May 2022 arXiv papers — page 5

Reshmi Biswas, Sarika Goyal, K. Sreenadh

In this article we study modified Kirchhoff-Schr\"odinger equations involving critical Stein-Weiss type nonlinearity and $p$-Laplacian

Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu

Data insufficiency problems (i.e., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affected the urban computing tasks in real scenarios. Prior transfer learning methods inspire an elegant solution to the data insufficiency, but are only concerned with one kind of ins

Tianheng Wang

We investigate the worldline quantum field theory (WQFT) formalism for scalar-QED and observe that a generating function emerges from WQFT, from which the scattering angle ensues. This generating function bears important similarities with the radial action in that it requires no consideration of exponentiation of lower-order contributions. We demonstrate the

Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda

Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the task using high-level events. The structure of RMs enables the decomposition of a task into simpler and independently solvable subtasks that help tackle long-horizon and/or sparse

Masayoshi Mase, Art B. Owen, Benjamin B. Seiler

The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple subjects. These inputs can be unlikely, physically impossible, or even logically impossible. As a result, the predictions for such cases can be based on data very unlike any the bla

Jiulong Liu, Zhaoqiang Liu

In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike conventional compressed sensing where the signal is assumed to be sparse, we assume that the signal lies in the range of an $L$-Lipschitz continuous generative model with bounded $k$-dimensiona

Ling Yang, Shenda Hong

Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks. Global structure of graphs helps discriminating representations and existing methods mainly utilize the global structure by imposing additional supervisions. However, their global semantics are usually invariant for all nodes/grap

M. Przewięźlikowski, P. Przybysz, J. Tabor, M. Zięba

The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this method is to learn the general weights of the meta-model, which are further adapted to spe

Zhuoyuan Mao, Chenhui Chu, Sadao Kurohashi

Massively multilingual sentence representation models, e.g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks. However, the use of a large amount of data or inefficient model architectures results in heavy computation to train a new model according to our preferred languages and domains. To resolve this issue, we int

Pieter Senden

For a finite abelian group $A$, the Reidemeister number of an endomorphism $\varphi$ equals the size of $\mathrm{Fix}(\varphi)$, the set of fixed points of $\varphi$. Consequently, the Reidemeister spectrum of $A$ is a subset of the set of divisors of $|A|$. We fully determine the Reidemeister spectrum of $|A|$, that is, which divisors of $|A|$ occur as the

Pierre Degond, Antoine Diez, Adam Walczak

Swarmalators are systems of agents which are both self-propelled particles and oscillators. Each particle is endowed with a phase which modulates its interaction force with the other particles. In return, relative positions modulate phase synchronization between interacting particles. In the present model, there is no force reciprocity: when a particle attra

Qiang Liu, Zhi Liu

Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the efficient estim

Juan S. Giraldo, Nataly Banol Arias, Edgar Mauricio Salazar Duque, Gerwin Hoogsteen

Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e.g., power quality and reliability. This paper proposes a compensation mechanism using EV flexibility management of a set of charging sessions

M. Brož, M. Ferrais, P. Vernazza, P. Ševeček

According to adaptive-optics observations by Ferrais et al., (22) Kalliope is a 150-km, dense and differentiated body. Here, we interpret (22) Kalliope in the context of bodies in its surroundings. While there is a known moon Linus, with a 5:1 size ratio, no family has been reported in the literature, which is in contradiction with the existence of the moon.

Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer

Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling. The structural (or topological) information is implicitly taken into account in these two steps. We propose in this work a novel point of view, which places distance

Wanying Kang, John Marshall, Tushar Mittal, Suyash Bire

Over the south pole of Enceladus, an icy moon of Saturn, geysers eject water into space in a striped pattern, making Enceladus one of the most attractive destinations in the search for extraterrestrial life. We explore the ocean dynamics and tracer/heat transport associated with geysers as a function of the assumed salinity of the ocean and various core-shel

Udo Schlegel, Samuel Schiegg, Daniel A. Keim

Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep neural networks to smaller sizes by only decreasing their performance as little as possible. However, such pruning algorithms are often hard

Felicia Ruppel, Florian Faion, Claudius Gläser, Klaus Dietmayer

We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as other data types, such as radar. Both track management and the detection of new tracks are performed by the same t

Dominic Dootson, John Magorrian

We study the explicitly time-dependent response of a razor-thin axisymmetric disc to externally imposed perturbations by recasting the linearized Collisionless Boltzmann equation as an integral equation and applying Kalnajs' matrix method. As an application we consider the idealized problem of calculating the dynamical friction torque on a steadily rotating,

Bijan Bagchi, Sauvik Sen

The modified Newtonian dynamics (MOND) paradigm is discussed in the context of asymptotic safe gravity. We estimate quantum correction to the logarithmic potential which is well known to account for the constancy of the circular velocity $v$ of the spiral galaxies. We determine plausible bounds on $v$.

Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang

Modeling dynamic scenes is important for many applications such as virtual reality and telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic scenes, existing methods based on Neural Radiance Fields (NeRF) suffer from slow convergence (i.e., model training time measured in days). In this paper, we present DeVRF, a novel re

Erna K. Delczeg-Czirjak, O. Eriksson, A. V. Ruban

It is demonstrated that thermally induced longitudinal spin fluctuations (LSF) play an important role in itinerant Co$_3$Mn$_2$Ge at an elevated temperature. The effect of LSF is taken into account during {\it ab initio} calculations via a simple model for the corresponding entropy contribution. We show that the magnetic entropy leads to the appearance of a

Khoa D. Doan, Peng Yang, Ping Li

Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. Hashing aims to learn a binary-output function that maps an image to a binary vector. For optimal retrieval performance, producing balanced hash codes with low-quantization error to bridge the gap between the learning stage's

Along He, Kai Wang, Tao Li, Wang Bo

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous stud

Qing Guo, Junyuan Liu, Shuangjie Peng

This paper deals with the following critical elliptic systems of Hamiltonian type, which are variants of the critical Lane-Emden systems and analogous to the prescribed curvature problem: \begin{equation*} \begin{cases} -\Delta u_1=K_1(y)u_2^{p},\ y\in \mathbb{R}^N,\\ -\Delta u_2=K_2(y)u_1^{q}, \ y\in \mathbb{R}^N,\\ u_1,u_2>0, \end{cases} \end{equation*} wh

Adam Mielke

With the covid-19 pandemic still ongoing and an enormous amount of test data available, the lessons learned over the last two years need to be developed to a point where they can provide understanding for tackling new variants and future diseases. The SIR-model commonly used to model disease spread, predicts exponential initial growth, which helps establish

Clément Berenfeld, Paul Rosa, Judith Rousseau

We study the Bayesian density estimation of data living in the offset of an unknown submanifold of the Euclidean space. In this perspective, we introduce a new notion of anisotropic H\"older for the underlying density and obtain posterior rates that are minimax optimal and adaptive to the regularity of the density, to the intrinsic dimension of the manifold,

Yiwei Fu, Dheeraj S. K. Kapilavai, Elliot Way

Factored decentralized Markov decision process (Dec-MDP) is a framework for modeling sequential decision making problems in multi-agent systems. In this paper, we formalize the learning of numerical methods for hyperbolic partial differential equations (PDEs), specifically the Weighted Essentially Non-Oscillatory (WENO) scheme, as a factored Dec-MDP problem.

Michael Lorke, Igor Khanonkin, Stephan Michael, Johann Peter Reithmaier

Tunneling-injection structures are incorporated in semiconductor lasers in order to overcome the fundamental dynamical limitation due to hot carrier injection by providing a carrier transport path from a cold carrier reservoir. The tunneling process itself depends on band alignment between quantum-dot levels and the injector quantum well, especially as in th

Maximilian Felde, Gerd Stumme

Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their

Silvia Severini, Viktor Hangya, Masoud Jalili Sabet, Alexander Fraser

Bilingual Word Embeddings (BWEs) are one of the cornerstones of cross-lingual transfer of NLP models. They can be built using only monolingual corpora without supervision leading to numerous works focusing on unsupervised BWEs. However, most of the current approaches to build unsupervised BWEs do not compare their results with methods based on easy-to-access

Michał Możdżonek, Anna Wróblewska, Sergiy Tkachuk, Szymon Łukasik

Product matching corresponds to the task of matching identical products across different data sources. It typically employs available product features which, apart from being multimodal, i.e., comprised of various data types, might be non-homogeneous and incomplete. The paper shows that pre-trained, multilingual Transformer models, after fine-tuning, are sui

CMS Collaboration, Ankush Reddy Kanuganti

The results from a search for chargino-neutralino or chargino pair production via electroweak interactions are summarized. The results are based on a sample of $\sqrt{s}=13~\mathrm{TeV}$ proton-proton collisions from the LHC, recorded with the CMS detector~\cite{cms} and corresponding to an integrated luminosity of $137~\mathrm{fb}^{-1}$. The search consider

Anastasios Papazafeiropoulos, Zaid Abdullah, Pandelis Kourtessis, Steven Kisseleff

In this paper, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisting a massive multiple-input multiple-output (mMIMO) system. In particular, we derive a closed-form expression for the coverage probability of a STAR-RIS assisted mMIMO system while accounting for correlated fading and phase-shift erro

Onel L. A. López, Dileep Kumar, Antti Tölli

The provision of accurate localization is an increasingly important feature of wireless networks. To this end, a reliable distinction between line-of-sight (LOS) and non-LOS (NLOS) radio links is necessary to avoid degenerative localization estimation biases. Interestingly, LOS and NLOS transmissions affect the polarization of the received signals differentl

Alastair Marshall, Alon Salhov, Martin Gierse, Christoph Müller

Magnetic resonance imaging of $^{13}$C-labeled metabolites enhanced by parahydrogen-induced polarization (PHIP) can enable real-time monitoring of processes within the body. We introduce a robust, easily implementable technique for transferring parahydrogen-derived singlet order into 13C magnetization using adiabatic radio-frequency sweeps at $\mu$T fields.

Emmanuel Wend-Benedo Zongo

In this paper, we analyze an eigenvalue problem for a quasi-linear elliptic operators involving Dirichlet boundary condition in an open smooth bounded set of $\mathbb{R}^N$. We investigate a bifurcation results (from trivial solution and from infinity) of an eigenvalue problem involving the $(p,2)$-Laplace operator, from the Fu\v cik spectrum of the Laplacia

George Dialektakis, Ilias Dimitriadis, Athena Vakali

The high growth of Online Social Networks (OSNs) over the last few years has allowed automated accounts, known as social bots, to gain ground. As highlighted by other researchers, most of these bots have malicious purposes and tend to mimic human behavior, posing high-level security threats on OSN platforms. Moreover, recent studies have shown that social bo

Adi Armoni

We consider an 'electric' $U(N)$ level $k$ QCD$_3$ theory with one adjoint Majorana fermion. Inspired by brane dynamics, we suggest that for $k \ge N/2$ the massive $m<0$ theory, in the vicinity of the supersymmetric point, admits a $U(k-\frac{N}{2})_{-(\frac{1}{2}k+\frac{3}{4}N),-(k+\frac{N}{2})}$ 'magnetic' dual with one adjoint Majorana fermion. The magne

Jie Luan, Philip St. J. Russell, David Novoa

We demonstrate generation of 7.6 fs near-UV pulses centered at 400 nm via 8-fold soliton-effect self-compression in an Ar-filled hollow-core kagom\'e-style photonic crystal fiber with ultrathin core walls. Analytical calculations of the effective compression length and soliton order permit adjustment of the experimental parameters, and numerical modelling of

Sumyeong Ahn, Seongyoon Kim, Se-young Yun

The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute are present, the trained model can provide unintended prejudgments and show significant inference errors (i.e., the dataset bias problem). Various methods have been proposed to miti

Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena, Jicheng Shi

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These techniques are compared in terms of data requirements, ease of use, computational burden, and robustnes

Rakshit P. Vyas, Mihir J. Joshi

The Barbero-Immirzi parameter ($\gamma$) is introduced in loop quantum gravity (LQG) whose physical significance is still a biggest open question; because of its profound traits. In some cases, it is real-valued; while, it is complex-valued in other cases. This parameter emerges out in the process of denoting a Lorentz connection with non compact group $SO(3

Antonio Cicone, Wing Suet Li, Haomin Zhou

The analysis of the time-frequency content of a signal is a classical problem in signal processing, with a broad number of applications in real life. Many different approaches have been developed over the decades, which provide alternative time-frequency representations of a signal each with its advantages and limitations. In this work, following the success

Rui Lu, Andrew Zhao, Simon S. Du, Gao Huang

While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited. Most previous analytical works could only assume that the representation function is already known to the agent or from linear function class, since analyz

Giovanni Morrone, Samuele Cornell, Enrico Zovato, Alessio Brutti

Continuous speech separation (CSS) is a recently proposed framework which aims at separating each speaker from an input mixture signal in a streaming fashion. Hereafter we perform an evaluation study on practical design considerations for a CSS system, addressing important aspects which have been neglected in recent works. In particular, we focus on the trad

Kevin Kamm, Michelle Muniz

In this paper, we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properties of stochastic matrices and its link to matrix Lie groups. We give a gentle introduction to this topic and demonstrate how It\^o-SDEs in R will gen

Arunangshu Debnath, Angel Rubio

We theoretically propose a biphoton entanglement-enhanced multidimensional spectroscopic technique as a probe for the dissipative polariton dynamics in the ultrafast regime. It is applied to the cavity-confined monomeric photosynthetic complex that represents a prototypical multi-site excitonic quantum aggregate. The proposed technique is shown to be particu

Sarwar Khan, Jagadheep D. Pandian, Dharam V. Lal, Michael R. Rugel

The dynamics of ionized gas around the W33 Main ultracompact HII region is studied using observations of hydrogen radio recombination lines and a detailed multiwavelength characterization of the massive star-forming region W33 Main is performed. We used the Giant Meterwave Radio Telescope (GMRT) to observe the H167$\alpha$ recombination line at 1.4 GHz at an

Neslihan Suzen, Alexander N. Gorban, Jeremy Levesley, Evgeny M. Mirkes

One major problem in Natural Language Processing is the automatic analysis and representation of human language. Human language is ambiguous and deeper understanding of semantics and creating human-to-machine interaction have required an effort in creating the schemes for act of communication and building common-sense knowledge bases for the 'meaning' in tex

Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic

We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution. We start by analyzing the scenario where the type characteristics are known and then move to two learning scenarios where the types are unknown: non-preemptive problems, where each started job must be completed before moving to another job; and

Henrik Axelsen, Johannes Rude Jensen, Omri Ross

The IS discourse on the potential of distributed ledger technology (DLT) in the financial services has grown at a tremendous pace in recent years. Yet, little has been said about the related implications for the costly and highly regulated process of compliance reporting. Working with a group of representatives from industry and regulatory authorities, we em

Samuel Balula, Dominic Liao-McPherson, Alisa Rupenyan, John Lygeros

We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers. The position of the precision motion stage is predicted with data-driven models, a linear low-fidelity model is used to optim

H. Mohseni Sadjadi, V. Anari

In the early Universe, dark energy may have a non-negligible contribution. If this dark energy corresponds to an early cosmological constant, it leads to a late-time huge dark energy density which is much larger than what is expected for the current expansion of the Universe. Using a conformal coupling of the quintessence to the dark matter, and based on the

David Criens, Lars Niemann

In this note we consider a family of nonlinear (conditional) expectations that can be understood as a multidimensional diffusion with uncertain drift and certain volatility. Here, the drift is prescribed by a set-valued function that depends on time and path in a Markovian way. We establish the Feller property for the associated sublinear Markovian semigroup

Christopher Carr, Peng Wang

Coverage Path Planning (CPP) aims at finding an optimal path that covers the whole given space. Due to the NP-hard nature, CPP remains a challenging problem. Bio-inspired algorithms such as Ant Colony Optimisation (ACO) have been exploited to solve the problem because they can utilise heuristic information to mitigate the path planning complexity. This paper

Abhishek Banerjee, Max Geier, Md Ahnaf Rahman, Daniel S. Sanchez

Andreev bound states with opposite phase-inversion asymmetries are observed in local and non-local tunneling spectra at the two ends of a superconductor-semiconductor-superconductor planar Josephson junction in the presence of a perpendicular magnetic field. Spectral signatures agree with a theoretical model, yielding a physical picture in which phase textur

Xin-Li Sheng, Lucia Oliva, Zuo-Tang Liang, Qun Wang

Polarized quarks and antiquarks in high-energy heavy-ion collisions can lead to the spin alignment of vector mesons formed by quark coalescence. Using the relativistic spin Boltzmann equation for vector mesons derived from Kadanoff-Baym equations with an effective quark-meson model for strong interaction and quark coalescence model for hadronizaton, we calcu

Zaishuo Xia, Zelin Li, Yanbing Bai, Jinze Yu

In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessme

Mohamed Faouzi Melalkia, Tecla Gabbrielli, Antoine Petitjean, Léandre Brunel

This work marks an important progress towards practical quantum optical technologies in the continuous variable regime, as it shows the feasibility of experiments where non-Gaussian state generation entirely relies on plug-&-play components from guided-wave optics technologies. This strategy is demonstrated experimentally with the heralded preparation of low

Luigi Sbailò, Ádám Fekete, Luca M. Ghiringhelli, Matthias Scheffler

We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive, the largest data

Jan Wiersig

Exceptional points are non-Hermitian degeneracies in open quantum and wave systems at which not only eigenenergies but also the corresponding eigenstates coalesce. This is in strong contrast to degeneracies known from conservative systems, so-called diabolic points, at which only eigenenergies degenerate. Here we connect these two kinds of degeneracies by in

Holger Gies, Felix Karbstein, Leonhard Klar

The fundamental theory of quantum electrodynamics predicts the vacuum to resemble a polarizable medium. This gives rise to effective nonlinear interactions between electromagnetic fields and light-by-light scattering phenomena. We study the collision of two optical laser pulses in a pump-probe setup using beams with circular and elliptic cross section and es

Rastko Ciric, Armin W. Thomas, Oscar Esteban, Russell A. Poldrack

Mapping the functional connectome has the potential to uncover key insights into brain organisation. However, existing workflows for functional connectomics are limited in their adaptability to new data, and principled workflow design is a challenging combinatorial problem. We introduce a new analytic paradigm and software toolbox that implements common oper

Sowmya S Sundaram, Sairam Gurajada, Marco Fisichella, Deepak P

From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we are still miles away from building robu

Gilbert Groenewald, Dawie Janse van Rensburg, Andre Ran, Madelein van Straaten

Let $\mathbb{H}$ be a field with $\mathbb{Q}\subset\mathbb{H}\subset\mathbb{C}$, and let $p(\lambda)$ be a polynomial in $\mathbb{H}[\lambda]$, and let $A\in\mathbb{H}^{n\times n}$ be nonderogatory. In this paper we consider the problem of finding a solution $X\in\mathbb{H}^{n\times n}$ to $p(X)=A$. A necessary condition for this to be possible is already kn

Ulrik Egede, Tom Hadavizadeh, Minni Singla, Peter Skands

Beauty and charm quarks are ideal probes of pertubative Quantum Chromodymanics in proton-proton collisions, owing to their large masses. In this paper the role of multi-parton interactions in the production of doubly-heavy hadrons is studied using simulation samples generated with Pythia, a Monte Carlo event generator. Comparisons are made to the stand-alone

Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela

Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especially in settings where the observations include images or complex high-dimensional data. In parallel, modern neural density estimators, such as normalizing flows, are becoming incre

Emilio De Santis, Fabio Spizzichino

Referring to a standard context of voting theory, and to the classic notion of voting situation, here we show that it is possible to observe any arbitrary set of elections' outcomes, no matter how paradoxical it may appear. On this purpose we use results, presented in a recent paper of us, that hinge on the concept of ranking pattern concordant with a probab

Shaofei Cai, Liang Li, Xinzhe Han, Jiebo Luo

Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information. Moreover, due to diverse mechanisms in the message passing,

Liang Hou, Qi Cao, Yige Yuan, Songtao Zhao

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores

Sambunath Das, Dayasindhu Dey, S. Ramasesha, Manoranjan Kumar

Entanglement entropy (EE) of a state is a measure of correlation or entanglement between two parts of a composite system and it may show appreciable change when the ground state (GS) undergoes a qualitative change in a quantum phase transition (QPT). Therefore, the EE has been extensively used to characterise the QPT in various correlated Hamiltonians. Simil

Pedro Hermosilla, Timo Ropinski

Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics. Unfortunately, the number of available structures is orders of magnitude lower than the training data sizes commonly used in computer vision and machine learning. Moreover, this number is reduced even further, when only annotated protein structures

Daniele Grattarola, Pierre Vandergheynst

We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal exists on some unknown topological space from which we sample a discrete graph. In the absence of a coord

Mehran Shakarami, Ashish Cherukuri, Nima Monshizadeh

This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their actions with the aim of maximizing their individual payoff functions, while a central regulator uses interventions to modify their marginal returns and maximize the social welfare

Stefano de Nicola, Roberto P. Saglia, Jens Thomas, Claudia Pulsoni

We discuss the statistical distribution of galaxy shapes and viewing angles under the assumption of triaxiality by deprojecting observed Surface Brightness (SB) profiles of 56 Brightest Cluster Galaxies coming from a recently published large deep-photometry sample. For the first time, we address this issue by directly measuring axis ratio profiles without li

M. Naydenov, V. Kozhuharov

The phenomenological implications to the decay of the neutral pion from the introduction of new dark particles are discussed. We calculate the contribution to the $\pi^0$ decay width and then we extend the theory by adding dark sector vector particles which interact through $\gamma^\mu$, $\sigma^{\mu\nu}$ and $\sigma^{\mu\nu}\gamma^5$. We calculate the total

Akash Patel, Björn Lindqvist, Christoforos Kanellakis, Ali-akbar Agha-mohammadi

Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying MAVs in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the robot such that new ground can

Olga Yufereva, Michael Persiianov, Pavel Dvurechensky, Alexander Gasnikov

Inspired by recent advances in distributed algorithms for approximating Wasserstein barycenters, we propose a novel distributed algorithm for this problem. The main novelty is that we consider time-varying computational networks, which are motivated by examples when only a subset of sensors can make an observation at each time step, and yet, the goal is to a

Duo Xu, Sander Damsma, Mircea Lazar

Motivated by increasing precision requirements for switched power amplifiers, this paper addresses the problem of model predictive control (MPC) design for discrete-time linear systems with a finite control set (FCS). Typically, existing solutions for FCS-MPC penalize the output tracking error and the control input rate of change, which can lead to arbitrary

Baoyu Jing, Yuchen Yan, Yada Zhu, Hanghang Tong

Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, information retrieval, and drug discovery. A fundamental challenge for bipartite graphs is how to learn informative node embeddings. Despite the success of recent self-supervised learn

Pramit Dutta, Ganesh Sistu, Senthil Yogamani, Edgar Galván

Generating a detailed near-field perceptual model of the environment is an important and challenging problem in both self-driving vehicles and autonomous mobile robotics. A Bird Eye View (BEV) map, providing a panoptic representation, is a commonly used approach that provides a simplified 2D representation of the vehicle surroundings with accurate semantic l

H. Li, T. del Pino Alemán, J. Trujillo Bueno, R. Casini

We present the Tenerife Inversion Code (TIC), which has been developed to infer the magnetic and plasma properties of the solar chromosphere and transition region via full-Stokes inversion of polarized spectral lines. The code is based on the HanleRT forward engine, which takes into account many of the physical mechanisms that are critical for a proper model

Chunlai Li, Shuguo Xing, Sebastian E. Lauro, Yan Su

Chang'e-3(CE-3) has been the first spacecraft to soft-land on the Moon since the Soviet Union's Luna 24 in 1976. The spacecraft arrived at Mare Imbrium on December 14, 2013 and the same day, Yutu lunar rover separated from lander to start its exploration of the surface and the subsurface around the landing site. The rover was equipped, among other instrument

Meiirkhan B. Borikhanov, Michael Ruzhansky, Berikbol T. Torebek

In the present paper, we study the Cauchy-Dirichlet problem to the nonlocal nonlinear diffusion equation with polynomial nonlinearities $$\mathcal{D}_{0|t}^{\alpha }u+(-\Delta)^s_pu=\gamma|u|^{m-1}u+\mu|u|^{q-2}u,\,\gamma,\mu\in\mathbb{R},\,m>0,q>1,$$ involving time-fractional Caputo derivative $\mathcal{D}_{0|t}^{\alpha}$ and space-fractional $p$-Laplacian

Hui Song, A. K. Qin, Chenggang Yan

Real-world electricity consumption prediction may involve different tasks, e.g., prediction for different time steps ahead or different geo-locations. These tasks are often solved independently without utilizing some common problem-solving knowledge that could be extracted and shared among these tasks to augment the performance of solving each task. In this

Veronika Chobanova, Matthew Wingate, Yosuke Yusa, Jeremy Dalseno

This summary reviews contributions to the CKM 2021 workshop in Working Group 4. In particular, theoretical and experimental progress in determining $B$ meson mixing properties are discussed.

Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff

Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or, professional content. To date, all summarization datasets operate under a one-size-fits-all paradigm that may not reflect the full range of organic summarization needs. Several recently prop

Niall Taggart

We show that there exists a suitable $C_2$-fixed points functor from calculus with Reality to the orthogonal calculus of Weiss which recovers orthogonal calculus ``up to a shift'' in an analogous way with the recovery of real topological $K$-theory from Atiyah's $K$-theory with Reality via appropriate $C_2$-fixed points.

Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu

Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. Several important works have investigated whether neural networks can effectively reason like algorithms, typically by learning to execute them. The common trend in the area, however, is to generate target

Wenshuo Zhou, Dalu Yang, Binghong Wu, Yehui Yang

Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc. We propose Feature Centroid Contrast Learning (FCCL), which can improve target

Valerio Arnaboldi, Andrea Passarella, Marco Conti, Robin Dunbar

We analyze the ego-alter Twitter networks of 300 Italian MPs and 18 European leaders, and of about 14,000 generic users. We find structural properties typical of social environments, meaning that Twitter activity is controlled by constraints that are similar to those shaping conventional social relationships. However, the evolution of ego-alter ties is very

Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar, A. K. Qin

Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However, these approaches suffer from two key challenges when applied to combinatorial problems: insufficient exploration and the requirement of many training examples of the search space

Rachel Howe, W. J. Chaplin, Y. P. Elsworth, S. J. Hale

We examine the solar-cycle variation of the power in the low-degree helioseismic modes by looking at binned power spectra from 45 years of observations with the Birmingham Solar Oscillations Network, which provides a more robust estimate of the mode power than that obtained by peak fitting. The solar-cycle variation of acoustic mode power in the five-minute

Mario Beraha, Jim E. Griffin

We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits

Le Yu, Leilei Sun, Bowen Du, Tongyu Zhu

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designe

Michael Frewer

In the study by Sadeghi & Oberlack [JFM 899, A10 (2020)] it is claimed that new scaling laws are derived for the case of passive scalar dynamics under the influence of a constant mean gradient in decaying homogeneous isotropic turbulence. However, these scaling laws are not new and have already been derived and discussed in Bahri (2016). No novel analytical

M. Cosset-Chéneau, M. Husien Fahmy, A. Kandazoglou, C. Grezes

The spin-dependent transport properties of paramagnetic metals are roughly invariant under rotation. By contrast, in ferromagnetic materials the magnetization breaks the rotational symmetry, and thus the spin Hall effect is expected to become anisotropic. Here, using a specific design of lateral spin valves, we measure electrically the spin Hall Effect aniso

Tilman Alemán, Martin Halla, Christoph Lehrenfeld, Paul Stocker

Driven by the challenging task of finding robust discretization methods for Galbrun's equation, we investigate conditions for stability and different aspects of robustness for different finite element schemes on a simplified version of the equations. The considered PDE is a second order indefinite vector-PDE which remains if only the highest order terms of G

Zu-Xing Yang, Xiao-Hua Fan, Tomoya Naito, Zhong-Ming Niu

Based on the back-propagation neural networks and density functional theory, a supervised learning is performed firstly to generate the nuclear charge density distributions. The charge density is further calibrated to the experimental charge radii by a composite loss function. It is found that, when the parity, pairing, and shell effects are taken into accou