November 2022 arXiv papers — page 30

Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a u

Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusio

Sohitri Ghosh, Matthew A. Feldman, Seongjin Hong, Claire E. Marvinney

Optomechanical sensors are capable of transducing external perturbations to resolvable optical signals. A particular regime of interest is that of high-bandwidth force detection, where an impulse is delivered to the system over a short period of time. Exceedingly sensitive impulse detection has been proposed to observe very weak signals like those due to lon

Atnafu Lambebo Tonja, Mesay Gemeda Yigezu, Olga Kolesnikova, Moein Shahiki Tash

Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Polit\'ecnico Nacional, Centro de Inve

T. Mageshwaran, Gargi Shaw, Sudip Bhattacharyya

We present both a disc-wind model on the optical/UV emission continuum and CLOUDY modelling on the spectral lines of the tidal disruption event (TDE) iPTF16axa to understand the disc-wind emission and the properties of the atmosphere that impacts the line luminosity of the TDE. Assuming the optical/UV emission from the wind due to the disc super-Eddington ph

M. J. Mehdipour, A. Rejali

Let $\frak{F}(X, A)$ be one of the Banach algebras $\hbox{Lip}(X, A)$ or $\hbox{lip}(X, A)$. In this paper, we show that $\frak{F}(X, A)$ is amenable if and only if $X$ is uniformly discrete and $A$ is amenable. We also prove that the result holds for $\hbox{lip}^\circ(X, A)$ instead of $\frak{F}(X, A)$. In the case where $A^*$ is separable, we establish tha

Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck

In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into

Tetsuya J. Kobayashi, Dimitri Loutchko, Atsushi Kamimura, Shuhei A. Horiguchi

We introduce a new information-geometric structure associated with the dynamics on discrete objects such as graphs and hypergraphs. The presented setup consists of two dually flat structures built on the vertex and edge spaces, respectively. The former is the conventional duality between density and potential, e.g., the probability density and its logarithmi

Qinian Jin, Wei Wang

We consider determining $\R$-minimizing solutions of linear ill-posed problems $A x = y$, where $A: {\mathscr X} \to {\mathscr Y}$ is a bounded linear operator from a Banach space ${\mathscr X}$ to a Hilbert space ${\mathscr Y}$ and ${\mathcal R}: {\mathscr X} \to [0, \infty]$ is a proper strongly convex penalty function. Assuming that multiple repeated inde

Michael Poli, Stefano Massaroli, Federico Berto, Jinykoo Park

Spectral analysis provides one of the most effective paradigms for information-preserving dimensionality reduction, as simple descriptions of naturally occurring signals are often obtained via few terms of periodic basis functions. In this work, we study deep neural networks designed to harness the structure in frequency domain for efficient learning of long

Maxim Lyutikov

We first derive a set of equations describing general stationary configurations of relativistic force-free plasma, without assuming any geometric symmetries. We then demonstrate that electromagnetic interaction of merging neutron stars is necessarily dissipative due to the effect of electromagnetic draping - creation of dissipative regions near the star (in

Jerred Chen, Frank Dellaert

Quadrupeds are robots that have been of interest in the past few years due to their versatility in navigating across various terrain and utility in several applications. For quadrupeds to navigate without a predefined map a priori, they must rely on SLAM approaches to localize and build the map of the environment. Despite the surge of interest and research d

Roberto Vila, Narayanaswamy Balakrishnan, Helton Saulo, Peter Zörnig

We present here a unit-log-symmetric model based on the bivariate log-symmetric distribution. It is a flexible family of distributions over the interval $(0, 1)$. We then discuss its mathematical properties such as stochastic representation, symmetry, modality, moments, quantile function, entropy and maximum likelihood estimators, paying particular attention

Xiaoyu Wang, Scott Sanner, Baher Abdulhai

Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches leveraging artificial intelligence based optimization and control algorithms such as reinforcement learning as well as conventional model predictive control. However, lack of cross-domai

Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang

Recently the Transformer structure has shown good performances in graph learning tasks. However, these Transformer models directly work on graph nodes and may have difficulties learning high-level information. Inspired by the vision transformer, which applies to image patches, we propose a new Transformer-based graph neural network: Patch Graph Transformer (

Iordanis Fostiropoulos, Laurent Itti

Transformations in the input space of Deep Neural Networks (DNN) lead to unintended changes in the feature space. Almost perceptually identical inputs, such as adversarial examples, can have significantly distant feature representations. On the contrary, Out-of-Distribution (OOD) samples can have highly similar feature representations to training set samples

Nanshu Liu, Cong Wang, Changlin Yan, Changsong Xu

A recent experiment reported type-II multiferroicity in monolayer (ML) NiI$_{2}$ based on a presumed spiral magnetic configuration (Spiral-B), which is, as we found here, under debate in the ML limit. Freestanding ML NiI$_{2}$ breaks its C$_{3}$ symmetry, as it prefers a striped antiferromagnetic order (AABB-AFM) along with an intralayer antiferroelectric (A

Quande Yuan, Yuzhen Pi, Lei Kou, Fangfang Zhang

Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification resu

J. E. Sánchez-Guevara, R. A. Zúñiga-Rojas

This paper presents a study of how the theory of categories leads to the creation of non classical logical systems. In particular, the case of the elementary topos of graphs, where there are three other truth values different from false and true. The approach in this article to the theory of categories avoids specialized mathematical training to understand i

Wentao Guo, Charlie Ruan, Claire Zhou

Photo Rater is a computer vision project that uses neural networks to help photographers select the best photo among those that are taken based on the same scene. This process is usually referred to as "culling" in photography, and it can be tedious and time-consuming if done manually. Photo Rater utilizes three separate neural networks to complete such a ta

Xiang Li, Haoyuan Cao, Shijie Zhao, Junlin Li

Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing the VSOD problem in 2D scenarios. With the rapid development of VR devices, panoramic videos have been a promising alternative to 2D videos to provide immersive feelings of the rea

Sumit Roy

Let $X$ be a compact Riemann surface of genus $g\geq 3$ and let $\mathcal{M}_{\mathrm{par}}$ be the moduli space of semistable parabolic bundles over $X$. Let $\mathcal{M}_{\mathrm{parH}}$ denote the moduli space of semistable parabolic Higgs bundles over $X$. In this article, we study the Picard group of $\mathcal{M}_{\mathrm{par}}$ and $\mathcal{M}_{\mathr

Oliver Neumann, Marcel Schilling, Markus Reischl, Ralf Mikut

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-site has s

Xuanyi Li, Changsong Xu, Boyu Liu, Xueyang Li

A realistic first-principle-based spin Hamiltonian is constructed for the type-II multiferroic NiI$_2$, using a symmetry-adapted cluster expansion method. Besides single ion anisotropy and isotropic Heisenberg terms, this model further includes the Kitaev interaction and a biquadratic term, and can well reproduce striking features of the experimental helical

Forrest Mozer, Stuart Bale, Paul Kellogg, Roberto Livi

The Parker Solar Probe is in a solar orbit with a perihelion for orbit 12 at 13.3 solar radii. The electric field experiment on this satellite observes what we call triggered ion-acoustic waves as the most dominant wave mode above a few Hz within the solar radial distance of 15-25 solar radii. In this mode, a few Hz electrostatic wave is typically accompanie

Thomas Letourmy, Leandro Vendramin

We define isoclinism of skew braces and present several applications. We study some properties of skew braces that are invariant under isoclinism. For example, we prove that right nilpotency is an isoclinism invariant. This result has application in the theory of set-theoretic solutions to the Yang-Baxter equation. We define isoclinic solutions and study mul

Okan K. Orhan, Mauricio Ponga

Light-weight alloys are essential pillars of transportation technologies. They also play a crucial role to achieve a more green and cost-effective aerospace technologies. Magnesium-lithium-aluminum (Mg-Li-Al) alloys are auspicious candidates due to their promising mechanical strengths at low densities. We herein present a systematic first-principles investig

Sebastián Belmar-Herrera, Leonardo Balart

We present a family of nonlinear electrodynamics models that are free of the infinite self-energy of the point charge. Each model is dependent on a dimensional nonlinearity parameter and is determined by the integer value of a dimensionless parameter n. The Born-Infeld model is recovered when n = 1. Some of the characteristics of this family are studied. In

Xin Li, Sumit Sinha, T. R. Kirkpatrick, D. Thirumalai

The complex spatiotemporal flow patterns in living tissues, driven by active forces, have many of the characteristics associated with inertial turbulence even though the Reynolds number is extremely low. Analyses of experimental data from two-dimensional epithelial monolayers in combination with agent-based simulations show that cell division and apoptosis l

Ryo Kuroiwa, J. Christopher Beck

For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the 'holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a new model-based paradigm based on dynamic programming (DP). While D

Luis Enrique Gutiérrez-Domínguez, Rodrigo Hernández-Gutiérrez

In a 2021 paper, Vladimir Tkachuk asked whether there is a non-separable LOTS $X$ such that $X^2\setminus\{\langle x,x\rangle\colon x\in X\}$ is functionally countable. In this paper we prove that such a space, if it exists, must be an Aronszajn line and admits a $\leq 2$-to-$1$ retraction to a subspace that is a Suslin line. After this, assuming the existen

Yang Cao, Xiaoyu Li, Zhao Song, Xin Yang

The famous theorem of Fritz John states that any convex body has a unique maximal volume inscribed ellipsoid, known as the John Ellipsoid. Computing the John Ellipsoid is a fundamental problem in convex optimization. In this paper, we focus on approximating the John Ellipsoid inscribed in a convex and centrally symmetric polytope defined by $ P := \{ x \in \

Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha

Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics is still missing. In this paper, we ask several important questions for providing a fundamental understanding of SNNs: What are temporal information dynamics inside SNNs? How can

Congsong Zhang, Yong Gao, James Nastos

Backtracking has been widely used for solving problems in artificial intelligence (AI), including constraint satisfaction problems and combinatorial optimization problems. Good branching heuristics can efficiently improve the performance of backtracking by helping prune the search space and leading the search to the most promising direction. In this paper, w

Wen-Lei Zhao, Huiqian Zhang

We investigate the quantum irreversibility and quantum diffusion in a non-Hermitian kicked rotor model for which the kicking strength is complex. Our results show that the exponential decay of Loschmidt echo gradually disappears with increasing the strength of the imaginary part of non-Hermitian driven potential, demonstrating the suppress of the exponential

Hardik Kothari

We propose the nonlinear restricted additive Schwarz (RAS) preconditioning strategy to improve the convergence speed of limited memory quasi-Newton (QN) methods. We consider both "left-preconditioning" and "right-preconditioning" strategies. As the application of the nonlinear preconditioning changes the standard gradients and Hessians to their preconditione

Aristides Milios, Parishad BehnamGhader

Although large pre-trained language models have achieved great success in many NLP tasks, it has been shown that they reflect human biases from their pre-training corpora. This bias may lead to undesirable outcomes when these models are applied in real-world settings. In this paper, we investigate the bias present in monolingual BERT models across a diverse

Bernie Boscoe, Tuan Do, Evan Jones, Yunqi Li

In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets are required for training and testing. H

Jonathan W. Siegel

Let $\Omega = [0,1]^d$ be the unit cube in $\mathbb{R}^d$. We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can approximate functions in the Sobolev spaces $W^s(L_q(\Omega))$ and Besov spaces $B^s_r(L_q(\Omega))$, with error measured in the $L_p(\Omega)$ norm. This problem i

Noriko Hirata-Kohno, Makoto Kawashima, Anthony Poëls, Yukiko Washio

In this article, we use Pad\'{e} approximations constructed for binomial functions, to give a new upper bound for the number of the solutions of the $S$-unit equation. Combining explicit formulae of these Pad\'{e} approximants with a simple argument relying on Mahler measure and on the local height, we refine the bound due to J.-H. Evertse.

S. Sulis, M. Lendl, H. Cegla, L. F. Rodriguez Diaz

Stellar granulation generates fluctuations in photometric and spectroscopic data whose properties depend on the stellar type, composition, and evolutionary state. In this study, we aim to detect the signatures of stellar granulation, link spectroscopic and photometric signatures of convection for main-sequence stars, and test predictions from 3D hydrodynamic

U. G. Aglietti, G. Ferrera

We consider the resummation of soft-gluon effects in heavy quark to heavy quark decays, namely the processes Q1 -> Q2 + (non QCD partons), where Q1 and Q2 are two different heavy quarks. We construct a new factorization scheme for threshold resummed spectra, which allows us to consistently evaluate the distribution of the final hadron invariant mass mX in al

Jay J. Yoo, Khashayar Namdar, Sean Carey, Sandra E. Fischer

Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normaliza

Cristian Simionescu

Recent advances in Deep Learning have greatly improved performance on various tasks such as object detection, image segmentation, sentiment analysis. The focus of most research directions up until very recently has been on beating state-of-the-art results. This has materialized in the utilization of bigger and bigger models and techniques which help the trai

William Shiao, Zhichun Guo, Tong Zhao, Evangelos E. Papalexakis

A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Notably, many state-of-the-art graph SSL methods are contrastive methods, which use a combination of positive and negative samples to learn node representations. Owing to challenges i

Han Wang, Leonardo F. Toso, James Anderson

We study the problem of learning a linear system model from the observations of $M$ clients. The catch: Each client is observing data from a different dynamical system. This work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federated learning problem and char

Francesco Bosia, Peikun Zheng, Alain Vaucher, Thomas Weymuth

Semi-empirical quantum chemical approaches are known to compromise accuracy for feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and for data-driven machine learning has shifted the emphasis towards calculation runtimes recently. This come

Md Ashiqur Rahman, Jasorsi Ghosh, Hrishikesh Viswanath, Kamyar Azizzadenesheli

We address the problem of generating 3D human motions in dyadic activities. In contrast to the concurrent works, which mainly focus on generating the motion of a single actor from the textual description, we generate the motion of one of the actors from the motion of the other participating actor in the action. This is a particularly challenging, under-explo

Amin Eslami Abyane, Steve Drew, Hadi Hemmati

Recently, a new distributed learning scheme called Federated Learning (FL) has been introduced. FL is designed so that server never collects user-owned data meaning it is great at preserving privacy. FL's process starts with the server sending a model to clients, then the clients train that model using their data and send the updated model back to the server

Scott E. Field, Sigal Gottlieb, Gaurav Khanna, Ed McClain

Linear wave equations sourced by a Dirac delta distribution $\delta(x)$ and its derivative(s) can serve as a model for many different phenomena. We describe a discontinuous Galerkin (DG) method to numerically solve such equations with source terms proportional to $\partial^n \delta /\partial x^n$. Despite the presence of singular source terms, which imply di

Luan M. Veríssimo, Marcelo L. Lyra, Roman Orus

In this work, we investigate the interplay between dissipation and symmetry-protected topological order. We considered the one-dimensional spin-1 Affleck-Kennedy-Lieb-Tasaki model interacting with an environment where the dissipative dynamics are described by the Lindladian master equation. The Markovian dynamics is solved by the implementation of a tensor n

Alexandros Efstratiou, Jeremy Blackburn, Tristan Caulfield, Gianluca Stringhini

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they

Rameshwar Garg, Shriya Barpanda, Girish Rao Salanke N S, Ramya S

Time series data is being used everywhere, from sales records to patients' health evolution metrics. The ability to deal with this data has become a necessity, and time series analysis and forecasting are used for the same. Every Machine Learning enthusiast would consider these as very important tools, as they deepen the understanding of the characteristics

Krishnaraj Kundavu, Parveen Kumar, R. P. Chauhan

Density functional theory based First Principles calculations were used to study the effect of Silicon (Si) doping on the structural, electronic and optical properties of CsCaI3. From our calculations, we predict that CsCaI3 can form stable perovskite structure. It is also observed that after substitutional doping of Si in CsCaI3, the material still can stay

Jacob Zietek, Nicholas Wade, Cole Roberts, Aref Malek

This technical report details VEX Robotics team BLRSAI's development of a fully autonomous robot for VEX Robotics' Tipping Point AI Competition. We identify and develop three separate critical components. This includes a Unity simulation and reinforcement learning model training pipeline, a malleable computer vision pipeline, and a data transfer pipeline to

Vytautas Dūdėnas, Thomas Gajdosik, Uladzimir Khasianevich, Wojciech Kotlarski

In the Grimus-Neufeld model (GNM) the neutrino mass generation from an extended Higgs sector leads to bounds from Charged Lepton Flavour Violating (cLFV) processes. Here we update bounds from the previous study by extending the parameter space to nonvanishing Majorana phase of the Pontecorvo-Maki-Nakagawa-Sakata matrix and to heavier charged Higgs boson mass

Yushun Dong, Song Wang, Jing Ma, Ninghao Liu

Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain demographic subgroups. Understanding how the bias in predictions arises is critical, as it guides the design of GNN debiasing mechanisms. How

Sudeep Kanur, Georgios Georgakarakos, Antti Siirilä, Jérémie Lagravière

The near channel performance of Low Density Parity Check Codes (LDPC) has motivated its wide applications. Iterative decoding of LDPC codes provides significant implementation challenges as the complexity grows with the code size. Recent trends in integrating Multiprocessor System on Chip (MPSoC) with Network on Chip (NoC) gives a modular platform for parall

Iosif Bena, Emilian Dudas, Mariana Graña, Gabriele Lo Monaco

Anti-D3 branes at the bottom of warped throats, commonly used to uplift the cosmological constant in String-Theory de Sitter proposals, source a plaethora of supersymmetry-breaking fluxes, that can interact nontrivially with other ingredients of the flux compactification. In this paper we perform a complex-structure decomposition of these fluxes, and compute

Gustavo Ávalos, Antonio Rojas, Marxil Sánchez, Genaro Toledo

The $\rho(1450)$ vector meson ($\rho^\prime$) is becoming increasingly important to properly describe precision observables. We analyse a set of decay modes and cross sections, in the low-energy regime, to determine the role played by this meson. This is done through the extraction of the parameters for its description, in the context of the vector meson dom

Hengshuai Yao

We show that unlike machine learning classifiers, there are no complex boundary structures in the decision boundaries for well-trained deep models. However, we found that the complicated structures do appear in training but they vanish shortly after shaping. This is a pessimistic news if one seeks to capture different levels of complexity in the decision bou

Victor E. Ambrus, S. Schlichting, C. Werthmann

We evaluate the full opacity dependence of collective flow in high-energy heavy-ion collisions within a microscopic kinetic description based on the Boltzmann equation in the conformal relaxation time approximation. By comparing kinetic theory calculations to hydrodynamic and hybrid simulations for an average initial state, we point out shortcomings and inac

Aaron Master, Lie Lu, Nathan Swedlow

Speech Enhancement (SE) systems typically operate on monaural input and are used for applications including voice communications and capture cleanup for user generated content. Recent advancements and changes in the devices used for these applications are likely to lead to an increase in the amount of two-channel content for the same applications. However, S

Fuyang Tay, Xiao Lin, Xihang Shi, Hongsheng Chen

Free-electron radiation is a fundamental photon emission process that is induced by fast-moving electrons interacting with optical media. Historically, it has been understood that, just like any other photon emission process, free-electron radiation must be constrained within a finite time interval known as the "formation time", whose concept is applicable t

Dimitry Gurevich, Pavel Saponov

By treating generators of the reflection equation algebra corresponding to a Hecke symmetry as quantum analogs of vector fields, we exhibit the corresponding Leibniz rule via the so-called quantum doubles. The role of the function algebra in such a double is attributed to another copy of the reflection equation algebra. We consider two types of quantum doubl

Zhen Chen, Anne Gelb, Yoonsang Lee

We propose a new data-driven method to learn the dynamics of an unknown hyperbolic system of conservation laws using deep neural networks. Inspired by classical methods in numerical conservation laws, we develop a new conservative form network (CFN) in which the network learns the flux function of the unknown system. Our numerical examples demonstrate that t

Gerhard Schindl

We characterize the inclusions of weighted classes of entire functions in terms of the defining weights resp. weight systems. First we treat weights defined in terms of a so-called associated weight function where the weight(system) is based on a given sequence. The abstract weight function case is then reduced to the weight sequence setting by using the so-

Jaime Barros Silva Filho, Fernando de Souza Bastos, Diogo da Silva Machado, Maria Luiza Ferreira Delfim

The study of compounds with antioxidant capabilities is of great interest to the scientific community, as it has implications in several areas, from Agricultural Sciences to Biological Sciences, including Food Engineering, Medicine and Pharmacy. In applications related to human health, it is known that antioxidant activity can delay or inhibit oxidative dama

Daniel Peixoto Pinto da Silva, Edresson Casanova, Lucas Rafael Stefanel Gris, Arnaldo Candido Junior

During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection in audios. We investigate which features are important for model decision process, investigating spectrograms,

Daniel Lawther, Marianne Vestergaard, Sandra Raimundo, Jun Yi Koay

Mrk 590 is a known changing-look AGN which almost turned off in 2012, and then in 2017 partially re-ignited into a repeat flaring state, unusual for an AGN. Our \emph{Swift} observations since 2013 allow us to characterise the accretion-generated emission and its reprocessing in the central engine of a changing-look AGN. The X-ray and UV variability amplitud

Jean-Marc Casandjian, Jean Ballet, Isabelle Grenier, Quentin Remy

Where dust and gas are uniformly mixed, atomic hydrogen can be traced through the detection of far-infrared (FIR) or UV emission of dust. We considered, for the origin of discrepancies observed between various direct and indirect tracers of gas outside the Galactic plane, possible corrections to the zero levels of the Planck-HFI detectors. We set the zero le

Leonard Tang, Alexander Cai, Steve Li, Jason Wang

Jokes are intentionally written to be funny, but not all jokes are created the same. Some jokes may be fit for a classroom of kindergarteners, but others are best reserved for a more mature audience. While recent work has shown impressive results on humor detection in text, here we instead investigate the more nuanced task of detecting humor subtypes, especi

Alberto Castaño Domínguez, Luis Narváez Macarro

Given two holomorphic functions $f$ and $g$ defined in two respective germs of complex analytic manifolds $(X,x)$ and $(Y,y)$, we know thanks to M. Saito that, as long as one of them is Euler homogeneous, the reduced (or microlocal) Bernstein-Sato polynomial of the Thom-Sebastiani sum $f+g$ can be expressed in terms of those of $f$ and $g$. In this note we g

Jiwoon Park

We study microscopic observables of the Discrete Gaussian model (i.e., the Gaussian free field restricted to take integer values) at high temperature using the renormalisation group method. In particular, we show the central limit theorem for the two-point function of the Discrete Gaussian model by computing the asymptotic of the moment generating function $

Gregory P. Spell, Simiao Ren, Leslie M. Collins, Jordan M. Malof

We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements. Recent work has shown impressive results using deep learning, but we note that there is a trade-off between model perfor

Devansh R. Agrawal, Dimitra Panagou

This paper addresses the synthesis of safety-critical controllers using estimate feedback. We propose an observer-controller interconnection to ensure that the nonlinear system remains safe despite bounded disturbances on the system dynamics and measurements that correspond to partial state information. The co-design of observers and controllers is critical,

Ivan Volkov

This paper describes an entropy regularization term for vector quantization (VQ) based on the analysis of persistent homology of the VQ embeddings. Higher embedding entropy positively correlates with higher codebook utilization, mitigating overfit towards the identity and codebook collapse in VQ-based autoencoders [1]. We show that homology-constrained regul

Shufan Li, Congxi Lu, Linkai Li, Haoshuai Zhou

Line Chart Data Extraction is a natural extension of Optical Character Recognition where the objective is to recover the underlying numerical information a chart image represents. Some recent works such as ChartOCR approach this problem using multi-stage networks combining OCR models with object detection frameworks. However, most of the existing datasets an

Devansh R Agrawal, Ruichang Chen, Dimitra Panagou

This paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method that ensures that trajectories of a nonlinear system satisfy safety constraints despite sensing limitations. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step to ensure that proposed trajecto

Viktor Scherbakov, Vladimir Mayorov

Most Named Entity Recognition (NER) models operate under the assumption that training datasets are fully labelled. While it is valid for established datasets like CoNLL 2003 and OntoNotes, sometimes it is not feasible to obtain the complete dataset annotation. These situations may occur, for instance, after selective annotation of entities for cost reduction

Andrés E. Rentería-Olivo

A proof-of-concept application of a quantum algorithm to multiloop Feynman integrals in the Loop-Tree Duality (LTD) framework is applied to a representative four-loop topology. Bootstrapping causality in the LTD formalism, is a suitable problem to address with quantum computers given the straightforward possibility to encode the two on-shell states of a prop

Zhixuan Zhou, Jiao Sun, Jiaxin Pei, Nanyun Peng

Fairy tales are a common resource for young children to learn a language or understand how a society works. However, gender bias, e.g., stereotypical gender roles, in this literature may cause harm and skew children's world view. Instead of decades of qualitative and manual analysis of gender bias in fairy tales, we computationally analyze gender bias in a f

Marc Chardin, Rafael Holanda

Notions of Castelnuovo-Mumford regularity and of $a^*$ invariant were extended from standard graded algebras to the toric setting. We here focus our attention on the standard multigraded case, which corresponds to a product of $k$ projective spaces. A natural notion for a $\mathbb Z^k$-graded module is its support: degrees in which it is not zero. A stabiliz

Victor E. Ambrus, S. Schlichting, C. Werthmann

We simulate the space-time dynamics of high-energy collisions based on a microscopic kinetic description in the conformal relaxation time approximation, in order to determine the range of applicability of an effective description in relativistic viscous hydrodynamics. We find that hydrodynamics provides a quantitatively accurate description of collective flo

D. O. Ignatyeva, T. V. Mikhailova, P. O. Kapralov, S. D. Lyashko

It is widely known that the magneto-optical Faraday effect is linear in magnetization, and therefore the Faraday angles for the states with opposite magnetizations are of opposite sign but equal in modulus. Here we demonstrate that under certain spatial symmetry-breaking conditions, an asymmetric Faraday effect (AFE) arises, meaning that the Faraday angles f

Ruonan Xu, Jeffrey M. Wooldridge

When observing spatial data, what standard errors should we report? With the finite population framework, we identify three channels of spatial correlation: sampling scheme, assignment design, and model specification. The Eicker-Huber-White standard error, the cluster-robust standard error, and the spatial heteroskedasticity and autocorrelation consistent st

Pierre-Henri Chavanis

We complete the kinetic theory of two-dimensional (2D) point vortices initiated in previous works. We use a simpler and more physical formalism. We consider a system of 2D point vortices submitted to a small external stochastic perturbation and determine the response of the system to the perturbation. We derive the diffusion coefficient and the drift by pola

O. Erken, B. Fazla, F. Romanò, J. B. Grotberg

Airway mucus is a complex material with both viscoelastic and viscoplastic properties that vary with healthy and pathological lung conditions. In this study, the effects of these conditions on airway closure are examined in a model problem, where an elastoviscoplastic (EVP) single liquid layer lines the inner wall of a rigid pipe and surrounds the air core.

Adrian Solymos, Carlos Vieira, Cristhiano Duarte, Zoltán Zimborás

The no-broadcasting theorem is a fundamental result in quantum information theory. It guarantees that a class of attacks on quantum protocols, based on eavesdropping and indiscriminate copying of quantum information, are impossible. Due to its fundamental importance, it is natural to ask whether it is an intrinsic quantum property or whether it also holds fo

Mathilde Gaudel, Jan H. Orkisz, Maryvonne Gerin, Jérôme Pety

Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent a

Maude Gull, Daniel R. Weisz, Peter Senchyna, Nathan R. Sandford

We characterize massive stars (M>8 M_sun) in the nearby (D~0.8 Mpc) extremely metal-poor (Z~5% Z_sun) galaxy Leo A using Hubble Space Telescope ultra-violet (UV), optical, and near-infrared (NIR) imaging along with Keck/LRIS and MMT/Binospec optical spectroscopy for 18 main sequence OB stars. We find that: (a) 12 of our 18 stars show emission lines, despite

Mattia Bulla

We present an improved version of the 3D Monte Carlo radiative transfer code POSSIS to model kilonovae from neutron star mergers, wherein nuclear heating rates, thermalization efficiencies and wavelength-dependent opacities depend on local properties of the ejecta and time. Using an axially-symmetric two-component ejecta model, we explore how simplistic assu

Luis Sa-Couto, Jose Miguel Ramos, Andreas Wichert

When several models have similar training scores, classical model selection heuristics follow Occam's razor and advise choosing the ones with least capacity. Yet, modern practice with large neural networks has often led to situations where two networks with exactly the same number of parameters score similar on the training set, but the deeper one generalize

A. Pereira, A. Onofre, A. Proenca

HEP-Frame is a new C++ package designed to efficiently perform analyses of data sets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high performance servers and mini-clusters, and it was designed for natural science experts with a user-friendly interface to access structured data

Jailson S. Alcaniz, Jacinto P. Neto, Farinaldo S. Queiroz, Deivid R. da Silva

The Standard Cosmological Model has experienced tremendous success at reproducing observational data by assuming a universe dominated by a cosmological constant and dark matter in a flat geometry. However, several studies, based on local measurements, indicate that the universe is expanding too fast, in disagreement with the Cosmic Microwave Background. Taki

Hafsa Billah, Mayur Arora, Sharma Chakravarthy

Query-based video situation detection (as opposed to manual or customized algorithms) is critical for diverse applications such as traffic monitoring, surveillance1 , and other types of environmental/infrastructure monitoring. Video contents are complex in terms of disparate object types and background information. Therefore, in addition to extracting comple

Christina Chaccour, Walid Saad, Merouane Debbah, Zhu Han

Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communicatio

Ragnhild Aurlien, Mathieu Remazeilles, Sebastian Belkner, Julien Carron

PICO is a concept for a NASA probe-scale mission aiming to detect or constrain the tensor to scalar ratio $r$, a parameter that quantifies the amplitude of inflationary gravity waves. We carry out map-based component separation on simulations with five foreground models and input $r$ values $r_{in}=0$ and $r_{in} = 0.003$. We forecast $r$ determinations usin

Martin Beneke, Stefan Lederer, Clara Peset

We consider the resummation of large electroweak Sudakov logarithms for the annihilation of neutralino DM with $\mathcal{O}$(TeV) mass to high-energy photons in the minimal supersymmetric standard model, extending previous work on the minimal wino and Higgsino models. We find that NLL resummation reduces the yield of photons by about $20\%$ for Higgsino-domi

Ilija Buric, Volker Schomerus

Conformal blocks are a central analytic tool for higher dimensional conformal field theory. We employ Harish-Chandra's radial component map to construct universal Casimir differential equations for spinning conformal blocks in any dimension $d$ of Euclidean space. Furthermore, we also build a set of differential ``shifting'' operators that allow to construct