May 2022 arXiv papers — page 61

Giuseppe De Palma, Saverio Giallorenzo, Jacopo Mauro, Matteo Trentin

Cloud-edge serverless applications or serverless deployments spanning multiple regions introduce the need to govern the scheduling of functions to satisfy their functional constraints or avoid performance degradation. For instance, functions may require to be allocated to specific private (edge) nodes that have access to specialised resources or to nodes wit

Martin Balla, Diego Perez-Liebana

Deep Reinforcement Learning has been very successful recently with various works on complex domains. Most works are concerned with learning a single policy that solves the target task, but is fixed in the sense that if the environment changes the agent is unable to adapt to it. Successor Features (SFs) proposes a mechanism that allows learning policies that

S. Gerakakis, M. Brilenkov, M. Ieronymaki, M. San

The BeyondPlanck and Cosmoglobe collaborations have implemented the first integrated Bayesian end-to-end analysis pipeline for CMB experiments. The primary long-term motivation for this work is to develop a common analysis platform that supports efficient global joint analysis of complementary radio, microwave, and sub-millimeter experiments. A strict prereq

Avinash Sudhodanan, Andrew Paverd

The ubiquity of user accounts in websites and online services makes account hijacking a serious security concern. Although previous research has studied various techniques through which an attacker can gain access to a victim's account, relatively little attention has been directed towards the process of account creation. The current trend towards federated

F. Lykou, P. Ábrahám, L. Chen, J. Varga

The disk of FU Orionis is marginally resolved with MATISSE, suggesting that the region emitting in the thermal infrared is rather compact. An upper limit of $\sim1.3\pm0.1$ mas (in $L$) can be given for the diameter of the disk region probed in the $L$ band, corresponding to 0.5 au at the adopted Gaia EDR3 distance. This represents the hot, gaseous region of

Muhammad Shahzad Anjum, Liang Chen, Minfeng Gu

The extragalactic $\gamma$-ray sky is dominated by blazars and their study plays an important role in understanding jet physics, cosmic evolution history and origin of ultra high energy cosmic rays. In this work, we study a large sample of BL Lac objects to investigate why some sources are detected in $\gamma$-rays, while others not. We selected 170 BL Lac o

C. Ciampi, S. Piantelli, G. Casini, G. Pasquali

An investigation of the isospin equilibration process in the reactions $^{58,64}$Ni+$^{58,64}$Ni at two bombarding energies in the Fermi regime ($32\,$MeV/nucleon and $52\,$MeV/nucleon) is presented. Data have been acquired during the first experimental campaign of the coupled INDRA-FAZIA apparatus in GANIL. Selecting from peripheral to semi-central collisio

Patrick Ciarlet, David Lassounon, Mahran Rihani

In this work, we present a new numerical method for solving the scalar transmission problem with sign-changing coefficients. In electromagnetism, such a transmission problem can occur if the domain of interest is made of a classical dielectric material and a metal or a metamaterial, with for instance an electric permittivity that is strictly negative in the

Yi Wang, Zhiqing Wei, Zhiyong Feng

Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, hig

Serge V. Repin, Mikhail A. Bugaev, Igor D. Novikov, Igor D. Novikov

The problem of the passage of light through the mouth of a zero-mass wormhole and the possibility of observing the objects from another asymptotically flat space-time through the mouth of a wormhole are considered. It is shown that an individual star can have several images and the fact that the image of a flat Lambertian screen has a complex brightness dist

Egor Illarionov, Rainer Arlt

Catalogs of the Zurich Observatory contain positional information on sunspots, prominences and faculae in late 19th and early 20th centuries. This database is given in handwritten tabular form and was not systematically analysed earlier. It is different from the sunspot number time series made in Zurich and was obtained with a larger telescope. We trained a

Victor Boutin, Lakshya Singhal, Xavier Thomas, Thomas Serre

Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing novel instances of unknown visual concepts from a single training example. Yet, a more precise comparison between these models and humans is not pos

L. A. López, Valeria Ramírez

In this contribution, we study the quasinormal modes of a Generic--class of a regular black hole with a magnetic charge in nonlinear electrodynamics, considering scalar and electromagnetic perturbations. The Generic--class contains the Bardeen--class, Hayward--class, and New--class solutions. As the Generic--class can represent a black hole with two horizons

Ronen Peretz

In this paper we give a counterexample to the conjecture: Let $S\in{\rm SInn}$. Then $z\cdot S$ is onto $U$.

Lorenzo Mella, Anita Pasotti

Square relative non-zero sum Heffter arrays, denoted by $\mathrm{N}\mathrm{H}_t(n;k)$, have been introduced as a variant of the classical concept of Heffter array. An $\mathrm{N}\mathrm{H}_t(n; k)$ is an $n\times n$ partially filled array with elements in $\mathbb{Z}_v$, where $v=2nk+t$, whose rows and whose columns contain $k$ filled cells, such that the su

Dongqi Cai, Yaozong Wu, Shangguang Wang, Felix Xiaozhu Lin

Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, our measurements show that FedNLP is prohibitively slow due to the large model sizes and the resul

Lia Bozarth, Daniele Quercia, Licia Capra, Sanja Scepanovic

Past research has attributed the online circulation of misinformation to two main factors - individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion) - and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geogr

M. R. Kennedy, C. Littlefield, P. M. Garnavich

We report XMM-Newton and TESS observations of V496 UMa, an AM Herculis-type cataclysmic variable. The XMM-Newton observation reveals that at times, two poles on the white dwarf accrete simultaneously, but accretion onto the secondary magnetic pole is erratic and can nearly cease in less than one binary orbit (1.5 h). Modelling of the X-ray spectrum during th

Jiankai Jin, Olga Ohrimenko, Benjamin I. P. Rubinstein

Adversarial examples pose a security risk as they can alter decisions of a machine learning classifier through slight input perturbations. Certified robustness has been proposed as a mitigation where given an input $\mathbf{x}$, a classifier returns a prediction and a certified radius $R$ with a provable guarantee that any perturbation to $\mathbf{x}$ with $

Rémy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome

Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Augmentations do not typically yield new labeled samples, as indiscriminately mixing contents creates be

Stefano Teso, Laurens Bliek, Andrea Borghesi, Michele Lombardi

It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variables are not known or are only partially specified. The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must sati

Shuo Li, Jakub Pachocki, Jakub Radoszewski

A \emph{power} is a word of the form $\underbrace{uu...u}_{k \; \text{times}}$, where $u$ is a word and $k$ is a positive integer; the power is also called a {\em $k$-power} and $k$ is its {\em exponent}. We prove that for any $k \ge 2$, the maximum number of different non-empty $k$-power factors in a word of length $n$ is between $\frac{n}{k-1}-\Theta(\sqrt

Maxim Lyutikov

We consider propagation of polarization in the inner parts of pair-symmetric magnetar winds, close to the light cylinder. Pair plasmas in magnetic field is birefringent, a $\propto B^2$ effect. As a result, such plasmas work as phase retarders: Stokes parameters follow a circular trajectory on the Poincare sphere. In the highly magnetized regime, $\omega, \,

I. Gódor, M. Luvisotto, S. Ruffini, K. Wang

Connectivity has a major role in the current transformation of smart manufacturing. 5G is foreseen as an integral part of an end-to-end networking infrastructure supporting smart manufacturing operation. The integration of 5G with time-sensitive networking (TSN)is seen as a holistic communication solution for smart factories. The next generation of industria

Joachim Sachs, Krister Landernäs

5G has been defined to address new use cases beyond consumer-focused mobile broadband services. In particular industrial use cases, for example in smart manufacturing, have been addressed in the 5G standardization, so that 5G can support industrial IoT services and wireless industrial networking. To this end, 5G needs to integrate with the industrial network

Rungang Han, Anru R. Zhang

We wholeheartedly congratulate Drs. Rohe and Zeng for their insightful paper \cite{rohe2020vintage} on vintage factor analysis with Varimax rotation. This note discusses the conditions to guarantee Varimax consistently recovers the subspace rotation.

Laura Covi, Sarif Khan

In the Standard Model a Dark Matter candidate is missing, but it is relatively simple to enlarge the model including one or more suitable particles. We consider in this paper one such extension, inspired by simplicity and by the goal to solve more than just the Dark Matter issue. Indeed we consider a local $U(1) $ extension of the SM providing an axion parti

Tobias Binder, Sreemanti Chakraborti, Shigeki Matsumoto, Yu Watanabe

We study a minimal model for a light scalar dark matter, requiring a light scalar mediator to address the core-cusp problem and interact with the standard model particles. We analyze the model comprehensively by focusing on the Breit-Wigner resonance for dark matter annihilation and self-scattering channels, considering the thermal relic abundance condition

Kentaro Kasai, Masahiro Kawasaki, Kai Murai

We study a primordial black hole (PBH) formation scenario based on the Affleck-Dine (AD) mechanism and investigate two PBH mass regions: $M \sim 30 M_\odot$ motivated by the LIGO-Virgo observations of the binary black hole mergers and $M \gtrsim 10^4 M_\odot$ motivated by the observations of supermassive black holes at the center of galaxies. In the previous

Courtney George, Christopher Manon

Work of Gonz\'alez, Hering, Payne, and S\"uss shows that it is possible to find both examples and non-examples of Mori dream spaces among projectivized toric vector bundles. This result, and the combinatorial nature of the data of projectivized toric vector bundles make them an ideal test class for the question: what makes a variety a Mori dream space? In th

Risheng Liu, Jiaxin Gao, Xuan Liu, Xin Fan

In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately, it has been proved that these alternating learning strategies cannot exactly reveal the intrinsic relationship between the generator and discriminator, thus easily result in a ser

Jana Vacková, Marek Bukáček

The standard definition of pedestrian density produces scattered values, hence, many approaches have been developed to improve the features of the estimated density. This paper provides a review of generally applied methods and presents a general framework based on various kernels that bring desired properties of density estimates (e.g., continuity) and inco

Lukas S. Huber, Robert Geirhos, Felix A. Wichmann

In laboratory object recognition tasks based on undistorted photographs, both adult humans and Deep Neural Networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last two

Angelo Felice Lopez, Roberto Muñoz, José Carlos Sierra

We give an almost complete classification of non-big Ulrich vector bundles on fourfolds. This allows to classify them in the case of Picard rank one fourfolds, of Mukai fourfolds and in the case of Del Pezzo $n$-folds for $n \le 4$. We also classify Ulrich bundles with non-big determinant on Del Pezzo and Mukai $n$-folds, $n \ge 2$.

Alexander George Mountogiannakis, Panagiotis Papanastasiou, Stefano Pirandola

The performance of a practical continuous-variable (CV) quantum key distribution (QKD) protocol depends significantly, apart from the loss and noise of the quantum channel, on the post-processing steps which lead to the extraction of the final secret key. A critical step is the reconciliation process, especially when one assumes finite-size effects in a comp

Xing Liu, Tarig Ballal, Mohanad Ahmed, Tareq Y. Al-Naffouri

We present an ambiguity resolution method for Global Navigation Satellite System (GNSS)-based attitude determination. A GNSS attitude model with nonlinear constraints is used to rigorously incorporate a priori information. Given the characteristics of the employed nonlinear constraints, we formulate GNSS attitude determination as an optimization problem on a

Toshimasa Morita

In the Coulomb explosion acceleration regime, an ion bunch with a narrow energy range exhibits a thin shell shape with a certain diameter. The ion cloud has a layered structure of these ion bunches with different energies. The divergences of the ion bunch in the laser inclination direction and perpendicular to it are different in an oblique incidence laser.

Giuseppe Alberti

The study begins by considering an abstract object (cellular automaton) able of moving -- by arbitrary decision -- between two given fixed positions. That is, at each clock step, it can change position or remain stationary in its current position. This object, which we call an Arbitrary Oscillator (ArbO), cannot evolve indefinitely since it may encounter 'en

Rémy Sun, Alexandre Ramé, Clément Masson, Nicolas Thome

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even gene

Ján Koloda, Jürgen Seiler, André Kaup

This paper presents a novel method for the reconstruction of images from samples located at non-integer positions, called mesh. This is a common scenario for many image processing applications, such as super-resolution, warping or virtual view generation in multi-camera systems. The proposed method relies on a set of initial estimates that are later refined

Qingzhong Wang, Haifang Li, Haoyi Xiong, Wen Wang

While China has become the biggest online market in the world with around 1 billion internet users, Baidu runs the world largest Chinese search engine serving more than hundreds of millions of daily active users and responding billions queries per day. To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understa

Juan S. Cruz, Stephan Brandt, Maximilian Urban

We study the effects of a fixed de Sitter geometry background in scenarios of false vacuum decay. It is currently understood that bubble nucleation processes associated with first order phase transitions are particularly important in cosmology. The geometry of spacetime complicates the interpretation of the decay rate of a metastable vacuum. However, the eff

Xifeng Su, Philippe Thieullen

Liv\v{s}ic theorem for flows asserts that a Lipschitz observable that has zero mean average along every periodic orbit is necessarily a coboundary, that is the Lie derivative of a Lipschitz function smooth along the flow direction. The positive Liv\v{s}ic theorem bounds from below the observable by such a coboundary as soon as the mean average along every pe

S. Rossi, E. Talamas Simola, M. Raimondo, M. Acciarri

The Rashba effect in Ge/Si$_{0.15}$Ge$_{0.85}$ multiple quantum wells embedded in a p-i-n diode is studied through polarization and time-resolved photoluminescence. In addition to a sizeable redshift arising from the quantum-confined Stark effect, a threefold enhancement of the circular polarization degree of the direct transition is obtained by increasing t

Alexander Bakhtin, Abdullah Al Maruf, Tomas Cerny, Davide Taibi

It is well recognized that design patterns improve system development and maintenance in many aspects. While we commonly recognize these patterns in monolithic systems, many patterns emerged for cloud computing, specifically microservices. Unfortunately, while various patterns have been proposed, available quality assessment tools often do not recognize many

Omar Raii, Florian Mintert, Daniel Burgarth

The Kitaev honeycomb model is a system allowing for experimentally realisable quantum computation with topological protection of quantum information. Practical implementation of quantum information processing typically relies on adiabatic, i.e. slow dynamics. Here we show that the restriction to adiabatic dynamics can be overcome with optimal control theory,

Joseph A. Ball, Vladimir Bolotnikov, Sanne ter Horst

We study a general metric constrained interpolation problem in a de Branges-Rovnyak space $\mathcal{H}(K_S)$ associated with a contractive multiplier $S$ between two Fock spaces along with its commutative counterpart, a de Branges-Rovnyak space associated with a Schur multiplier on the Drury-Arveson space of the unit ball of $\mathbb{C}^n$.

Naian Liao

Local H\"older regularity is established for certain weak solutions to a class of parabolic fractional $p$-Laplace equations with merely measurable kernels. The proof uses DeGiorgi's iteration and refines DiBenedetto's intrinsic scaling method. The control of a nonlocal integral of solutions in the reduction of oscillation plays a crucial role and entails de

Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han

Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels can easily result in overfitting to nois

Y. Kutovyi, M. M. Jansen, S. Qiao, C. Falter

Isolated impurity states in epitaxially grown semiconductor systems possess important radiative features such as distinct wavelength emission with a very short radiative lifetime and low inhomogeneous broadening which makes them promising for the generation of indistinguishable single photons. In this study, we investigate chlorine-doped ZnSe/ZnMgSe quantum

Taihei Kimoto, Takayuki Miyadera

By preparing an input state and measuring an observable for the output state, we can measure a quantum channel. Following the formulation given by Xiao et al., we study an uncertainty relation for ancilla-free measurements of random unitary channels acting on a qubit. We obtain an explicit formula and give a necessary and sufficient condition for this formul

L. Domingo, G. Carlo, F. Borondo

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to per

David Ireland, Giovanni Montana

Combinatorial Optimisation problems arise in several application domains and are often formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are not always needed. Several heuristics have been developed to provide near-optimal solutions; however, they do not typically scale well with the size of the graph. We propose a low-co

Esther Galby, Dániel Marx, Philipp Schepper, Roohani Sharma

The Edge Multicut problem is a classical cut problem where given an undirected graph $G$, a set of pairs of vertices $\mathcal{P}$, and a budget $k$, the goal is to determine if there is a set $S$ of at most $k$ edges such that for each $(s,t) \in \mathcal{P}$, $G-S$ has no path from $s$ to $t$. Edge Multicut has been relatively recently shown to be fixed-pa

S. Paradiso, L. P. L. Colombo, K. J. Andersen, R. Aurlien

We present cosmological parameter constraints as estimated using the Bayesian BeyondPlanck (BP) analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data to final cosmological parameters. As a first demonstration of the method, we analyze time-ordered Planck LFI observations, combined with selected external dat

Soham Sen, Rituparna Mandal, Sunandan Gangopadhyay

The near horizon aspects (and beyond) of a black hole metric, which belongs to a large class of static spherically symmetric black holes, are considered here. It has been realized recently that an atom falling into a black hole leads to the generation of acceleration radiation through virtual transitions. In recent studies, it has been argued that this accel

Hyerin Jang, Beibei Liu, Anders Johansen

Streaming instability is hypothesized to be triggered at particular protoplanetary disk locations where the volume density of the solid particles is enriched comparable to that of the gas. A ring of planetesimals thus forms when this condition is fulfilled locally. These planetesimals collide with each other and accrete inward drifting pebbles from the outer

Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement. Among these algorithms, deep unfolding methods demonstrate promising performance but suffer from two issues. Firstly, they do not estimate the degradation

Jing Wang, Haotian Fan, Xiaoxia Hou, Yitian Xu

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains un settled due to the v

Jonas Sonnenschein, Mirian Tsulaia

Using the method of the "exact discretization" of the Schr\"odinger equation, we propose a particular discretized version of the N=2 Supersymmetric Quantum Mechanics. After defining the corresponding shape invariance condition, we show that the energy spectra and wavefunctions for discretized Quantum Mechanical systems can be found using the technique of N=2

Moshe White

An abstract simplicial complex is said to be $d$-representable if it records the intersection pattern of a collection of convex sets in $\mathbb{R}^d$. In this paper, we show that $d$-representability of a simplicial complex is equivalent to the existence of a map with certain properties, from a closely related simplicial complex into $\mathbb{R}^d$. This eq

Takuto Otomo, Hiroshi Kera, Kazuhiko Kawamoto

We address adversarial attacks on the actuators at the joints of legged robots trained by deep reinforcement learning. The vulnerability to the joint attacks can significantly impact the safety and robustness of legged robots. In this study, we demonstrate that the adversarial perturbations to the torque control signals of the actuators can significantly red

Kirill Melnikov

I review theoretical talks presented at the session on QCD and high-energy interactions of the Moriond 2022 conference.

Macarena Arenas, Daniel T. Wise

We show that word-hyperbolic groups satisfy linear isoperimetric functions for all homotopy types of surface diagrams. This generalises the linear isoperimetric functions for disc and annular diagrams.

Samuel Sousa, Roman Kern

Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures. Data protection laws, such as the European Union's General Data Protection Regulation (GDPR), thereby enforce the need for privacy. Although many privacy-preserving NLP methods have been proposed in recent ye

Francis Brown

To any graph with external half-edges and internal masses, we associate canonical integrals which depend non-trivially on particle masses and momenta, and are always finite. They are generalised Feynman integrals which satisfy graphical relations obtained from contracting edges in graphs, and a coproduct involving both ultra-violet and infra-red subgraphs. T

Tao Yang, Yuwang Wang, Yan Lu, Nanning Zheng

Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition. Towards this goal, we propose an unsupervised transformer-based Visual Concepts Tokenization framework, dub

Jayabrata Chowdhury, Suresh Sundaram, Nishanth Rao, Narasimhan Sundararajan

To ensure the safety and efficiency of its maneuvers, an Autonomous Vehicle (AV) should anticipate the future intentions of surrounding vehicles using its sensor information. If an AV can predict its surrounding vehicles' future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present such a Deep Spatio-Temporal Context-Awa

Shi-Xin Zhang, Jonathan Allcock, Zhou-Quan Wan, Shuo Liu

TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceler

Francisco J. Fernández, Ignacio Márquez Albés, F. Adrián F. Tojo

This work revolves around the study of differentiability in the Stieltjes sense of a product of functions. A formula for the first order derivative has been obtained in the past, which is similar to the usual one with some extra terms in its expression. The aim of this paper is to take this behavior into account to study under which conditions we can guarant

Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm, however, performs poorly with small batch sizes, and is inapplicable to differential privacy. To address these limitations, we propose the kernel normalization (KernelNorm) and kernel normalized convolutional lay

Hlynur D. Hlynsson, Steindór Ellertsson, Jón F. Daðason, Emil L. Sigurdsson

Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time c

A. Tichai, P. Arthuis, K. Hebeler, M. Heinz

It was recently observed that chiral two-body interactions can be efficiently represented using matrix factorization techniques such as the singular value decomposition. However, the exploitation of these low-rank structures in a few- or many-body framework is nontrivial and requires reformulations that explicitly utilize the decomposition format. In this wo

Ratha Siv, Matei Mancas, Bernard Gosselin, Dona Valy

In our previous paper, we introduced PoseTReID which is a generic framework for real-time 2D multi-person tracking in distributed interaction spaces where long-term people's identities are important for other studies such as behavior analysis, etc. In this paper, we introduce a further study of PoseTReID framework in order to give a more complete comprehensi

Anirban Paul, Dhananjay Nandi

Complete dissociation dynamics of low-energy electron attachment to carbon disulfide have been studied using the velocity slice imaging (VSI) technique. The ion yields of S- and CS- fragment ions as the function of incident electron energy in the range 5 to 11 eV have been obtained. Two resonances for S- ions at around 6.2 eV and 7.7 eV and only one resonanc

Pablo Sánchez-Palencia, Said Hamad, Pablo Palacios, Ricardo Grau-Crespo

Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination of density functional theory and machine learning, using the cubic spinel nitride GeSn$_2$N$_4$ as a case study, exploring how formation energy and elec

Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash

We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skeleton of causal graphs with cycles may not be possible from merely the observational distribution. Fu

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca

Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleatoric uncertainty is then typically quantified via the Bayes error, and epistemic uncertainty via the size of the set. In this paper, we extend the notion of calibration, which is com

Xiuxiu Bai, Zhe Liu, Yao Gao, Bin Liu

Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery. Grid cells (2014 Nobel Prize) in the entorhinal cortex support a periodic representation as a metric for coding space. Here, we develop a self-supervised convolutional neural network to perfor

Mark Deutel, Philipp Woller, Christopher Mutschler, Jürgen Teich

Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing technologies, such as the Internet of Things, interpreting large quantities of data generated by sensors is becoming an increasingly important task. However, in many applic

Raghav Singhal, Jiten C Kalita

In the present work, we propose a novel hybrid explicit jump immersed interface approach in conjunction with a higher order compact (HOC) scheme for simulating transient complex flows governed by the streamfunction-vorticity ($\psi$-$\zeta$) formulation of the Navier-Stokes (N-S) equations for incompressible viscous flows. A new strategy has been adopted for

John Hartley, Sotirios A. Tsaftaris

Neural networks pose a privacy risk due to their propensity to memorise and leak training data. We show that unique features occurring only once in training data are memorised by discriminative multi-layer perceptrons and convolutional neural networks trained on benchmark imaging datasets. We design our method for settings where sensitive training data is no

Maksud Sharipov, Ulugbek Salaev

This work presents a morphological analyzer for the Uzbek language using a finite state machine. The proposed methodology is a morphologic analysis of Uzbek words by using an affix striping to find a root and without including any lexicon. This method helps to perform morphological analysis of words from a large amount of text at high speed as well as it is

Thibaut Lacroix, Brendon W. Lovett, Alex W. Chin

Nanodevices exploiting quantum effects are critically important elements of future quantum technologies (QT), but their real-world performance is strongly limited by decoherence arising from local `environmental' interactions. Compounding this, as devices become more complex, i.e. contain multiple functional units, the `local' environments begin to overlap,

Nir Weinberger

The DNA storage channel is considered, in which a codeword is comprised of $M$ unordered DNA molecules. At reading time, $N$ molecules are sampled with replacement, and then each molecule is sequenced. A coded-index concatenated-coding scheme is considered, in which the $m$th molecule of the codeword is restricted to a subset of all possible molecules (an in

Aldo Antognini, Franziska Hagelstein, Vladimir Pascalutsa

Laser spectroscopy of muonic atoms has been recently used to probe properties of light nuclei with unprecedented precision. We introduce nuclear effects in hydrogen-like atoms, nucleon structure quantities (form factors, structure functions, polarizabilities) and their effects in the Lamb shift and hyperfine splitting (HFS) of muonic hydrogen ($\mu$H). Updat

Giovanni De Gasperis, Sante Dino Facchini, Alessio Susco

Tax credit stimulus and fiscal bonuses had a very important impact on Italian economy in the last decade. Along with a huge expansion in constructions a relevant increase in scams and frauds has come too. The aim of this article is to design a possible system to track and control the whole tax credit process from its generation to its redeem through a Decent

Markus Hittmeir

This paper elaborates on a sieving technique that has first been applied in 2018 for improving bounds on deterministic integer factorization. We will generalize the sieve in order to obtain a polynomial-time reduction from integer factorization to a specific instance of the multiple-choice subset-sum problem. As an application, we will improve upon special p

Philip Beck, Lucas Schneider, Roland Wiesendanger, Jens Wiebe

Magnetic adatoms coupled to an $s$-wave superconductor give rise to local bound states, so-called Yu-Shiba-Rusinov states. Focusing on the ultimate goal of tailoring chains of such adatoms into a topologically superconducting phase, we investigate basic building blocks - single Fe and Mn adatoms and Mn dimers on clean superconducting Ta(110) - using scanning

Jesper Larsson Träff

We give a fast(er), communication-free, parallel construction of optimal communication schedules that allow broadcasting of $n$ distinct blocks of data from a root processor to all other processors in $1$-ported, $p$-processor networks with fully bidirectional communication. For any $p$ and $n$, broadcasting in this model requires $n-1+\lceil\log_2 p\rceil$

Razvan Brinzea, Bulat Khaertdinov, Stylianos Asteriadis

Human Activity Recognition is a field of research where input data can take many forms. Each of the possible input modalities describes human behaviour in a different way, and each has its own strengths and weaknesses. We explore the hypothesis that leveraging multiple modalities can lead to better recognition. Since manual annotation of input data is expens

Max Klabunde, Florian Lemmerich

Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems. In this paper, we systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs). With our experiments, we establish t

V. V. Val'kov, M. S. Shustin, S. V. Aksenov, A. O. Zlotnikov

We discuss the properties of topologically nontrivial superconducting phases and the conditions for their realization in condensed matter, and the principles for identifying Majorana bound states (MBSs). Along with the well-known Kitaev chain and superconducting nanowire (SW) models with spin-orbit coupling in an external magnetic field, we discuss models of

Wenxuan Wang, Wenxiang Jiao, Shuo Wang, Zhaopeng Tu

Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation~(MNMT) system. However, its quality is still not satisfactory due to off-target issues. In this paper, we aim to understand and alleviate the off-target issues from the perspective of uncertainty in zero-shot translation. By carefully examining

S. S. Manna

The well-known problem of gradient percolation has been revisited to study the probability distribution of island sizes. It is observed that as the ordinary percolation, this distribution is also described by a power-law decaying function but the associated critical exponents are found to be different. Because of the underlying gradient for the occupation pr

Arzu Kurt, Matteo A. C. Rossi, Jyrki Piilo

We report the results of an in-depth study of the role of graph topology on quantum transport efficiency in random removal and Watts-Strogatz networks. By using four different environmental models -- noiseless, driving by classical random telegraph noise (RTN), thermal quantum bath, and bath+RTN -- we compare the role of the environment and of the change in

Kanghui He, Shengling Shi, Ton van den Boom, Bart De Schutter

Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies to overcome the

Giacinto Gelli, Ivan Iudice, Domenico Pascarella

Integration of Unmanned Aerial Vehicles (UAVs) or "drones" into the civil aviation airspace is a problem of increasing interest in the aviation community, as testified by many initiatives developed worldwide. Many traditional surveillance solutions for manned aircrafts employ the Automatic Dependent System-Broadcast (ADS-B) technology, which however might pr

Xiang Li, Wenhai Wang, Lingfeng Yang, Jian Yang

Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-training efficiency and fine-tuning accuracy. Notably, the success of the asymmetric structure relies on the "global" property of Vanilla Vision Transformer (ViT), whose self-attentio

Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li

PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-t