January 2022 arXiv papers — page 5

Alexandre Anahory Simões, Sebastián J. Ferraro, Juan Carlos Marrero, David Martín de Diego

Using the nonholonomic exponential map, we generalize the well-known family of Newmark methods for nonholonomic systems. We give numerical examples including a test problem where the structure of reversible integrability responsible for good energy behaviour as described in [16] is lost. We observe that the composition of two Newmark methods is able to produ

János Barát, Mátyás Czett

The dichromatic number of a directed graph is at most 2, if we can 2-color the vertices such that each monochromatic part is acyclic. An oriented graph arises from a graph by orienting its edges in one of the two possible directions. We study oriented graphs, which have dichromatic number more than 2. Such a graph $D$ is $3$-dicritical if the removal of any

Luis Gerhorst, Benedict Herzog, Stefan Reif, Wolfgang Schröder-Preikschat

Operating systems rely on system calls to allow the controlled communication of isolated processes with the kernel and other processes. Every system call includes a processor mode switch from the unprivileged user mode to the privileged kernel mode. Although processor mode switches are the essential isolation mechanism to guarantee the system's integrity, th

Magnus C. Ørke

We study traveling waves for a class of fractional Korteweg--De Vries and fractional Degasperis--Procesi equations with a parametrized Fourier multiplier operator of order $-s \in (-1, 0)$. For both equations there exist local analytic bifurcation branches emanating from a curve of constant solutions, consisting of smooth, even and periodic traveling waves.

Anja Rey, Lisa Rey

Hedonic games formalize coalition formation scenarios where players evaluate an outcome based on the coalition they are contained in. Due to a large number of possible coalitions, compact representations of these games are crucial. We complement known compact representation models by a distance-based approach: Players' preferences are encoded in a bipolar ma

Augusto Peres, Eduardo Dias, Luís Sarmento, Hugo Penedones

We propose a message passing neural network architecture designed to be equivariant to column and row permutations of a matrix. We illustrate its advantages over traditional architectures like multi-layer perceptrons (MLPs), convolutional neural networks (CNNs) and even Transformers, on the combinatorial optimization task of recovering a set of deleted entri

Shany Shumeli, Petros Drineas, Haim Avron

Models in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in data science applications. It is often desirable for algorithms to take advantage of such structures, avoiding costly matrix computations that often require cubic time and quadratic storage. This is often accomplished by performing operat

Jacques Kluska, Hans Van Winckel, Quentin Coppée, Glenn-Michael Oomen

Post-asymptotic giant branch (post-AGB) binaries are surrounded by massive disks of gas and dust that are similar to protoplanetary disks around young stars. We assembled a catalog of all known Galactic post-AGB binaries with disks. We explore the correlations between the different observables with the aim of learning more about potential disk-binary interac

Alejandro Ortega

In this work we study the existence of solutions to the following critical fractional problem with concave-convex nonlinearities, \begin{equation*} \left \{ \begin{array}{l} (-\Delta)^su=\lambda u^q+u^{2_s^*-1},\ u>0\quad\text{in }\Omega,\\[3pt] \mkern+51mu u=0\quad\text{on } \Sigma_{\mathcal{D}}\\ \mkern+36mu \displaystyle \frac{\partial u}{\partial \nu}=0\

Marco Cesati

This article proposes a new method to inject backdoors in RSA and other cryptographic primitives based on the Integer Factorization problem for balanced semi-primes. The method relies on mathematical congruences among the factors of the semi-primes modulo a large prime number, which acts as a "designer key" or "escrow key". In particular, two different backd

M. O. Archer, D. J. Southwood, M. D. Hartinger, L. Rastaetter

System-scale magnetohydrodynamic (MHD) waves within Earth's magnetosphere are often understood theoretically using box models. While these have been highly instructive in understanding many fundamental features of the various wave modes present, they neglect the complexities of geospace such as the inhomogeneities and curvilinear geometries present. Here we

Konstantinos Ntougias, Ioannis Krikidis

This study considers the Joint Transmit/Reflect Beamforming and Power Splitting (JTRBPS) optimization problem in a spectrum underlay setting, such that the transmit sum-energy of the intelligent reflecting surface (IRS)-aided secondary transmitter (ST) is minimized subject to the quality-of-service requirements of the PS-simultaneous wireless information and

Elias R. Most, Anton Motornenko, Jan Steinheimer, Veronica Dexheimer

Binary neutron-star mergers and heavy-ion collisions are related through the properties of the hot and dense nuclear matter formed during these extreme events. In particular, low-energy heavy-ion collisions offer exciting prospects to recreate such {extreme} conditions in the laboratory. However, it remains unexplored to what degree those collisions can actu

I. Kriuchevskyi, J. P. Wittmer, O. Benzerara, H. Meyer

Focusing on simulated polymer glasses well below the glass transition, we confirm the validity and the efficiency of the recently proposed simple-average expression $G(t) = \mu_A - h(t)$ for the computational determination of the shear stress relaxation modulus $G(t)$. Here, $\mu_A = G(0)$ characterizes the affine shear transformation of the system at $t=0$

Janek Ebbers, Romain Serizel, Reinhold Haeb-Umbach

Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain

Houjun Qian, Mikhail Krasilnikov, Anusorn Lueangaramwong, Xiangkun Li

Slice energy spread is one of the key parameters in free electron laser optimizations, but its accurate measurement is not straightforward. Two recent studies from high energy ($>$100 MeV) photoinjectors at SwissFEL and European XFEL have reported much higher slice energy spread than expected at their XFEL working points (200 - 250 pC). In this paper, a new

S. A. Venikoudis, K. V. Fasoulakos, F. P. Fronimos

In this work we present the late-time behaviour of the Universe in the context of Einstein-Gauss-Bonnet gravitational theory. The theory involves a scalar field, which represents low-effective quantum corrections, assisted by a function $f(\mathcal{G})$ solely depending from the Gauss-Bonnet topological invariant $\mathcal{G}$. It is considered that the dark

Shailesh Garg, Harshit Gupta, Souvik Chakraborty

Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time. We investigate the efficacy of recently proposed DeepONet in solving time dependent reliability analysis and uncertainty quantification of systems subje

Maarten Grachten, Carlos Cancino-Chacón, Thassilo Gadermaier

This demo paper introduces partitura, a Python package for handling symbolic musical information. The principal aim of this package is to handle richly structured musical information as conveyed by modern staff music notation. It provides a much wider range of possibilities to deal with music than the more reductive (but very common) piano roll-oriented appr

Jiaying Guo, Long Cheng, Shen Wang

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and emissions, improvements of traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Diff

Florian Aigner, Ilse Fischer

We introduce a new family $\mathcal{A}_{n,k}$ of Schur positive symmetric functions, which are defined as sums over totally symmetric plane partitions. In the first part, we show that, for $k=1$, this family is equal to a multivariate generating function involving $n+3$ variables of objects that extend alternating sign matrices (ASMs), which have recently be

M. Dolgushev, J. P. Wittmer, A. Johner, O. Benzerara

Assuming Gaussian chain statistics along the chain contour, we generate by means of a proper fractal generator hyperbranched polymer trees which are marginally compact. Static and dynamical properties, such as the radial intrachain pair density distribution or the shear-stress relaxation modulus, are investigated theoretically and by means of computer simula

Maël Dumas, Anthony Perez, Mathis Rocton, Ioan Todinca

We consider edge modification problems towards block and strictly chordal graphs, where one is given an undirected graph $G = (V,E)$ and an integer $k \in \mathbb{N}$ and seeks to edit (add or delete) at most $k$ edges from $G$ to obtain a block graph or a strictly chordal graph. The completion and deletion variants of these problems are defined similarly by

M. Iskin

We consider the three-body problem in a generic multiband lattice, and analyze the dispersion of the trimer states that are made of two spin-$\uparrow$ fermions and a spin-$\downarrow$ fermion due to an onsite attraction in between. Based on a variational approach, we first obtain the exact solution in the form of a set of coupled integral equations, and the

Lorenzo Piccolo, Sandro Cadeddu, Luca Frontini, Adriano Lai

This work presents the first measurements performed on the Timespot1 ASIC. As the second prototype developed for the TimeSPOT project, the ASIC features a 32x32 channels hybrid-pixel matrix. Targeted to space-time tracking applications in High Energy Physics experiments, the system aims to achieve a time resolution of 30 ps or better at a maximum event rate

Severin Selzer, Leandro Salemi, András Deák, Eszter Simon

It is well established that it is possible to switch certain antiferromagnets electrically, yet the interplay of N\'{e}el-spin-orbit torques and thermal activation is only poorly understood. Combining ab initio calculations and atomistic spin dynamics simulations we develop a multiscale model to study the current induced switching in Mn2Au. We compute from f

John Cotrina, Raúl Fierro

We deal with inverse maximum theorems, which are inspired by the ones given by Aoyama, Komiya, Li et al., Park and Komiya, and Yamauchi. As a consequence of our results, we state and prove an inverse maximum Nash theorem and show that any generalized Nash game can be reduced to a classical Nash game, under suitable assumptions. Additionally, we show that a r

Tohru Koma

We present a lattice model for superconducting fermions whose nearest-neighbour two-body interactions are a Bardeen-Cooper-Schrieffer-type pairing on the hypercubic lattice $\mathbb{Z}^d$ with the dimension $d\ge 3$. Although these effective interactions between two electrons are believed to be caused by electron-phonon interactions, we assume that the inter

B. Pal, P. Kumar

In this article, we study the problem of the existence and nonexistence of warping function associated with constant scalar curvature on pseudo-Riemannian Poisson warped product space under the assumption that fiber space has constant scalar curvature. We characterize the warping function on Einstein Poisson warped space by taking the various dimensions of b

Timofey Zolkin, Yaroslav Kharkov, Sergei Nagaitsev

We present a new automated method for finding integrable symplectic maps of the plane. These dynamical systems possess a hidden symmetry associated with an existence of conserved quantities, i.e. integrals of motion. The core idea of the algorithm is based on the knowledge that the evolution of an integrable system in the phase space is restricted to a lower

Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh, Yingyu Liang

Mixtures of ranking models are standard tools for ranking problems. However, even the fundamental question of parameter identifiability is not fully understood: the identifiability of a mixture model with two Bradley-Terry-Luce (BTL) components has remained open. In this work, we show that popular mixtures of ranking models with two components (BTL, multinom

Cynthia Michalkowski, Kilian Weishaupt, Veronika Schleper, Rainer Helmig

Fluid flow through layered materials with different wetting behavior is observed in a wide range of applications in biological, environmental and technical systems. Therefore, it is necessary to understand the occuring transport mechanisms of the fluids at the interface between the layered constituents. Of special interest is the water transport in polymer e

Alberto Megías, Andrés Santos, Antonio Prados

Loosely speaking, the Mpemba effect appears when hotter systems cool sooner or, in a more abstract way, when systems further from equilibrium relax faster. In this paper, we investigate the Mpemba effect in a molecular gas with nonlinear drag, both analytically (by employing the tools of kinetic theory) and numerically (direct simulation Monte Carlo of the k

A. A. Saeed, W. A. Yahya, O. K. Azeez

In recent years, artificial neural network (ANN) has been successfully applied in nuclear physics and some other areas of physics. This study begins with the calculations of {\alpha}-decay half-lives for some neutron-deficient nuclei using Coulomb and proximity potential model (CPPM), temperature dependent Coulomb and proximity potential model (CPPMT), Royer

Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard

Maximizing a monotone submodular function is a fundamental task in machine learning. In this paper, we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present cons

Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE). Both the KL-divergence and the IPMs are widely used in various fields in applications such as generative modeling. However, a unified understanding of these c

Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti, Alessandro Pedretti

Nowadays there is a big spotlight cast on the development of techniques of explainable machine learning. Here we introduce a new computational paradigm based on Group Equivariant Non-Expansive Operators, that can be regarded as the product of a rising mathematical theory of information-processing observers. This approach, that can be adjusted to different si

Atsuo Kuniba, Grégoire Misguich, Vincent Pasquier

We explore several aspects of the current fluctuations and correlations in the box-ball system (BBS), an integrable cellular automaton in one space dimension. The state we consider is an ensemble of microscopic configurations where the box occupancies are independent random variables (i.i.d. state), with a given mean ball density. We compute several quantiti

Prathamesh Kalamkar, Aman Tiwari, Astha Agarwal, Saurabh Karn

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent

Kwangmin Lee, Seongmin Kim, Seongil Jo, Jaeyong Lee

In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence, we use serological surveys (also called the serosurveys) conducted within each country. When the serosurveys are incorporated to estimate world seroprevalence, there are two issues. First, there are countries in

Eustache Diemert, Romain Fabre, Alexandre Gilotte, Fei Jia

Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed under the Improving Web Advertising Business Group at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this pr

Joydev Halder, Bhargav Kumar Kakumani, Suman Kumar Tumuluri

In this article, we consider a semilinear pseudo parabolic heat equation with the nonlinearity which is the product of logarithmic and polynomial functions. Here we prove the global existence of solution to the problem for arbitrary dimension $n \geq 1$ and power index $p>1$. Asymptotic behaviour of the solution has been addressed at different energy levels.

A. Zuevsky

Starting from the axiomatic description of meromorphic functions with prescribed analytic properties, we introduce the cosimplicial cohomology of restricted meromorphic functions defined on foliations of smooth complex manifolds. Spaces for double chain-cochain complexes and coboundary operators are constructed. Multiplications of several restricted meromorp

Robin Booth

Arguably our current cosmological paradigm, the so-called $\Lambda$CDM `concordance model', faces an existential crisis. This has largely been brought about by its reliance on the twin concepts of dark matter and dark energy, and the continued inability of the observational and theoretical physics community to find viable candidates for these postulated phen

Hans L. Bodlaender, Carla Groenland, Hugo Jacob, Lars Jaffke

In this paper, we showcase the class XNLP as a natural place for many hard problems parameterized by linear width measures. This strengthens existing $W[1]$-hardness proofs for these problems, since XNLP-hardness implies $W[t]$-hardness for all $t$. It also indicates, via a conjecture by Pilipczuk and Wrochna [ToCT 2018], that any XP algorithm for such probl

Andrea Droghetti, Milos M. Radonjić, Liviu Chioncel, Ivan Rungger

We present the combination of Density Functional Theory (DFT) and Dynamical Mean Field Theory (DMFT) for computing the electron transmission through two-terminals nanoscale devices. The method is then applied to metallic junctions presenting alternating Cu and Co layers, which exhibit spin-dependent charge transport and giant magnetoresistance (GMR) effect.

Alexander G. D. G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows. The normalizing flows are directly trained to transport between annealing temperatures using a KL divergence for

Robin Guehne, Carsten Kattinger, Marko Bertmer, Simon Welzmiller

The ternary semiconductor AgInTe$_2$ is a thermoelectric material with chalcopyrite-type structure that transforms reversibly into a rocksalt-type structure under high pressure. Nuclear magnetic resonance (NMR) is considered to provide unique insight into material properties on interatomic length scales, especially in the context of structural phase transiti

Sayantan Kumar, Zachary Abrams, Suzanne Schindler, Nupur Ghoshal

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogeneous with a variety of different symptoms that progress at different rates. In this work, we used an unsupervised data-driven K-Means clustering approach on

Tomáš Kaiser, Petr Vrána

We prove that every 52-connected line graph of a rank 3 hypergraph is Hamiltonian. This is the first result of this type for hypergraphs of bounded rank other than ordinary graphs.

Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan

Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields. Moreover, a growing amount of research work tends to transfer deterministic dynamical systems to stochastic dynamical systems, especially those driven by non-Gaussian multiplicative noise. However, lots of log

V. M. Biryukov

We suggest a way to compare the experiments and simulations of electron dechanneling length in bent crystals performed at different energies and different radii. As an example, we compare the SLAC experiment at 3-14 GeV with earlier published predictions of two different Monte Carlo codes for 855 MeV and 50 GeV, and with MAMI experiment at 855 MeV.

Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi

In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necessary to solve the optimization problem by taking into account the uncertainty of the environmental variables. Chance-constrained (CC) problem, the problem of maximizing the expected

Ayesha Ekanayaka, Emily Kang, Peter Kalmus, Amy Braverman

We describe an improved statistical downscaling method for Earth science applications using multivariate Basis Graphical Lasso (BGL). We demonstrate our method using a case study of sea surface temperature (SST) projections from CMIP6 Earth system models, which has direct applications for studies of multi-decadal projections of coral reef bleaching. We find

Mohamed Maghenem, Alessandro Melis, Ricardo G. Sanfelice

In this paper, we propose necessary and sufficient conditions for a scalar function to be nonincreasing along solutions to general differential inclusions with state constraints. The problem of determining if a function is nonincreasing appears in the study of stability and safety, typically using Lyapunov and barrier functions, respectively. The results in

Antti Ranni, Elsa T. Mannila, Axel Eriksson, Dmitry S. Golubev

We measure the tunneling rates and coupling coefficients for local Andreev, non-local Andreev and elastic cotunneling processes. The non-local Andreev process, giving rise to Cooper pair splitting, exhibits the same coupling coefficient as the elastic co-tunneling whereas the local Andreev process is more than two orders of magnitude stronger than the corres

Harshdeep Singh, Kapish Chand Meena

In this paper, we find a necessary and sufficient condition for multi-twisted Reed-Solomon codes to be MDS. In particular, we introduce a new class of MDS double-twisted Reed-Solomon codes $\mathcal{C}_{\bm \alpha, \bm t, \bm h, \bm \eta}$ with twists $\bm t = (1, 2)$ and hooks $\bm h = (0, 1)$ over the finite field $\mathbb{F}_q$, providing a non-trivial ex

Serge Kernbach

Para- and ortho- isomers of water have different chemical and physical properties. Excitations by magnetic field, laser emission or hydrodynamic cavitation are reported to change energetic levels and spin configurations of water molecules that in turn change macroscopically measurable properties of aqueous solutions. Similar scheme is also explored for disso

Mohamed Maghenem, Ricardo. G. Sanfelice

This paper presents converse theorems for safety in terms of barrier functions for unconstrained continuous-time systems modeled as differential inclusions. Via a counterexample, we show the lack of existence of autonomous and continuous barrier functions certifying safety for a nonlinear system that is not only safe but also has a smooth right-hand side. Gu

Federico Adolfi, Todd Wareham, Iris van Rooij

Computational feasibility is a widespread concern that guides the framing and modeling of biological and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about the search space and complexity of a subcomputation. However, a mistaken intuition might make such initial conceptualizatio

Murad Banaji

A family of results, referred to as inheritance results, tell us which enlargements of a chemical reaction network (CRN) preserve its capacity for nontrivial behaviours such as multistationarity and oscillation. In this paper, the following inheritance result is proved: under mild assumptions, splitting chemical reactions and inserting complexes involving so

Qianwei Qu, Sergei N. Yurchenko, Jonathan Tennyson

An algorithm for the calculation of hyperfine structure and spectra of diatomic molecules based on the variational nuclear motion is presented. Hyperfine coupling terms considered are Fermi-contact, nuclear spin-electron spin dipole-dipole, nuclear spin-orbit, nuclear spin-rotation and nuclear electric quadrupole interactions. Initial hyperfine-unresolved wa

Christof Naumzik, Stefan Feuerriegel

False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. Specifically, our model is the first to formalize the

Maged Abdelaty, Sandra Scott-Hayward, Roberto Doriguzzi-Corin, Domenico Siracusa

Machine Learning (ML) has proven to be effective in many application domains. However, ML methods can be vulnerable to adversarial attacks, in which an attacker tries to fool the classification/prediction mechanism by crafting the input data. In the case of ML-based Network Intrusion Detection Systems (NIDSs), the attacker might use their knowledge of the in

Ralf Blossey, Rudolf Podgornik

We develop of a field-theoretic approach for the treatment of both the non-local and the non-linear response of structured liquid dielectrics. Our systems of interest are composed of dipolar solvent molecules and simple salt cations and anions. We describe them by two independent order parameters, the polarization field for the solvent and the charge density

Anh Duc Le

Social networking services have became an important communication channel in time of emergency. The aim of this study is to create a machine learning language model that is able to investigate if a person or area was in danger or not. The ubiquitousness of smartphones enables people to announce an emergency they are observing in real-time. Because of this, m

Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek

We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective. The image encoder is trained to minimise the distance between representations of the original and that of a masked image. The masking function, conversely, aims at maximising

Lorenzo Balzotti, Paolo G. Franciosa

We study the problem of computing the vitality of edges and vertices with respect to the $st$-max flow in undirected planar graphs, where the vitality of an edge/vertex is the $st$-max flow decrease when the edge/vertex is removed from the graph. This allows us to establish the vulnerability of the graph with respect to the $st$-max flow. We give efficient a

G. Franciolini, P. Pani

One fundamental goal of the newly born gravitational wave astronomy is discovering the origin of the observed binary black hole mergers. Towards this end, identifying features in the growing wealth of data may help in distinguishing different formation pathways. While large uncertainties still affect the binary formation models, spin-mass relations remain ch

Mathieu Even, Laurent Massoulié, Kevin Scaman

In personalized Federated Learning, each member of a potentially large set of agents aims to train a model minimizing its loss function averaged over its local data distribution. We study this problem under the lens of stochastic optimization. Specifically, we introduce information-theoretic lower bounds on the number of samples required from all agents to a

Elias Frantar, Dan Alistarh

The recent focus on the efficiency of deep neural networks (DNNs) has led to significant work on model compression approaches, of which weight pruning is one of the most popular. At the same time, there is rapidly-growing computational support for efficiently executing the unstructured-sparse models obtained via pruning. Yet, most existing pruning methods mi

Beatrice Acciaio, Anastasis Kratsios, Gudmund Pammer

Several problems in stochastic analysis are defined through their geometry, and preserving that geometric structure is essential to generating meaningful predictions. Nevertheless, how to design principled deep learning (DL) models capable of encoding these geometric structures remains largely unknown. We address this open problem by introducing a universal

Srikanth Korse, Nicola Pia, Kishan Gupta, Guillaume Fuchs

The quality of speech coded by transform coding is affected by various artefacts especially when bitrates to quantize the frequency components become too low. In order to mitigate these coding artefacts and enhance the quality of coded speech, a post-processor that relies on a-priori information transmitted from the encoder is traditionally employed at the d

Jaroslav Albert

Gene transcription is a stochastic process that involves thousands of reactions. The first set of these reactions, which happen near a gene promoter, are considered to be the most important in the context of stochastic noise. The most common models of transcription are primarily concerned with the effect of activators/repressors on the overall transcription

Hao Chen, Daniel Freese

We point out an interesting connection between fluid dynamics and minimal surface theory: When gluing helicoids into a minimal surface, the limit positions of the helicoids correspond to a "vortex crystal", an equilibrium of point vortices in 2D fluid that move together as a rigid body. While vortex crystals have been studied for almost 150 years, the gluing

I. V. Lukin, Yu. V. Slyusarenko, A. G. Sotnikov

We study theoretically transitions between the localized and chaotic many-body regimes in one-dimensional quantum lattice systems with long-range couplings between particles and linear external potential. In terms of established criteria characterizing localization, we construct effective phase diagrams for several types of lattice systems with variable ampl

Sara Di Ruzza

The n-body problem is one of the most important issue in Celestial Mechanics. This article aims to retrace the historical and scientific events that led the Paduan mathematician, Tullio Levi-Civita, to deal with the problem first from a classic and then a relativistic point of view. We describe Levi-Civita's contributions to the theory of relativity focusing

Daniele Funaro, Alessandro Chiolerio

Biconical-type antennas featuring high directivity have been designed, created, and tested in anechoic chamber. Results in the range between 1 and 5 GHz are presented in this article. In particular, two different configurations have been tested, with and without dielectric lenses, both involving rapid prototyping tools (3D printing) for the dielectric and th

Massimo Vaccaro

We determine the invariants characterizing the $Sp(n)$-orbits in the real Grassmannian $Gr^\R(2k,4n)$ of the $2k$-dimensional complex and $\Sigma$-complex subspaces of a $4n$-dimensional Hermitian quaternionic vector space. A $\Sigma$-complex subspace is the orthogonal sum of complex subspaces by different, up to sign, compatible complex structure. The resul

Debjyoti Majumdar

The melting phase diagram of a double-stranded DNA in poor solvent is studied using the pruned and enriched Rosenbluth method on a simple cubic lattice. As the solvent quality is changed from good to poor, there is a non-monotonic change in the melting temperature. First-order melting transition, as in good solvent, gives way to continuous transition and the

Tianyue Chu, Alvaro Garcia-Recuero, Costas Iordanou, Georgios Smaragdakis

We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing GDPR-sensitive content related to categories such as health, sexual preference, political beliefs, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers,it is still vulnerable to poison

Hao Wang, J. Li, X. M. Zhou, Y. Tan

Solid usually becomes harder and tougher under compression, and turns softer at elevated temperature. Recently, compression-induced softening and heating-induced hardening (CISHIH) dual anomaly was predicted in group VB elements such as vanadium. Here, the evidence for this counterintuitive phenomenon is reported. By using accurate high-temperature high-pres

Christian Rathgeb, Robert Nichols, Mathias Ibsen, Pawel Drozdowski

We investigate the potential of fusing human examiner decisions for the task of digital face manipulation detection. To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision. Conducted experiments are based on a psychophysical evaluation of digital face ima

Priyanka Gupta, Sathvik Bhaskarpandit, Manik Gupta

Using deep learning models to classify time series data generated from the Internet of Things (IoT) devices requires a large amount of labeled data. However, due to constrained resources available in IoT devices, it is often difficult to accommodate training using large data sets. This paper proposes and demonstrates a Similarity Learning-based Few Shot Lear

Haojie Chen, Xiaolan Nie

Motivated by a recent work of Chen-Zheng [8] on Strominger space forms, we prove that a compact Hermitian surface with pointwise constant holomorphic sectional curvature with respect to a Gauduchon connection $\nabla^t $ is either K\"ahler, or an isosceles Hopf surface with an admissible metric and $t=-1$ or $t=3$. In particular, a compact Hermitian surface

Ioana Ciotir, Dan Goreac, Ionut Munteanu

The aim of the present paper is to provide necessary and sufficient conditions to maintain a stochastic coupled system, with porous media components and gradient-type noise in a prescribed set of constraints by using internal controls. This work is a continuation of the results in [10], as we consider the case of divergence type noise perturbation. On the ot

Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger

Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these method

Seung Jin Lee, Sue Kyong Y. Soh

In 1993, Stanley and Stembridge conjectured that a chromatic symmetric function of any $(3+1)$-free poset is $e$-positive. Guay-Paquet reduced the conjecture to $(3+1)$- and $(2+2)$-free posets which are also called natural unit interval orders. Shareshian and Wachs defined chromatic quasisymmetric functions, generalizing chromatic symmetric functions, and c

Abhishek Ranjan, Anowarul Habib, Azeem Ahmad, Balpreet Singh Ahluwalia

We demonstrate surface acoustic waves inside polystyrene microspheres of different sizes experimentally through photoacoustic microscopy and validate the experimental result with simulation. A novel method for sample preparation of a lifted sample is also presented where the microparticles are suspended in agarose above the surface of the petridish. Another

Riccardo Angelo Giro, Giancarlo Bernasconi, Giuseppe Giunta, Simone Cesari

The detection of leaks in pipeline transportation systems is a matter of serious concern for operators, who pursue the integrity of their assets, the reduction of losses and the prevention of environmental hazards. Whenever a hole occurs in a pressurized pipeline, the corresponding fluid leakage is characterized by a turbulent flow and a peculiar acoustic no

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The feature extraction module uses an encoder-de

Chi Hong, Jiyue Huang, Lydia Y. Chen

Deep machine learning models are increasingly deployedin the wild for providing services to users. Adversaries maysteal the knowledge of these valuable models by trainingsubstitute models according to the inference results of thetargeted deployed models. Recent data-free model stealingmethods are shown effective to extract the knowledge of thetarget model wi

Kavyashree U, K N Deeksha, Suma Ballal, Vitina Mary Dsouza

A Bio-metrics system is actually a pattern recognition system that utilizes various patterns like iris, retina and biological traits like fingerprint, voice recognition, facial geometry and hand geometry. What makes Bio-metrics really attractive is that the various security codes like passwords and ID cards can be interchanged, stolen or duplicated. To enhan

Víctor Almeida, Jorge J. Betancor, Pablo Quijano, Lourdes Rodríguez-Mesa

In this paper we establish $L^p$ boundedness properties for maximal operators, Littlewood-Paley functions and variation operators involving Poisson semigroups and resolvent operators associated with nonsymmetric Ornstein-Uhlenbeck operators. We consider the Ornstein-Uhlenbeck operators defined by the identity as the covariance matrix and having a drift given

Nick James, Max Menzies

We introduce new frameworks to study spatio-temporal patterns in carbon dioxide emissions, demographic trends and economic patterns across 50 countries over the past 50 years. Our analysis is broken up into four sections. First, we introduce a new method to classify countries into one of three characteristic emissions classes based on a one, two or three-seg

Yutaka Itoh

We report the $^{63}$Cu and $^{65}$Cu nuclear spin-lattice relaxation rate measurements of cuprous oxide Cu$_2$O in a zero field Cu nuclear quadrupole resonance at $T$ = 77$-$325 K. From the detailed isotopic measurements of the relaxation rates, we successfully estimated a finite magnetic relaxation rate $^{63}W_M$ and a predominant nuclear quadrupole relax

Ivana Balažević

Encoding facts as representations of entities and binary relationships between them, as learned by knowledge graph representation models, is useful for various tasks, including predicting new facts, question answering, fact checking and information retrieval. The focus of this thesis is on (i) improving knowledge graph representation with the aim of tackling

Avital Friedland, Jonathan Zeltser, Omer Levy

Two languages are considered mutually intelligible if their native speakers can communicate with each other, while using their own mother tongue. How does the fact that humans perceive a language pair as mutually intelligible affect the ability to learn a translation model between them? We hypothesize that the amount of data needed to train a neural ma-chine

P. Fedorets, C. Zheng, R. Engels, I. Engin

A laser-driven spin-polarized 3He2+ beam source for nuclear-physics experiments and for the investigation of polarized nuclear fusion demands a high-density polarized 3He gas-jet target. Such a target requires a magnetic system providing a permanent homogeneous holding field for the nuclear spins plus a set of coils for adjusting the orientation of the polar

Koray Koksal, Fatih Ahmet Celik

We study the possibility of the generation of the photo-induced image currents at a distance from the surface of nano-sized metal clusters by using time-dependent perturbation theory. We reveal that the wave function of an electron excited to the image state is localized outside the surface and current flows in a spherical shell whose radius is a few times t