference (EI) by maximizing the penalty term from Invariant Risk Minimization (IRM) framework.

Xuan Luo, Satoshi X. Nakamura

Recent LHCb's amplitude analysis on $B^+\to J/\psi \phi K^+$ suggests the existence of exotic $X$ and $Z_{cs}$ hadrons, based on an assumption that Breit-Wigner resonances describe all the peak structures. However, all the peaks and also dips in the spectra are located at relevant meson-meson thresholds where threshold kinematical cusps might cause such stru

Eoin Long, Laurentiu Ploscaru

An old conjecture of Erd\H{o}s and McKay states that if all homogeneous sets in an $n$-vertex graph are of order $O(\log n)$ then the graph contains induced subgraphs of each size from $\{0,1,\ldots, \Omega (n^2)\}$. We prove a bipartite analogue of the conjecture: if all balanced homogeneous sets in an $n \times n$ bipartite graph are of order $O(\log n)$ t

Wolfgang Weingarten

Small (compared to coherence length) weak superconducting defects when located at the surface, combined with the proximity and percolation effects, are claimed responsible for various observations with superconducting rf accelerating cavities with non-constant Q-value, such as "Q-slope" and "Q-drop", the role of temperature, the result of "nitrogen doping" a

Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the refer

Maria Lefter, David Šiška, Łukasz Szpruch

We produce uniform and decaying bounds in time for derivatives of the solution to the backwards Kolmogorov equation associated to a stochastic processes governed by a time dependent dynamics. These hold under assumptions over the integrability properties in finite time of the derivatives of the transition density associated to the process, together with the

F. Barra, C. Pinto, D. J. Walton, P. Kosec

Despite two decades of studies, it is still not clear whether ULX spectral transitions are due to stochastic variability in the wind or variations in the accretion rate or in the source geometry. The compact object is also unknown for most ULXs. In order to place constraints onto such scenarios and on the structure of the accretion disc, we studied the tempo

Zohre Habibolahi, Karim Ghorbani, Parsa Ghorbani

We consider an extension to the Standard Model (SM) with two extra real singlet scalars which interact with the SM Higgs particle. The lighter scalar is taken as the dark matter (DM) candidate. We show that the model successfully explains the relic abundance of the DM in the universe and evades the strong bounds from direct detection experiments while respec

Alberto Ambrosetti, Giorgio Palermo, Pier Luigi Silvestrelli

Water-flow in carbon nanotubes (CNT's) starkly contradicts classical fluid mechanics, with permeabilities that can exceed no-slip Haagen-Poiseuille predictions by two to five orders of magnitude. Semi-classical molecular dynamics accounts for enhanced flow-rates, that are attributed to curvature-dependent lattice mismatch. However, the steeper permeability-e

Oliver R. Gittus, Fernando Bresme

Temperature gradients induce mass separation in mixtures in a process called thermodiffusion and quantified by the Soret coefficient. The existence of minima in the Soret coefficient of aqueous solutions was controversial until fairly recently, where a combination of experiments and simulations provided evidence for the existence of this physical phenomenon.

Hui Xia, Xiugui Yang, Xiangyun Qian, Rui Zhang

During the generation of invisible backdoor attack poisoned data, the feature space transformation operation tends to cause the loss of some poisoned features and weakens the mapping relationship between source images with triggers and target labels, resulting in the need for a higher poisoning rate to achieve the corresponding backdoor attack success rate.

Richard J. Szabo, Michelangelo Tirelli

This is a mini-review about generalized instantons of noncommutative gauge theories in dimensions 4, 6 and 8, with emphasis on their realizations in type II string theory, their geometric interpretations, and their applications to the enumerative geometry of non-compact toric varieties.

Gianluca Faraco

We consider translation surfaces with poles on surfaces. We shall prove that any finite group appears as the automorphism group of some translation surface with poles. As a direct consequence we obtain the existence of structures achieving the maximal possible number of automorphisms allowed by their genus and we finally extend the same results to branched p

Bibhu Prasad Tripathy, Bijan Kumar Patel

In this paper, we find all the sums of three Fibonacci numbers which are close to a power of 2. This paper continues and extends the previous work of Hasanalizade \cite{Hasanalizade}.

Tomoki Uchiyama, Naoya Sogi, Satoshi Iizuka, Koichiro Niinuma

This paper proposes a method for visually explaining the decision-making process of video recognition networks with a temporal extension of occlusion sensitivity analysis, called Adaptive Occlusion Sensitivity Analysis (AOSA). The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure th

Saad Shafiq, Christoph Mayr-Dorn, Atif Mashkoor, Alexander Egyed

In software development teams, developer turnover is among the primary reasons for project failures as it leads to a great void of knowledge and strain for the newcomers. Unfortunately, no established methods exist to measure how knowledge is distributed among development teams. Knowing how this knowledge evolves and is owned by key developers in a project h

Toluwani Aremu, Li Zhiyuan, Reem Alameeri, Mustaqeem Khan

Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the \emph{Smart-City CCTV Violence Detection (SCVD)} dataset, specifically designed to facilitate the learning of weapon distribution in surveillance videos. To tackle the complexities of analyzing 3D surveil

Joshua Mitton, Simon Peter Mekhail, Miles Padgett, Daniele Faccio

We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an $\mathrm{SO}^{+}(2,1)$-equivariant neural network. This model takes advantage of the of the azimuthal correlations known to exist in fibre speckle patterns and naturally accounts for the difference in spatial a

Jyatsnasree Bora, Umananda Dev Goswami

We have calculated the static and spherically symmetric solutions for compact stars in the $f(\mathcal{R},T)$ gravity metric formalism. To describe the matter of compact stars, we have used the MIT Bag model equation of state (EoS) and the color-flavor-locked (CFL) EoS. Solving the hydrostatic equilibrium equations i.e., the modified TOV equations in $f(\mat

J. L. Gach, D. Boutolleau, T. Carmignani, F. Clop

We present the evolutions of the C-BLUE One family of cameras (formerly introduced as C-MORE), a laser guide star oriented wavefront sensor camera family. Within the Opticon WP2 european funded project, which has been set to develop LGS cameras, fast path solutions based on existing sensors had to be explored to provide working-proven cameras to ELT projects

Kunal Garg, Mayank Baranwal

This study develops a fixed-time convergent saddle point dynamical system for solving min-max problems under a relaxation of standard convexity-concavity assumption. In particular, it is shown that by leveraging the dynamical systems viewpoint of an optimization algorithm, accelerated convergence to a saddle point can be obtained. Instead of requiring the ob

Hassen M. Yesuf

This work uses multiscale environments and structures of galaxies in the Sloan Digital Sky Survey as consistency checks of the evolution from starburst to quiescence at redshift $z < 0.2$. The environmental indicators include fixed aperture mass overdensities ($\delta_{x\mathrm{Mpc}}$, $x \in \{0.5, 1, 2, 4, 8\}\,h^{-1}$Mpc), $k$-nearest neighbor distances,

Nikolay Bobev, Pieter-Jan De Smet, Xuao Zhang

We study correlation functions of local operators and Wilson loop expectation values in the planar limit of a 4d $\mathcal{N}=2$ superconformal ${\rm SU}(N)$ YM theory with hypermultiplets in the symmetric and antisymmetric representations of the gauge group. This so-called $\mathbf{E}$ theory is closely related to $\mathcal{N}=4$ SYM and has a holographic d

Victor G. Turrisi da Costa, Giacomo Zara, Paolo Rota, Thiago Oliveira-Santos

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extensive literature available for images, the field of videos is still relatively unexplored. On the other hand, the performance of a model in action recognition is heavily affected by

Kevin Gildea, Clara Mercadal-Baudart, Richard Blythman, Aljosa Smolic

Computer vision/deep learning-based 3D human pose estimation methods aim to localize human joints from images and videos. Pose representation is normally limited to 3D joint positional/translational degrees of freedom (3DOFs), however, a further three rotational DOFs (6DOFs) are required for many potential biomechanical applications. Positional DOFs are insu

Mohamed Akel

In this work, we introduce a new generalized integral transform involving many potentially known or new transforms as special cases. Basic properties of the new integral transform, that investigated in this work, include the existence theorem, the scaling property, elimination property a Parseval-type identity, and inversion formula. The relationships of the

Shaojie Tang, Jing Yuan

Many sequential decision making problems, including pool-based active learning and adaptive viral marketing, can be formulated as an adaptive submodular maximization problem. Most of existing studies on adaptive submodular optimization focus on either monotone case or non-monotone case. Specifically, if the utility function is monotone and adaptive submodula

Takayuki Umakoshi, Hiroshi Arata, Prabhat Verma

Optical control of electronic properties is essential for future electric devices. Manipulating such properties has been limited to the microscale in spatial volume due to the wave nature of light; however, scaling down the volume is in extremely high demand. In this study, we demonstrate optical switching within a nanometric spatial volume in an organic ele

Johann S. Brauchart, Peter J. Grabner, Ian H. Sloan, Robert S. Womersley

Spherical needlets were introduced by Narcowich, Petrushev, and Ward to provide a multiresolution sequence of polynomial approximations to functions on the sphere. The needlet construction makes use of integration rules that are exact for polynomials up to a given degree. The aim of the present paper is to relax the exactness of the integration rules by repl

Kumar Ghosh, Sumit Ghosh

In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurity we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magne

Yachun Li, Lizhen Zhang

The global existence of martingale solutions to the compressible Navier-Stokes equations driven by stochastic external forces, with density-dependent viscosity and vacuum, is established in this paper. This work can be regarded as a stochastic version of the deterministic Navier-Stokes equations \cite{Vasseur-Yu2016} (Vasseur-Yu, Invent. Math., 206:935--974,

Ziqiao Ao, Gergely Horvath, Chunyuan Sheng, Yifan Song

In this paper, we compare different methods to extract skill requirements from job advertisements. We consider three top-down methods that are based on expert-created dictionaries of keywords, and a bottom-up method of unsupervised topic modeling, the Latent Dirichlet Allocation (LDA) model. We measure the skill requirements based on these methods using a U.

Qianhui Men, Clare Teng, Lior Drukker, Aris T. Papageorghiou

Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scanning skills on how to manipulate the probe to achieve the desired plane. In this paper, a multimodal guidance approach (Multimodal-GuideNet) is proposed to capture the stepwise dep

Bennett Clayton, Jean-Luc Guermond, Matthias Maier, Bojan Popov

This paper is concerned with the approximation of the compressible Euler equations supplemented with an arbitrary or tabulated equation of state. The proposed approximation technique is robust, formally second-order accurate in space, invariant-domain preserving, and works for every equation of state, tabulated or analytic, provided the pressure is nonnegati

Phung Lai, Han Hu, NhatHai Phan, Ruoming Jin

In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently b

Linjie Yang, Pingzhi Fan, Des McLernon, Li X Zhang

In most existing grant-free (GF) studies, the two key tasks, namely active user detection (AUD) and payload data decoding, are handled separately. In this paper, a two-step dataaided AUD scheme is proposed, namely the initial AUD step and the false alarm correction step respectively. To implement the initial AUD step, an embedded low-density-signature (LDS)

Yixuan Zhang, Yifan Sun, Joseph D. Gaggiano, Neha Kumar

During the COVID-19 pandemic, a number of data visualizations were created to inform the public about the rapidly evolving crisis. Data dashboards, a form of information dissemination used during the pandemic, have facilitated this process by visualizing statistics regarding the number of COVID-19 cases over time. In this research, we conducted a qualitative

Paul-Elliot Anglès d'Auriac, Bastien Mignoty, Lu Liu, Ludovic Patey

We study the reverse mathematics of infinitary extensions of the Hales-Jewett theorem, due to Carlson and Simpson. These theorems have multiple applications in Ramsey's theory, such as the existence of finite big Ramsey numbers for the triangle-free graph, or the Dual Ramsey theorem. We show in particular that the Open Dual Ramsey theorem holds in $\mathsf{A

Andrea Mazzolini, Thierry Mora, Aleksandra M Walczak

Chronic infections of the human immunodeficiency virus (HIV) create a very complex co-evolutionary process, where the virus tries to escape the continuously adapting host immune system. Quantitative details of this process are largely unknown and could help in disease treatment and vaccine development. Here we study a longitudinal dataset of ten HIV-infected

Daniel Potts, Laura Weidensager

We use hyperbolic wavelet regression for the fast reconstruction of high-dimensional functions having only low dimensional variable interactions. Compactly supported periodic Chui-Wang wavelets are used for the tensorized hyperbolic wavelet basis on the torus. With a variable transformation we are able to transform the approximation rates and fast algorithms

N. Schopohl, N. S. Cetin

The Dirac Hamiltonian $H^{\left(D\right)}$ for relativistic charged fermions minimally coupled to (possibly time-dependent) electromagnetic fields is transformed with a purpose-built flow equation method, so that the result of that transformation is unitary equivalent to $H^{\left(D\right)}$ and granted to strive towards a limiting value $H^{\left(NW\right)}

Kaifeng Zhao, Shaofei Wang, Yan Zhang, Thabo Beeler

Synthesizing natural interactions between virtual humans and their 3D environments is critical for numerous applications, such as computer games and AR/VR experiences. Our goal is to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications as pairs of action categories and object instances, e.g., "sit on the chair"

De Biao Li, Vítor H. Fernandes

In this paper, we characterize the monoid of endomorphisms of the semigroup of all oriented full transformations of a finite chain, as well as the monoid of endomorphisms of the semigroup of all oriented partial transformations and the monoid of endomorphisms of the semigroup of all oriented partial permutations of a finite chain. Characterizations of the mo

J. A. Gracey

We renormalize models with scalar chiral superfields with an odd superpotential to several orders in perturbation theory. These extensions of the cubic Wess-Zumino model are renormalizable in spacetime dimensions which are rational. When endowed with an $O(N)$ symmetry it is shown that they share the same property as their non-supersymmetric counterparts in

Y. Lahlou, L. Baqmou, B. Maroufi, M. Daoud

The Gaussian states are essential ingredients in many tasks of quantum information processing. The presence of the noises imposes limitations on achieving these quantum protocols. Therefore, examining the evolution of quantum entanglement and quantum correlations under the coherence of Gaussian states in noisy channels is of paramount importance. In this pap

Morten H. Christensen, Turan Birol, Brian M. Andersen, Rafael M. Fernandes

Experiments in the recently discovered vanadium-based kagome metals have suggested that their charge-ordered state displays not only bond distortions, characteristic of a ``real" charge density-wave (rCDW), but also time-reversal symmetry-breaking, typical of loop currents described by an ``imaginary" charge density-wave (iCDW). Here, we combine density-func

Yabin Wang, Zhiwu Huang, Xiaopeng Hong

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL).

David Ginzburg, David Soudry

In this paper, we propose a formula relating certain residues of Eisenstein series on symplectic groups. These Eisenstein series are attached to parabolic data coming from Speh representations. The proposed formula bears a strong similarity to the regularized Siegel-Weil formula, established by Kudla and Rallis for symplectic-orthogonal dual pairs. Their wor

Michal Balazia, Philipp Müller, Ákos Levente Tánczos, August von Liechtenstein

Body language is an eye-catching social signal and its automatic analysis can significantly advance artificial intelligence systems to understand and actively participate in social interactions. While computer vision has made impressive progress in low-level tasks like head and body pose estimation, the detection of more subtle behaviors such as gesturing, g

Tejumade Afonja, Lucas Bourtoule, Varun Chandrasekaran, Sageev Oore

It is perhaps no longer surprising that machine learning models, especially deep neural networks, are particularly vulnerable to attacks. One such vulnerability that has been well studied is model extraction: a phenomenon in which the attacker attempts to steal a victim's model by training a surrogate model to mimic the decision boundaries of the victim mode

José Niño-Mora

Motivated by time-sensitive e-service applications, we consider the design of effective policies in a Markovian model for the dynamic control of both admission and routing of a single class of real-time transactions to multiple heterogeneous clusters of web servers, each having its own queue and server pool. Transactions come with response-time deadlines, st

Tijmen Vermeij, Ron Peerlings, Marc Geers, Johan Hoefnagels

Crystallographic slip system identification methods are widely employed to characterize the fine scale deformation of metals. While powerful, they usually rely on the occurrence of discrete slip bands with clear slip traces and can struggle when complex mechanisms such as cross-slip, curved slip, diffuse slip and/or intersecting slip occur. This paper propos

Mirjana Ivanovic, Serge Autexier, Miltiadis Kokkonidis

In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the col

Joanna LOs, Aleksandra Drozd-Rzoska, Sylwester J. Rzoska, Krzysztof Czuprynski

The report shows the temperature behavior of the real part of dielectric permittivity in the static (dielectric constant) and lo-frequency (LF) domains in bulk samples of 11CB and BaTiO3-based nanocolloids. The study covers the Isotropic Liquid (I), Nematic (N), Smectoc A (SmA) and Solid CRystal (Cr) phases. For each phase, the dominance of pretransitional f

Otte Heinävaara

We prove that any two-dimensional real subspace of Schatten-3 can be linearly isometrically embedded into $L_{3}$. This resolves the case $p = 3$ of Hanner's inequality for Schatten classes, conjectured by Ball, Carlen and Lieb. We conjecture that similar isometric embedding is possible for any $p \geq 1$.

I. Abaloszewa, M. Z. Cieplak, A. Abaloszew

In this work, we provide a systematic study of the magnetic field penetration process and avalanche formation in niobium films of different thicknesses deposited on glass substrates. The research was carried out by means of direct visualization of the magnetic flux using magneto-optical imaging. The experimental data were compared with theoretical prediction

Geoffroy Delamare, Ulisse Ferrari

The inverse Ising model is used in computational neuroscience to infer probability distributions of the synchronous activity of large neuronal populations. This method allows for finding the Boltzmann distribution with single neuron biases and pairwise interactions that maximizes the entropy and reproduces the empirical statistics of the recorded neuronal ac

Somrita Ray

We explore the effect of stochastic resetting on the first-passage properties of Feller process. The Feller process can be envisioned as space-dependent diffusion, with diffusion coefficient $D(x)=x$, in a potential $U(x)=x\left(\frac{x}{2}-\theta \right)$ that owns a minimum at $\theta$. This restricts the process to the positive side of the origin and ther

Enhao Zhang, Chuanxing Geng, Songcan Chen

Data augmentation for minority classes is an effective strategy for long-tailed recognition, thus developing a large number of methods. Although these methods all ensure the balance in sample quantity, the quality of the augmented samples is not always satisfactory for recognition, being prone to such problems as over-fitting, lack of diversity, semantic dri

Nachiappan Valliappan, Fabian Ruch, Carlos Tomé Cortiñas

Fitch-style modal lambda calculi enable programming with necessity modalities in a typed lambda calculus by extending the typing context with a delimiting operator that is denoted by a lock. The addition of locks simplifies the formulation of typing rules for calculi that incorporate different modal axioms, but each variant demands different, tedious and see

Maher Me'meh, Ali Saraeb

We prove the transformation laws of the four Jacobi theta functions using Gordon's proof for the transformation law of the Dedekind eta function.

Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis Voloch

The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genetic edits (perturbations) or combinations

Giang Nguyen, Mohammad Reza Taesiri, Anh Nguyen

Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as opposed to post-hoc explanations) by harnessing the

Yan Song, Wenlin Dai, Marc G. Genton

Low-rank approximation is a popular strategy to tackle the "big n problem" associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing

Oluwasegun Taiwo Ojo, Antonio Fernández Anta, Marc G. Genton, Rosa E. Lillo

We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detectio

Maher Me'meh, Ali Saraeb

We present a new proof of the transformation law of $\vartheta_1$ under the action of the generator of the full modular group $\Gamma$ using Siegel's method.

Neil K. Chada

The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems, by updating an ensemble of particles through a sample mean and covariance matrices. In this chapter we present a relatively recent topic which is the application of the EnKF to inverse problems, known

Massimo Tomellini, Riccardo Polini

An analytical mean field approach for describing the time evolution of film growth by seeding has been developed. The modeling deals with the generic case of anisotropic growth with different growth rates, respectively on -- and normal to -- the substrate plane. The finite size of the seeds is considered by including spatial correlation effects among seeds t

Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong, Seok-Bae Yun

With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs. Physics-informed neural networks (PINNs) have recently emerged and succeeded in various forward and inverse PDE problems thanks to their excellent properties, such as flexibility, mesh-free solution

K. Mahesh Krishna

Based on the solution of \textbf{Paulsen Problem} by Kwok, Lau, Lee, and Ramachandran [\textit{STOC'18-Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018}] and independently by Hamilton, and Moitra [\textit{Isr. J. Math., 2021}] we study Paulsen Problem and Projection Problem in the context of Hilbert C*-modules. We show that fo

Job Feldbrugge, Neil Turok

Many interesting physical theories have analytic classical actions. We show how Feynman's path integral may be defined non-perturbatively, for such theories, without a Wick rotation to imaginary time. We start by introducing a class of smooth regulators which render interference integrals absolutely convergent and thus unambiguous. The analyticity of the reg

Xinjie Liu

The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber

Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induc

Kristen Menou, Hong Tao Zhang

Differential settling and growth of dust grains impact the structure of the radiative envelopes of gaseous planets during formation. Sufficiently rapid dust growth can result in envelopes with substantially reduced opacities for radiation transport, thereby facilitating planet formation. We revisit the problem and establish that dust settling and grain growt

Mohammadamin Rakeei, Rosario Giustolisi, Gabriele Lenzini

We study coercion-resistance for online exams. We propose two properties, Anonymous Submission and Single-Blindness which, if hold, preserve the anonymity of the links between tests, test takers, and examiners even when the parties coerce one another into revealing secrets. The properties are relevant: not even Remark!, a secure exam protocol that satisfied

Rui Qian, Shuangrui Ding, Xian Liu, Dahua Lin

In this paper, we propose a novel learning scheme for self-supervised video representation learning. Motivated by how humans understand videos, we propose to first learn general visual concepts then attend to discriminative local areas for video understanding. Specifically, we utilize static frame and frame difference to help decouple static and dynamic conc

Koichi Hattori, Masaru Hongo, Xu-Guang Huang

Relativistic magnetohydrodynamics (RMHD) provides an extremely useful description of the low-energy long-wavelength phenomena in a variety of physical systems from quark-gluon plasma in heavy-ion collisions to matters in supernovas, compact stars, and early universe. We review the recent theoretical progresses of RMHD, such as a formulation of RMHD from the

Yue Zhang, Yajie Zou, Yuanchang Xie, Lei Chen

A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model and graph structure to identify the difference in dynamic interaction network

T. Morel, A. Blazère, T. Semaan, E. Gosset

We present a spectroscopic analysis of the GIRAFFE and UVES data collected by the Gaia-ESO survey for the young open cluster NGC 3293. Archive spectra from the same instruments obtained in the framework of the `VLT-FLAMES survey of massive stars' are also analysed. Atmospheric parameters, non-LTE chemical abundances for six elements, or variability informati

Chiara Guidolin, Jonathan Mac Intyre, Emmanuelle Rio, Antti Puisto

Foams are unstable jammed materials. They evolve over timescales comparable to their "time of use", which makes the study of their destabilisation mechanisms crucial for applications. In practice, many foams are made from viscoelastic fluids, which are observed to prolong their lifetimes. Despite their importance we lack understanding of the coarsening mecha

Zhanwei Yu, Tao Deng, Yi Zhao, Di Yuan

In multi-access edge computing (MEC) systems, there are multiple local cache servers caching contents to satisfy the users' requests, instead of letting the users download via the remote cloud server. In this paper, a multi-cell content scheduling problem (MCSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents an

Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt

Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to gro

Victor E. Ambrus, S. Schlichting, C. Werthmann

We employ an effective kinetic description to study the space-time dynamics and development of transverse flow of small and large collision systems. By combining analytical insights in the few interactions limit with numerical simulations at higher opacity, we are able to describe the development of transverse flow from very small to very large opacities, re

Mogeng Li, Detlef Lohse, Sander G. Huisman

We experimentally investigate the evaporation of very volatile liquid droplets (Novec 7000 Engineered Fluid) in a turbulent spray. Droplets with diameters of the order of a few micrometers are produced by a spray nozzle and then injected into a purpose-built enclosed dodecahedral chamber, where the ambient temperature and relative humidity in the chamber are

Mathew Owens, Luke Barnard, Benjamin Pope, Mike Lockwood

Severe geomagnetic storms appear to be ordered by the solar cycle in a number of ways. They occur more frequently close to solar maximum and declining phase, are more common in larger solar cycles and show different patterns of occurrence in odd- and even-numbered solar cycles. Our knowledge of the most extreme space weather events, however, comes from the s

Pablo Cobreros, Paul Egré, David Ripley, Robert van Rooij

This paper explores the relations between two logical approaches to vagueness: on the one hand the fuzzy approach defended by Smith (2008), and on the other the strict-tolerant approach defended by Cobreros, Egr\'e, Ripley and van Rooij (2012). Although the former approach uses continuum many values and the latter implicitly four, we show that both approache

Ankit Mishra, Sarika Jalan

Localization behaviours of Laplacian eigenvectors of complex networks provide understanding to various dynamical phenomena on the corresponding complex systems. We numerically investigate role of hyperedges in driving eigenvector localization of hypergraphs Laplacians. By defining a single parameter \gamma which measures the relative strengths of pair-wise a

Andreas Triantafyllopoulos, Markus Fendler, Anton Batliner, Maurice Gerczuk

Chronic obstructive pulmonary disease (COPD) causes lung inflammation and airflow blockage leading to a variety of respiratory symptoms; it is also a leading cause of death and affects millions of individuals around the world. Patients often require treatment and hospitalisation, while no cure is currently available. As COPD predominantly affects the respira

Yicong Li, Xiang Wang, Junbin Xiao, Tat-Seng Chua

Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual scenes in video and linguistic semantics in question. However, most leading VideoQA models work as black boxes, which make the visual-linguistic alignment behind the answering process

Marco Salucci, Lorenzo Poli, Paolo Rocca, Claudio Massagrande

An innovative millimeter-wave (mm-wave) microstrip edge-fed antenna (EFA) for 77 GHz automotive radars is proposed. The radiator contour is modeled with a sinusoidal spline-shaped (SS) profile characterized by a reduced number of geometrical descriptors, but still able to guarantee a high flexibility in the modeling for fulfilling challenging user-defined re

Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring te

ATLAS Collaboration

Exclusive production of dielectron pairs, $\gamma\gamma\rightarrow e^+e^-$, is studied using $\mathcal{L}_\mathrm{int}=1.72\; \mathrm{nb^{-1}}$ of data from ultraperipheral collisions of lead nuclei at $\sqrt{s_{_{\text{NN}}}} = 5.02$ TeV recorded by the ATLAS detector at the LHC. The process of interest proceeds via photon-photon interaction in the strong e

A. Astoul, A. J. Barker

In close exoplanetary systems, tidal interactions drive orbital and spin evolution of planets and stars over long timescales. Tidally-forced inertial waves (restored by the Coriolis acceleration) in the convective envelopes of low-mass stars and giant gaseous planets contribute greatly to the tidal dissipation when they are excited and subsequently damped (e

Karthik Prasad, Sayan Ghosh, Graham Cormode, Ilya Mironov

Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees. However, communication efficiency remains a major bottleneck when scaling federated learning to production environments, particularly due to bandwidth constraints du

Taras Banakh, Serhii Bardyla

Let $\mathcal C$ be a class of topological semigroups. A semigroup $X$ is called $absolutely$ $\mathcal C$-$closed$ if for any homomorphism $h:X\to Y$ to a topological semigroup $Y\in\mathcal C$, the image $h[X]$ is closed in $Y$. Let $\mathsf{T_{\!1}S}$, $\mathsf{T_{\!2}S}$, and $\mathsf{T_{\!z}S}$ be the classes of $T_1$, Hausdorff, and Tychonoff zero-dime

Taikei Fujii, Takahiko Nobukawa

We introduce a configuration of a $q$-difference equation and characterize the variants of the $q$-hypergeometric equation, which were defined by Hatano-Matsunawa-Sato-Takemura, by configurations. We show integral solutions and series solutions for the variants of the $q$-hypergeometric equation.

G. Holgado, S. Simón-Díaz, A. Herrero, R. H. Barbá

Stellar rotation is of key importance for the formation process, evolution, and final fate of massive stars. In this paper we review results from the study of the spin rate properties of a sample of more than 400 Galactic O-type stars surveyed by the IACOB and OWN projects. By combining vsini, Teff, and logg estimates (resulting from a detailed quantitative

A. Rettaroli, C. Barone, M. Borghesi, S. Capelli

The DARTWARS project has the goal of developing high-performing innovative travelling wave parametric amplifiers with high gain, large bandwidth, high saturation power, and nearly quantum-limited noise. The target frequency region for its applications is 5 - 10 GHz, with an expected noise temperature of about 600 mK. The development follows two different app

Iordan Ganev, Robin Walters

We develop a uniform theoretical approach towards the analysis of various neural network connectivity architectures by introducing the notion of a quiver neural network. Inspired by quiver representation theory in mathematics, this approach gives a compact way to capture elaborate data flows in complex network architectures. As an application, we use paramet

Dmitry Chernyak, Azat M. Gainutdinov, Hubert Saleur

We introduce new $U_q\mathfrak{sl}_2$-invariant boundary conditions for the open XXZ spin chain. For generic values of $q$ we couple the bulk Hamiltonian to an infinite-dimensional Verma module on one or both boundaries of the spin chain, and for $q=e^{\frac{i\pi}{p}}$ a $2p$-th root of unity $ - $ to its $p$-dimensional analogue. Both cases are parametrised