August 2022 arXiv papers — page 38

Shrey Jain, Leon Erichsen, Glen Weyl

In this article, we explore the tension between abstraction and composability in web3 today, specifically within identity solutions, and argue that the current standard DID v1.0 is sufficiently under specified, allowing for many methods and instantiations, including blockchain based certificates. We view experiments today in web3 identity as additive and com

Shōta Inoue

We discuss moments of the Riemann zeta-function in this paper. The purpose of this paper is to give an upper bound of exponential moments of the logarithm of the Riemann zeta-function twisted by arguments. Our results contain an improvement of Najnudel result for exponential moments of the argument of the Riemann zeta-function and an unconditional upper boun

Katarzyna Bolonek-Lason, Joanna Gonera, Piotr Kosinski

It is shown that by an appropriate canonical transformation Kepler dynamics can be put in the form which allows to exhibit the structure of the symmetry transformations related to the superintegrability. They appear to fit nicely into general scheme of nonlinear realizations. In new coordiantes the Kepler dynamics results from dimensional reduction of that d

Kalana Abeywardena, Shechem Sumanthiran, Sanoojan Baliah, Nadarasar Bahavan

Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation. While part localization has shown significant success, it uses either na{\i}ve position-based partitions or static feature templates. These, however, hypothesize the p

Adrian Wiltz, Fei Chen, Dimos V. Dimarogonas

In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints. In the proposed control scheme, neighbor-to-neighbor communication suffices and all subsystems solve their local optimization problem in parallel. The approa

Laura Mack, Annette Rudolph, Peter Névir

Atmospheric fronts are associated with precipitation and strong diabatic processes. Therefore, detecting fronts objectively from reanalyses is a prerequisite for the long-term study of their weather impacts. For this purpose, several algorithms exist, e.g., based on the thermic front parameter (TFP) or the F diagnostic that combines relative vorticity and ho

C. Martin Gaskell, Frances C. Anderson, Sufia Á. Birmingham, Samhita Ghosh

We use seven different methods to estimate broad-line and continuum reddenings of NGC 5548. We investigate two possible reddening curves considered for active galactic nuclei (AGNs): the mean AGN reddening curve of Gaskell & Benker (2007) which is relatively flat in the ultraviolet, and a curve that rises strongly into the ultraviolet like a Small Magellanic

Mohammadreza Amirian, Friedhelm Schwenker, Thilo Stadelmann

The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image such that misclassification is evoked while still looking normal to a human observer -- they are thus not easily detectable. In a different context, backpropagated activations of CNN

Yuwei Sun, Hideya Ochiai

Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a robust decentralized learning framework for various client models where centralized data collection is refrained due to confidentiality concerns. This work aims to tackle privacy-pres

Cheng Han, Qichao Zhao, Shuyi Zhang, Yinzi Chen

Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper

Zihao Yuan, Martin Spindler

The major contributions of this paper lie in two aspects. Firstly, we focus on deriving Bernstein-type inequalities for both geometric and algebraic irregularly-spaced NED random fields, which contain time series as special case. Furthermore, by introducing the idea of "effective dimension" to the index set of random field, our results reflect that the sharp

N. Losacco

Baryon density and magnetic field effect on chaos for the holographic dual of a $Q \bar Q$ system at finite temperature is studied. A string in an AdS Reissner Nordstrom background, and in a metric with magnetic field near the black hole horizon is considered and small time-dependent perturbations of the static configurations are investigated. The proximity

Igor Baskov

We study the dg-algebra $\Omega ^\bullet_{A|\mathbb{R}}$ of algebraic de Rham forms of a real soft function algebra $A$, i.e., the algebra of global sections of a soft subsheaf of $C_X$, the sheaf of continuous functions on a space $X$. We obtain a canonical splitting $\mathrm H ^n (\Omega ^\bullet_{A|\mathbb{R}}) \cong \mathrm H ^n (X,\mathbb{R})\oplus V$,

Ricardo Simão, Lucas Wardil

Some political strategies to win elections over the last years were based heavily on fomenting general distrust in information institutions and favoring distrustful sources. The misinformation pandemic has the straightforward consequence that people do not believe any information unless it is compatible with their own beliefs. We present a simple model to st

Sai Krishna Chada, Daniel Görges, Achim Ebert, Roman Teutsch

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), name

Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Giorgio Fabbro, Stefan Uhlich

Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e.g., a mixing engineer). The automation of music production tasks has become an emerging field in recent years, where rule-based methods and machine learning approaches have been e

Yapeng Zhang, Ignas A. G. Snellen, Aurélien Wyttenbach, Louise D. Nielsen

Ultra-hot Jupiters (UHJs), rendering the hottest planetary atmospheres, offer great opportunities of detailed characterisation with high-resolution spectroscopy. MASCARA-4 b is a recently discovered close-in gas giant belonging to this category. In order to refine system and planet parameters, we carried out radial velocity measurements and transit photometr

Jiaqing Kou, Oscar A. Marino, Esteban Ferrer

Jump penalty stabilisation techniques have been recently proposed for continuous and discontinuous high order Galerkin schemes [1,2,3]. The stabilisation relies on the gradient or solution discontinuity at element interfaces to incorporate localised numerical diffusion in the numerical scheme. This diffusion acts as an implicit subgrid model and stablises un

Galit Ashkenazi-Golan, János Flesch, Eilon Solan

It was shown in Flesch and Solan (2022) with a rather involved proof that all two-player stochastic games with finite state and action spaces and shift-invariant payoffs admit an $\epsilon$-equilibrium, for every $\epsilon>0$. Their proof also holds for two-player absorbing games with tail-measurable payoffs. In this paper we provide a simpler proof for the

Manel Farhat, Houda Chaabouni-Chouayakh, Achraf Ben-Hamadou

Feature matching and finding correspondences between endoscopic images is a key step in many clinical applications such as patient follow-up and generation of panoramic image from clinical sequences for fast anomalies localization. Nonetheless, due to the high texture variability present in endoscopic images, the development of robust and accurate feature ma

Peter Opsomer, Daan Huybrechs

Gaussian quadrature rules are a classical tool for the numerical approximation of integrals with smooth integrands and positive weight functions. We derive and expicitly list asymptotic expressions for the points and weights of Gaussian quadrature rules for three general classes of positive weight functions: analytic functions on a bounded interval with alge

C. Q. Su, Y. H Gao, Y Zhou, Y. Q Sun

Light field microscopy is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU accelerated I

Hiroyuki Yamase, Matias Bejas, Andres Greco

Metallic systems exhibit plasmons as elementary charge excitations. This fundamental concept was reinforced also in high-temperature cuprate superconductors recently, although cuprates are not only layered systems but also strongly correlated electron systems. Here, we study how such ubiquitous plasmons leave their marks on the electron dispersion in cuprate

Andreas Crivellin, Fiona Kirk, Marco Schreck

We address the question whether a violation of Lorentz symmetry can explain the tension between the measurement and the Standard-Model prediction of the anomalous magnetic moment of the muon ($(g-2)_{\mu}$) and whether it can significantly impact the one of the electron ($(g-2)_{e}$). While anisotropic Lorentz-violating effects are, in general, expected to p

Ion I. Dinca

We provide a generalization of Bianchi's Hazzidakis transformation from $2$-dimensional quadrics to generic higher dimensional quadrics.

David S. Robertson, James M. S. Wason, Aaditya Ramdas

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time.

Aram Dermenjian

In this paper, we study two different subposets of the $\nu$-Tamari lattice: one in which all elements have maximal in-degree and one in which all elements have maximal out-degree. The maximal in-degree and maximal out-degree of a $\nu$-Dyck path turns out to be the size of the maximal staircase shape path that fits weakly above $\nu$. For $m$-Dyck paths of

Sahel Ashhab, Olga A. Ilinskaya, Sergey N. Shevchenko

In the standard Landau-Zener-St\"uckelberg-Majorana (LZSM) problem, the bias sweep rate and gap are both time independent and fully characterize the LZSM problem. We consider the nonlinear LZSM problem, in which at least one of the two characteristic parameters varies as the system traverses the avoided crossing region. This situation results in what could b

ATLAS Collaboration

A search for flavour-changing neutral current (FCNC) $tqH$ interactions involving a top quark, another up-type quark ($q=u$, $c$), and a Standard Model (SM) Higgs boson decaying into a $\tau$-lepton pair ($H\rightarrow \tau^+\tau^-$) is presented. The search is based on a dataset of $pp$ collisions at $\sqrt{s}=13$ TeV that corresponds to an integrated lumin

Carla Binucci, Walter Didimo, Maurizio Patrignani

The problem of orienting the edges of an undirected graph such that the resulting digraph is acyclic and has a single source s and a single sink t has a long tradition in graph theory and is central to many graph drawing algorithms. Such an orientation is called an st-orientation. We address the problem of computing st-orientations of undirected graphs with

Xia Zhang, Jan Paseka, Jianjun Feng, Yudong Chen

In this paper, we show that marked quantales have a reflection into quantales. To obtain the reflection we construct free quantales over marked quantales using appropriate lower sets. A marked quantale is a posemigroup in which certain admissible subsets are required to have joins, and multiplication distributes over these. Sometimes are the admissible subse

Dorin Bucur, Eloi Martinet, Mickaël Nahon

We prove that the second nontrivial Neumann eigenvalue of the Laplace-Beltrami operator on the unit sphere $\mathbb{S}^n \subseteq \mathbb{R}^{n+1}$ is maximized by the union of two disjoint, equal, geodesic balls among all subsets of $\mathbb{S}^n$ of prescribed volume. In fact, the result holds in a stronger version, involving the harmonic mean of the eige

Davide Grassano, Luca Binci, Nicola Marzari

Topological materials have been a main focus of studies in the past decade due to their protected properties that can be exploited for the fabrication of new devices. Among them, Weyl semimetals are a class of topological semimetals with non-trivial linear band crossing close to the Fermi level. The existence of such crossings requires the breaking of either

Zijian Jiang, Ziming Chen, Tianqi Hou, Haiping Huang

Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain. Here, we integrate the experimental observation of wide synaptic integration window into our model of sequence retrieval in the continuous time dynamics. The model with non-normal neuron-interactions is theoretically studied by deri

Susumu Yada, Kazem Bazesefidpar, Outi Tammisola, Gustav Amberg

Using experiments and numerical simulations, we investigate the spontaneous spreading of droplets of aqueous glycerol (Newtonian) and aqueous polymer (shear-thinning) solutions on smooth surfaces. We find that in the first millisecond the spreading of the shear-thinning solutions is identical to the spreading of water, regardless of the polymer concentration

Mohammad Pouranvari

This study investigates the entanglement properties of disordered free fermion systems undergoing an Anderson phase transition from a delocalized to a localized phase. The entanglement entropy is employed to quantify the degree of entanglement, with the system randomly divided into two subsystems. To explore this phenomenon, one-dimensional tight-binding fer

Jacqueline Wastensteiner, Tobias M. Weiss, Felix Haag, Konstantin Hopf

Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to

Johanna Frischauf, Martin Pfurner, Daniel F. Scharler, Hans-Peter Schröcker

We present a closed-loop 8R mechanism with two degrees of freedom whose motion exhibits curious properties. In any point of a two-dimensional component of its configuration variety it is possible to fix every second joint while retaining one degree of freedom. This shows that the even and the odd axes, respectively, always form a Bennett mechanism. In this m

Ernesto Rodrigues, Iarley P. Lobo

We discuss the conditions for mapping the geometric description of the kinematics of particles that probe a given Hamiltonian in phase space to a description in terms of Finsler geometry (and vice-versa).

Ángel Martín, Daniel Mejías, Zaloa Fernández, Roberto Viola

Vehicles shipping sensors for onboard systems are gaining connectivity. This enables information sharing to realize a more comprehensive understanding of the environment. However, peer communication through public cellular networks brings multiple networking hurdles to address, needing in-network systems to relay communications and connect parties that canno

Felix Haag, Konstantin Hopf, Pedro Menelau Vasconcelos, Thorsten Staake

The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often hide the reasoning behind the prediction from their users, which can lead to lower adoption and lack of insight. Motivated

Muhammad Hamza, Muhammad Azeem Akbar

The smart healthcare system has gained significant attention for the improvement of the customary healthcare system. The system is comprised of several key stakeholders that make the whole ecosystem successful. However, these stakeholders offer considerable challenges that need much research to address for making the system acceptable and reliable. Furthermo

Ashish Cherukuri

This paper focuses on a class of variational inequalities (VIs), where the map defining the VI is given by the component-wise conditional value-at-risk (CVaR) of a random function. We focus on solving the VI using sample average approximation, where solutions of the VI are estimated with solutions of a sample average VI that uses empirical estimates of the C

Paul Primus, Gerhard Widmer

Standard machine learning models for tagging and classifying acoustic signals cannot handle classes that were not seen during training. Zero-Shot (ZS) learning overcomes this restriction by predicting classes based on adaptable class descriptions. This study sets out to investigate the effectiveness of self-attention-based audio embedding architectures for Z

Mohammadreza Amirian, Friedhelm Schwenker

Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of

Seyed Mohammadhossein Tabatabaee, Anne Bouillard, Jean-Yves Le Boudec

In feed-forward time-sensitive networks with Deficit Round-Robin (DRR), worst-case delay bounds were obtained by combining Total Flow Analysis (TFA) with the strict service curve characterization of DRR by Tabatabaee et al. The latter is the best-known single server analysis of DRR, however the former is dominated by Polynomial-size Linear Programming (PLP),

Matteo M. Defranchis, Jan Kieseler, Katerina Lipka, Javier Mazzitelli

The running of the top quark mass ($m_\mathrm{t}$) is probed at the next-to-next-to-leading order in quantum chromodynamics for the first time. The result is obtained by comparing calculations in the modified minimal subtraction ($\mathrm{\overline{MS}}$) renormalisation scheme to the CMS result on differential measurement of the top quark-antiquark ($\mathr

Patricia Vitoria, Stamatios Georgoulis, Stepan Tulyakov, Alfredo Bochicchio

Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself. Complementary information from auxiliary sensors such event sensors are being explored to address these limitations. The latter can record changes in a logarithmic intensity asynchronously, called events, with high temporal reso

Emma Joy Dodson, Nicholas Werren, Yossi Paltiel, Erik Gauger

Phycobilisomes (PBS) are massive structures that absorb and transfer light energy to photochemical reaction centers. Among the range of light harvesting systems, PBS are considered to be excellent solutions for absorption cross-sections but relatively inefficient energy transferring systems. This is due to the combination of a large number of chromophores wi

K. Altwegg, M. Combi, S. A. Fuselier, N. Hänni

Ammonium hydrosulphide has long since been postulated to exist at least in certain layers of the giant planets. Its radiation products may be the reason for the red colour seen on Jupiter. Several ammonium salts, the products of NH3 and an acid, have previously been detected at comet 67P/Churyumov-Gerasimenko. The acid H2S is the fifth most abundant molecule

Felix Liu, Måns I. Andersson, Albin Fredriksson, Stefano Markidis

The modern workflow for radiation therapy treatment planning involves mathematical optimization to determine optimal treatment machine parameters for each patient case. The optimization problems can be computationally expensive, requiring iterative optimization algorithms to solve. In this work, we investigate a method for distributing the calculation of obj

Xiaoyang Wang, Yinchenguang Lyu, Changyu Yao, Xiao Yuan

We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then, since the spreading process can be modeled as a classical Markovian process, we show that the spreading process can be simulated using the evolution o

Dimitrios Kelesis, Spyros Basilakos, Vicky Papadopoulou Lesta, Dimitris Fotakis

In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct spatial methods which we suitably adjust for (i) detecting the topology of the cosmic web and (ii) categorizing various co

Mattia Bianchi, Sergio Grammatico, Jorge Cortés

We consider the design of state feedback control laws for both the switching signal and the continuous input of an unknown switched linear system, given past noisy input-state trajectories measurements. Based on Lyapunov-Metzler inequalities, we derive data-dependent bilinear programs whose solution directly returns a provably stabilizing controller and ensu

Yang Chen, Ziyan Luo

This article aims to seek a selection and estimation procedure for a class of tensor regression problems with multivariate covariates and matrix responses, which can provide theoretical guarantees for model selection in finite samples. Considering the frontal slice sparsity and low-rankness inherited in the coefficient tensor, we formulate the regression pro

P. Milde, M. Langenhorst, H. Hölscher, J. Rottmann-Matthes

The fundamental sensitivity limit of atomic force microscopy is strongly correlated to the thermal noise of the cantilever oscillation. A method to suppress this unwanted noise is to reduce the bandwidth of the measurement, but this approach is limited by the speed of the measurement and the width of the cantilever resonance, commonly defined through the qua

Theodore Papamarkou

In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth

Doris Antensteiner, Silvia Bucci, Arushi Goel, Marah Halawa

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana. It provides a voice to a minority (female) group in the computer vision community and focuses on increasing the visibility of these researchers, both in academia and industry. WiCV believes that such an e

Áulide Martínez-Tapia, Samuel Corona-Aquino, Chenglong You, Rui-Bo Jin

Recent investigations suggest that the use of non-classical states of light, such as entangled photon pairs, may open new and exciting avenues in experimental two-photon absorption spectroscopy. Despite several experimental studies of entangled two-photon absorption (eTPA), there is still a heated debate on whether eTPA has truly been observed. This interest

L. N. Carenza, G. Gonnella, G. Negro

In the last years self-motile droplets attracted the attention of scientists from different fields ranging from applied biology to theoretical physics, because of their promising technological applications and important biological implications. In this Chapter we review the state of the art of the research on active droplets with a particular focus on theore

Sahil Manchanda, Sayan Ranu

Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has gained attention due to its success in reducing the running time for solving MIPs. However, existing learning-to-branch methods assume that the entire training data is available i

Zhiyuan Yao, Yoann Desmouceaux, Juan-Antonio Cordero-Fuertes, Mark Townsley

Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, inf

Yoji Tomita, Riku Togashi, Daisuke Moriwaki

Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender systems (RRSs) and matching theory. We present our ongoing project to deploy a matching theory-based recommende

J. C. Bellizotti Souza, N. P. Vizarim, C. J. O. Reichhardt, C. Reichhardt

Using a particle based model, we investigate the skyrmion dynamical behavior in a channel where the upper wall contains divots of one depth and the lower wall contains divots of a different depth. Under an applied driving force, skyrmions in the channels move with a finite skyrmion Hall angle that deflects them toward the upper wall for $-x$ direction drivin

Liming Zhao, Lin-chun Wan, Ming-Xing Luo

Probabilistic graphical models play a crucial role in machine learning and have wide applications in various fields. One pivotal subset is undirected graphical models, also known as Markov random fields. In this work, we investigate the structure learning methods of Markov random fields on quantum computers. We propose a quantum algorithm for structure learn

Julian Rosen, Yoshihiro Takeyama, Koji Tasaka, Shuji Yamamoto

In this paper, we develop an explicit method to express finite algebraic numbers (in particular, certain idempotents among them) in terms of linear recurrent sequences, and give applications to the characterization of the splitting primes in a given finite Galois extension over the rational field.

Samuel Palmer, Konstantinos Karagiannis, Adam Florence, Asier Rodriguez

In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackle non-convex portfolio optimization problems using quantum annealing that would otherwise be challenging for classical algorithms. Being able to use cardinality constraints for por

Ikki Fukuda, Hiroyuki Hirayama

We consider the Cauchy problem for the generalized Kadomtsev--Petviashvili--Burgers equation in 2D. This is one of the nonlinear dispersive-dissipative type equations, which has a spatial anisotropic dissipative term. Under some suitable regularity assumptions on the initial data $u_{0}$, especially the condition $\partial_{x}^{-1}u_{0} \in L^{1}(\mathbb{R}^

Anand Louis, Meghana Nasre, Prajakta Nimbhorkar, Govind S. Sankar

Matching problems with group-fairness constraints and diversity constraints have numerous applications such as in allocation problems, committee selection, school choice, etc. Moreover, online matching problems have lots of applications in ad allocations and other e-commerce problems like product recommendation in digital marketing. We study two problems inv

Mattia Bianchi, Sergio Grammatico

Multi-agent decision problems are typically solved via distributed iterative algorithms, where the agents only communicate between themselves on a peer-to-peer network. Each agent usually maintains a copy of each decision variable, while agreement among the local copies is enforced via consensus protocols. Yet, each agent is often directly influenced by a sm

Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Jie Chen

Hyperspectral and multispectral image fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquisition conditions. This paper presents a general imaging model that considers inter-image variability of data f

Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen

The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks. In order to preserve semantic information behind pixels for intelligent tasks during wireless image transmission, we propose a joint source-channel coding method based on semantics of pixe

Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme

Asynchronous Time Series is a multivariate time series where all the channels are observed asynchronously-independently, making the time series extremely sparse when aligning them. We often observe this effect in applications with complex observation processes, such as health care, climate science, and astronomy, to name a few. Because of the asynchronous na

Seongwoo Cho, Iaroslav Gaponenko, Kumara Cordero-Edwards, Jordi Barceló-Mercader

Artificially induced asymmetric tribological properties of ferroelectrics offer an alternative route to visualize and control ferroelectric domains. Here, we observe the switchable friction and wear behavior of ferroelectrics using a nanoscale scanning probe where down domains having lower friction coefficient than up domains can be used as smart masks as th

Marcela Carvalho, Oussama Ennaffi, Sylvain Chateau, Samy Ait Bachir

In spite of the legal advances in personal data protection, the issue of private data being misused by unauthorized entities is still of utmost importance. To prevent this, Privacy by Design is often proposed as a solution for data protection. In this paper, the effect of camera distortions is studied using Deep Learning techniques commonly used to extract s

Wenbin Gan, Yuan Sun

The ever growing abundance of learning traces in the online learning platforms promises unique insights into the learner knowledge assessment (LKA), a fundamental personalized-tutoring technique for enabling various further adaptive tutoring services in these platforms. Precise assessment of learner knowledge requires the fine-grained Q-matrix, which is gene

Philip Bille, Inge Li Gørtz, Simon J. Puglisi, Simon R. Tarnow

Relative Lempel-Ziv (RLZ) parsing is a dictionary compression method in which a string $S$ is compressed relative to a second string $R$ (called the reference) by parsing $S$ into a sequence of substrings that occur in $R$. RLZ is particularly effective at compressing sets of strings that have a high degree of similarity to the reference string, such as a se

Stefan Kutschera, Wilhelm Zugaj, Wolfgang Slany

We aim to access entropy sources available within smartphones in order to construct and evaluate a random number generator which is competitive in comparison with existing and proven random number generators. A prototype utilizing the herein proposed algorithm shall generate data that can be tested against the Statistical Test Suit provided by NIST. Although

Divya D Joshi, Prashant M Gade, Sachin Bhalekar

We study the fractional maps of complex order, $\alpha_0e^{i r \pi/2}$ for $0<\alpha_0<1$ and $0\le r<1$ in 1 and 2 dimensions. In two dimensions, we study H{\'e}non and Lozi map and in $1d$, we study logistic, tent, Gauss, circle, and Bernoulli maps. The generalization in $2d$ can be done in two different ways which are not equivalent for fractional-order a

Xuqi Zhang, Fanqin Meng, Haiqi Liu, Xiaojing Shen

This paper considers the problem of tracking a large-scale number of group targets. Usually, multi-target in most tracking scenarios are assumed to have independent motion and are well-separated. However, for group target tracking (GTT), the targets within groups are closely spaced and move in a coordinated manner, the groups can split or merge, and the numb

J. Schultheiß, G. Picht, J. Wang, Y. A. Genenko

Ferroelectrics have a spontaneous electrical polarization that is arranged into domains and can be reversed by an externally applied field. This high versatility makes them useful in enabling components such as capacitors, sensors, and actuators. The key to tuning their dielectric, piezoelectric, and electromechanical performance is to control the domain str

Frieder Uhlig, Lukas Struppek, Dominik Hintersdorf, Thomas Göbel

Approximate matching (AM) is a concept in digital forensics to determine the similarity between digital artifacts. An important use case of AM is the reliable and efficient detection of case-relevant data structures on a blacklist, if only fragments of the original are available. For instance, if only a cluster of indexed malware is still present during the

Goran Erceg, Aljosa Subasic, Tanja Vojkovic

The work in this paper is motivated by I. Bani\v{c}'s and A. Taranenko's recent paper, where they introduced a new notion, the span of a graph. Their goal was to solve the problem of keeping a safety distance while two actors are moving through a graph and they present three different types of graph spans, depending on the movement rules. We observe the same

Raphaël Pagès

We present an algorithm for factoring linear differential operators with coefficients in a finite separable extension of F p (x). Our methods rely on specific tools arising in positive characteristic: p-curvature, structure of simple central algebras and p-Riccati equations.

Mohamed Maghenem, Masoumeh Ghanbarpour

This paper establishes the equivalence between robust safety and the existence of a barrier function certificate for differential inclusions. More precisely, for a robustly-safe differential inclusion, a barrier function is constructed as the time-to-impact function with respect to a specifically-constructed reachable set. Using techniques from set-valued an

Weiming Sun, Zimao Zhang

Fourier series multiscale method, a concise and efficient analytical approach for multiscale computation, will be developed out of this series of papers. In the fifth paper, the usual structural analysis of plates on an elastic foundation is extended to a thorough multiscale analysis for a system of a fourth order linear differential equation (for transverse

Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte

Principal component analysis (PCA), a ubiquitous dimensionality reduction technique in signal processing, searches for a projection matrix that minimizes the mean squared error between the reduced dataset and the original one. Since classical PCA is not tailored to address concerns related to fairness, its application to actual problems may lead to disparity

Zijian Gao, Kele Xu, Yuanzhao Zhai, Dawei Feng

Under sparse extrinsic reward settings, reinforcement learning has remained challenging, despite surging interests in this field. Previous attempts suggest that intrinsic reward can alleviate the issue caused by sparsity. In this article, we present a novel intrinsic reward that is inspired by human learning, as humans evaluate curiosity by comparing current

The Gravity Plus Consortium, Florentin Millour, Philippe Berio, Stéphane Lagarde

We present the testbench aimed at integrating the GRAVITY+ adaptive optics GPAO. It consists of two independent elements, one reproducing the Coud{\'e} focus of the telescope, including the telescope deformable mirror mount (with its surface facing down), and one reproducing the Coud{\'e} room opto-mechanical environment, including a downwards-propagating be

P. M. Gueye, B. Gómez-Ferrer, C. Kaden, C. Pareige

After irradiation of Fe-Cr alloys of low purity (model alloys of F-M steels), minor solute elements as P, Ni and Si have been shown to create solute clusters which significantly contribute to hardening and might be associated with small dislocation loops. In order to understand the role of each impurity on the formation of the nano-features formed under irra

Yuan Gao, Siman Yang

Locally repairable codes (LRCs) are a class of erasure codes that are widely used in distributed storage systems, which allow for efficient recovery of data in the case of node failures or data loss. In 2014, Tamo and Barg introduced Reed-Solomon-like (RS-like) Singleton-optimal $(r,\delta)$-LRCs based on polynomial evaluation. These constructions rely on th

Woon Hyung Cho, Jiseon Shin, Young Duck Kim, George J. Jung

Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise clas

Jin-Hui Fang, Csaba Sándor

Two sets of nonnegative integers $A=\{a_1<a_2<\cdots\}$ and $B=\{b_1<b_2<\cdots\}$ are defined as \emph{disjoint}, if $\{A-A\}\bigcap\{B-B\}=\{0\}$, namely, the equation $a_i+b_t=a_j+b_k$ has only trivial solution. In 1984, Erd\H os and Freud [J. Number Theory 18 (1984), 99-109.] constructed disjoint sets $A,B$ with $A(x)>\varepsilon\sqrt{x}$ and $B(x)>\vare

Gongjie Zhang, Zhipeng Luo, Zichen Tian, Jingyi Zhang

Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors. In this paper, we propose Iterative Multi-scale Feature Aggregation (IMFA) -- a generic paradigm that enables efficient use of multi-scale features in Transfo

Chiranjeevi Pallerla, K. Sriram

We report the cross-correlation function studies of a Neutron star low mass X-ray binary, a Z source GX 5-1 using SXT and LAXPC energy bands onboard AstroSat. For the first time, we report the lag between soft (0.8-2.0 keV, SXT) and hard X-ray energy bands (10-20 keV and 16-40 keV, LAXPC) in GX 5-1 and detected lags of the order of a few tens to hundreds of

Jim de Groot

We characterise non-distributive positive logic as the fragment of a single-sorted first-order language that is preserved by a new notion of simulation called a meet-simulation. Meet-simulations distinguish themselves from simulations because they relate pairs of states from one model to single states from another. En route to this result we use a more tradi

Haochen Liu, Zhiyu Huang, Xiaoyu Mo, Chen Lv

Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinforcement learning (RL)-based decision-making scheme is promising to handle urban driving scenarios, it suffers from low sample efficiency and poor adaptability. In this paper, we pro

Xuecheng Wu, Mengmeng Tian, Lanhang Zhai

Recently, the domestic COVID-19 epidemic situation is serious, but in public places, some people do not wear masks or wear masks incorrectly, which requires the relevant staff to instantly remind and supervise them to wear masks correctly. However, in the face of such an important and complicated work, it is very necessary to carry out automated mask-wearing

Yuebao Yang, Jingyi Yang, Mingtao Li

Ai4EComponentLib.jl(Ai4EComponentLib) is a component-base model library based on Julia language, which relies on the differential equation solver DifferentialEquations.jl and the symbolic modeling tool Modelingtoolkit.jl. To handle problems in different physical domains, Ai4EComponentLib tries to build them with component-base model. Supported by a new gener

Qi Wei, Haoliang Sun, Xiankai Lu, Yilong Yin

Sample selection is an effective strategy to mitigate the effect of label noise in robust learning. Typical strategies commonly apply the small-loss criterion to identify clean samples. However, those samples lying around the decision boundary with large losses usually entangle with noisy examples, which would be discarded with this criterion, leading to the