December 2020 arXiv papers — page 133

Taosha Fan, Todd Murphey

In this paper, we generalize proximal methods that were originally designed for convex optimization on normed vector space to non-convex pose graph optimization (PGO) on special Euclidean groups, and show that our proposed generalized proximal methods for PGO converge to first-order critical points. Furthermore, we propose methods that significantly accelera

Ilya Archakov, Peter Reinhard Hansen, Asger Lunde

We propose a novel class of multivariate GARCH models that incorporate realized measures of volatility and correlations. The key innovation is an unconstrained vector parametrization of the conditional correlation matrix, which enables the use of factor models for correlations. This approach elegantly addresses the main challenge faced by multivariate GARCH

Harald Garcke, Robert Nürnberg

We present variational approximations of boundary value problems for curvature flow (curve shortening flow) and elastic flow (curve straightening flow) in two-dimensional Riemannian manifolds that are conformally flat. For the evolving open curves we propose natural boundary conditions that respect the appropriate gradient flow structure. Based on suitable w

Nicolas Wagner, Anirban Mukhopadhyay

Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning. Features can be learned with 13 algorithms that span from simple classification to more complex state of theart contrastive pretext tasks. The framework is easy to use and allows for pretraining any PyTorch neural network with only two lines of code. Simultaneously, f

Kaiyu Zheng, Deniz Bayazit, Rebecca Mathew, Ellie Pavlick

Humans use spatial language to naturally describe object locations and their relations. Interpreting spatial language not only adds a perceptual modality for robots, but also reduces the barrier of interfacing with humans. Previous work primarily considers spatial language as goal specification for instruction following tasks in fully observable domains, oft

Simeon Kublenz, Sebastian Siebertz, Alexandre Vigny

We show that the dominating set problem admits a constant factor approximation in a constant number of rounds in the LOCAL model of distributed computing on graph classes with bounded expansion. This generalizes a result of Czygrinow et al. for graphs with excluded topological minors.

Simon Boudet, Flavio Bombacigno, Giovanni Montani, Massimiliano Rinaldi

In the context of f(R) generalizations to the Holst action, endowed with a dynamical Immirzi field, we derive an analytic solution describing asymptotically Anti-de Sitter black holes with hyperbolic horizon. These exhibit a scalar hair of the second kind, which ultimately depends on the Immirzi field radial behaviour. In particular, we show how the Immirzi

Ilya Archakov, Peter Reinhard Hansen

We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Ga

LHCb collaboration, R. Aaij, C. Abellán Beteta, T. Ackernley

Long-lived particles decaying to $e^\pm μ^\mp ν$, with masses between 7 and $50$ GeV/c$^2$ and lifetimes between 2 and $50$ ps, are searched for by looking at displaced vertices containing electrons and muons of opposite charges. The search is performed using $5.4$ fb$^{-1}$ of $pp$ collisions collected with the LHCb detector at a centre-of-mass energy of $\

María Cumplido, Alexandre Martin, Nicolas Vaskou

We show that the geometric realisation of the poset of proper parabolic subgroups of a large-type Artin group has a systolic geometry. We use this geometry to show that the set of parabolic subgroups of a large-type Artin group is stable under arbitrary intersections and forms a lattice for the inclusion. As an application, we show that parabolic subgroups o

Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. Whil

Tom Bachmann, Hana Jia Kong, Guozhen Wang, Zhouli Xu

We define the Chow $t$-structure on the $\infty$-category of motivic spectra $SH(k)$ over an arbitrary base field $k$. We identify the heart of this $t$-structure $SH(k)^{c\heartsuit}$ when the exponential characteristic of $k$ is inverted. Restricting to the cellular subcategory, we identify the Chow heart $SH(k)^{cell, c\heartsuit}$ as the category of even

Arnout Devos, Yatin Dandi

In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning with different task distributions can first improve adaptability by meta-finetuning on related tasks befor

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We search for the process $e^{+}e^{-}\rightarrow π^{+}π^{-} χ_{cJ}$ ($J=0,1,2$) and for a charged charmonium-like state in the $π^{\pm} χ_{cJ}$ subsystem. The search uses data sets collected with the BESIII detector at the BEPCII storage ring at center-of-mass energies between 4.18 GeV and 4.60 GeV. No significant $π^{+}π^{-} χ_{cJ}$ signals are observed at

Niall Bootland, Victorita Dolean, Pierre Jolivet, Pierre-Henri Tournier

Solving time-harmonic wave propagation problems in the frequency domain and within heterogeneous media brings many mathematical and computational challenges, especially in the high frequency regime. We will focus here on computational challenges and try to identify the best algorithm and numerical strategy for a few well-known benchmark cases arising in appl

Giulio Cimini, Rossana Mastrandrea, Tiziano Squartini

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network rec

Ranwa Al Mallah, Talal Halabi, Bilal Farooq

Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITS), such as Traffic Signal Control (TSC) for urban traffic congestion management. However, their involvement will expand the space of security vulnerabil

Adrian Hutter

We consider a scenario in which two reinforcement learning agents repeatedly play a matrix game against each other and update their parameters after each round. The agents' decision-making is transparent to each other, which allows each agent to predict how their opponent will play against them. To prevent an infinite regress of both agents recursively p

Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi

We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the present paper, we expose vulnerabilities of the protocol and demonstrate its inherent insecurity by introducing general attack strategies targeting the reconstruction of clients&#39

Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter

Probabilistic inference in pairwise Markov Random Fields (MRFs), i.e. computing the partition function or computing a MAP estimate of the variables, is a foundational problem in probabilistic graphical models. Semidefinite programming relaxations have long been a theoretically powerful tool for analyzing properties of probabilistic inference, but have not be

Ali Motazedifard, Seyed Ahmad Madani, N. S. Vayaghan

Using the type-I SPDC process in BBO nonlinear crystal (NLC), we generate a polarization-entangled state near to the maximally-entangled Bell-state with high-visibility (high-brightness) $ 98.50 \pm 1.33 ~ \% $ ($ 87.71 \pm 4.45 ~ \% $) for HV (DA) basis. We calculate the CHSH version of the Bell inequality, as a nonlocal realism test, and find a strong viol

Markus Brill, Ulrike Schmidt-Kraepelin, Warut Suksompong

Tournament solutions are standard tools for identifying winners based on pairwise comparisons between competing alternatives. The recently studied notion of margin of victory (MoV) offers a general method for refining the winner set of any given tournament solution, thereby increasing the discriminative power of the solution. In this paper, we reveal a numbe

İsmail Güzel, Atabey Kaygun

In this article, we define a new non-archimedean metric structure, called cophenetic metric, on persistent homology classes of all degrees. We then show that zeroth persistent homology together with the cophenetic metric and hierarchical clustering algorithms with a number of different metrics do deliver statistically verifiable commensurate topological info

Dario Bambusi, Beatrice Langella, Riccardo Montalto

We prove a $\langle t\rangle^\varepsilon$ bound on the growth of Sobolev norms for unbounded time dependent perturbations of the Laplacian on flat tori.

Mojtaba Shahin, Olivia Ilic, Chris Gonsalvez, Jon Whittle

Despite dedicated effort and research in the last two decades, the entrepreneurship field is still limited by little evidence-based knowledge of the impacts of entrepreneurship programs on the entrepreneurial intention of students in pre-university levels of study. Further, gender equity continues to be an issue in the entrepreneurial sector, particularly in

Kelly Holley-Bockelmann, :, Jillian Bellovary, Peter Bender

The NASA LISA Study Team was tasked to study how NASA might support US scientists to participate and maximize the science return from the Laser Interferometer Space Antenna (LISA) mission. LISA is gravitational wave observatory led by ESA with NASA as a junior partner, and is scheduled to launch in 2034. Among our findings: LISA science productivity is great

Taylor Mordan, Matthieu Cord, Patrick Pérez, Alexandre Alahi

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. These encompass visual appearance and behavior, and also include the forecasting of road crossing, which is

Songyang Zhang, Houwen Peng, Jianlong Fu, Yijuan Lu

We address the problem of retrieving a specific moment from an untrimmed video by natural language. It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they do not fully consider the temporal contexts between temporal momen

Ahmad Abdellatif, Khaled Badran, Diego Elias Costa, Emad Shihab

Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input

Edward Fish, Jon Weinbren, Andrew Gilbert

Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal content of movies. By l

Daniel Perez

In this paper we give a metric construction of a tree which correctly identifies connected components of superlevel sets of $\mathbb{R}$-valued continuous functions $f$ on $X$ and show that it is possible to retrieve the $H_0$-persistent diagram from this tree. We revisit the notion of homological dimension previously introduced by Schweinhart and give some

Caitlin Jones, Giulio Gasbarri, Angelo Bassi

Currently there is not a satisfactory relativistic spontaneous collapse model. Here we show the impossibility of a simple generalization of the continuous spontaneous collapse (CSL) model to the relativistic framework. We consider a mass coupled model as in the non-relativistic limit this gives the CSL model. We show that a Lorentz covariant collapse equatio

Johannes Branahl, Alexander Hock, Raimar Wulkenhaar

The analogue of Kontsevich's matrix Airy function, with the cubic potential $\operatorname{Tr}\big(Φ^3\big)$ replaced by a quartic term $\operatorname{Tr}\big(Φ^4\big)$ with the same covariance, provides a toy model for quantum field theory in which all correlation functions can be computed exactly and explicitly. In this paper we show that distinguished

Christian Bessiere, Mohamed-Bachir Belaid, Nadjib Lazaar

Itemset mining is one of the most studied tasks in knowledge discovery. In this paper we analyze the computational complexity of three central itemset mining problems. We prove that mining confident rules with a given item in the head is NP-hard. We prove that mining high utility itemsets is NP-hard. We finally prove that mining maximal or closed itemsets is

İbrahim Güllü, Ali Övgün

In this paper, we derive an exact black hole spacetime metric in the Einstein-Hilbert-Bumblebee (EHB) gravity around global monopole field. We study the horizon, temperature, and the photon sphere of the black hole. Using the null geodesics equation, we obtain the shadow cast by the Schwarzschild-like black hole with a topological defect in bumblebee gravity

M. Antonello, I. J. Arnquist, E. Barberio, T. Baroncelli

Ultra-pure NaI(Tl) crystals are the key element for a model-independent verification of the long standing DAMA result and a powerful means to search for the annual modulation signature of dark matter interactions. The SABRE collaboration has been developing cutting-edge techniques for the reduction of intrinsic backgrounds over several years. In this paper w

Samet Uzun, Nazim Kemal Ure, Behcet Acikmese

This paper introduces a decentralized state-dependent Markov chain synthesis (DSMC) algorithm for finite-state Markov chains. We present a state-dependent consensus protocol that achieves exponential convergence under mild technical conditions, without relying on any connectivity assumptions regarding the dynamic network topology. Utilizing the proposed cons

Peter Jachim, Filipo Sharevski, Emma Pieroni

This paper presents TrollHunter2020, a real-time detection mechanism we used to hunt for trolling narratives on Twitter during the 2020 U.S. elections. Trolling narratives form on Twitter as alternative explanations of polarizing events like the 2020 U.S. elections with the goal to conduct information operations or provoke emotional response. Detecting troll

Na Li, Zied Bouraoui, Jose Camacho Collados, Luis Espinosa-Anke

While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context. In this paper, we explore how the contextualised embeddings predicted by BERT can be used

J. L. F. Barbon, J. Martin-Garcia, M. Sasieta

Holographic complexity, in the guise of the Complexity = Volume prescription, comes equipped with a natural correspondence between its rate of growth and the average infall momentum of matter in the bulk. This Momentum/Complexity correspondence can be related to an integrated version of the momentum constraint of general relativity. In this paper we propose

Paul Labonne

This paper presents a new way to account for downside and upside risks when producing density nowcasts of GDP growth. The approach relies on modelling location, scale and shape common factors in real-time macroeconomic data. While movements in the location generate shifts in the central part of the predictive density, the scale controls its dispersion (akin

Irum Sanaullah, Nael Alsaleh, Shadi Djavadian, Bilal Farooq

The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11)

Peter Jachim, Filipo Sharevski, Paige Treebridge

This paper presents TrollHunter, an automated reasoning mechanism we used to hunt for trolls on Twitter during the COVID-19 pandemic in 2020. Trolls, poised to disrupt the online discourse and spread disinformation, quickly seized the absence of a credible response to COVID-19 and created a COVID-19 infodemic by promulgating dubious content on Twitter. To co

Vadim Schechtman, Alexander Varchenko

In this paper we strengthen the results of [SV] by presenting their derived version. Namely, we define a "derived Knizhnik - Zamolodchikov connection"\ and identify it with a "derived Gauss - Manin connection".

Chongchao Wang, Xianfeng Zhao, Dechao Zheng

In this paper, we completely characterize when two dual truncated Toeplitz operators are essentially commuting and when the semicommutator of two dual truncated Toeplitz operators is compact. Our main idea is to study dual truncated Toeplitz operators via Hankel operators, Toeplitz operators and function algebras.

Matthias Klupsch

We show that irreducible unipotent representations of split Levi subgroups of finite groups of Lie type extend to their stabilizers inside the normalizer of the given Levi subgroup. For this purpose, we extend the multiplicity-freeness theorem from regular embeddings to arbitrary isotypies.

Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki

Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL),

Valentin Burcea

It is formally constructed a normal form for a class of real-formal surfaces defined near a CR Singularity.

N. Zhao, A. Sud, H. Sukegawa, S. Komori

We report current-induced spin torques in epitaxial NiMnSb films on a commercially available epi-ready GaAs substrate. The NiMnSb was grown by co-sputtering from three targets using optimised parameter. The films were processed into micro-scale bars to perform current-induced spin-torque measurements. Magnetic dynamics were excited by microwave currents and

Jacek Bochnak, Wojciech Kucharz

Let X be a complex nonsingular affine algebraic variety, K a holomorphically convex subset of X, and Y a homogeneous variety for some complex linear algebraic group. We prove that a holomorphic map f:K-->Y can be uniformly approximated on K by regular maps K-->Y if and only if f is homotopic to a regular map K-->Y. However, it can happen that a null homotopi

Rodrigo Guedes Lang, Andrew M. Taylor, Vitor de Souza

Recent data from the Pierre Auger Observatory has revealed the presence of a large-scale dipole in the arrival direction distribution of ultra-high energy cosmic rays (UHECR). In this work, we build up an understanding of the diffusive origin of such a dipolar behavior as well as its dependency on energy and astrophysical source assumptions such as extragala

Ivailo Hartarsky, Fabio Martinelli, Cristina Toninelli

The Fredrickson-Andersen 2-spin facilitated model on $\mathbb{Z}^d$ (FA-2f) is a paradigmatic interacting particle system with kinetic constraints (KCM) featuring dynamical facilitation, an important mechanism in condensed matter physics. In FA-2f a site may change its state only if at least two of its nearest neighbours are empty. Although the process is re

Luca Banszerus, Katrin Hecker, Eike Icking, Stefan Trellenkamp

Graphene and bilayer graphene quantum dots are promising hosts for spin qubits with long coherence times. Although recent technological improvements make it possible to confine single electrons electrostatically in bilayer graphene quantum dots, and their spin and valley texture of the single particle spectrum has been studied in detail, their relaxation dyn

R. J. Jesus, M. L. Antunes, R. A. da Costa, S. N. Dorogovtsev

The function and performance of neural networks is largely determined by the evolution of their weights and biases in the process of training, starting from the initial configuration of these parameters to one of the local minima of the loss function. We perform the quantitative statistical characterization of the deviation of the weights of two-hidden-layer

V. V. Obukhov

All equivalence classes for electromagnetic potentials and space-time metrics of Stackel spaces, provided that Hamilton-Jacobi equation and Klein-Gordon-Fock equation for a charged test particle can be integrated by the method of complete separation of variables are found. The separation is carried out using the complete sets of mutually-commuting integrals

R. S. Vieira, A. Lima-Santos

The formal derivatives of the Yang-Baxter equation with respect to its spectral parameters, evaluated at some fixed point of these parameters, provide us with two systems of differential equations. The derivatives of the $R$ matrix elements, however, can be regarded as independent variables and eliminated from the systems, after which two systems of polynomi

Nando Metzger, Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner

Optical satellite sensors cannot see the Earth's surface through clouds. Despite the periodic revisit cycle, image sequences acquired by Earth observation satellites are therefore irregularly sampled in time. State-of-the-art methods for crop classification (and other time series analysis tasks) rely on techniques that implicitly assume regular temporal

Konstantin Clauß, Felix Kunzmann, Arnd Bäcker, Roland Ketzmerick

We conjecture that in chaotic quantum systems with escape the intensity statistics for resonance states universally follows an exponential distribution. This requires a scaling by the multifractal mean intensity which depends on the system and the decay rate of the resonance state. We numerically support the conjecture by studying the phase-space Husimi func

S. -H. Park, Y. -J. Kwon, I. Adachi, H. Aihara

We present a search for the dark photon $A^{\prime}$ in the $B^0 \to A^{\prime} A^{\prime}$ decays, where $A^{\prime}$ subsequently decays to $e^+ e^-$, $μ^+ μ^-$, and $π^+ π^-$. The search is performed by analyzing $772 \times 10^6$ $B\overline{B}$ events collected by the Belle detector at the KEKB $e^+ e^-$ energy-asymmetric collider at the $Υ(4S)$ resonan

Rufin VanRullen, Ryota Kanai

Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace theory refers to a large-scale system integrating and distributing information am

Potsawee Manakul, Mark Gales

In this paper, we describe our approach for the Podcast Summarisation challenge in TREC 2020. Given a podcast episode with its transcription, the goal is to generate a summary that captures the most important information in the content. Our approach consists of two steps: (1) Filtering redundant or less informative sentences in the transcription using the at

Alper Ahmetoglu, M. Yunus Seker, Justus Piater, Erhan Oztop

We propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-groun

Shubham Rai, Walter Lau Neto, Yukio Miyasaka, Xinpei Zhang

Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is completely-specified, the implementation accurately represents the function. If the function is incompletely-specified, the implementation has to be true only on the care set. While

Fariborz Taherkhani, Hadi Kazemi, Ali Dabouei, Jeremy Dawson

Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training. SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification.

Alex Hernandez-Garcia

The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave rise to a hype loaded with ambitious promises and overstatements. Soon the community realised that the success owed much to the availability of thousands of labe

Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim

The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples. The motivation is to curtail undesirable oscillations by its implicit model constraint to behave linearly at in-between observed data points and promo

Gianmassimo Tasinato

Brief periods of non-slow-roll evolution during inflation can produce interesting observable consequences, as primordial black holes, or an inflationary gravitational wave spectrum enhanced at small scales. We develop a model independent, analytic approach for studying the predictions of single-field scenarios which include short phases of slow-roll violatio

Mohit Raghavendra, Pravan Omprakash, B R Mukesh, Sowmya Kamath

Biometric systems based on Machine learning and Deep learning are being extensively used as authentication mechanisms in resource-constrained environments like smartphones and other small computing devices. These AI-powered facial recognition mechanisms have gained enormous popularity in recent years due to their transparent, contact-less and non-invasive na

Florian Bridoux, Amélia Durbec, Kévin Perrot, Adrien Richard

A Boolean network (BN) with $n$ components is a discrete dynamical system described by the successive iterations of a function $f:\{0,1\}^n \to \{0,1\}^n$. This model finds applications in biology, where fixed points play a central role. For example, in genetic regulations, they correspond to cell phenotypes. In this context, experiments reveal the existence

Davide Cozzolino, Andreas Rössler, Justus Thies, Matthias Nießner

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal

Shinji Isogami, Keisuke Masuda, Yoshio Miura, Rajamanickam Nagalingam

Ferrimagnetic Mn$_4$N is a promising material for heat flux sensors based on the anomalous Nernst effect (ANE) because of its sizable uniaxial magnetic anisotropy ($K_{\rm u}$) and low saturation magnetization ($M_{\rm s}$). We experimentally and theoretically investigated the ANE and anomalous Hall effect in sputter-deposited Mn$_4$N films. It was revealed

Tomohiro Abe, Koichi Hamaguchi, Natsumi Nagata

We reexamine the inverse Primakoff scattering of axions, whose scattering cross section depends on the distribution of electrons in target atoms. We evaluate it using a form factor computed with a relativistic Hartree-Fock wave function and compare it with the previous results obtained with those based on the screened Coulomb potential for the electrostatic

Damai Dai, Jing Ren, Shuang Zeng, Baobao Chang

Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. These methods either focus more on the e

Claudio Agostini, Eugenio Colla

Recently, Solecki introduced the notion of Ramsey monoid to produce a common generalization to theorems such as Hindman's theorem, Carlson's theorem, and Gowers' FIN$_k$ theorem. He proved that an entire class of finite monoids is Ramsey. Here we improve this result, enlarging this class and finding a simple algebraic characterization of finite R

Nathan F. Lepora, John Lloyd

This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features such as edges and surfaces. PBTS control is implemented with a soft curved optical

Marco Cantarini, Alessandro Gambini, Alessandro Zaccagnini

In this paper we extend and improve all the previous results known in literature about weighted average, with Ces\`aro weight, of representations of an integer as sum of a positive arbitrary number of prime powers and a non-negative arbitrary number of squares. Our result includes all cases dealt with so far and allows us to obtain the best possible outcome

Marco Boschi, Luigi Di Stefano, Martino Alessandrini

This work introduces a new framework, named SAFFIRE, to automatically extract a dominant recurrent image pattern from a set of image samples. Such a pattern shall be used to eliminate pose variations between samples, which is a common requirement in many computer vision and machine learning tasks. The framework is specialized here in the context of a machine

Shion Yamashika, Ryosuke Yoshii, Shunji Tsuchiya

We study collective modes and superfluidity of spin-1 bosons with antiferromagnetic interactions in an optical lattice based on the time-dependent Ginzburg-Landau (TDGL) equation derived from the spin-1 Bose-Hubbard model. Specifically, we examine the stability of supercurrents in the polar phase in the vicinity of the Mott insulating phase with even filling

H. G. Dosch, S. J. Brodsky, G. F. de Téramond, M. Nielsen

Supersymmetric Light Front Holographic QCD is a holographic theory, which not only describes the spectroscopy of mesons and baryons, but also predicts the existence and spectroscopy of tetraquarks. A discussion of the limitations of the theory is also presented.

Frauke Wilm, Christof A. Bertram, Christian Marzahl, Alexander Bartel

Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to improve tumour prognostication. Pathologists are the gold standard for database development, however, labelling errors may hamper development of a

Songfang Han, Jiayuan Gu, Kaichun Mo, Li Yi

Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performance in the deep learning era. However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have v

Philip Klement, Natalie Dehnhardt, Chuan-Ding Dong, Florian Dobener

Low-dimensional organic-inorganic perovskites synergize the virtues of two unique classes of materials featuring intriguing possibilities for next-generation optoelectronics: they offer tailorable building blocks for atomically thin, layered materials while providing the enhanced light harvesting and emitting capabilities of hybrid perovskites. Here, we go b

P. Noterdaeme, S. Balashev, J. -K. Krogager, P. Laursen

We present UV to NIR spectroscopic observations of the quasar J0015+1842 and its proximate molecular absorber at z=2.631. The [OIII] emission line of the quasar is composed of a broad (FWHM~1600 km/s), spatially-unresolved component, blueshifted by ~600 km/s from a narrow, spatially-resolved component (FWHM~650 km/s). The wide, blueshifted, unresolved compon

Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generalize to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense

Lucas Baier, Niklas Kühl, Jakob Schöffer, Gerhard Satzger

As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it remains unclear which effect these measures, so-called non-pharmaceutical interventions (NPIs), have on the spread of the virus. In this article, we use machine learning and apply d

Rim Alrifai, Virginie Coda, Jonathan Peltier, Andon A. Rangelov

We show that a system of three parallel waveguides, among which the central one is dissipative, leads to an ultrabroadband power splitting associated with an overall 50% power loss. The present approach is reminiscent of non-Hermitian systems in quantum mechanics and does not require a perfect effective index matching between the external and the central wav

Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Sha

István Mező

Based on a Problem and its solution published on the pages of SIAM Review, we give an interesting integral representation for the Lambert $W$ function in this short note. In particular, our result yields a new integral representation for the $Ω=W(1)$ constant as well.

Lu Huang, Zhiqi Huang, Xiaolin Luo, Xinbo He

The correlation between the peak spectra energy ($E_p$) and the equivalent isotropic energy ($E_{\rm iso}$) of long gamma-ray bursts (GRBs), the so-called Amati relation, is often used to constrain the high-redshift Hubble diagram. Assuming Lambda cold dark matter ($Λ$CDM) cosmology, Wang et al. found a $\gtrsim 3σ$ tension in the data-calibrated Amati coeff

Hengrong Lan, Changchun Yang, Fei Gao

Photoacoustic (PA) computed tomography (PACT) reconstructs the initial pressure distribution from raw PA signals. The standard reconstruction of medical image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. Most works remove the artifacts from imag

Chanh Duc Ngo, Fabrizio Pastore, Lionel Briand

Apps' pervasive role in our society led to the definition of test automation approaches to ensure their dependability. However, state-of-the-art approaches tend to generate large numbers of test inputs and are unlikely to achieve more than 50% method coverage. In this paper, we propose a strategy to achieve significantly higher coverage of the code affec

Nan Tang, Ju Fan, Fangyi Li, Jianhong Tu

Can AI help automate human-easy but computer-hard data preparation tasks that burden data scientists, practitioners, and crowd workers? We answer this question by presenting RPT, a denoising auto-encoder for tuple-to-X models (X could be tuple, token, label, JSON, and so on). RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and the

Cen Liu, Chang Tian, Peixi Liu

We investigate the reconfigurable intelligent surface (RIS) assisted downlink secure transmission where only the statistical channel of eavesdropper is available. To handle the stochastic ergodic secrecy rate (ESR) maximization problem, a deterministic lower bound of ESR (LESR) is derived. We aim to maximize the LESR by jointly designing the transmit beamfor

Naini Dudhe, Colin Benjamin

Pigou's problem has many applications in real life scenarios like traffic networks, graph theory, data transfer in internet networks, etc. The two player classical Pigou's network has an unique Nash equilibrium with the Price of Stability and Price of Anarchy agreeing with each other. The situation changes for the $k-$person classical Pigou's net

Jesus Emilio Dominguez, Carlos Segovia

The present work introduces new perspectives in order to extend finite group actions from surfaces to 3-manifolds. We consider the Schur multiplier associated to a finite group $G$ in terms of principal $G$-bordisms in dimension two, called $G$-cobordisms. We are interested in the question of when a free action of a finite group on a closed oriented surface

Nhat Minh Doan

This paper is about a type of quantitative density of closed geodesics and orthogeodesics on complete finite-area hyperbolic surfaces. The main results are upper bounds on the length of the shortest closed geodesic and the shortest doubly truncated orthogeodesic that are $\varepsilon$-dense on a given compact set on the surface.

Ankush Goswami, Robert Osburn

We explicitly prove the quantum modularity of partial theta series with even or odd periodic coefficients. As an application, we show that the Kontsevich-Zagier series $\mathscr{F}_t(q)$ which matches (at a root of unity) the colored Jones polynomial for the family of torus knots $T(3,2^t)$, $t \geq 2$, is a weight $3/2$ quantum modular form. This generalize

Mingyang Yi, Ruoyu Wang, Zhi-Ming Ma

We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong globally strongly convexity or global growth conditions e.g., PL-inequality, we only require the population risk to be \emph{locally} strongly convex around its local minima. Conc

G. I. Lykasov, A. I. Malakhov, A. A. Zaitsev

A calculation of the inclusive spectra of pions and kaons produced in $pp$ collisions as functions of their transverse momentum $p_t$ at mid-rapidity is presented within the self-similarity approach. A satisfactory description of the data within a wide range of initial energies is presented. We focus mainly on the ratio of cross-sections of $K^\pm$ to $π^\pm

Lorenzo Brandolese, Sylvie Monniaux

We establish the existence and the uniqueness for the Boussinesq system in the whole 3D space in the critical space of continuous in time with values in the power 3 integrable in space functions for the velocity and square integrable in time with values in the power 3/2 integrable in space.