October 2022 arXiv papers — page 60

Nicholas St. John, Soumyajit Mandal, Grzegorz W. Deptuch, Eric Raguzin

A line driver with configurable pre-emphasis is implemented in a 65 nm CMOS process. The driver utilizes a three-tap feed-forward equalization (FFE) architecture. The relative delays between the taps are selectable in increments of 1/16th of the unit interval (UI) via an 8-stage delay-locked loop (DLL) and digital interpolator. It is also possible to control

Pradip Pramanick, Chayan Sarkar

The usage of automatic speech recognition (ASR) systems are becoming omnipresent ranging from personal assistant to chatbots, home, and industrial automation systems, etc. Modern robots are also equipped with ASR capabilities for interacting with humans as speech is the most natural interaction modality. However, ASR in robots faces additional challenges as

Enrico Angelelli, Renata Mansini, Romeo Rizzi

The profitable tour problem (PTP) is a well-known NP-hard routing problem searching for a tour visiting a subset of customers while maximizing profit as the difference between total revenue collected and traveling costs. PTP is known to be solvable in polynomial time when special structures of the underlying graph are considered. However, the computational c

Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian, David Gesbert

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) serve

Wei Ju, Yiyang Gu, Binqi Chen, Gongbo Sun

This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent years have witnessed the success of deep clustering coupled with graph neural networks (GNNs). However, existing methods focu

M. A. Lara-Lopez, L. S. Pilyugin, J. Zaragoza-Cardiel, I. A. Zinchenko

NGC~6946, also known as the `Fireworks' galaxy, is an unusual galaxy that hosts a total of 225 supernova remnant (SNR) candidates, including 147 optically identified with high [SII]/Ha line ratios. In addition, this galaxy shows prominent HI holes, which were analyzed in previous studies. Indeed, the connection between SNRs and HI holes together with their p

Murilo Marques Marinho, Juan José Quiroz-Omaña, Kanako Harada

There are a large number of robotic platforms with two or more arms targeting surgical applications. Despite that, very few groups have employed such platforms for scientific exploration. Possible applications of a multi-arm platform in scientific exploration involve the study of the mechanisms of intractable diseases by using organoids, i.e., miniature huma

Tom Reichert, Jan Steinheimer, Volodymyr Vovchenko, Benjamin Dönigus

A comparison of light hypernuclei production, from UrQMD+coalescence and the thermal model, in heavy ion collisions over a wide range of beam energies and system sizes is presented. We find that both approaches provide generally similar results, with differences in specific details. Especially the ratios of hypertriton to $\Lambda$ are affected by both the s

Alexandre R. Coates, Brendon W. Lovett, Erik M Gauger

Understanding energy transport in quantum systems is crucial for an understanding of light-harvesting in nature, and for the creation of new quantum technologies. Open quantum systems theory has been successfully applied to predict the existence of environmental noise-assisted quantum transport (ENAQT) as a widespread phenomenon occurring in biological and a

Alberto Natali, Geert Leus

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering

Tommaso Cea

We study the inter-valley scattering induced by the Coulomb repulsion as a purely electronic mechanism for the origin of superconductivity in few layers of graphene. The pairing is strongly favored by the presence of van Hove singularities (VHS's) in the density of states (DOS). We consider three different hetherostructures: twisted bilayer graphene (TBG), r

Sofia Fellini, Massimo Marro, Annika Vittoria Del Ponte, Marilina Barulli

Greening cities is a key solution to improve the urban microclimate and mitigate the impact of climate change. However, the effect of tree planting on pollutant dispersion in streets is still a debated topic. To shed light on this issue, we present a wind-tunnel experiment aimed at investigating the effect of trees on street canyon ventilation. An idealized

H. C . Shi, B. Wang, H. Y. Shi, C. H. Yu

A high-level beam background is a crucial challenge to future upgrades to the BEPCII collider. We report on the first separate measurement of the main beam background components at BEPCII. The separation measurement enables the beam background extrapolation towards the beam parameters assumed for the upgraded BEPCII. The measured rates of each background com

Minchul Lee, Kijong Han, Myeong Cheol Shin

BERT has shown a lot of sucess in a wide variety of NLP tasks. But it has a limitation dealing with long inputs due to its attention mechanism. Longformer, ETC and BigBird addressed this issue and effectively solved the quadratic dependency problem. However we find that these models are not sufficient, and propose LittleBird, a novel model based on BigBird w

Dmitry A. Pasechnyuk, Alexander Gasnikov, Martin Takáč

The paper studies the properties of stochastic gradient methods with preconditioning. We focus on momentum updated preconditioners with momentum coefficient $\beta$. Seeking to explain practical efficiency of scaled methods, we provide convergence analysis in a norm associated with preconditioner, and demonstrate that scaling allows one to get rid of gradien

Thimo M. Kasper, Nicolas Dietrich, Wolfgang Trutschnig

Motivated by a recently established result saying that within the class of bivariate Archimedean copulas standard pointwise convergence implies weak convergence of almost all conditional distributions this contribution studies the class $\mathcal{C}_{ar}^d$ of all $d$-dimensional Archimedean copulas with $d \geq 3$ and proves the afore-mentioned implication

Georg Gottwald, Ian Melbourne

We give a complete description and clarification of the structure of the Levy area correction to Ito/Stratonovich stochastic integrals arising as limits of time-reversible deterministic dynamical systems. In particular, we show that time-reversibility forces the Levy area to vanish only in very specific situations that are easily classified.

Marion Vanwelde, Cédric Hernalsteens, S. Alex Bogacz, Shinji Machida

The coupling of transverse motion is a natural occurrence in particle accelerators, either in the form of a residual coupling arising from imperfections or originating by design from strong systematic coupling fields. While the first can be treated perturbatively, the latter requires a robust approach adapted to strongly coupled optics and a parametrization

Jiaju Zhang, M. A. Rajabpour

In this paper, we propose a novel truncation method for determining the trace distance between two Gaussian states in fermionic systems. For two fermionic Gaussian states, characterized by their correlation matrices, we consider the von Neumann entropies and dissimilarities between their correlation matrices and truncate the correlation matrices to facilitat

Xiang Wang, Elena V. Konstantinova

V. Levenshtein first proposed the sequence reconstruction problem in 2001. This problem studies the model where the same sequence from some set is transmitted over multiple channels, and the decoder receives the different outputs. Assume that the transmitted sequence is at distance $d$ from some code and there are at most $r$ errors in every channel. Then th

Meenakshi Sharma

The ALICE experiment has studied strangeness production in different collision systems (pp, p-Pb, Xe-Xe and Pb-Pb) and energies. The ratio of the strange particle yield to pion yield as a function of multiplicity for different collision energies and systems follow a continuously increasing trend ("enhancement") from low multiplicity pp to high-multiplicity P

Takahiro Aoi

Let $(X,L_X)$ be a polarized manifold and $D$ be a smooth hypersurface such that $D \in | L_X |$. In this paper, we show that if there is no nontrivial holomorphic vector field on $D$ and ${\rm Aut}_0 ((X,L_X); D)$ is trivial, then constant scalar curvature K\"{a}hler metrics of Poincar\'{e} type on $X \setminus D$ can be approximated by constant scalar curv

Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics. Contextualized embeddings have analogously been found to capture these phenomena at distinctive layers and frequencies. Leveraging these findings, we develop a fully learnable frequency filter to identify spectral profiles

Lionel Soulhac, Sofia Fellini, Chi Vuong Nguyen, Pietro Salizzoni

To predict pollutant concentration in urban areas, it is crucial to take into account the chemical transformations of reactive pollutants in operational dispersion models. In this work, we derive and discuss different NO-NO2-O3 chemical street canyon models with increasing complexity and we analytically evaluate their applicability in different urban context

Xiaoyu Feng, Zhonghua Qiao, Shuyu Sun, Xiuping Wang

Varieties of energy-stable numerical methods have been developed for incompressible two-phase flows based on the Navier-Stokes-Cahn-Hilliard (NSCH) model in the Eulerian framework, while few investigations have been made in the Lagrangian framework. Smoothed particle hydrodynamics (SPH) is a popular mesh-free Lagrangian method for solving complex fluid flows

A. Moór, P. Ábrahám, Á. Kóspál, K. Y. L. Su

Extreme debris discs are characterized by unusually strong mid-infrared excess emission, which often proves to be variable. The warm dust in these discs is of transient nature and is likely related to a recent giant collision occurring close to the star in the terrestrial region. Here we present the results of a 877 days long, gap-free photometric monitoring

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Supervised learning in reproducing kernel Hilbert space (RKHS) and vector-valued RKHS (vvRKHS) has been investigated for more than 30 years. In this paper, we provide a new twist to this rich literature by generalizing supervised learning in RKHS and vvRKHS to reproducing kernel Hilbert $C^*$-module (RKHM), and show how to construct effective positive-defini

Shinya Sugiyama, Tommaso Ghigna, Yurika Hoshino, Nobuhiko Katayama

We measured the vibration of a prototype superconducting magnetic bearing (SMB) operating at liquid nitrogen temperature. This prototype system was designed as a breadboard model for LiteBIRD low-frequency telescope (LFT) polarization modulator unit. We set an upper limit of the vibration amplitude at $36~\mathrm{\mu m}$ at the rotational synchronous frequen

Filip Blaschke, Petr Beneš

We present a "primitive" way of realizing finite-mass Dirac monopoles in $U(1)$ gauge theories involving a single non-minimally interacting scalar field. Typically, the energy density of this type of monopole is not concentrated at its core, but it is distributed in a spherical shell, as we illustrate on several exact solutions in the Bogomol'nyi-Prasad-Somm

Pedro D. Alvarez, Benjamin Koch, Cristobal Laporte, Angel Rincon

This paper examines a cosmological model of scale-dependent gravity. The gravitational action is taken to be the Einstein-Hilbert term supplemented with a cosmological constant, where the couplings, $G_k$ and $\Lambda_k$, run with the energy scale $k$. % Also, notice that, by construction, our formalism recovers general relativity when in the limit of consta

Pablo Dorta-González

Purpose: This paper explores some influencing factors of Twitter mentions of scientific research. The results can help to understand the relationships between various altmetrics. Design/methodology/approach: Data on research mentions in Altmetric and a multiple linear regression analysis are used. Findings: Among the variables analyzed, the number of mainstr

Marta Molero, Francesca Matteucci, Luca Ciotti

We study the formation and evolution of elliptical galaxies and how they suppress star formation and maintain it quenched. A one-zone chemical model which follows in detail the time evolution of gas mass and its chemical abundances during the active and passive evolution, is adopted. The model includes both gas infall and outflow as well as detailed stellar

Marco Fanizza, Michalis Skotiniotis, John Calsamiglia, Ramon Muñoz-Tapia

Operating quantum sensors and quantum computers would make data in the form of quantum states available for purely quantum processing, opening new avenues for studying physical processes and certifying quantum technologies. In this Perspective, we review a line of works dealing with measurements that reveal structural properties of quantum datasets given in

A. F. Krasnikov

In this paper we prove the theorem on freedom for free sums of Lie algebras with a single relation (analogous with the well-known result of Shirshov) and a generalized Freiheitssatz for free sums of Lie algebras (analogous with the well-known result of Kharlampovich).

Gerard Navó, Moritz Reichert, Martin Obergaulinger, Almudena Arcones

We present core-collapse supernova simulations including nuclear reaction networks that impact explosion dynamics and nucleosynthesis. The different composition treatment can lead to changes in the neutrino heating in the vicinity of the shock by modifying the number of nucleons and thus the neutrino-opacity of the region. This reduces the ram pressure outsi

Juan Cabello-Sánchez, Vladimir Drakinskiy, Jan Stake, Helena Rodilla

The planar Goubau line is a promising low-loss metal waveguide for terahertz interconnects. To enable advanced system architectures and multi-port measurements based on planar Goubau lines, there is a strong need for broadband impedance-matched loads, which can be used to absorb the energy and minimize standing waves in a system. In this work, we propose a m

Jasmina Gajcin, Ivana Dusparic

While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations

David Widmann, Fredrik Lindsten, Dave Zachariah

Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point estimates. Such models can be a valuable tool in decision-making under uncertainty, provided that the model output

Thomas Izgin, Stefan Kopecz, Andreas Meister

Modified Patankar-Runge-Kutta (MPRK) methods preserve the positivity as well as conservativity of a production-destruction system (PDS) of ordinary differential equations for all time step sizes. As a result, higher order MPRK schemes do not belong to the class of general linear methods, i.e. the iterates are generated by a nonlinear map $\mathbf g$ even whe

Xenia Miscouridou, Samir Bhatt, George Mohler, Seth Flaxman

Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can model the occurrence of the times and locations of events. Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior

Rui Xie, Tianxiang Hu, Wei Ye, Shikun Zhang

Code summarization generates brief natural language descriptions of source code pieces, which can assist developers in understanding code and reduce documentation workload. Recent neural models on code summarization are trained and evaluated on large-scale multi-project datasets consisting of independent code-summary pairs. Despite the technical advances, th

Kristof Takacs, Balint Varga, Viktor Farkas, Andras Perczel

Methods from artificial intelligence (AI), in general, and machine learning, in particular, have kept conquering new territories in numerous areas of science. Most of the applications of these techniques are restricted to the classification of large data sets, but new scientific knowledge can seldom be inferred from these tools. Here we show that an AI-based

Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein

Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are 'small' but 'realistic' semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts, or are limit

Reiner S. Thomä, Carsten Andrich, Julia Beuster, Heraldo Cesar Alves Costa

Integrated sensing and communication (ISAC) qualifies mobile radio systems for detecting and localizing of passive objects by means of radar sensing. Advanced ISAC networks rely on meshed mobile radio access nodes (infrastructure and/or user equipment, resp.) establishing a distributed, multistatic MIMO radar system in which each target reveals itself by its

Nathania Santoso, Haris Javaid

Permissioned blockchains like Hyperledger Fabric have become quite popular for implementation of enterprise applications. Recent research has mainly focused on improving performance of permissioned blockchains without any consideration of their power/energy consumption. In this paper, we conduct a comprehensive empirical study to understand energy efficiency

Yuxuan Yang

In this paper, we continue the study of locating-paired-dominating set, abbreviated LPDS, in graphs introduced by McCoy and Henning. Given a finite or infinite graph $G=(V,E)$, a set $S\subset V$ is paired-dominating if the induced subgraph $G[S]$ has a perfect matching and every vertex in $V$ is adjacent to a vertex in $S$. The other condition for LPDS requ

Aglaia Aggistrioti, Efstathia Bambili, Nikoleta Gkatzoli, Athina Kontostavlaki

Between 2019 and 2022, Greek media attention has been attracted by a rather unusually high number of femicide cases which have been trending for several weeks up to months in the public debate and one of the contributing factors is the feedback loop between traditional media and social media. In this paper we are investigating the use of the term "\{gamma}\u

Matthias Bitzer, Mona Meister, Christoph Zimmer

Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, evaluating the model selection criteria for Gaussian processes typically scales cubically in the sample size, re

Laurent Besacier, Swen Ribeiro, Olivier Galibert, Ioan Calapodescu

In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-the-art speech2unit encoders like HuBERT to convert speech utterances into discrete acoustic units. We then propose a simple and easily replicable neural architecture that learns a s

Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang

We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form. The challenge is to maximize the total reward without violating the budget for each resource. We study this problem under a general realizability set

Xiren Zhou, Shikang Liu, Ao Chen, Yizhan Fan

Ground Penetrating Radar (GPR) has been widely used to estimate the healthy operation of some urban roads and underground facilities. When identifying subsurface anomalies by GPR in an area, the obtained data could be unbalanced, and the numbers and types of possible underground anomalies could not be acknowledged in advance. In this paper, a novel method is

Richard G. Freedman

Since the first AI-HRI held at the 2014 AAAI Fall Symposium Series, a lot of the presented research and discussions have emphasized how artificial intelligence (AI) developments can benefit human-robot interaction (HRI). This portrays HRI as an application, a source of domain-specific problems to solve, to the AI community. Likewise, this portrays AI as a to

Marius Schlegel, Kai-Uwe Sattler

The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iteratio

Antoine Henry, Ravi Raghunathan, Guillaume Ricard, Baptiste Lefaucher

We propose novel methods for the exact synthesis of single-qubit unitaries with high success probability and gate fidelity, considering both time-bin and frequency-bin encodings. The proposed schemes are experimentally implementable with a spectral linear-optical quantum computation (S- LOQC) platform, composed of electro-optic phase modulators and phase-onl

Antonio Laface, Luca Ugaglia

Let $C$ be a smooth curve of genus $g \geq 1$ and let $C^{(2)}$ be its second symmetric product. In this note we prove that if $C$ is very general, then the blow-up of $C^{(2)}$ at a very general point has non-polyhedral pseudo-effective cone. The strategy is to consider first the case of hyperelliptic curves and then to show that having polyhedral pseudo-ef

Peter Müllner, Stefan Schmerda, Dieter Theiler, Stefanie Lindstaedt

Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the appl

San-Dong Guo, Yu-Ling Tao, Zhuo-Yan Zhao, Bing Wang

One of the key problems in valleytronics is to realize valley polarization. Ferrovalley (FV) semiconductor and half-valley-metal (HVM) have been proposed, which possess intrinsic spontaneous valley polarization. Here, we propose the concept of quasi-half-valley-metal (QHVM), including electron and hole carriers with only a type of carriers being valley polar

Beerend G. A. Gerats, Jelmer M. Wolterink, Ivo A. M. J. Broeders

3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition. However, the surgical environment makes it challenging to find poses due to sterile clothing, frequent occlusions, and limited public data. Methods specifically designed for the OR are generally based on the fusion of detected pos

Yael Septon, Tobias Huber, Elisabeth André, Ofra Amir

Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and delayed rewards. Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and gl

Roland Assaraf, Emmanuel Giner, Vijay Gopal Chilkuri, Pierre-François Loos

The sampling of the configuration space in diffusion Monte Carlo (DMC) is done using walkers moving randomly. In a previous work on the Hubbard model [\href{https://doi.org/10.1103/PhysRevB.60.2299}{Assaraf et al.~Phys.~Rev.~B \textbf{60}, 2299 (1999)}], it was shown that the probability for a walker to stay a certain amount of time in the same state obeys a

David Winderl, Nicola Franco, Jeanette Miriam Lorenz

Variational Quantum optimization algorithms, such as the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabili

Oliver Ester, Fabian Hörst, Constantin Seibold, Julius Keyl

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and th

Enrico Morgante, Nicklas Ramberg, Pedro Schwaller

We analyze the phase transition in improved holographic QCD to obtain an estimate of the gravitational wave signal emitted in the confinement transition of a pure SU(3) Yang-Mills dark sector. We derive the effective action from holography and show that the energy budget and duration of the phase transition can be calculated with minor errors. These are used

Pablo Donato, Pierre-Yves Strub, Benjamin Werner

We explore the features of a user interface where formal proofs can be built through gestural actions. In particular, we show how proof construction steps can be associated to drag-and-drop actions. We argue that this can provide quick and intuitive proof construction steps. This work builds on theoretical tools coming from deep inference. It also resumes an

Qingchu Wu, Tarik Hadzibeganovic, Xiao-Pu Han

Understanding the interplay between human behavioral phenomena and infectious disease dynamics has been one of the central challenges of mathematical epidemiology. However, socio-cognitive processes critical for the initiation of desired behavioral responses during an outbreak have often been neglected or oversimplified in earlier models. Combining the micro

Yvon Bossut

We adapt the properties of Kim-independence in NSOP1 theories with existence proven in [5],[4] and [2] by Ramsey, Kaplan, Chernikov, Dobrowolski and Kim to hyperimaginaries by adding the assumption of existence for hyperimaginaries. We show that Kim-independence over hyperimaginaries satisfies a version of Kim's lemma, symmetry, the independence theorem, tra

Shuche Wang, Yuanyuan Tang, Jin Sima, Ryan Gabrys

The problem of correcting deletions has received significant attention, partly because of the prevalence of these errors in DNA data storage. In this paper, we study the problem of correcting a consecutive burst of at most $t$ deletions in non-binary sequences. We first propose a non-binary code correcting a burst of at most 2 deletions for $q$-ary alphabets

Jingqi Li, Jiaqi Gao, Yuzhen Zhang, Hongming Shan

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a n

A. Di Siena, P. Rodriguez-Fernandez, N. T. Howard, A. Banon Navarro

The recent progress in high-temperature superconductor technologies has led to the design and construction of SPARC, a compact tokamak device expected to reach plasma breakeven with up to $25$MW of external ion cyclotron resonant heating (ICRH) power. This manuscript presents local (flux-tube) and radially global gyrokinetic GENE (Jenko et al 2000 Phys. Plas

Jules BOURCIER, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier

In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances. Such data are challenging to obtain as it requires military experts, and some observables are intrinsically rare. This limited labeling capability, as well as the lar

Philippe Chassaing, Jules Flin, Alexis Zevio

We consider four examples of combinatorial triangles $\left(T(n,k)\right)_{0\le k\le n}$ (Pascal, Stirling of both types, Euler) : through saddle-point asymptotics, their \emph{Pascal's formulas} define four vector fields, together with their field lines that turn out to be the conjectured limit of sample paths of four well known Markov chains. We prove this

Qing Wang, Rui Song, Ning Hao

We propose that a spin-dependent second-order topological insulator can be realized in monolayer FeSe/GdClO heterostructure, in which substrate GdClO helps to stabilize and enhance the antiferromagnetic order in FeSe. The second-order topological insulator is free from spin-orbit coupling and in-plane magnetic field. We also find that there exist two types o

Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume

We propose a new neural network, called isomorphic mesh generator (iMG), which generates isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects have a unified mesh structure even though the objects belong to different classes. This unified representation enables surface models to be handled by DNNs. Mor

Thierry Bodineau, Isabelle Gallagher, Laure Saint-Raymond, Sergio Simonella

We derive linear fluctuating hydrodynamics as the low density limit of a deterministic system of particles at equilibrium. The proof builds upon previous results of the authors where the asymptotics of the covariance of the fluctuation field is obtained, and on the proof of the Wick rule for the fluctuation field.

Yuehan Yang

This paper models categorical data with two or multiple responses, focusing on the interactions between responses. We propose an efficient iterative procedure based on sufficient dimension reduction. We study the theoretical guarantees of the proposed method under the two- and multiple-response models, demonstrating the uniqueness of the proposed estimator a

Moshe Eliasof, Nir Ben Zikri, Eran Treister

Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harnesses recent advances in unsupervised learning using a combination of Mutual Information Maximization (MIM), Neural Superpixel Segmentation and Graph Neural Networks (GNNs) in an e

Hannah Diamond-Lowe, João M. Mendonça, David Charbonneau, Lars A. Buchhave

Nearby M dwarf systems currently offer the most favorable opportunities for spectroscopic investigations of terrestrial exoplanet atmospheres. The LTT~1445 system is a hierarchical triple of M dwarfs with two known planets orbiting the primary star, LTT~1445A. We observe four transits of the terrestrial world LTT~1445Ab ($R=1.3$ R$_\oplus$, $M=2.9$ M$_\oplus

Kaixin Kang, Jingtao Shi

This paper is concerned with a three-level stochastic linear-quadratic Stackelberg differential game with asymmetric information, in which three players participate credited as Player 1, Player 2 and Player 3. Player 3 acts as the leader of Player 2 and Player 1, Player 2 acts as the leader of Player 1 and Player 1 acts as the follower. The asymmetric inform

Yingbo Gao, Christian Herold, Zijian Yang, Hermann Ney

Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. For machine translation, despite the evolution from long short-term memory networks to Transformer networks, plus the introduction and development of attention mechanism, encoder-decoder is still the de facto neural network architecture for state-of-the-art models. While

Li Chong, Denghao Ma, Yueguo Chen

As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning techniques. Distinct from the previous solutions, we propose to construct fine-grained semantic representations of a question by a learned

Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester

For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Among recent models aiming to avoid this spurious pattern problem, adding extra counterfactual samples to the training data has proven to be ver

Yunhua Zhou, Peiju Liu, Yuxin Wang, Xipeng QIu

Discovering new intents is of great significance to establishing Bootstrapped Task-Oriented Dialogue System. Most existing methods either lack the ability to transfer prior knowledge in the known intent data or fall into the dilemma of forgetting prior knowledge in the follow-up. More importantly, these methods do not deeply explore the intrinsic structure o

Yingbo Gao, Christian Herold, Zijian Yang, Hermann Ney

Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models. The calculation is cheap to perform and the fact that the translation improvement almost comes for free, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean

Fatma Chaari, Sébastien Boyas, Sonia Sahli, Thouraya Fendri

Background: Recent studies reported postural balance disorders in patients and soccer players with groin pain (GP) compared to controls. Since postural balance asymmetry identified after an initial injury contributes for subsequent injuries, identification of this asymmetry in soccer players with GP may highlight the risk of sustaining subsequent noncontact

Elias Hanna, Alex Coninx, Stéphane Doncieux

This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model. Dynamics models approximate the true transition function of a given task, in order to perform policy search directly on the model rather than on the costly real system. This study aims to determine how to bootstrap a model as efficiently as poss

Zhen Wan, Qianying Liu, Zhuoyuan Mao, Fei Cheng

Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation types, caused by language complexity and data sparsity. In this paper, we introduce a simple enhancement of RE using $k$ nearest neigh

Rui Ge, Xiongshuo Yan, Zhaokang Liang, Hao Li

In this paper, by cascading several bichromatic photonic crystals we demonstrate that the quality factor can be much larger compared with that in an isolated cavity without increasing the total size of the device. We take lithium niobate photonic crystal as an example to illustrate that the simulated quality factor of the cascaded cavity can attain 10^5 with

Alicja Jaworska-Pastuszak, Grzegorz Pastuszak, Grzegorz Bobiński

Assume that $K$ is an algebraically closed field and denote by $KG(R)$ the Krull-Gabriel dimension of $R$, where $R$ is a locally bounded $K$-category (or a bound quiver $K$-algebra). Assume that $C$ is a tilted $K$-algebra and $\widehat{C},\check{C},\widetilde{C}$ are the associated repetitive category, cluster repetitive category and cluster-tilted algebra

Jack D. Betteridge, Colin J. Cotter, Thomas H. Gibson, Matthew J. Griffith

Compatible finite element discretisations for the atmospheric equations of motion have recently attracted considerable interest. Semi-implicit timestepping methods require the repeated solution of a large saddle-point system of linear equations. Preconditioning this system is challenging since the velocity mass matrix is non-diagonal, leading to a dense Schu

Christopher Diehl, Janis Adamek, Martin Krüger, Frank Hoffmann

Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundaries (e.g., obstacles) restrict the robot's motions. Direct methods from optimal control solve a constrained optimization problem. However, in many applications finding a proper cost

Zhile Yang, Shangqi Guo, Ying Fang, Jian K. Liu

One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware. Among them, reward-modulated spike-timing-dependent plasticity (R-STDP) is a recent branch with good potential in energy efficiency. However, current R-STDP methods rely on heuristic design

Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-Noguer

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we

Aosong Feng, Irene Li, Yuang Jiang, Rex Ying

Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity. Sparse Transformer is a popular approach to improving the efficiency of Transformers by restricting self-attention to locations specified by the predefined sparse patterns. However, leveraging sparsity may sacrifice expressiveness comp

V. V. Bobylev, A. T. Bajkova

A linear Ogorodnikov-Milne model is applied to study the three-dimensional kinematics of classical Cepheids in the Milky Way. A sample of 832 classical Cepheids from Mr'oz et al. (2019) with distances, line-of-sight velocities, and proper motions from the Gaia DR2 catalogue is used. The Cepheid space velocities have been freed from the differential Galactic

Aleksejus Kononovicius, Bronislovas Kaulakys

We analyze the power spectral density of a signal composed of nonoverlapping rectangular pulses. First, we derive a general formula for the power spectral density of a signal constructed from the sequence of nonoverlapping pulses. Then we perform a detailed analysis of the rectangular pulse case. We show that pure $1/f$ noise can be observed until extremely

Eric Chan, Marek Chrobak, Mohsen Lesani

Extreme valuation and volatility of cryptocurrencies require investors to diversify often which demands secure exchange protocols. A cross-chain swap protocol allows distrusting parties to securely exchange their assets. However, the current models and protocols assume predefined user preferences for acceptable outcomes. This paper presents a generalized mod

Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar

Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the cur

Zhongzi Wang, Ying Zhang

We explicitly find the minima as well as the minimum points of the geodesic length functions for the family of filling (hence non-simple) closed curves, $a^2b^n$ ($n\ge 3$), on a complete one-holed hyperbolic torus in its relative Teichm\"uller space, where $a, b$ are simple closed curves on the one-holed torus which intersect exactly once transversely. This

Khyati Sharma, A. Satyanarayana Reddy

A finite group is said to be $n$-cyclic if it contains $n$ cyclic subgroups. For a finite group $G$, the ratio of the number of cyclic subgroups to the number of subgroups is known as the cyclicity degree of the group $G$ and is denoted by $cdeg (G)$. In this paper, we classify all $12$-cyclic groups. We also prove that the set of cyclicity degrees for all t

Prafulla Kumar Choubey, Ruihong Huang

We propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs. Our key observation is that the functional roles of sentences used for profiling news discourse signify different time frames relevant to a news story and can, therefore, help to recover the global temporal structure of a docum