February 2022 arXiv papers — page 2

Jiaming Liang, Yongxin Chen

We study sampling problems associated with potentials that lack smoothness. The potentials can be either convex or non-convex. Departing from the standard smooth setting, the potentials are only assumed to be weakly smooth or non-smooth, or the summation of multiple such functions. We develop a sampling algorithm that resembles proximal algorithms in optimiz

Nathanaël Beau, Benoît Crabbé

Considering the seq2seq architecture of TranX for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decod

Gyorgy Szenes

A systematic study of the $R_e$-$T_m$ relation ($R_e$ = track radius, $T_m$ = melting temperature) in insulators reveals new features of track formation. A quantitative relationship is demonstrated between $R_e$ values measured in various solids when $\langle s_e\rangle$=constant ($\langle s_e\rangle$ = atomic stopping power). This effect is the consequence

Phan Thành Nam, Julien Ricaud, Arnaud Triay

We review our recent study on the ground state energy of dilute Bose gases with three-body interactions. The main feature of our results is the emergence of the 3D energy-critical Schrödinger equation to describe the ground state energy of a Bose-Einstein condensate, where the nonlinearity strength is determined by a zero scattering problem. Several open que

Suman Mondal, Ashirbad Padhan, Tapan Mishra

We show that in a system of one dimensional spinless fermions a topological phase and phase transition can emerge only through interaction. By allowing a dimerized or bond-alternating nearest neighbour interaction we show that the system exhibits a symmetry protected topological phase while its non-interacting limit is a gapless state. The non-trivial topolo

Hossein T. Dinani, Diego Tancara, Felipe F. Fanchini, Ariel Norambuena

Several applications of quantum machine learning (QML) rely on a quantum measurement followed by training algorithms using the measurement outcomes. However, recently developed QML models, such as variational quantum circuits (VQCs), can be implemented directly on the state of the quantum system (quantum data). Here, we propose to use a qubit as a probe to e

Rohit Kumar, Satyabrata Adhikari

Braunstein et. al. have started the study of entanglement properties of the quantum states through graph theoretical approach. Their idea was to start from a simple unweighted graph $G$ and then they have defined the quantum state from the Laplacian of the graph $G$. A lot of research had already been done using the similar idea. We ask here the opposite one

Nicolás Andruskiewitsch, Dirceu Bagio, Saradia Della Flora, Daiana Flores

We consider a class of Nichols algebras $\mathscr{B} (\mathfrak L_q( 1, \mathscr{G}))$ introduced in [3] which are domains and have many favorable properties like AS-regular and strongly noetherian. We classify their finite-dimensional simple modules and their point modules.

Ken R. Duffy, Wei An, Muriel Medard

Error correction techniques traditionally focus on the co-design of restricted code-structures in tandem with code-specific decoders that are computationally efficient when decoding long codes in hardware. Modern applications are, however, driving demand for ultra-reliable low-latency communications (URLLC), rekindling interest in the performance of shorter,

Pallav Kant, César Pairetti, Youssef Saade, Stéphane Popinet

We combine experiments and numerical computations to examine underlying fluid mechanical processes associated with bioaerosol generation during violent respiratory manoeuvres, such as coughing or sneezing. Analogous experiments performed in a cough machine -- consisting of a strong shearing airflow over a thin liquid film, allow us to illustrate the changes

Dawid Bugajewski

The aim of this article is to investigate the issues of multiplicative inverses and composition in the set of formal Laurent series. We show the lack of general uniqueness of inverses of formal Laurent series; necessary and sufficient conditions for the existence of inverses of formal Laurent series satisfying some weak assumptions are also provided. Moreove

Alessio Gianelle, Patrick Koppenburg, Donatella Lucchesi, Davide Nicotra

Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computati

Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato

Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos. For training an object detector, a method called Mixup, which overlays two training images to mitigate data bias, has been empirically shown to be effective for data augmentation. However, in hand-

Michael Carley

A method of noise reduction proposed for the next generation of aircraft is to shield noise from the propulsion system, by positioning the noise source over a wing or another surface. In this paper, an approximate analysis is developed for the acoustic field far from a circular source placed near the edge of a semi-infinite plate, a model problem for shieldi

C. R. Muniz, H. R. Christiansen, M. S. Cunha, J. Furtado

In this paper, we obtain a static black string solution for a bilocal gravitational source in 3+1 dimensions. The solution is regular at the origin and tends asymptotically to the ordinary static uncharged black string solution of general relativity. It allows an event horizon and an internal horizon depending on the value of the mass density. A mass remnant

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen

This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. The "Graphormer-V2" could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on downstream tasks.

Sinnou David, Noriko Hirata-Kohno, Makoto Kawashima

For a given rational number $x$ and an integer $s\geq 1$, let us consider a generalized polylogarithmic function, often called the Lerch function, defined by $$Φ_{s}(x,z)= \sum_{k=0}^{\infty}\frac{z^{k+1}}{(k+x+1)^s}\enspace.$$ We prove the linear independence over any number field $K$ of the numbers $1$ and $Φ_{s_j}(x_j,α_i)$ with any choice of distinct shi

Giulio Caldarelli

Blockchain technology truly opened the gate to a wave of unparalleled innovations; however, despite the rapidly growing load of hype, the integration into the business, apart from a few applications, seems to be coming at a slower rate. One reason for that delay may be the need in the real-world applications for the so-called trust model. Trust models are ra

Marc Noy, Clément Requilé, Juanjo Rué

We provide the first solution to the problem of counting rooted 3-connected bipartite planar maps. Our starting point is the enumeration of bicoloured planar maps according to the number of edges and monochromatic edges, following Bernardi and Bousquet-Mélou [J. Comb. Theory Ser. B, 101 (2011), 315-377]. The decomposition of a map into 2- and 3-connected com

Steffen W. R. Werner, Benjamin Peherstorfer

Learning controllers from data for stabilizing dynamical systems typically follows a two step process of first identifying a model and then constructing a controller based on the identified model. However, learning models means identifying generic descriptions of the dynamics of systems, which can require large amounts of data and extracting information that

Hadi Sehat, Anders Lindskov Kloborg, Christian Mørup, Elena Pagnin

Cloud Service Providers (CSPs) offer a vast amount of storage space at competitive prices to cope with the growing demand for digital data storage. Dual deduplication is a recent framework designed to improve data compression on the CSP while keeping clients' data private from the CSP. To achieve this, clients perform lightweight information-theoretic tr

Ben Craps, Marine De Clerck, Oleg Evnin, Philip Hacker

We address the difference between integrable and chaotic motion in quantum theory as manifested by the complexity of the corresponding evolution operators. Complexity is understood here as the shortest geodesic distance between the time-dependent evolution operator and the origin within the group of unitaries. (An appropriate `complexity metric' must be

Gabriele Di Bona, Leonardo Di Gaetano, Vito Latora, Francesco Coghi

Maximum entropy random walks (MERWs) are maximally dispersing and play a key role in optimizing information spreading in various contexts. However, building MERWs comes at the cost of knowing beforehand the global structure of the network, a requirement that makes them totally inadequate in real case scenarios. Here, we propose an adaptive random walk (ARW),

Anthony J. Young, Charles R. Keeton, Andrew J. Baker

Integral field spectroscopy of high-redshift galaxies has become a powerful tool for understanding their dynamics and evolutionary states. However, in the case of gravitationally lensed systems, it has proved difficult to model both lensing and intrinsic kinematics in a way that takes full advantage of the information available in the spectral domain. In thi

Eiichiro Komatsu

Cosmology requires new physics beyond the Standard Model of elementary particles and fields. What is the fundamental physics behind dark matter and dark energy? What generated the initial fluctuations in the early Universe? Polarised light of the cosmic microwave background (CMB) may hold the key to answers. In this article, we discuss two new developments i

J. T. Wai, M. D. Boyer, E. Kolemen

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two neural networks relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade (NSTX-

Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, Siva Reddy

A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potentially small) inventory. In turn, skills correspond to parameter-efficient (sparse / low-rank) model para

Michael Ruderman, Andrei Zagvozdkin, Dmitrii Rachinskii

Understanding the dynamics of two inertial bodies coupled via a friction interface is essential for a wide range of systems and motion control applications. Coupling terms within the dynamics of an inertial pair connected via a passive frictional contact are non-trivial and have long remained understudied in system communities. This problem is particularly c

Anthony D. Forbes

We report some group divisible designs with block size five, including types $6^{15}$ and $10^{15}$. As a consequence we are able to extend the known spectrum for 5-GDDs of type $g^u$.

Glynn Winskel

The article bridges between two major paradigms in computation, the functional, at basis computation from input to output, and the interactive, where computation reacts to its environment while underway. Central to any compositional theory of interaction is the dichotomy between a system and its environment. Concurrent games and strategies address the dichot

Lian Hu, Songxiao Li, Rong Yang

A bounded linear operator $T$ on a separable complex Hilbert space $H$ is called $C$-normal if there is a conjugation $C$ on $H$ such that $ CT^\ast TC=TT^\ast$. Let $φ$ be a linear fractional self-map of $\mathbb{D}$. In this paper, we characterize the necessary and sufficient condition for the composition operator $C_φ$ and weighted composition operator $W

A. A. Araújo Filho

In this work, we analyse the thermodynamical behavior of massive and massless particles within Loop Quantum Gravity formalism. We investigate a modified dispersion relation which suffices to derive all our results of interest in an \textit{analytical} manner. Initially, we study the massive case where, in essence, we examine how the mass modifies the thermod

Thomas Louail, Marc Barthelemy

Characterizing the spatial organization of urban systems is a challenge which points to the more general problem of describing marked point processes in spatial statistics. We propose a non-parametric method that goes beyond standard tools of point pattern analysis and which is based on a mapping between the points and a "dominance tree", constructed

Jianwei Urbain Yang

We prove an $L^2\times L^2\to L^q_tL^r_x$ bilinear adjoint Fourier restriction estimate for $n$-dimensional elliptic paraboloids, with $n\ge 2$ and $1\le q \le \infty$, $1\le r\le 2$ being on the endline $\frac{1}{q}=\frac{n+1}{2}\bigl(1-\frac{1}{r}\bigr)$ except for the critical index. This includes the endpoint case when $q=r=\frac{n+3}{n+1}$, a question l

Daniele Gregoris, Yen Chin Ong

The Chandrasekhar limit for white dwarfs has been confirmed by many astrophysical observations. However, how to obtain it theoretically in models which rely on other-than-Heisenberg's uncertainty principles, which are predicted by some quantum gravity theories, is not a trivial task. In this manuscript, we will derive the Chandrasekhar mass assuming an u

Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, ca

Cui-Zu Chang, Chao-Xing Liu, Allan H. MacDonald

The quantum Hall (QH) effect, quantized Hall resistance combined with zero longitudinal resistance, is the characteristic experimental fingerprint of Chern insulators - topologically non-trivial states of two-dimensional matter with broken time-reversal symmetry. In Chern insulators, non-trivial bulk band topology is expressed by chiral states that carry cur

ATLAS Collaboration

Several observables sensitive to the fragmentation of $b$-quarks into $b$-hadrons are measured using 36 fb${}^{-1}$ of $\sqrt s = 13$ TeV proton-proton collision data collected with the ATLAS detector at the LHC. Jets containing $b$-hadrons are obtained from a sample of dileptonic $t\bar{t}$ events, and the associated set of charged-particle tracks is separa

Yasmina Becis-Aubry

This contribution proposes a recursive set-membership method for the ellipsoidal state characterization for discrete-time linear time-varying models with additive unknown disturbances vectors, bounded by possibly degenerate zonotopes and polytopes, impacting respectively, the state evolution equation and the sporadic measurement vectors, which are expressed

Ivan Limonchenko, Grigory Solomadin

In this paper we prove that the quotient of any real or complex moment-angle complex by any closed subgroup in the naturally acting compact torus on it is equivariantly homotopy equivalent to the homotopy colimit of a certain toric diagram. For any quotient we prove an equivariant homeomorphism generalizing the well-known Davis-Januszkiewicz construction for

Shiyi Kong, Jun Ai, Minyan Lu, Shuguang Wang

Modern software systems have become increasingly complex, which makes them difficult to test and validate. Detecting software partial anomalies in complex systems at runtime can assist with handling unintended software behaviors, avoiding catastrophic software failures and improving software runtime availability. These detection techniques aim to identify th

Sudipta Hensh, Jan Schee, Zdeněk Stuchlík

We investigate the light curve of an isolated bright spot in a Keplerian orbit around a Kerr black hole candidate to probe possible deviations from the standard event-horizon picture. In particular, we explore the presence of a reflective surface, namely a "mirror" near the horizon, and its observable consequences. We present estimates on the time de

Hiroki Hayashi, Daegeun Jo, Dongwook Go, Tenghua Gao

Modern spintronics relies on the generation of spin currents through spin-orbit coupling. The spin-current generation has been believed to be triggered by current-induced orbital dynamics, which governs the angular momentum transfer from the lattice to the electrons in solids. The fundamental role of the orbital response in the angular momentum dynamics sugg

Chul-Ung Woo, Heiko Rieger, Jae Dong Noh

We investigate the phase transitions of the $q$-state Brownian Potts model in two dimensions (2d) comprising Potts spins that diffuse like Brownian particles and interact ferromagnetically with other spins within a fixed distance. With extensive Monte Carlo simulations we find a continuous phase transition from a paramagnetic to a ferromagnetic phase even fo

Damir Ferizović

Given any full rank lattice and a natural number N , we regard the point set given by the scaled lattice intersected with the unit square under the Lambert map to the unit sphere, and show that its spherical cap discrepancy is at most of order N , with leading coefficient given explicitly and depending on the lattice only. The proof is established using a le

Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen

Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage and sample scarcity of many offline datasets, the principle of pessimism has been recently introduced to mitigate high bias of the estimated values. While pessimistic variants of m

Nir Goren, Efi Fogel, Dan Halperin

We introduce bindings that enable the convenient, efficient, and reliable use of software modules of CGAL (Computational Geometry Algorithm Library), which are written in C++, from within code written in Python. There are different tools that facilitate the creation of such bindings. We present a short study that compares three main tools, which leads to the

Paola Bonizzoni, Matteo Costantini, Clelia De Felice, Alessia Petescia

Feature embedding methods have been proposed in literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure prediction. Recent theoretical results showed that the well-known Lyndon factorization preserves common factors in overlapping strings. Surprisingly, the fin

Jinn-Ouk Gong, Maria Mylova

We examine the validity of the classical approximation of the waterfall phase transition in hybrid inflation from an effective field theory (EFT) point of view. The EFT is constructed by integrating out the waterfall field fluctuations, up to one-loop order in the perturbative expansion. Assuming slow-roll conditions are obeyed, right after the onset of the

N. A. Carella

The existence of infinitely many consecutive prime triples $p_n$, $ p_{n+1}$, and $p_{n+2}$ as $n \to \infty$, is sufficient to prove that the Catalan constant $β(2)=0.9159655941\ldots $ is an irrational number. This note provides the detailed analysis. Moreover, the numerical data suggests that the irrationality measure is $μ(β(2))=2$, the same as almost ev

Zhengyun Zhao, Qiao Jin, Fangyuan Chen, Tuorui Peng

Objective: Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper,

Gianluca Calcagni, Maria Grazia Di Luca, Tomáš Fodran

These lecture notes introduce the reader to the hot big bang model, cosmological perturbations, gravitational waves, the cosmic microwave background, inflation, the singularity problem, the cosmological constant problem and the cosmology of quantum gravity.

Mitja Jančič, Gregor Kosec

The popularity of local meshless methods in the field of numerical simulations has increased greatly in recent years. This is mainly due to the fact that they can operate on scattered nodes and that they allow a direct control over the approximation order and basis functions. In this paper we analyse two popular variants of local strong form meshless methods

Bat-Od Battseren

We will show that group exactness is a von Neumann equivalence invariant. This result generalizes the previously known fact stating that group exactness is invariant under measure equivalence and W*-equivalence.

Md Ahmed Al Muzaddid, William J. Beksi

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated

Sijie Zhang, Zhiyuan Sun, Qiaomei Liu, Zixiao Wang

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillat

José Barrientos, Adolfo Cisterna, Cristóbal Corral, Marcelo Oyarzo

We present novel regular Euclidean solutions to General Relativity in presence of Maxwell and conformally coupled scalar fields. In particular, we consider metrics of the Eguchi-Hanson and Taub-NUT families to solve the field equations analytically. The solutions have nontrivial topology labeled by the Hirzebruch signature and Euler characteristic that we co

Rafael Rodrigues, Susana Quijano-Roy, Robert-Yves Carlier, Antonio M. G. Pinheiro

Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based o

Menglin Yang, Min Zhou, Tong Zhang, Jiahong Liu

Graph representation learning in Euclidean space, despite its widespread adoption and proven utility in many domains, often struggles to effectively capture the inherent hierarchical and complex relational structures prevalent in real-world data, particularly for datasets exhibiting a highly non-Euclidean latent anatomy or power-law distributions. Hyperbolic

Q. Fable, A. Chbihi, M. Boisjoli, J. D. Frankland

In this article we investigate $^{40,48}$Ca+$^{40,48}$Ca peripheral and semi-peripheral reactions at 35 MeV/nucleon. Data were obtained using the unique coupling of the VAMOS high acceptance spectrometer and the INDRA charged particle multidetector.The spectrometer allowed high resolution measurement of charge, mass and velocity of the cold projectile-like f

Alice Pagano, Sebastian Weber, Daniel Jaschke, Tilman Pfau

We study the implementation of a high fidelity controlled-phase gate in a Rydberg quantum computer. The protocol is based on a symmetric gate with respect to the two qubits as experimentally realized by Levine et al [Phys. Rev. Lett. 123, 170503 (2019)], but allows for arbitrary pulse shapes with time-dependent detuning. Optimizing the pulse shapes, we intro

Lefteris Kirousis, John Livieratos

The {\em acyclic chromatic number} of a graph is the least number of colors needed to properly color its vertices so that none of its cycles has only two colors. The {\em acyclic chromatic index} is the analogous graph parameter for edge colorings. We first show that the acyclic chromatic index is at most $2Δ-1$, where $Δ$ is the maximum degree of the graph.

K. J. Kapcia, V. Tkachenko, F. Capotondi, A. Lichtenstein

In this work, we report on modelling results obtained with our recently developed simulation tool enabling nanoscopic description of electronic processes in X-ray irradiated ferromagnetic materials. With this tool, we have studied the response of Co/Pt multilayer system irradiated by an ultrafast extreme ultraviolet pulse at the M-edge of Co (photon energy $

Tiziana Calamoneri, Angelo Monti, Fabrizio Petroni

A graph G is a multi-interval PCG if there exist an edge weighted tree T with non-negative real values and disjoint intervals of the non-negative real half-line such that each node of G is uniquely associated to a leaf of T and there is an edge between two nodes in G if and only if the weighted distance between their corresponding leaves in T lies within any

Tianyun Yang, Ziyao Huang, Juan Cao, Lei Li

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable

Marzena Śniegowska, Swayamtrupta Panda, Bożena Czerny, Đorge Savić

Narrow Line Seyfert 1 (NLSy1) galaxies have been shown to have high Eddington ratios and relatively small black hole masses. The measurement of the black hole masses is based on the virial relation which is dependent on the distribution of the line-emitting gas and the viewing angle to the source. Spectropolarimetry enables us to probe the geometry of this l

Tonghua Yu, Ryotaro Arita, Motoaki Hirayama

Topological phases usually are unreachable in molecular solids, which are characteristic of weakly dispersed energy bands with a large gap, in contrast to topological materials. In this work, however, we propose that nontrivial electronic topology may ubiquitously emerge in a class of molecular crystals that contain interstitial electronic states, the bands

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato

Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment. This paper develops a method, termed as the linearised deep image prior (DIP), to estimate the uncertainty associated with reconstructions produced by the DIP with total variation regula

M. Bouvier, C. Ceccarelli, A. López-Sepulcre, N. Sakai

Understanding the chemical past of our Sun and how life appeared on Earth is no mean feat. The best strategy we can adopt is to study newborn stars located in an environment similar to the one in which our Sun was born and assess their chemical content. In particular, hot corinos are prime targets since recent studies showed correlations between interstellar

Mohit Tekriwal, Avi Tachna-Fram, Jean-Baptiste Jeannin, Manos Kapritsos

Distributed architectures are used to improve performance and reliability of various systems. Examples include drone swarms and load-balancing servers. An important capability of a distributed architecture is the ability to reach consensus among all its nodes. Several consensus algorithms have been proposed, and many of these algorithms come with intricate p

Rok Venturini, Anže Mraz, Igor Vaskivskyi, Yevhenii Vaskivskyi

Progress in high-performance computing demands significant advances in memory technology. Among novel memory technologies that promise efficient device operation on a sub-ns timescale, resistance switching between charge ordered phases of the 1T-TaS$_2$ has shown to be potentially useful for the development of high-speed, energy efficient non-volatile memory

Patrik Christen

Self-modifying code has many intriguing applications in a broad range of fields including software security, artificial general intelligence, and open-ended evolution. Having control over self-modifying code, however, is still an open challenge since it is a balancing act between providing as much freedom as possible so as not to limit possible solutions, wh

Sihan Feng, Yong Zhang, Fuming Wang, Hong Zhao

Despite their great success, neural networks still remain as black-boxes due to the lack of interpretability. Here we propose a new analyzing method, namely the weight pathway analysis (WPA), to make them transparent. We consider weights in pathways that link neurons longitudinally from input neurons to output neurons, or simply weight pathways, as the basic

Steven Cunnington

A goal for pathfinder intensity mapping (IM) surveys will be detecting features in the neutral hydrogen (HI) power spectrum, which serve as conclusive evidence of cosmological signals. Observing such features at the expected scales in HI IM auto-correlations, where contribution from systematics is uncertain, will provide a more convincing cosmological detect

Nikita Kuzmin, Igor Fedorov, Alexey Sholokhov

Recently, hyperspherical embeddings have established themselves as a dominant technique for face and voice recognition. Specifically, Euclidean space vector embeddings are learned to encode person-specific information in their direction while ignoring the magnitude. However, recent studies have shown that the magnitudes of the embeddings extracted by deep ne

Nicola Grittini, Marco Antonio Pellegrini

In this paper, we prove the existence of a relation between the prime divisors of the order of a Sylow normalizer and the degree of characters having values in some small cyclotomic fields. This relation is stronger when the group is solvable.

Luca Razzoli, Paolo Bordone, Matteo G. A. Paris

A fully connected vertex $w$ in a simple graph $G$ of order $N$ is a vertex connected to all the other $N-1$ vertices. Upon denoting by $L$ the Laplacian matrix of the graph, we prove that the continuous-time quantum walk (CTQW) -- with Hamiltonian $H=γL$ -- of a walker initially localized at $\vert w \rangle$ does not depend on the graph $G$. We also prove

Jason Gross, Théo Zimmermann, Rajashree Agrawal, Adam Chlipala

As the adoption of proof assistants increases, there is a need for efficiency in identifying, documenting, and fixing compatibility issues that arise from proof assistant evolution. We present the Coq Bug Minimizer, a tool for reproducing buggy behavior with minimal and standalone files, integrated with coqbot to trigger automatically on Coq reverse CI failu

Léo Régnier, Maxim Dolgushev, Sidney Redner, Olivier Bénichou

We develop a framework to determine the complete statistical behavior of a fundamental quantity in the theory of random walks, namely, the probability that $n_1$, $n_2$, $n_3$, . . . distinct sites are visited at times $t_1$, $t_2$, $t_3$, ... . From this multiple-time distribution, we show that the visitation statistics of 1d random walks are temporally cor

T. Blaineau, M. Moniez, C. Afonso, J. -N. Albert

We have searched for long duration microlensing events originating from intermediate mass Black Holes (BH) in the halo of the Milky Way, using archival data from EROS-2 and MACHO photometric surveys towards the Large Magellanic Cloud. We combined data from these two surveys to create a common database of light curves for 14.1 million objects in LMC, covering

Yichen Yang, Xiaosen Wang, Kun He

We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to inconsistent outputs, and we propose a novel robust training method, termed Fast Triplet Metric Learning (FTML). Specifically, we argue that the original sample should have similar

Julius Ross, Matei Toma

We prove a version of the Hodge-Riemann bilinear relations for Schur polynomials of Kähler forms and for Schur polynomials of positive forms on a complex vector space.

A. S. Il'yn, A. V. Kopyev, V. A. Sirota, K. P. Zybin

We consider finite-dimensional systems of linear stochastic differential equations ${\partial_t}{x_k}\left( t \right) = {A_{kp}}\left( t \right){x_p}\left( t \right)$, ${\bf A}(t)$ being a stationary continuous statistically isotropic stochastic process with values in real $d \times d$ matrices. We suppose also that the laws of ${\bf A}(t)$ satisfy the large

Vladimir Lazić, Shin-ichi Matsumura, Thomas Peternell, Nikolaos Tsakanikas

We prove that if $(X,Δ)$ is a threefold pair with mild singularities such that ${-}(K_X+Δ)$ is nef, then the numerical class of ${-}(K_X+Δ)$ is effective.

Agnieszka Cichy, Konrad Jerzy Kapcia, Andrzej Ptok

The band structure of noninteracting fermions in the honeycomb lattice exhibits the Dirac cones at the corners of the Brillouin zone. As a consequence, fermions in this lattice manifest a semiconducting behavior below some critical value of the onsite attraction, $U_{c}$. However, above $U_{c}$, the superconducting phase can occur. We discuss an interplay be

Joya Chen, Kai Xu, Yuhui Wang, Yifei Cheng

A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the backward pass. We propose a novel method to reduce this footprint - Dropping Intermediate Tensors (DropIT). DropIT drops min-k elements of the intermediate tensors and approximates gra

Ankush Deshmukh, Bhargava B C, A V Narasimhadhan

Machine learning (ML) and Deep Learning (DL) tasks primarily depend on data. Most of the ML and DL applications involve supervised learning which requires labelled data. In the initial phases of ML realm lack of data used to be a problem, now we are in a new era of big data. The supervised ML algorithms require data to be labelled and of good quality. Labell

G. Fabbiano, M. Elvis

Imaging X-ray spectroscopy of nearby AGNs, mostly with Chandra, has shown that extended soft (<2.5 keV) emission-line dominated X-ray biconical structures, of kiloparsec scale, are widespread in highly absorbed Compton Thick (CT) AGNs. The X-ray emission is complex, requiring both photoionized and shock-ionized gas. It originates from high ionization regions

Zhuangwei Shi

Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation. Subspace learning maps high-dimensional features on low-dimensional subspace to capture efficient representation

Junseok Oh, Donghwee Yoon, Injung Kim

We propose a one-shot ultra-high-resolution generative adversarial network (OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) images from a single training image and is trainable on a single consumer GPU. OUR-GAN generates an initial image that is visually plausible and varied in shape at low resolution, and then gradually increases the

Qiang Zou, Zhihong Tang, Hsi-Yung Feng, Shuming Gao

This paper presents a comprehensive review of geometric constraint solving in parametric computer-aided design (CAD), with the major focus on its advances in the last 15 years. Geometric constraint solving can date back to the very first CAD prototype, Sketchpad, in the 1960s, but serious research studies were carried out only after parametric CAD was introd

Maria Luisa Chiofalo, Dario Grasso, Massimo Mannarelli, Silvia Trabucco

A transonic fluid flow generates an acoustic hole that is the hydrodynamic analogue of a gravitational black hole. Acoustic holes emit a detectable thermal radiation of phonons at a characteristic Hawking temperature. The crucial concept is that the spontaneous phonon emission at the horizon produces an irreversible heat increase at the expenses of the bulk

ANTARES Collaboration, A. Albert, S. Alves, M. André

This work presents a new search for magnetic monopoles using data taken with the ANTARES neutrino telescope over a period of 10 years (January 2008 to December 2017). Compared to previous ANTARES searches, this analysis uses a run-by-run simulation strategy, with a larger exposure as well as a new simulation of magnetic monopoles taking into account the Kasa

Christian Eder, Pierre Lairez, Rafael Mohr, Mohab Safey El Din

Polynomial system solving arises in many application areas to model non-linear geometric properties. In such settings, polynomial systems may come with degeneration which the end-user wants to exclude from the solution set. The nondegenerate locus of a polynomial system is the set of points where the codimension of the solution set matches the number of equa

Jake Fawkes, Robin Evans, Dino Sejdinovic

In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the prediction that would have been made if someone&#39;s race, gender or religion were counterfactually different. In order to achieve this, we must have causal models that are able to c

Alexandre Didier, Jerome Sieber, Melanie N. Zeilinger

We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial initial conditions. The dynamic regret is defined as the difference between the true incurred cost of the system and the cost which could have optimally been achieved under any inpu

Marek Bożejko, Wiktor Ejsmont

In this article, we introduce the notion of a double Fock space of type B. We will show that this new construction is compatible with combinatorics of counting positive and negative inversions on a hyperoctahedral group.

Lucas Brivadis, Cyprien Tamekue, Antoine Chaillet, Jean Auriol

Recently, [2] proved that the closed-loop system resulting from the output proportional feedback stabilization of a class of delayed neural fields is input-to-state stable (ISS) for sufficiently high gain, subject to the existence of an equilibrium point for the closed-loop system. In the present paper, we show that a sufficient condition for such an equilib

Florian Hörsch, Zoltán Szigeti

We show that the problem of deciding whether a given graph $G$ has a well-balanced orientation $\vec{G}$ such that $d_{\vec{G}}^+(v)\leq \ell(v)$ for all $v \in V(G)$ for a given function $\ell:V(G)\rightarrow \mathbb{Z}_{\geq 0}$ is NP-complete. We also prove a similar result for best-balanced orientations. This improves a result of Bern\&#39; ath, Iwata, K

Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for