October 2022 arXiv papers — page 3

Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer

Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This challenge is amplified in multi-agent reinforcement learning (MARL) where credit assignment of these rewards needs to happen not only across time, but also across agents. We propose Agent-Time Attention (ATA), a neural network model with auxiliary losses for redistributi

Mingru Yang, Bram Vanhecke, Norbert Schuch

New or enlarged symmetries can emerge at the low-energy spectrum of a Hamiltonian that does not possess the symmetries, if the symmetry breaking terms in the Hamiltonian are irrelevant under the renormalization group flow. In this letter, we propose a tensor network based algorithm to numerically extract lattice operator approximation of the emergent conserv

Michèle Levi, Roger Morales, Zhewei Yin

We confirm the generalized actions of the complete NLO cubic-in-spin interactions for generic compact binaries which were first tackled via an extension of the EFT of spinning gravitating objects. We first reduce these generalized actions to standard actions with spins, where the interaction potentials are found to consist of $6$ independent sectors, includi

Konstantinos N. Anagnostopoulos, Takehiro Azuma, Kohta Hatakeyama, Mitsuaki Hirasawa

The type IIB matrix model, also known as the IKKT model, has been proposed as a promising candidate for a non-perturbative formulation of superstring theory. Based on this proposal, various attempts have been made to explain how our four-dimensional space-time can emerge dynamically from superstring theory. In this article, we review the progress in numerica

Martin Hutzenthaler, Robert Link

In this article we establish strong convergence rates on the whole probability space for explicit full-discrete approximations of stochastic Burgers equations with multiplicative trace-class noise. The key step in our proof is to establish uniform exponential moment estimates for the numerical approximations.

O. Grace Telford, Kristen B. W. McQuinn, John Chisholm, Danielle A. Berg

Metal-poor, star-forming dwarf galaxies produce extreme nebular emission and likely played a major role in cosmic reionization. Yet, determining their contribution to the high-redshift ionizing photon budget is hampered by the lack of observations constraining the ionizing spectra of individual massive stars more metal-poor than the Magellanic Clouds (20-50%

Goutam Das, Chinmoy Dey, M. C. Kumar, Kajal Samanta

We study the threshold effect for neutral and charged Drell-Yan productions, associated production of Higgs boson with a massive vector boson and Higgs production in bottom quark annihilation at LHC to the third order in QCD. Using the third order soft-virtual results for these processes and exploiting the universality of the threshold logarithms, we extract

Georgios Antoniou, Alexandros Papageorgiou, Panagiota Kanti

We study a number of well-motivated theories of modified gravity with the common overarching theme that they predict the existence of compact objects such as black holes and wormholes endowed with scalar hair. We compute the shadow radius of the resulting compact objects and demonstrate that black hole images such as that of M87$^*$ or the more recent SgrA$^

Matthew Badger, Max Engelstein, Tatiana Toro

In Kenig and Toro's two-phase free boundary problem, one studies how the regularity of the Radon-Nikodym derivative $h= d\omega^-/d\omega^+$ of harmonic measures on complementary NTA domains controls the geometry of their common boundary. It is now known that $\log h \in C^{0,\alpha}(\partial \Omega)$ implies that pointwise the boundary has a unique blow-up,

Jens Kammerer, Rachel A. Cooper, Thomas Vandal, Deepashri Thatte

Kernel phase imaging (KPI) enables the direct detection of substellar companions and circumstellar dust close to and below the classical (Rayleigh) diffraction limit. We present a kernel phase analysis of JWST NIRISS full pupil images taken during the instrument commissioning and compare the performance to closely related NIRISS aperture masking interferomet

A. Gerbershagen, C. Ahdida, J. Bernhard, V. Clerc

NA60+ is a fixed target experiment proposed in the framework of the Physics Beyond Colliders programme at CERN. It aims to precisely measure the hard and electromagnetic probes in nuclear collisions. Initially proposed for the underground cavern ECN3 with very high beam intensities, the experiment now foresees a location in the EHN1 surface hall which was sh

Shuvro Chowdhury, Kerem Y. Camsari, Supriyo Datta

Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various special purpose devices and domain specific hardware, it has become increasingly important to establish clear benchmarks o

Reinald Kim Amplayo, Kellie Webster, Michael Collins, Dipanjan Das

Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficult to evaluate. We resolve the difficulties to evaluate long-form output by doing both tasks at once -- to do q

Valentin Huguin

In this article, we show that every rational map whose multipliers all lie in a given number field is a power map, a Chebyshev map or a Latt\`{e}s map. This strengthens a conjecture by Milnor concerning rational maps with integer multipliers, which was recently proved by Ji and Xie.

Adam Smith, Abhradeep Thakurta

We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst. Such an analyst selects mechanisms (to be run on a sensitive data set) and their privacy budgets adaptively, that is, based on the answers from other mechanism

Daniel Kessler, Keith Levin, Elizaveta Levina

We consider the setting where many networks are observed on a common node set, and each observation comprises edge weights of a network, covariates observed at each node, and an overall response. The goal is to use the edge weights and node covariates to predict the response while identifying an interpretable set of predictive features. Our motivating applic

Dan Li, Chunmei Wang, Junping Wang

This paper introduces an extension of the well-known Morley element for the biharmonic equation, extending its application from triangular elements to general polytopal elements using the weak Galerkin finite element methods. By leveraging the Schur complement of the weak Galerkin method, this extension not only preserves the same degrees of freedom as the M

Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang

Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i) mathematical abilities e.g., arithmeti

Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi

One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is unaffected by the treatments assigned to other experimental units. This assumption can be violated in real-life experiments, which significantly complicates the task of causal inference. As the n

Mahsa Derakhshan, Alireza Farhadi

In the stochastic weighted matching problem, the goal is to find a large-weight matching of a graph when we are uncertain about the existence of its edges. In particular, each edge $e$ has a known weight $w_e$ but is realized independently with some probability $p_e$. The algorithm may query an edge to see whether it is realized. We consider the well-studied

Thomas Cook, Harsh Vardhan Dubey, Ji Ah Lee, Guangyu Zhu

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized $\alpha$-investing framework which enables control of the false discovery rate i

Daniele Amato, Paolo Facchi, Arturo Konderak

We discuss several aspects concerning the asymptotic dynamics of dicrete-time semigroups associated with a quantum channel. By using an explicit expression of the asymptotic map, which describes the action of the quantum channel on its attractor manifold, we investigate the role of permutations in the asymptotic dynamics. We show that, in general, they make

Nigel Hitchin

The article considers some concrete solutions to the Dirac equation coupled to a vector bundle with connection, arising in the study of Yang-Mills equations and vector bundles on Riemann surfaces.

Nikita Samarin, Aparna Krishnan, Moses Namara, Joanne Ma

Recent events have placed a renewed focus on the issue of racial justice in the United States and other countries. One dimension of this issue that has received considerable attention is the security and privacy threats and vulnerabilities faced by the communities of color. Our study focuses on community-level advocates who organize workshops, clinics, and o

David Prinz

We study the perturbative quantization of gauge theories and gravity. Our investigations start with the geometry of spacetimes and particle fields. Then we discuss the various Lagrange densities of (effective) Quantum General Relativity coupled to the Standard Model. In addition, we study the corresponding BRST double complex of diffeomorphisms and gauge tra

Swapnil Shankar, Philipp Mösta, Steven R. Brandt, Roland Haas

We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to GPU-based exascale systems. GRaM-X supports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einste

Laura Bocchi, Julien Lange, Simon Thompson, A. Laura Voinea

Existing models for the analysis of concurrent processes tend to focus on fail-stop failures, where processes are either working or permanently stopped, and their state (working/stopped) is known. In fact, systems are often affected by grey failures: failures that are latent, possibly transient, and may affect the system in subtle ways that later lead to maj

Michael Ray, Davide Lazzati, Rosalba Perna

Both long and short gamma-ray bursts (GRBs) are expected to occur in the dense environments of active galactic nuclei (AGN) accretion disks. As these bursts propagate through the disks they live in, they photoionize the medium causing time-dependent opacity that results in transients with unique spectral evolution. In this paper we use a line-of-sight radiat

C. J. Ho, J. Lim, B. E. Sauer, M. R. Tarbutt

Theories that extend the Standard Model of particle physics often introduce new interactions that violate charge-parity (CP) symmetry. CP-violating effects within an atomic nucleus can be probed by measuring its nuclear magnetic quadrupole moment (MQM). The sensitivity of such a measurement is enhanced when using a heavy polar molecule containing a nucleus w

Roberto Casadei, Stefano Mariani, Danilo Pianini, Mirko Viroli

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coo

Di Wu, Shuang-Qing Wu

Recently, the so-called ``consistent thermodynamics'' of the Lorentzian Reissner-Nordstr\"{o}m (RN)-NUT-AdS$_4$ spacetimes has been pursued by a lot of efforts via different means. Among these attempts, we had proposed a novel idea that ``The NUT charge is a thermodynamical multihair" to successfully tackle with the subject. In this paper, we will adopt this

Sharadh Jois, Jose L. Lado, Genda Gu, Qiang Li.

Scattering processes in quantum materials emerge as resonances in electronic transport, including confined modes, Andreev states, and Yu-Shiba-Rusinov states. However, in most instances, these resonances are driven by a single scattering mechanism. Here we show the appearance of resonances due to the combination of two simultaneous scattering mechanisms, one

N. Castillo, O. Costin, R. D. Costin

We construct a new type of convergent, and asymptotic, representations, dyadic expansions. Their convergence is geometric and the region of convergence often extends from infinity down to $0^+$. We show that dyadic expansions are numerically efficient representations. For special functions such as Bessel, Airy, Ei, erfc, Gamma, etc. the region of convergence

Nicholas F. Marshall, Oscar Mickelin, Yunpeng Shi, Amit Singer

Principal component analysis (PCA) plays an important role in the analysis of cryo-EM images for various tasks such as classification, denoising, compression, and ab-initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-electron microscopy projection images that enables fast PCA comp

Jing Wang, James J. Sohn, Yang Lei, Wei Nie

Objective: Proton therapy offers an advantageous dose distribution compared to the photon therapy, since it deposits most of the energy at the end of range, namely the Bragg peak (BP). Protoacoustic technique was developed to in vivo determine the BP locations. However, it requires large dose delivery to the tissue to obtain an averaged acoustic signal with

Martin Dindoš, Jill Pipher

In this paper we present in concise form recent results, with illustrative proofs, on solvability of the $L^p$ Dirichlet, Regularity and Neumann problems for scalar elliptic equations on Lipschitz domains with coefficients satisfying a variety of Carleson conditions. More precisely, with $L=\mbox{div}(A\nabla)$, we assume the matrix $A$ is elliptic and satis

Paolo Antonelli, David N. Reynolds

We study the asymptotic emergent dynamics of two models that can be thought of as extensions of the well known Schr\"odinger-Lohe model for quantum synchronization. More precisely, the interaction strength between different oscillators is determined by intrinsic parameters, following Cucker-Smale communication protocol. Unlike the original Schr\"odinger-Lohe

Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher

Pre-trained Language Models recently gained traction in the Natural Language Processing (NLP) domain for text summarization, generation and question-answering tasks. This stems from the innovation introduced in Transformer models and their overwhelming performance compared with Recurrent Neural Network Models (Long Short Term Memory (LSTM)). In this paper, w

Su Yan, Jun-Jie Huang, Herman Verinaz-Jadan, Nathan Daly

Macro X-ray Fluorescence (MA-XRF) scanning is increasingly widely used by researchers in heritage science to analyse easel paintings as one of a suite of non-invasive imaging techniques. The task of processing the resulting MA-XRF datacube generated in order to produce individual chemical element maps is called MA-XRF deconvolution. While there are several e

Sina Mahdipour Saravani, Sadaf Ghaffari, Yanye Luther, James Folkestad

This study introduces and investigates the capabilities of three different text mining approaches, namely Latent Semantic Analysis, Latent Dirichlet Analysis, and Clustering Word Vectors, for automating code extraction from a relatively small discussion board dataset. We compare the outputs of each algorithm with a previous dataset that was manually coded by

Gisèle Mophou, Cyrille Kenne, Mahamadi Warma

We consider a bilinear optimal control for an evolution equation involving the fractional Laplace operator of order $0<s<1$. We first give some existence and uniqueness results for the considered evolution equation. Next, we establish some weak maximum principle results allowing us to obtain more regularity of our state equation. Then, we consider an optimal

Daniel Altman

A system of linear equations in $\mathbb{F}_p^n$ is \textit{Sidorenko} if any subset of $\mathbb{F}_p^n$ contains at least as many solutions to the system as a random set of the same density, asymptotically as $n\to \infty$. A system of linear equations is \textit{common} if any 2-colouring of $\mathbb{F}_p^n$ yields at least as many monochromatic solutions

Alexander Sakhnovich

We construct so-called Darboux transformations and solutions of the dynamical Hamiltonian systems with several space variables $\frac{\partial \psi}{\partial t}=\sum_{k=1}^r H_k(t)\frac{\partial \psi}{\partial \zeta_k}\,$ $( H_k(t)= H_k(t)^*)$. In particular, such systems are analogs of the port-Hamiltonian systems in the important and insufficiently studied

Julian Whitman, Howie Choset

Modular robots can be reconfigured to create a variety of designs from a small set of components. But constructing a robot's hardware on its own is not enough -- each robot needs a controller. One could create controllers for some designs individually, but developing policies for additional designs can be time consuming. This work presents a method that uses

Artyom M. Grigoryan, Sos S. Agaian, Karen Panetta

As the availability of imagery data continues to swell, so do the demands on transmission, storage and processing power. Processing requirements to handle this plethora of data is quickly outpacing the utility of conventional processing techniques. Transitioning to quantum processing and algorithms that offer promising efficiencies over conventional methods

Ajit Diwan, Aniruddha Joshi

The $k$th power of a graph $G$, denoted $G^k$, has the same vertex set as $G$, and two vertices are adjacent in $G^k$ if and only if there exists a path between them in $G$ of length at most $k$. A $K_r$-factor in a graph is a spanning subgraph in which every component is a complete graph of order $r$. It is easy to show that for any connected graph $G$ of o

Serdar Elhatisari, Lukas Bovermann, Yuanzhuo Ma, Evgeny Epelbaum

Ab initio calculations play an essential role in our fundamental understanding of quantum many-body systems across many subfields, from strongly correlated fermions to quantum chemistry and from atomic and molecular systems to nuclear physics. One of the primary challenges is to perform accurate calculations for systems where the interactions may be complica

Evan Hall, Nancy Aggarwal

We present a method to search for scalar field ultralight dark matter directly interacting with gravitational-wave interferometers via a modulation of the fine structure constant and the electron mass. This modulation induces an effective strain in solid materials at a frequency determined by the mass of the dark matter particle. We study the prospects for l

Julian Whitman, Howie Choset

Control policy learning for modular robot locomotion has previously been limited to proprioceptive feedback and flat terrain. This paper develops policies for modular systems with vision traversing more challenging environments. These modular robots can be reconfigured to form many different designs, where each design needs a controller to function. Though o

Jian-Hao Zhang, Yang Qi, Zhen Bi

Topological phases in open quantum systems have become an exciting area of research, driven by the rising importance of noisy intermediate-scale quantum platforms. The concept of Average Symmetry-Protected Topological (ASPT) phases extends symmetry-protected topological phases to quantum systems affected by disorder or decoherence. To identify nontrivial ASP

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar

We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which ena

Quynh L. Nguyen, Ryan A. Duncan, Gal Orenstein, Yijing Huang

We report ultrafast x-ray scattering experiments of the quasi-1D charge density wave (CDW) material (TaSe$_4$)$_2$I following photoexcitation with femtosecond infrared laser pulses. From the time-dependent diffraction signal at the CDW sidebands we identify an amplitude mode derived primarily from the transverse acoustic component of the CDW static distortio

Mosab Diab, Mostafa Mohammadkarimi, Raj Thilak Rajan

In this paper, we study path planning algorithms of resource constrained mobile agents in unknown cluttered environments, which include but are not limited to various terrestrial missions e.g., search and rescue missions by drones in jungles, and space missions e.g., navigation of rovers on the Moon. In particular, we focus our attention on artificial potent

Sung Min Jo, Simone Venturi, Jae Gang Kim, Marco Panesi

This work constructs a rovibrational state-to-state model for the $\text{O}_2$+$\text{O}_2$ system leveraging high-fidelity potential energy surfaces and quasi-classical trajectory calculations. The model is used to investigate internal energy transfer and non-equilibrium reactive processes in dissociating environment using a master equation approach, whereb

Leandro Arosio, Matteo Fiacchi, Lorenzo Guerini, Anders Karlsson

We study the interplay between the backward dynamics of a non-expanding self-map $f$ of a proper geodesic Gromov hyperbolic metric space $X$ and the boundary regular fixed points of $f$ in the Gromov boundary. To do so, we introduce the notion of stable dilation at a boundary regular fixed point of the Gromov boundary, whose value is related to the dynamical

Weilong Ren, Kambiz Ghazinour, Xiang Lian

In a wide spectrum of real-world applications, it is very important to analyze and mine graph data such as social networks, communication networks, citation networks, and so on. However, the release of such graph data often raises privacy issue, and the graph privacy preservation has recently drawn much attention from the database community. While prior work

Xiang Li, Junchi Yang, Niao He

Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent ascent (GDA) combined with adaptive stepsizes require car

Simon Badger, Matteo Becchetti, Ekta Chaubey, Robin Marzucca

We consider the case of a two-loop five-point pentagon-box integral configuration with one internal massive propagator that contributes to top-quark pair production in association with a jet at hadron colliders. We construct the system of differential equations for all the master integrals in a canonical form where the analytic form is reconstructed from num

Anthony Lazzeroni

We introduce a new $P$ basis for the Hopf algebra of quasisymmetric functions that refine the symmetric powersum basis. Unlike the quasisymmetric power sums of types 1 and 2, our basis is defined combinatorially: its expansion in quasisymmetric monomial functions is given by fillings of matrices. This basis has a shuffle product, a deconcatenate coproduct, a

Carlos Hurtado, Sarath Shekkizhar, Javier Ruiz-Hidalgo, Antonio Ortega

Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dataset. In this work, we propose a framework to study the geometric structure of the data. We make use of our recently introduced non-negative kernel (NNK) regression graphs to estim

Afsaneh Mahmoudi, José Mairton Barros Da Silva Júnior, Hossein S. Ghadikolaei, Carlo Fischione

Federated Learning (FL) plays a prominent role in solving machine learning problems with data distributed across clients. In FL, to reduce the communication overhead of data between clients and the server, each client communicates the local FL parameters instead of the local data. However, when a wireless network connects clients and the server, the communic

Diogo A. P. Nunes, Joana Ferreira-Gomes, Daniela Oliveira, Carlos Vaz

Chronic pain is a multi-dimensional experience, and pain intensity plays an important part, impacting the patients emotional balance, psychology, and behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for pain, fail to capture this burden. Moreover, this type of tools is susceptible to a degree of subjectivity, dependent on the patie

Stan Owocki

Both stars and planets can lose mass through an expansive wind outflow, often constrained or channeled by magnetic fields that form a surrounding magnetosphere. The very strong winds of massive stars are understood to be driven by line-scattering of the star's radiative momentum, while in the Sun and even lower-mass stars a much weaker mass loss arises from

Kenneth MacSporran Mayer, Laura Cottatellucci, Robert Schober

Current trends in communication system design precipitate a change in the operating regime from the traditional far-field to the radiating near-field (Fresnel) region. We investigate the optimal transmit antenna placement for a multiple-input single-output (MISO) wireless power transfer (WPT) system designed for a three-dimensional cuboid room under line-of-

Marwan Gebran, Frédéric Paletou, Ian Bentley, Rose Brienza

In this follow-up paper, we investigate the use of Convolutional Neural Network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the derivation of Teff, log g, [M/H], and vesini. The network was constrained by applying it to databases of AFGK syn

Saeed Razavikia, Jaume Anguera Peris, Jose Mairton B. da Silva, Carlo Fischione

Federated Edge Learning (FEEL) is a distributed machine learning technique where each device contributes to training a global inference model by independently performing local computations with their data. More recently, FEEL has been merged with over-the-air computation (OAC), where the global model is calculated over the air by leveraging the superposition

Yury N. Eroshenko

In this paper, a class of the warp drive (WD) type metrics is proposed in the form of spherical and plane waves or shells. In particular, these metrics can describe the passage of spherical WD through the horizon of a black hole from the inside out. In this metrics, non-singular evolution of physical fields is possible, which is demonstrated by examples of s

Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability. To address this issue, we study iterative teaching under a continuous input space where the input example (i.e., im

Daniel Altman, Zachary Chase

We prove that any increasing sequence of real numbers with average gap $1$ and Poisson pair correlations has some gap that is at least $3/2+10^{-9}$. This improves upon a result of Aistleitner, Blomer, and Radziwill.

Tingnan Gong, Junghwan Lee, Xiuyuan Cheng, Yao Xie

Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficiency from recursive computation and constant memory requirement, and it enjoys statistical optimality. CUSUM req

Yunqi Luo, Rabayet Sadnan, Bala Krishnamoorthy, Anamika Dubey

The massive integration of distributed energy resources changes the operational demands of the electric power distribution system, motivating optimization-based approaches. The added computational complexities of the resulting optimal power flow (OPF) problem have generally been managed by approximated or relaxed models; however, they may lead to infeasible

Oliver Withington, Laurissa Tokarchuk

As academic interest in procedural content generation (PCG) for games has increased, so has the need for methodologies for comparing and contrasting the output spaces of alternative PCG systems. In this paper we introduce and evaluate a novel approach for visualising the generative spaces of level generation systems, using embeddings extracted from a trained

Lekan Raheem, Maab Elrashid

In multilingual colloquial settings, it is a habitual occurrence to compose expressions of text or speech containing tokens or phrases of different languages, a phenomenon popularly known as code-switching or code-mixing (CMX). We present our approach and results for the Code-mixed Machine Translation (MixMT) shared task at WMT 2022: the task consists of two

Piotr Bizoń, Maciej Maliborski

We comment upon a black hole with Yang-Mills hair presented in a recent preprint by Chen, Du, and Yau.

Francesca Aicardi, Diego Arcis, Jesús Juyumaya

Ramified monoids are a class of monoids introduced by the authors. The main motivation for considering these monoids comes from knot theory, see [3, 4, 5]. Thus, in [2] we have studied the ramified monoids of the symmeytric group and of the Brauer monoid, among others. This paper study the ramified of the inverse symmetric monoid, which plays a notable role

Felix Tennie, Tim Palmer

Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a non-technical, yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical we

Carlos Mauricio Correa

Cosmic voids are promising cosmological laboratories for studying the dark energy phenomenon and alternative gravity theories. They are receiving special attention nowadays in view of the new generation of galaxy spectroscopic surveys, which are covering an unprecedented volume and redshift range. There are two primary statistics in void studies: (i) the voi

Diego Córdoba, Luis Martínez-Zoroa, Wojciech Ożański

We construct solutions of the 2D incompressible Euler equations in $\mathds{R}^2\times [0,\infty)$ such that initially the velocity is in the super-critical Sobolev space $H^\beta$ for $1<\beta<2$, but are not in $H^{\beta'}$ for $\beta'>1+\frac{(3-\beta)(\beta-1)}{2 - (\beta-1)^2}$ for $0<t<\infty$. These solutions are not in the Yudovich class, but they ex

P. F. Antonietti, N. Farenga, E. Manuzzi, G. Martinelli

Agglomeration-based strategies are important both within adaptive refinement algorithms and to construct scalable multilevel algebraic solvers. In order to automatically perform agglomeration of polygonal grids, we propose the use of Machine Learning (ML) strategies, that can naturally exploit geometrical information about the mesh in order to preserve the g

I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen, Tassadaq Hussain

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via utilizing multi-modal self-supervised embeddings. Nevertheless, it is unclear if such representations can be ge

Ola Kenji Forslund, Yuqing Ge, Hiroto Ohta, Chennan Wang

The van der Waals ferromagnet (FM), VI$_3$, was studied by muon spin relaxation ($\mu^+$SR) and first principle calculations based on density functional theory (DFT). Temperature dependent zero field muon spin relaxation ($\mu^+$SR) measurements confirm the onset of long range FM order and the time spectra exhibits clear muon spin precession frequencies for

Sahl Rowther, Rebecca Nealon, Farzana Meru

We carry out three dimensional smoothed particle hydrodynamics simulations to study the impact of planet-disc interactions on a gravitationally unstable protoplanetary disc. We find that the impact of a planet on the disc's evolution can be described by three scenarios. If the planet is sufficiently massive, the spiral wakes generated by the planet dominate

Laura B. Balzer, Erica Cai, Lucas Godoy Garraza, Pracheta Amaranath

Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address

Pavamana K J, Chandramani Kishore Singh

Mobile networks are experiencing prodigious increase in data volume and user density , which exerts a great burden on mobile core networks and backhaul links. An efficient technique to lessen this problem is to use caching i.e. to bring the data closer to the users by making use of the caches of edge network nodes, such as fixed or mobile access points and e

Lukai Wang, Lei Wang

Network public opinion analysis is obtained by a combination of natural language processing (NLP) and public opinion supervision, and is crucial for monitoring public mood and trends. Therefore, network public opinion analysis can identify and solve potential and budding social problems. This study aims to realize an analysis of Chinese sentiment in social m

Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu

Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting in increased cost for storing, sharing and serving the models. To address this, parameter-efficient fine-tuning (PEFT) techniques were intr

Joan Palacios, Nuria González-Prelcic

Greedy sparse recovery has become a popular tool in many applications, although its complexity is still prohibitive when large sparsifying dictionaries or sensing matrices have to be exploited. In this paper, we formulate first a new class of sparse recovery problems that exploit multidimensional dictionaries and the separability of the measurement matrices

Qianyi Li, Haim Sompolinsky

Recently proposed Gated Linear Networks present a tractable nonlinear network architecture, and exhibit interesting capabilities such as learning with local error signals and reduced forgetting in sequential learning. In this work, we introduce a novel gating architecture, named Globally Gated Deep Linear Networks (GGDLNs) where gating units are shared among

Anthony Conway

This short survey, which was written to accompany a minicourse at the BIRS conference "Topology in dimension 4.5", concerns invariants of knotted $2$-spheres in $S^4$, also known as $2$-knots. It covers invariants extracted from the algebraic topology of the knot exterior, including Alexander invariants, the Farber-Levine pairing and Casson-Gordon invariants

Giovanna Cottin, Juan Carlos Helo, Martin Hirsch, Cristián Peña

We study the LHC discovery potential in the search for heavy neutral leptons (HNL) with a new signature: a displaced shower in the CMS muon detector, giving rise to a large cluster of hits forming a displaced shower. A new Delphes module is used to model the CMS detector response for such displaced decays. We reinterpret a dedicated CMS search for neutral lo

Amirali Hannani, Matthew Rosenzweig, Gigliola Staffilani, Minh-Binh Tran

Starting from a stochastic Zakharov-Kuznetsov (ZK) equation on a lattice, the previous work [ST21] by the last two authors gave a derivation of the homogeneous 3-wave kinetic equation at the kinetic limit under very general assumptions: the initial condition is out of equilibrium, the dimension $d\ge 2$, the smallness of the nonlinearity $\lambda$ is allowed

Sijie Mai, Ying Zeng, Haifeng Hu

Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated multimodal embedding may be redundant, and the discriminative unimodal information may be ignored, which often interferes with accurate prediction and leads to a higher risk of over

Burigede Liu, Eric Ocegueda, Margaret Trautner, Andrew M. Stuart

This paper concerns the study of history dependent phenomena in heterogeneous materials in a two-scale setting where the material is specified at a fine microscopic scale of heterogeneities that is much smaller than the coarse macroscopic scale of application. We specifically study a polycrystalline medium where each grain is governed by crystal plasticity w

Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani

Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interesting. Recently, several deep convolutional spiking neural networks have been proposed. These networks aim to increase biological plausibility while creating powerful tools to be appli

Eemeli Tomberg

Stochastic inflation can resolve strong inflationary perturbations, which seed primordial black holes. I present a fast and accurate way to compute these perturbations in typical black hole producing single-field models, treating the short-wavelength Fourier modes beyond the de Sitter approximation. The squeezing and freezing of the modes reduces the problem

Nianzu Ma, Sahisnu Mazumder, Alexander Politowicz, Bing Liu

Much of the existing work on text novelty detection has been studied at the topic level, i.e., identifying whether the topic of a document or a sentence is novel or not. Little work has been done at the fine-grained semantic level (or contextual level). For example, given that we know Elon Musk is the CEO of a technology company, the sentence "Elon Musk acte

Patrick Bastian, Holger Dette, Johannes Heiny

This paper takes a different look on the problem of testing the mutual independence of the components of a high-dimensional vector. Instead of testing if all pairwise associations (e.g. all pairwise Kendall's $\tau$) between the components vanish, we are interested in the (null)-hypothesis that all pairwise associations do not exceed a certain threshold in a

J. Cai, E. Griffin, V. Guarochico-Moreira, D. Barry

Recent experiments demonstrated that interfacial water dissociation (H2O = H+ + OH-) could be accelerated exponentially by an electric field applied to graphene electrodes, a phenomenon related to the Wien effect. Here we report an order-of-magnitude acceleration of the interfacial water dissociation reaction under visible-light illumination. This process is

Avyav Kumar Singh, Ekaterina Shutova, Helen Yannakoudakis

Existing approaches to few-shot learning in NLP rely on large language models (LLMs) and/or fine-tuning of these to generalise on out-of-distribution data. In this work, we propose a novel few-shot learning approach based on soft-label prototypes (SLPs) designed to collectively capture the distribution of different classes across the input domain space. We f

Dominique Maldague

We prove a sharp (up to $C_\epsilon R^\epsilon$) $L^7$ square function estimate for the moment curve in $\mathbb{R}^3$.