April 2020 arXiv papers — page 33

Muhammad Waseem, Muhammad Nehal Khan, Shahid Qamar

We consider the dynamics of the photon states in distant resonators coupled to a common bus resonator at different positions. The frequencies of distant resonators from a common bus resonator are equally detuned. These frequency detunings are kept larger than the coupling strengths of each resonator to the common bus resonator to satisfy the dispersive inter

Emeka Ogbuju, Moses Onyesolu

There are publicly available general purpose sentiment lexicons in some high resource languages but very few exist in the low resource languages. This makes it difficult to directly perform sentiment analysis tasks in such languages. The objective of this work is to create a general purpose sentiment lexicon for the Igbo language that can determine the senti

I. I. Artemenko, E. N. Nerush, I. Yu. Kostyukov

The photon emission by an ultrarelativistic charged particle in extremely strong magnetic field is analyzed, with vacuum polarization and photon recoil taken into account. The vacuum polarization is treated phenomenologically via refractive index. The photon emission occurs in the synergic (cooperative) synchrotron-Cherenkov process [J. Schwinger, W. Tsai an

Mandar Joshi, Kenton Lee, Yi Luan, Kristina Toutanova

We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply our method to reading comprehension tasks by encoding questions and passages together with background sentences about the entities they mention. We show that integrating background

Arnaud Marsiglietti, James Melbourne

We investigate geometric and functional inequalities for the class of log-concave probability sequences. We prove dilation inequalities for log-concave probability measures on the integers. A functional analogue of this geometric inequality is derived, giving large and small deviation inequalities from a median, in terms of a modulus of regularity. Our metho

Seonghyeon Jeong

Generated Jacobian equations are Monge-Amp\`ere type equations which contain optimal transport as a special case. Therefore, optimal transport case has its own special structure which is not necessarily true for more general generated Jacobian equations. Hence the theory for optimal transport can not be directly transplanted to generated Jacobian equations.

D. M. Kriete, G. R. McKee, L. Schmitz, D. R. Smith

Detailed 2D turbulence measurements from the DIII-D tokamak provide an explanation for how resonant magnetic perturbations (RMPs) raise the L-H power threshold $P_\textrm{LH}$ [P. Gohil et al., Nucl. Fusion 51, 103020 (2011)] in ITER-relevant, low rotation, ITER-similar-shape plasmas with favorable ion $\nabla B$ direction. RMPs simultaneously raise the turb

Jay Mardia, Hilal Asi, Kabir Aladin Chandrasekher

We study the planted clique problem in which a clique of size k is planted in an Erdos-Renyi graph G(n,1/2) and one is interested in recovering this planted clique. It is widely believed that it exhibits a statistical-computational gap when computational efficiency is equated with the existence of polynomial time algorithms. We study this problem under a mor

Jonathan M. Fraser

We prove a general nonlinear projection theorem for Assouad dimension. This theorem has several applications including to distance sets, radial projections, and sum-product phenomena. In the setting of distance sets we are able to completely resolve the planar distance set problem for Assouad dimension, both dealing with the awkward `critical case' and provi

Egor Burkov, Igor Pasechnik, Artur Grigorev, Victor Lempitsky

We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its s

Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler

Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets. We hypothesize that this issue is not primarily caused by the pretrained model's limitations, but rather by the paucity of c

Alexander Mason, Victor Reiner, Shruthi Sridhar

Prompted by a question of Jim Propp, this paper examines the cyclic sieving phenomenon (CSP) in certain cyclic codes. For example, it is shown that, among dual Hamming codes over $F_q$, the generating function for codedwords according to the major index statistic (resp. the inversion statistic) gives rise to a CSP when $q=2$ or $q=3$ (resp. when $q=2$). A by

Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, Emily Pitler

Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal. We propose four alternat

Ramy Yammine

Let $H$ be a connected Hopf algebra acting on an algebra $A$. Working over a base field having characteristic $0$, we show that for a given prime (semi-prime, completely prime) ideal $I$ of $A$, the largest $H$-stable ideal of A contained in $I$ is also prime (semi-prime, completely prime). We also prove a similar result for certain subrings of convolution a

Xingyu Li, Konstantinos N. Plataniotis

Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aiming to build effective pathology image diagnosis models. Since transferability of knowledge heavily depends on

Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari

Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-specific driving behaviors. Traditional transfer learning methods often focus on image data and are black-box m

Tanwi Mallick, Patha Pratim Das, Arun Kumar Majumdar

Indian Classical Dance is an over 5000 years' old multi-modal language for expressing emotions. Preservation of dance through multimedia technology is a challenging task. In this paper, we develop a system to generate a parseable representation of a dance performance. The system will help to preserve intangible heritage, annotate performances for better tuto

Dimitrios Chiotis, Zinaida Lykova, N. J. Young

We develop a theory of pointwise wedge products of vector-valued functions on the circle and the disc, and obtain results which give rise to a new approach to the analysis of the matricial Nehari problem. We investigate properties of pointwise creation operators and pointwise orthogonal complements in the context of operator theory and the study of vector-va

Bram Wallace, Bharath Hariharan

Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods

A. Kunold, J. C. Sandoval-Santana, V. G. Ibarra-Sierra, Gerardo G. Naumis

We analyze the quasienergy-spectrum and the valence to conduction-band transition probabilities of a tilted anisotropic Dirac material subject to linearly and circularly polarized electromagnetic fields. The quasienergy-spectrum is numerically calculated from the monodromy matrix of the Schr\"odinger equation via the Floquet theorem for arbitrarily intense e

Heerok Banerjee, V. Ganapathy, V. M. Shenbagaraman

One of the arduous tasks in supply chain modelling is to build robust models against irregular variations. During the proliferation of time-series analyses and machine learning models, several modifications were proposed such as acceleration of the classical levenberg-marquardt algorithm, weight decaying and normalization, which introduced an algorithmic opt

Hao-Yu Liu, Zhong-Bo Kang, Xiaohui Liu

We study the single hadron inclusive production in the forward rapidity region in proton-nucleus collisions. We find the long-standing negative cross section at next-to-leading-order (NLO) is driven by the large negative threshold logarithmic contributions. We established a factorization theorem for resumming these logarithms with systematically improvable a

Subhradeep Kayal, Florian Dubost, Harm A. W. M. Tiddens, Marleen de Bruijne

Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of

Tian Shi, Xuchao Zhang, Ping Wang, Chandan K. Reddy

Using attention weights to identify information that is important for models' decision-making is a popular approach to interpret attention-based neural networks. This is commonly realized in practice through the generation of a heat-map for every single document based on attention weights. However, this interpretation method is fragile, and easy to find cont

Vasileios Klimis, George Parisis, Bernhard Reus

In software-defined networks (SDN), a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and service disruption and security loopholes. The automatic detection of bugs or, even better, verification of their absenc

D. S. Grun, L. H. Ymai, Karin Wittmann Wilsmann, A. P. Tonel

High sensitivity quantum interferometry requires more than just access to entangled states. It is achieved through deep understanding of quantum correlations in a system. Integrable models offer the framework to develop this understanding. We communicate the design of interferometric protocols for an integrable model that describes the interaction of bosons

Junjie Zhang, Minghao Ye, Zehua Guo, Chen-Yu Yen

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwardin

Alan Anwer Abdulla, Sabah A. Jassim, Harin Sellahewa

Performance indicators characterizing modern steganographic techniques include capacity (i.e. the quantity of data that can be hidden in the cover medium), stego quality (i.e. artifacts visibility), security (i.e. undetectability), and strength or robustness (intended as the resistance against active attacks aimed to destroy the secret message). Fibonacci ba

Maurizio Serva

We recently introduced a new family of processes which describe particles which only can move at the speed of light c in the ordinary 3D physical space. The velocity, which randomly changes direction, can be represented as a point on the surface of a sphere of radius c and its trajectories only may connect the points of this variety. A process can be constru

Yang Qiu, Zhenghan Wang

Topological quantum computing is believed to be inherently fault-tolerant. One mathematical justification would be to prove that ground subspaces or ground state manifolds of topological phases of matter behave as error correction codes with macroscopic distance. While this is widely assumed and used as a definition of topological phases of matter in the phy

A. Ćiprijanović, G. F. Snyder, B. Nord, J. E. G. Peek

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experi

Rishiraj Saha Roy, Avishek Anand

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithm

Christopher Evans, Deborah Ferguson, Bhavesh Khamesra, Pablo Laguna

A popular approach in numerical simulations of black hole binaries is to model black holes as punctures in the fabric of spacetime. The location and the properties of the black hole punctures are tracked with apparent horizons, namely outermost marginally outer trapped surfaces (MOTSs). As the holes approach each other, a common apparent horizon suddenly app

Andrea Bellotti, Sergey Antopolskiy, Anna Marchenkova, Alessia Colucciello

Nowadays, the possibility to run advanced AI on embedded systems allows natural interaction between humans and machines, especially in the automotive field. We present a custom portable EEG-based Brain-Computer Interface (BCI) that exploits Event-Related Potentials (ERPs) induced with an oddball experimental paradigm to control the infotainment menu of a car

Nicole Machado Utpott, Vanessa Bielefeldt Leotti, Laura Bannach Jardim

Background: Scale equating is a statistical technique used to establish equivalence relations between different scales. Its use is quite popular in educational evaluation, however, unusual in the health area, where scales of measures are tools that integrate clinical practice. With the use of different scales, there is a difficulty in comparing scientific re

Alan Anwer Abdulla, Harin Sellahewa, Sabah A. Jassim

This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and stego quality. A new technique based on a specific representation is used to decompose pixel intensity values into 16 (virtua

Evani Radiya-Dixit, Xin Wang

State-of-the-art performance on language understanding tasks is now achieved with increasingly large networks; the current record holder has billions of parameters. Given a language model pre-trained on massive unlabeled text corpora, only very light supervised fine-tuning is needed to learn a task: the number of fine-tuning steps is typically five orders of

Rodrigo Antonio Samprogna, Jacson Simsen

In this work we obtain theoretical results on continuity of selected pullback attractors and we apply them to reaction diffusion equations with dynamical boundary conditions

Sy David Friedman, Giorgio Laguzzi

In this paper we introduce a tree-like forcing notion extending some properties of the random forcing in the context of the generalised Cantor space and study its associated ideal of null sets and notion of measurability. This issue was also addressed by Shelah ([11, Problem 0.5]) and concerns the definition of a forcing which is $\kappa^kappa$-bounding, $<

Alan A. Abdulla, Harin Sellahewa, Sabah A. Jassim

Digital steganography is becoming a common tool for protecting sensitive communications in various applications such as crime(terrorism) prevention whereby law enforcing personals need to remotely compare facial images captured at the scene of crime with faces databases of known criminals(suspects); exchanging military maps or surveillance video in hostile e

Pradumn Kumar Pandey, Bibhas Adhikari

We analyze the time series data of number of districts or cities in India that are affected by COVID-19 from March 01, 2020 to April 17, 2020. We study the data in the framework of time series network data. The networks are defined by using the geodesic distances of the districts or cities specified by the latitude and longitude coordinates. We particularly

Jonathan David Farley

It is proven that every geometric lattice of finite rank greater than 1 has a matching between the points and hyperplanes. This answers a question of P\'olya Prize-winner Anders Bj\"orner from the 1981 Banff Conference on Ordered Sets, which he raised as a "challenging question" in 1976.

Cristiano Nisoli

We relate the anomalous noise found experimentally in spin ice to the subdiffusion of magnetic monopoles. Because monopoles are emergent particles, they do not move in a structureless vacuum. Rather, the underlying spin ensemble filters the thermal white noise, leading to non-trivial coevolution. Thus, monopoles can be considered as random walkers under the

Hana Al-Theiabat, Aisha Al-Sadi

This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this

Amir Javadpour

Computer networks covered the entire world and a serious and new development has not formed for many years. But companies and consumer organizations complain about the failure to add new features to their networks and according to their need, like much of the works to be done automatically and they also like to develop and expand their networks on the softwa

Shatian Wang, Shuoguang Yang, Zhen Xu, Van-Anh Truong

We propose a cumulative oversampling (CO) method for online learning. Our key idea is to sample parameter estimations from the updated belief space once in each round (similar to Thompson Sampling), and utilize the cumulative samples up to the current round to construct optimistic parameter estimations that asymptotically concentrate around the true paramete

D. Milovančev, N. Vokić, F. Laudenbach, C. Pacher

We demonstrate high-rate CV-QKD supporting a secure-key rate of 22Mb/s through spectral tailoring and optimal use of quantum receiver bandwidth. Co-existence with 11 adjacent carrier-grade C-band channels spaced by only 20nm is accomplished at >10Mb/s.

Alexey Ovchinnikov, Isabel Cristina Pérez Verona, Gleb Pogudin, Mirco Tribastone

Motivation: Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimens

Arnab Das, Ian Briggs, Ganesh Gopalakrishnan, Pavel Panchekha

Automated techniques for rigorous floating-point round-off error analysis are important in areas including formal verification of correctness and precision tuning. Existing tools and techniques, while providing tight bounds, fail to analyze expressions with more than a few hundred operators, thus unable to cover important practical problems. In this work, we

José D. Querales-Flores, Pablo Aguado-Puente, Đorđe Dangić, Jiang Cao

The temperature renormalization of the bulk band structure of a topological crystalline insulator, SnTe, is calculated using first principles methods. We explicitly include the effect of thermal-expansion-induced modification of electronic states and their band inversion on electron-phonon interaction. We show that the direct gap decreases with temperature,

Arka Ghosh, Sankha Subhra Mullick, Shounak Datta, Swagatam Das

Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback has hitherto been the norm for such adversarial attacks. However, black-box attacks are much more practical for real-world applications. Universal perturbations applicable across mu

Kiwoon Kwon

This purpose of this paper is to locate a single localized source from three range measurements with multiplicative noises. Although some minimization approaches for additive noise have been found, studies on the existence of solutions are rare. We analyzed a situation with one or two solutions for the same multiplicative noise at three measurement sensors.

Ranjita Thapa, Noah Snavely, Serge Belongie, Awais Khan

Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We hav

Yohei Fuji, Yuto Ashida

We investigate entanglement phase transitions from volume-law to area-law entanglement in a quantum many-body state under continuous position measurement on the basis of the quantum trajectory approach. We find the signatures of the transitions as peak structures in the mutual information as a function of measurement strength, as previously reported for rand

Ryan M. Corey, Andrew C. Singer

Augmented listening devices, such as hearing aids and augmented reality headsets, enhance human perception by changing the sounds that we hear. Microphone arrays can improve the performance of listening systems in noisy environments, but most array-based listening systems are designed to isolate a single sound source from a mixture. This work considers a sou

Mohammed Barkatou

In this paper, we begin by giving a necessary and sufficient condition of existence for the quadrature surfaces problem in the case where the term source is a uniform density supported by a segment. Then, we use the Steiner continuous symmetrization to prove that the obtained solution is symmetric with respect the x-coordinate axis and that its boundary is a

Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang

We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of images as a pivot between the source and target languages by getting captions for such images in both languages independently. Human evaluations on the English-Hindi comparable cor

Johannes Bleher, Michael Bleher, Thomas Dimpfl

We propose a microscopic model to describe the dynamics of the fundamental events in the limit order book (LOB): order arrivals and cancellations. It is based on an operator algebra for individual orders and describes their effect on the LOB. The model inputs are arrival and cancellation rate distributions that emerge from individual behavior of traders, and

Freek Witteveen, Michael Walter

Entanglement renormalization is a unitary real-space renormalization scheme. The corresponding quantum circuits or tensor networks are known as MERA, and they are particularly well-suited to describing quantum systems at criticality. In this work we show how to construct Gaussian bosonic quantum circuits that implement entanglement renormalization for ground

Chris Gartland

We prove that the Lipschitz free space over a certain type of discrete metric space has the Radon-Nikod\'ym property. We also show that the Lipschitz free space over a complete, locally compact metric space has the Schur or approximation property whenever the Lipschitz free space over each compact subset also has this property.

Leon A Takhtajan

Based on the notion of the resolvent and on the Hilbert identities, this paper presents a number of classical results in the theory of differential operators and some of their applications to the theory of automorphic functions and number theory from a unified point of view. For instance, for the Sturm-Liouville operator there is a derivation of the Gelfand-

Taran Driver, Vitali Averbukh, Leszek J. Frasinski, Jon P. Marangos

Two-dimensional partial covariance mass spectrometry (2D-PC-MS) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the same parent (e.g. peptide) ion. Here, we apply 2D-PC-MS to the analysis of intact protein ions

Anh Tran, John A. Mitchell, Laura P. Swiler, Tim Wildey

Determining a process-structure-property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process-property linkage by bypassing the materials structure, or in the con

J. Kubalík, E. Derner, R. Babuška

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models th

Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu

Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are very computationally intensive and reach the hardware limit of the existing server and IoT devices. Thus, finding better model architectures with much less amount of computation wh

N. Canosa, R. Rossignoli, Javier Garcia, Swapan Mandal

We investigate nonlinear effects on the dynamics of entanglement and other quantum observables in a system of two harmonic modes coupled through angular momentum. The nonlinearity arises from a quartic anharmonic term in each mode. The emergence and evolution of entanglement, non-gaussianity, photon number, photon antibunching and squeezing are examined for

Stefan Forcey, Drew Scalzo

We describe Galois connections which arise between two kinds of combinatorial structures, both of which generalize trees with labelled leaves, and then apply those connections to a family of polytopes. The graphs we study can be imbued with metric properties or associated to vectors. Famous examples are the Billera-Holmes-Vogtmann metric space of phylogeneti

Maria-Alexandra Barina, Gabriel Barina

Converting the city into a "smart" one is the emerging strategy of alleviating the problems generated by the rapid population growth in most urban areas, i.e., urbanisation. However, as the rate in which the different concepts of the smart city architecture are implemented is very high, academic research pertaining these advancements simply can not keep up.

Leandro Salemi, Marco Berritta, Peter M. Oppeneer

Electrical control of magnetization is of crucial importance for integrated spintronics devices. Spin-orbit torques (SOT) in heavy-metal/ferromagnetic heterostructures have emerged as promising tool to achieve efficiently current-induced magnetization reversal. However, the microscopic origin of the SOT is being debated,with the spin Hall effect (SHE) due to

Wojciech Domitrz, Shyuichi Izumiya, Hiroshi Teramoto

We introduce the volume-preserving equivalence among symmetric matrix-valued map-germs which is the unimodular version of Bruce's $\mathcal{G}$-equivalence. The key concept to deduce unimodular classification out of classification relative to $\mathcal{G}$-equivalence is symmetrical quasi-homogeneity, which is a generalization of the condition for a $2 \time

Mattia Zeni, Ivano Bison, Britta Gauckler, Fernando Reis Fausto Giunchiglia

This article assesses the experience with i-Log at the European Big Data Hackathon 2019, a satellite event of the New Techniques and Technologies for Statistics (NTTS) conference, organised by Eurostat. i-Log is a system that allows to capture personal big data from smartphones' internal sensors to be used for time use measurement. It allows the collection o

A. Durán

Considered in this paper is a bi-directional model for the propagation of interfacial capillary-gravity waves in a two-layer system of fluids with rigid lid condition for the upper layer and lower layer with a much larger or infinite depth. The system is derived from a reformulation of the Euler equations for internal waves with nonnegligible surface tension

Michael Zhu, Kevin Murphy, Rico Jonschkowski

Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However, resampling remains a challenge in these works, as it is inherently non-differentiable. We address this challenge by replacing traditional resampling with a learned neural network

Mamadou Moustapha Kanté, Christophe Paul, Dimitrios M. Thilikos

The graph parameter of pathwidth can be seen as a measure of the topological resemblance of a graph to a path. A popular definition of pathwidth is given in terms of node search where we are given a system of tunnels that is contaminated by some infectious substance and we are looking for a search strategy that, at each step, either places a searcher on a ve

Amy Secunda, Jillian Bellovary, Mordecai-Mark Mac Low, K. E. Saavik Ford

The masses, rates, and spins of merging stellar-mass binary black holes (BBHs) detected by aLIGO and Virgo provide challenges to traditional BBH formation and merger scenarios. An active galactic nucleus (AGN) disk provides a promising additional merger channel, because of the powerful influence of the gas that drives orbital evolution, makes encounters diss

Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant in

Ruohong Zhang, Yu Hao, Donghan Yu, Wei-Cheng Chang

Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore the dependency structures entirely or rely on the (unrealistic) assumption that the correlation stru

Bastian Haase, Daniel Krashen, Max Lieblich

We use Tannakian methods to show that patching for coherent sheaves implies patching for objects in any Noetherian algebraic stack with affine stabilizers. Among other things, this gives a straightforward way to prove patching for torsors under linear algebraic groups, as well as patching for sheaves and torsors on proper algebraic spaces.

Gabriel Caceres-Aravena, Luis E F Foa, Rodrigo A Vicencio

We report on a study of a one-dimensional linear photonic lattice hosting, simultaneously, fundamental and dipolar modes at every site. We show how, thanks to the interaction between the different orbital modes, this minimal model exhibits rich transport and topological properties. By varying the detuning coefficient we find a regime where bands become flatt

Behnaz Arzani, Bita Rouhani

Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw data. Machine Learning (ML) models are useful tools that enable operators to either leverage this data to solve such pro

Dhruv Mubayi, Sayan Mukherjee

Given graphs $T$ and $H$, the generalized Tur\'an number ex$(n,T,H)$ is the maximum number of copies of $T$ in an $n$-vertex graph with no copies of $H$. Alon and Shikhelman, using a result of Erd\H os, determined the asymptotics of ex$(n,K_3,H)$ when the chromatic number of $H$ is greater than 3 and proved several results when $H$ is bipartite. We consider

Grigor Aslanyan, Richard Easther, Nathan Musoke, Layne C. Price

Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate these slow calculations, these approaches are only safe if the approximations are well understood and controlled. This workshop submission reviews and updates a previously publishe

Carlo Danieli, Alexei Andreanov, Sergej Flach

We generate translationally invariant systems exhibiting many-body localization from All-Bands- Flat single particle lattice Hamiltonians dressed with suitable short-range many-body interactions. This phenomenon - dubbed Many-Body Flatband Localization (MBFBL) - is based on symmetries of both single particle and interaction terms in the Hamiltonian, and it h

D. Nóbrega-Siverio, J. Martínez-Sykora, F. Moreno-Insertis, M. Carlsson

Ambipolar diffusion is a physical mechanism related to the drift between charged and neutral particles in a partially ionized plasma that is key in many different astrophysical systems. However, understanding its effects is challenging due to basic uncertainties concerning relevant microphysical aspects and the strong constraints it imposes on the numerical

Oliver Vipond

An ideal invariant for multiparameter persistence would be discriminative, computable and stable. In this work we analyse the discriminative power of a stable, computable invariant of multiparameter persistence modules: the fibered bar code. The fibered bar code is equivalent to the rank invariant and encodes the bar codes of the 1-parameter submodules of a

Elijah Flenner, Grzegorz Szamel

Active matter systems are driven out of equilibrium at the level of individual constituents. One widely studied class are systems of athermal particles that move under the combined influence of interparticle interactions and self-propulsions, with the latter evolving according to the Ornstein-Uhlenbeck stochastic process. Intuitively, these so-called active

Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni, Bruno Lepri

A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, alon

Víctor Hernández-Santamaría, Kévin Le Balc'h

In this paper, we prove the local-controllability to positive constant trajectories of a nonlinear system of two coupled ODE equations, posed in the one-dimensional spatial setting, with nonlocal spatial nonlinearites, and using only one localized control with a moving support. The model we deal with is derived from the well-known nonlinear reaction-diffusio

Leila Ben Saad, Baltasar Beferull-Lozano

Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet of Things (IoT) paradigm. The recent emerging Graph Signal Processing field can also contribute to enabling the IoT by providing key tools, such as graph filters, for processing the data associated with the sensor devices. Graph filters can be performed over WSNs in a

Stella Koch Ocker, James M. Cordes, Shami Chatterjee

Pulsar dispersion measures (DMs) have been used to model the electron density of the interstellar medium (ISM) in the Galactic disk as a plane-parallel medium, despite significant scatter in the DM-distance distribution and strong evidence for inhomogeneities in the ISM. We use a sample of pulsars with independent distance measurements to evaluate a model of

Alina Czajka, Sigtryggur Hauksson, Chun Shen, Sangyong Jeon

In this paper the nonequilibrium correction to the distribution function containing a time and space dependent mass is obtained. Given that, fully consistent fluid dynamic equations are formulated. Then, the physics of the bulk viscosity is elaborated for Boltzmann and Bose-Einstein gases within the relaxation time approximation. It is found that the paramet

Frank F. Deppisch, Lukas Graf, Werner Rodejohann, Xun-Jie Xu

Neutrino Self-Interactions ($\nu$SI) beyond the Standard Model are an attractive possibility to soften cosmological constraints on neutrino properties and also to explain the tension in late and early time measurements of the Hubble expansion rate. The required strength of $\nu$SI to explain the $4\sigma$ Hubble tension is in terms of a point-like effective

Antonio Bazco-Nogueras, Paul de Kerret, David Gesbert, Nicolas Gresset

We analyze the high-SNR regime of the MxK Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting with perfect CSI sharing, in which all the transmitters are

Andrea Franchetto, Benedetta Vulcani, Bianca M. Poggianti, Marco Gullieuszik

Exploiting the data from the GAs Stripping Phenomena in galaxies with MUSE (GASP) survey, we study the gas-phase metallicity scaling relations of a sample of 29 cluster galaxies undergoing ram-pressure stripping and of a reference sample of (16 cluster and 16 field) galaxies with no significant signs of gas disturbance. We adopt the PYQZ code to infer the me

Yang Yang, Natalia B. Perkins, Fulya Koç, Chi-Huei Lin

The spin-1/2 Heisenberg kagome antiferromagnet is one of the paradigmatic playgrounds for frustrated quantum magnetism, with an extensive number of competing resonating valence bond (RVB) states emerging at low energies, including gapped and gapless spin liquids and valence bond crystals. Here we revisit the crossover from this quantum RVB phase to a semicla

Jonathan R. Gaunt, Maximilian Stahlhofen

The fully-differential beam function (dBF) is a universal ingredient in resummed predictions of hadron collider observables that probe the full kinematics of the incoming parton from each colliding proton -- the virtuality and transverse momentum as well as the light-cone momentum fraction $x$. In this paper we compute the matching coefficients between the u

Hiromichi Tagawa, Zoltan Haiman, Imre Bartos, Bence Kocsis

The astrophysical origin of gravitational wave (GW) events is one of the most timely problems in the wake of the LIGO/Virgo discoveries. In active galactic nuclei (AGN), binaries form and evolve efficiently by dynamical interactions and gaseous dissipation. Previous studies have suggested that binary black hole (BBH) mergers in AGN disks can contribute signi

E. R. Stanway, A. A. Chrimes, J. J. Eldridge, H. F. Stevance

Binary stars have been shown to have a substantial impact on the integrated light of stellar populations, particularly at low metallicity and early ages - conditions prevalent in the distant Universe. But the fraction of stars in stellar multiples as a function of mass, their likely initial periods and distribution of mass ratios are all known empirically fr

T. K. Daisy Leung, Karen P. Olsen, Rachel S. Somerville, Romeel Dave

We present the first predictions for the $L_{\rm [CII]}$ - SFR relation and [CII] luminosity function (LF) in the Epoch of Reionization (EoR) based on cosmological hydrodynamics simulations using the SIMBA suite plus radiative transfer calculations via S\'IGAME. The sample consists of 11,137 galaxies covering halo mass $\log M_{\rm halo}\in$[9, 12.4] $M_\odo

M Mezcua

Detecting the seed black holes from which quasars formed is extremely challenging; however, those seeds that did not grow into supermassive should be found as intermediate-mass black holes (IMBHs) of 100-10$^5$ M$_{\odot}$ in local dwarf galaxies. The use of deep multiwavelength surveys has revealed that a population of actively accreting IMBHs (low-mass AGN