July 2022 arXiv papers — page 54

Seba Anna Varghese, Alireza Dehlaghi Ghadim, Ali Balador, Zahra Alimadadi

Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control

Andreas Bärtschi, Stephan Eidenbenz

We present short-depth circuits to deterministically prepare any Dicke state |Dn,k>, which is the equal-amplitude superposition of all n-qubit computational basis states with Hamming Weight k. Dicke states are an important class of entangled quantum states with a large variety of applications, and a long history of experimental creation in physical systems.

Taekyun Kim, Dae San Kim

Broder introduced the r-Stirling numbers of the first kind and of the second kind which enumerate restricted permutations and respectively restricted partitions, the restriction being that the first r elements must be in distinct cycles and respectively in distinct subsets. Kim-Kim-Lee-Park constructed the degenerate r-Stirling numbers of both kinds as degen

Hans Nikolaus Beck, Nayel Fabian Salem, Veronica Haber, Matthias Rauschenbach

Considering information as the basis of action, it may be of interest to examine the flow and acquisition of information between the actors in traffic. The central question is: Which signals does an automated driving system (which will be referred to as an automaton in the remainder of this paper) in traffic have to receive, decode or send in road traffic in

D. Camarena, V. Marra, Z. Sakr, S. Nesseris

The Copernican principle, the notion that we are not at a special location in the Universe, is one of the cornerstones of modern cosmology and its violation would invalidate the Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric, causing a major change in our understanding of the Universe. Thus, it is of fundamental importance to perform observational te

Hongye Yu, Yusheng Zhao, Tzu-Chieh Wei

Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than one hundred, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum syste

Clément Dallard, Fedor V. Fomin, Petr A. Golovach, Tuukka Korhonen

The independence number of a tree decomposition is the maximum of the independence numbers of the subgraphs induced by its bags. The tree-independence number of a graph is the minimum independence number of a tree decomposition of it. Several NP-hard graph problems, like maximum weight independent set, can be solved in time n^{O(k)} if the input n-vertex gra

A. Chacoma, M. N. Kuperman, O. V. Billoni

In this article, we study the dynamics of marking in football matches. To do this, we surveyed and analyzed a database containing the trajectories of players from both teams on the field of play during three professional games. We describe the dynamics through the construction of temporal bipartite networks of proximity. Based on the introduced concept of pr

James P. Long, Yumeng Yang, Kim-Anh Do

In cell line perturbation experiments, a collection of cells is perturbed with external agents (e.g. drugs) and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational (in silico) models which can predict cellular respo

Christopher K. Zeitler, Joseph C. Chapman, Eric Chitambar, Paul G. Kwiat

We experimentally investigate the properties of hyperentangled states displaying simultaneous entanglement in multiple degrees of freedom, and find that Bell tests beyond the standard Clauser, Horne, Shimony, Holt inequality can reveal a higher-dimensional nature in a device-independent way. Specifically, we show that hyperentangled states possess more than

Federico Cornalba, Tony Shardlow

The Regularised Inertial Dean-Kawasaki model (RIDK) -- introduced by the authors and J. Zimmer in earlier works -- is a nonlinear stochastic PDE capturing fluctuations around the mean-field limit for large-scale particle systems in both particle density and momentum density. We focus on the following two aspects. Firstly, we set up a Discontinuous Galerkin (

Xin Lai, Zhuotao Tian, Xiaogang Xu, Yingcong Chen

Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of the existing domain-invariant learning framework. (1) Being

Ale Jan Homburg, Charlene Kalle

We analyze the two-point motions of iterated function systems on the unit interval generated by expanding and contracting affine maps, where the expansion and contraction rates are determined by a pair $(M,N)$ of integers. This dynamics depends on the Lyapunov exponent. For a negative Lyapunov exponent we establish synchronization, meaning convergence of orb

Roberto Feola, Jessica Elisa Massetti

We consider a one-parameter family of beam equations with Hamiltonian non-linearity in one space dimension under periodic boundary conditions. In a unified functional framework we study the long time evolution of initial data in two categories of differentiability: (i) a subspace of Sobolev regularity, (ii) a subspace of infinitely many differentiable functi

Chris Sedgwick, Stephen Serjeant

We have explored a method for finding giant planets in the outer Solar System by detecting their thermal emission and proper motion between two far-infrared all-sky surveys separated by 23.4 years, taken with the InfraRed Astronomical Satellite (IRAS) and the AKARI Space Telescope. An upper distance limit of about 8,000 AU is given by both the sensitivities

Eric Bourreau, Gérard Fleury, Philippe Lacomme

This paper concerns quantum heuristics able to extend the domain of quantum computing, defining a promising way in the large number of well-known classical algorithms. Quantum approximate heuristics take advantage of alternation between a Hamiltonian defining the problem to solve and a mixing Hamiltonian. The adiabatic theorem initially defined in quantum ph

Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang

Generating sound effects that humans want is an important topic. However, there are few studies in this area for sound generation. In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variational Autoencoder (VQ-VAE), a decoder, a

Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse, Sarah E. Robertson

When individuals participating in a randomized trial differ with respect to the distribution of effect modifiers compared compared with the target population where the trial results will be used, treatment effect estimates from the trial may not directly apply to target population. Methods for extending -- generalizing or transporting -- causal inferences fr

Surajit Mandal, Sudhaker Upadhyay, Yerlan Myrzakulov, Gulmira Yergaliyeva

We discuss the shadow cast by the charged Reissner-Nordstr\"om (RN) AdS black hole. With the help of Killing equation and Hamilton-Jacobi equation, we calculate the geodesic equations for null particle. With the help of geodesics of null particle, we then determine the celestial coordinates ($\alpha$, $\beta$) and the shadow radius of the RN AdS black hole.

Peter Jüstel, Sebastian Röhrborn, Sven Eckert, Vladimir Galindo

We present results on the synchronization of the helicity in a liquid-metal Rayleigh-B\'enard (RB) experiment under the influence of a tide-like electromagnetic forcing with azimuthal wavenumber m=2. We show that for a critical forcing strength the typical Large Scale Circulation (LSC) in the cylindrical vessel of aspect ratio unity is entrained by the perio

Yihong Chen, Pushkar Mishra, Luca Franceschi, Pasquale Minervini

Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs). However, unlike GNNs, FMs struggle to incorporate node features and generalise to unseen nodes in inductive settings. Our work bridges the gap between FMs and GNNs by proposing ReFacto

Yu. M. Andreev, D. Banerjee, B. Banto-Oberhauser, J. Bernhard

A search for a new $Z'$ gauge boson associated with (un)broken B-L symmetry in the keV-GeV mass range is carried out for the first time using the missing-energy technique in the NA64 experiment at the CERN SPS. From the analysis of the data with 3.22e11 electrons on target collected during 2016-2021 runs no signal events were found. This allows to derive new

Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich

We introduce a method for instance proposal generation for 3D point clouds. Existing techniques typically directly regress proposals in a single feed-forward step, leading to inaccurate estimation. We show that this serves as a critical bottleneck, and propose a method based on iterative bilateral filtering with learned kernels. Following the spirit of bilat

Martha Constantinou, Haralambos Panagopoulos

In this paper we present an improved RI-type prescription appropriate for the non-perturbative renormalization of gauge invariant nonlocal operators. In this prescription, the non-perturbative vertex function is improved by subtracting unwanted finite lattice spacing ($a$) effects, calculated in lattice perturbation theory. The method is versatile and can be

Alexandra Kozyreva, Jakub Klencki, Alexei V. Filippenko, Petr Baklanov

The majority of Type II-plateau supernovae (SNe IIP) have light curves that are not compatible with the explosions of stars in a vacuum; instead, the light curves require the progenitors to be embedded in circumstellar matter (CSM). We report on the successful fitting of the well-observed SN IIP 2021yja as a core-collapse explosion of a massive star with an

Omar Otoniel Flores-Cortez, Ronny Adalberto Cortez, Veronica Rosa

Environmental pollution is a factor that represents a significant health risk. In El Salvador, The entity in charge of monitoring air quality is the ministry of Environment and Natural Resources, currently said ministry only has 3 stations for monitoring air quality throughout the territory of the country. The main objective of this work was the application

Yanzeng Zhang, Jun Li, Xian-Zhu Tang

A large class of Laboratory, Space, and Astrophysical plasmas is nearly collisionless. When a localized energy or particle sink, for example, in the form of a radiative cooling spot or a black hole, is introduced into such a plasma, it can trigger a plasma thermal collapse, also known as a thermal quench in tokamak fusion. Here we show that the electron ther

Laurie Letertre

Quantum nonseparability is a central feature of quantum mechanics, and raises important philosophical questions. Interestingly, a particular theoretical development of quantum mechanics, called the process matrix formalism (PMF), features another kind of nonseparability, called causal nonseparability. The PMF appeals to the notion of quantum process, which i

David Eppstein, Daniel Frishberg

We prove that the well-studied triangulation flip walk on a convex point set mixes in time O(n^3 log^3 n), the first progress since McShine and Tetali's O(n^5 log n) bound in 1997. In the process we give lower and upper bounds of respectively Omega(1/(sqrt n log n)) and O(1/sqrt n) -- asymptotically tight up to an O(log n) factor -- for the expansion of the

Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Peter Bjørn Jørgensen

Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale exploration. Machine Learning (ML) models have turned out to be excellent emulators of small molecule DFT calculations and could po

Elijah D. Andrews, Ivo R. Peters

A gas or vapor bubble collapsing in the vicinity of a rigid boundary displaces towards the boundary and produces a high-speed jet directed at the boundary. This behavior has been shown to be a function of the 'anisotropy' of the collapse, measured by a dimensionless representation of the Kelvin impulse known as the anisotropy parameter [Supponen et al., J. F

Rolf Nelson van Lieshout, Kevin Dalmeijer

Both evaluating the service quality of a public transport system and understanding how passengers choose between modes or routes is imperative for public transport operators, providers of competing mobility services and policy makers. However, the literature does not offer consensus on how either of these tasks should be performed, which can lead to inconsis

Bao-Min Gu, Fu-Wen Shu, Ke Yang, Yu-Peng Zhang

Primordial black holes (PBHs) could be formed if large perturbations are generated on small scales in inflation. We study a toy inflation model with a local minimum. The curvature perturbations are enhanced when the inflaton passes through the local minimum, with more efficient amplification rate than that of quasi-inflection point inflation, leading to the

Pasquale Bosso, Mitja Fridman, Giuseppe Gaetano Luciano

In this work, we consider the implications of a phenomenological model of quantum gravitational effects related to a minimal length, implemented via the Generalized Uncertainty Principle. Such effects are applied to the Bekenstein-Hawking entropy to derive a modified law of gravity through Verlinde's conjecture. Implications on galactic scales, and in partic

Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke, Zeynep Akata

Audio-visual generalised zero-shot learning for video classification requires understanding the relations between the audio and visual information in order to be able to recognise samples from novel, previously unseen classes at test time. The natural semantic and temporal alignment between audio and visual data in video data can be exploited to learn powerf

Zhaoyangfan Huang, Kun Hu, Xingjun Wang

In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine removal module. Firstly, the highlight

Franz Krause, Tobias Weller, Heiko Paulheim

Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way. In terms of graph-based domain applications, such as embeddings and graph neural networks, current research is increasingly taking into account the time-related evolution of the information encoded withi

Yawen Cui, Zitong Yu, Wei Peng, Li Liu

Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is built by mimicking the general class-incremental learning setting, while it is not totally appropriate due to the different data configurat

Paul W. Goldberg, Matthew J. Katzman

Lipschitz games, in which there is a limit $\lambda$ (the Lipschitz value of the game) on how much a player's payoffs may change when some other player deviates, were introduced about 10 years ago by Azrieli and Shmaya. They showed via the probabilistic method that $n$-player Lipschitz games with $m$ strategies per player have pure $\epsilon$-approximate Nas

Keita Imaizumi

We demonstrate how the Exact WKB analysis works in the study of the quasi-normal modes (QNMs). We apply the Exact WKB analysis to a massless scalar perturbation to the D3-brane metric as a concrete example. The Exact WKB analysis provides an exact condition for the QNMs. We numerically check our exact condition by using the Borel-Pad\'{e} approximation. We a

David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier

As learning machines increase their influence on decisions concerning human lives, analyzing their fairness properties becomes a subject of central importance. Yet, our best tools for measuring the fairness of learning systems are rigid fairness metrics encapsulated as mathematical one-liners, offer limited power to the stakeholders involved in the predictio

A. Hammad, Myeonghun Park, Raymundo Ramos, Pankaj Saha

We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods,

Xiaotian Guo, Andy D. Pimentel, Todor Stefanov

Deep Learning approaches based on Convolutional Neural Networks (CNNs) are extensively utilized and very successful in a wide range of application areas, including image classification and speech recognition. For the execution of trained CNNs, i.e. model inference, we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring

Guang-Yong Chen, Min Gan, Jing Chen, Long Chen

This paper presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinea

Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam, Chen Chen

Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift. If we could estimate the performance that a pre-trained model would achieve on data from a specific deployment setting, for example a certain clinic, we could judge whether the mo

Zhenqiang Ying, Deepti Ghadiyaram, Alan Bovik

Video conferencing, which includes both video and audio content, has contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic forced millions of people to work and learn from home. Global Internet traffic of video conferencing has dramatically increased Because of this, efficient and accurate video quality tools are needed to monitor a

Ryien Hosseini, Filippo Simini, Venkatram Vishwanath

As Graph Neural Networks (GNNs) increase in popularity for scientific machine learning, their training and inference efficiency is becoming increasingly critical. Additionally, the deep learning field as a whole is trending towards wider and deeper networks, and ever increasing data sizes, to the point where hard hardware bottlenecks are often encountered. E

Matthew D. Butler, Thomas D. Montenegro-Johnson

Thermo-responsive hydrogels are a promising material for creating controllable actuators for use in micro-scale devices, since they expand and contract significantly (absorbing or expelling fluid) in response to relatively small temperature changes. Understanding such systems can be difficult because of the spatially- and temporally-varying properties of the

Inhwan Bae, Jin-Hwi Park, Hae-Gon Jeon

Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particular set of individual actions to implicitly model group actions. In this paper, we present a novel architecture named GP-Graph which has collective group representations for effectiv

Bertrand Deroin, Julien Marché

We prove that the Fibonacci quantum representations $\rho_{g,n}:\rm{Mod}_{g,n}\to \rm{PU}(p,q)$ for $(g,n)\in\{(0,4),(0,5),(1,2),(1,3),(2,1)\}$ are holonomy representations of complex hyperbolic structures on some compactifications of the corresponding moduli spaces $\mathcal{M}_{g,n}$. As a corollary, the forgetful map between the corresponding compactifica

Bruno Gašperov, Zvonko Kostanjčar

The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte C

Bulat Nasrulin, Georgy Ishmaev, Johan Pouwelse

Decentralized reputation systems are emerging as promising mechanisms to enhance the effectiveness of token-based economies. Unlike traditional monetary incentives, these systems reward participants based on the actual value of their contributions to the network. However, the advantages and challenges associated with such systems remain largely unexplored. I

Jiajun Su, Chunyu Wang, Xiaoxuan Ma, Wenjun Zeng

While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked. In this work, we perform a systematic evaluation of the existing methods and find that they get notably larger errors when tested on different cameras, human poses and appearance. To address the problem,

K. Mukherjee, C. De Santi, S. You, K. Geens

Developing high quality GaN/dielectric interfaces is a fundamental step for manufacturing GaN vertical power transistors. In this paper, we quantitatively investigate the effect of planar etching treatment and trench formation on the performance of GaN-based MOS (metal oxide semiconductor) stacks. The results demonstrate that (i) blanket etching the GaN surf

Grzegorz Gabor, Krzysztof Rykaczewski

We derive conditions for the existence of fixed points of cone mappings without assuming scalability of functions. Monotonicity and scalability are often inseparable in the literature in the context of searching for fixed points of interference mappings. In applications, such mappings are approximated by non-negative neural networks. It turns out, however, t

Yvain Bruned, Usama Nadeem

In this work, we show a convergence result for the discrete formulation of the generalised KPZ equation $\partial_t u = (\Delta u) + g(u)(\nabla u)^2 + k(\nabla u) + h(u) + f(u)\xi_t(x)$, where the $\xi$ is a real-valued random field, $\Delta$ is the discrete Laplacian, and $\nabla$ is a discrete gradient, without fixing the spatial dimension. Our convergenc

Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen

Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To achieve this, DG is commonly formulated as an average- or worst-case problem over the set of possible domains. However, predictors that perform well on average lack robustness while p

Jinyong Hahn, David W. Hughes, Guido Kuersteiner, Whitney K. Newey

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimat

Wenhao Liang, Tao Hou, Junjie Zeng, Zheng Liu

Recently, the magnetic domain walls have been experimentally observed in antiferromagnetic topological insulators MnBi$_2$Te$_4$, where we find that the topological zero-line modes (ZLMs) appear along the domain walls. Here, we theoretically demonstrate that these ZLMs are layer-dependent in MnBi$_2$Te$_4$ multilayers. For domain walls with out-of-plane ferr

Felix Paries, Oliver Boidol, Georg von Freymann, Daniel Molter

Terahertz time-domain spectroscopy systems based on resonator-internal repetition-rate modulation, such as SLAPCOPS [12] and ECOPS [11], rely on electronic phase detectors which are typically prone to exhibit both a non-negligible random and systematic timing error. This limits the quality of the recorded information significantly. Here, we present the resul

Judith Beestermöller, Costas Busch, Roger Wattenhofer

A distributed directory is an overlay data structure on a graph $G$ that helps to access a shared token $t$. The directory supports three operations: publish, to announce the token, lookup, to read the contents of the token, and move, to get exclusive update access to the token. The directory is built upon a hierarchical partition of the graph using either w

Subham Dutta, Pralay Kumar Karmakar

The evolutionary existence of plasma fireballs is a generic phenomenon realizable in diversified physical plasma-dominated circumstances starting from the laboratory to the astrocosmic scales of space and time. A fair understanding of such fireballs and associated instabilities is indeed needed to enrich astroplasmic communities from various perspectives of

Matteo Ravasi

Seismic data processing heavily relies on the solution of physics-driven inverse problems. In the presence of unfavourable data acquisition conditions (e.g., regular or irregular coarse sampling of sources and/or receivers), the underlying inverse problem becomes very ill-posed and prior information is required to obtain a satisfactory solution. Sparsity-pro

Gabriele Fici, Svetlana Puzynina

We survey known results and open problems in abelian combinatorics on words. Abelian combinatorics on words is the extension to the commutative setting of the classical theory of combinatorics on words. The extension is based on \emph{abelian equivalence}, which is the equivalence relation defined in the set of words by having the same Parikh vector, that is

Liu Yang, Yinzhi Lu, Simon X. Yang, Tan Guo

Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is

Xize Wang, Greg Lindsey, Jessica E. Schoner, Andrew Harrison

The purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in Minneapolis - St. Paul Metropolitan Area in Minnesota. We obtained the number of trips to and from each of the 116 bike share stations operating in 2011 from Nice Ride Minnesota. Data for independent variables included in models com

Xin Yu, Peng Dai, Wenbo Li, Lan Ma

With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i.e., ultra-high-definition) images. However, for image demoireing, a challenging task in low-level vision, existing works are generally carried out on low-resolution or synthetic images. Hence, the effectiveness of these methods on

Oskar Natan, Jun Miura

In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models that handle these tasks separately, DeepIPC innovatively combines a perception module, which processes RGBD images for semantic segmentation and generates bird's eye view (BEV) mappi

Luojie Huang, Yikang Liu, Li Chen, Eric Z. Chen

In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose, the resulting images are often noisy. To check the correct placement of these devices, typically multiple motion-compensated

Ornella Mattei, Graeme W. Milton, Mihai Putinar

A body $\Theta$ containing two phases, which may form a periodic composite with microstructure much smaller that the body, or which may have structure on a length scale comparable to the body, is subjected to slowly time varying boundary conditions that would produce an approximate uniform field in $\Theta$ were it filled with homogeneous material. Here slow

Ganggang Xu, Jingfei Zhang, Yehua Li, Yongtao Guan

Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for mark-point dependence when estimating the mean and covariance functions of the mark process, given independent replicates of the marked point process. We assume that the mark process

Simon Daniel Reiß, Peter van Loock

This work examines secret key rates of key distribution based on quantum repeaters in a broad parameter space of the communication distance and coherence time of the quantum memories. As the first step in this task, a Markov decision process modeling the distribution of entangled quantum states via quantum repeaters is developed. Based on this model, a simul

Robert Burklund, Tomer M. Schlank, Allen Yuan

We show that Lubin--Tate theories attached to algebraically closed fields are characterized among $T(n)$-local $\mathbb{E}_{\infty}$-rings as those that satisfy an analogue of Hilbert's Nullstellensatz. Furthermore, we show that for every $T(n)$-local $\mathbb{E}_{\infty}$-ring $R$, the collection of $\mathbb{E}_\infty$-ring maps from $R$ to such Lubin-Tate

Tommaso Crepax, Jan Tobias Muehlberg

Despite the increasing awareness from academia, civil society and media to the issue of child manipulation online, the current EU regulatory system fails at providing sufficient levels of protection. Given the universality of the issue, there is a need to combine and further these scattered efforts into a unitary, multidisciplinary theory of digital manipula

Nikolaos Gkalelis, Dimitrios Daskalakis, Vasileios Mezaris

In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and frame features, and a head network to process these features for the task of event recognition and explanation in video, is proposed. The ViGAT head consists of graph attention networ

Leiyang Xu, Qiang Wang, Xiaotian Lin, Lin Yuan

Temporal action segmentation (TAS) aims to classify and locate actions in the long untrimmed action sequence. With the success of deep learning, many deep models for action segmentation have emerged. However, few-shot TAS is still a challenging problem. This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentat

Aditi Mandal, Sylvain Tricot, Rakesh Choubisa, Didier Sebilleau

We present here the MsSpec Dielectric Function module (MsSpec-DFM), which generates dielectric functions in an electron gas or a liquid, either isolated or embedded into an environment. In addition to standard models such as the plasmon pole and the RPA, this module also provides more involved methods incorporating local field corrections (in order to accoun

Jonas Odermatt, Krzysztof Barczynski, Louise K. Harra, Conrad Schwanitz

Context. Solar active regions are known to have jets. These jets are associated with heating and the release of particles into the solar wind. Aim. Our aim is to understand the spatial distribution of coronal jets within active regions to understand if there is a preferential location for them to occur. Methods. We analysed five active regions using Solar Dy

Gerhard Zauner

There are several approaches to define an eigenvector decomposition of the finite Fourier Transform, which is in some sense unique, and at best resembles the eigenstates of the quantum harmonic oscillator. A solution given by Balian and Itzykson in 1986 for prime dimensions d = 3 (mod 4) is revisited. It is shown, that by applying the Weyl-Heisenberg matrice

Ze-Chun Hu, Han Zhao, Qian-Qian Zhou

The Gaussian product inequality (GPI) conjecture is one of the most famous inequalities associated with Gaussian distributions and has attracted a lot of concerns. In this note, we investigate the quantitative versions of the two-dimensional Gaussian product inequalities. For any centered non-degenerate two-dimensional Gaussian random vector $(X_1, X_2)$ wit

Luke Boegner, Manbir Gulati, Garrett Vanhoy, Phillip Vallance

Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and exp

M. Wezgowiec, R. Beck, M. Hanasz, M. Soida

In several spiral galaxies that are observed face-on, large-scale ordered magnetic fields (the so-called magnetic arms) were found. One of the explanations was the action of the magnetic reconnection, which leads to a higher ordering of the magnetic fields. Because it simultaneously converts the energy of the magnetic fields into thermal energy of the surrou

Nir Sochen

Calculus of Variation combined with Differential Geometry as tools of modelling and solving problems in image processing and computer vision were introduced in the late 80's and the 90s of the 20th century. The beginning of an extensive work in these directions was marked by works such as Geodesic Active Contours (GAC), the Beltrami framework, level set meth

Frank Emrich, Jan Stolarek, James Cheney, Sam Lindley

FreezeML is a new approach to first-class polymorphic type inference that employs term annotations to control when and how polymorphic types are instantiated and generalised. It conservatively extends Hindley-Milner type inference and was first presented as an extension to Algorithm W. More modern type inference techniques such as HM(X) and OutsideIn($X$) em

Qi Xin, Zhi-Gang Wang, Xiao-Song Yang

In this work, we study the $D\bar{D}$, $DD$, $D\bar{D}_s$, $DD_s$, $D_s\bar{D}_s$ and $D_sD_s$ tetraquark molecular states with the $J^{PC}=0^{++}$ via the QCD sum rules. The prediction $M_{D_s\bar {D}_s} = 3.98\pm0.10\, \rm{GeV}$ is in very good agreement with the experimental value $M_{X(3960)} = 3956 \pm 5\pm 10 \,\rm{MeV}$ from the LHCb collaboration and

Naoki Akai

This paper presents a simultaneous localization and map-assisted environment recognition (SLAMER) method. Mobile robots usually have an environment map and environment information can be assigned to the map. Important information for mobile robots such as no entry zone can be predicted if localization has succeeded since relative pose of them can be known. H

William Howard, R. Michael Buehrer

Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks. Rather than model each radar node as an independent learner, entirely unable to swap information with other nodes in a network, in this work we construct a "central coordinator" to facilitate the

Ying-Hui Ge, Xiao-Hai Liu, Hong-Wei Ke

We investigate the process $B\to \bar{D}D_s \pi$ via several rescattering processes. It is shown that the triangle singularity (TS) peak around the $D^*K^*$ threshold generated from the $\chi_{c1}K^* D^*$ loop is relatively narrow, which may simulate the resonance-like structure $T_{c\bar{s}}(2900)$ recently observed by LHCb in the $D_s\pi$ spectrum. However

Tim Rädsch, Annika Reinke, Vivienn Weru, Minu D. Tizabi

Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their optimization remains largely unexplored. Here, we present the first systematic study of labeling instructions and their impact on annotation quality in the field. Through comprehensi

A. Kusina, Z. Was

With the increasing precision requirements and growing spectrum of applications of Monte Carlo simulations the evaluation of different components of such simulations and their systematic ambiguities become of utmost interest. In the following, we will address the question of systematic errors for Photos Monte Carlo for simulation of bremsstrahlung correction

Beren Millidge, Christopher L Buckley

Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian filtering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors. An alternative, but less explored, model-free RL algorithm is the successor representation, which expresses the value function in terms of a succe

Jaime Ruiz-Zapatero, David Alonso, Pedro G. Ferreira, Carlos Garcia-Garcia

In the context of modified gravity, at linear level, the growth of structure in the Universe will be affected by modifications to the Poisson equation and by the background expansion rate of the Universe. It has been shown that these two effects lead to a degeneracy which must be properly accounted for if one is to place reliable constraints on new forces on

Alex James, Alwen Tiu, Nisansala Yatapanage

We present an investigation into the design and implementation of a parallel model checker for security protocol verification that is based on a symbolic model of the adversary, where instantiations of concrete terms and messages are avoided until needed to resolve a particular assertion. We propose to build on this naturally lazy approach to parallelise thi

X. C. Zheng, H. J. A. Röttgering, A. van der Wel, K. Duncan

Links between the properties of radio-loud active galactic nuclei (RLAGNs) and the morphology of their hosts may provide important clues for our understanding of how RLAGNs are triggered. In this work, focusing on passive galaxies, we study the shape of the hosts of RLAGNs selected from the Karl G. Jansky Very Large Array Cosmic Evolution Survey (VLA-COSMOS)

Jean Cazalis

In this article, we show that, in the dissociation regime and under a non-degeneracy assumption, the reduced Hartree-Fock theory of graphene presents Dirac points at the vertices of the first Brillouin zone and that the Fermi level is exactly at the coincidence point of the cones. For this purpose, we first consider a general Schr\"odinger operator $H=-\Delt

Giulio Falcioni

In QCD the anomalous dimensions of gauge invariant operators of twist 2 play a key role, because they control the scale dependence of the parton distribution functions. Notably, the flavour singlet operators, such as those associated to the gluon distribution, mix under renormalisation with a set of unphysical operators, also known as aliens. Missing this ef

Jeongseop Han, Youngjo Lee, Jae Kwang Kim

Maximum likelihood (ML) estimation is widely used in statistics. The h-likelihood has been proposed as an extension of Fisher's likelihood to statistical models including unobserved latent variables of recent interest. Its advantage is that the joint maximization gives ML estimators (MLEs) of both fixed and random parameters with their standard error estimat

Cigdem Beyan, Alessandro Vinciarelli, Alessio Del Bue

Automated co-located human-human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, personality traits), social roles/relations, and interaction dynami

Rudolf A. Römer

Anderson localization provides a challenge to numerical approaches due to the inherent randomness, and hence absence of simple symmetries, in its discrete Hamiltonian representation. Numerous algorithmic approaches have been developed or adopted from other fields and have been collected in this encyclopedia entry. In the discussions below, the emphasis is on

Nathaniel Robinson, Perez Ogayo, Swetha Gangu, David R. Mortensen

Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data. For many such languages, audio and text are available separately, but not audio with transcriptions. Using text, speech can be synthetically produced via text-to-speech (TTS) systems. However, many low-resource languages