May 2022 arXiv papers — page 65

Xingzhi Guo, Baojian Zhou, Steven Skiena

Tracking a targeted subset of nodes in an evolving graph is important for many real-world applications. Existing methods typically focus on identifying anomalous edges or finding anomaly graph snapshots in a stream way. However, edge-oriented methods cannot quantify how individual nodes change over time while others need to maintain representations of the wh

G. Lambiase, L. Mastrototaro

Using idealized models of the accretion disk, we investigate the effects induced by the modified theories of gravity on the annihilation of the neutrino pair annihilation into electron-positron pairs ($\nu{\bar \nu}\to e^-e^+$), occurring near the rotational axis. For the accretion disk, we have considered the models with temperature $T=constant$ and $T\prop

Wonjune Kang, Mark Hasegawa-Johnson, Deb Roy

Zero-shot voice conversion is becoming an increasingly popular research topic, as it promises the ability to transform speech to sound like any speaker. However, relatively little work has been done on end-to-end methods for this task, which are appealing because they remove the need for a separate vocoder to generate audio from intermediate features. In thi

Kevin Beanland, Daniel Freeman

For a Banach space $X$ with a shrinking Schauder frame $(x_i,f_i)$ we provide an explicit method for constructing a shrinking associated basis. In the case that the minimal associated basis is not shrinking, we prove that every shrinking associated basis of $(x_i,f_i)$ dominates an uncountable family of incomparable shrinking associated bases of $(x_i,f_i)$.

Ralph Blumenhagen, Niccolò Cribiori, Christian Kneissl, Andriana Makridou

Starting from an already known solution in the literature, we study the dynamical cobordism induced by the backreaction of a non-supersymmetric, positive tension domain wall in string theory. This could e.g. be a non-BPS D8-brane of type I or a $\overline{D8}/O8$ stack of a non-supersymmetric type IIA orientifold. The singularities which typically appear ind

Cameron Calcluth, Alessandro Ferraro, Giulia Ferrini

We consider a computational model composed of ideal Gottesman-Kitaev-Preskill stabilizer states, Gaussian operations - including all rational symplectic operations and all real displacements -, and homodyne measurement. We prove that such architecture is classically efficiently simulatable, by explicitly providing an algorithm to calculate the probability de

Jizhou Wu, Barry C. Sanders

A multiphoton collective phase is a multiphoton-scattering feature that cannot be reduced to a sequence of two-photon scattering events, and the three-photon "triad phas" is the smallest nontrivial example. Observing a higher-order collective phase is experimentally challenging, and only triad and four-photon tetrad collective phases have been observed. We i

Matthew J. Colbrook, Lorna J. Ayton, Máté Szőke

Dynamic Mode Decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of turbulence and is closely related to Koopman operators. However, verifying the decomposition, equivalently the computed spectral features of Koopman operators, remains a

Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan, Simeon Adebola

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping into that power from a robot is non-trivial. We present FogRO

Ioana A. Zelko, Tommaso Treu, Kevork N. Abazajian, Daniel Gilman

The nature of dark matter is one of the most important unsolved questions in science. Some dark matter candidates do not have sufficient nongravitational interactions to be probed in laboratory or accelerator experiments. It is thus important to develop astrophysical probes which can constrain or lead to a discovery of such candidates. We illustrate this usi

Nicolas PD Sawaya

Preparing problems for execution on quantum computers can require many compilation steps. Automated compilation software is useful not only for easier and faster problem execution, but also for facilitating the comparison between different algorithmic choices. Here we describe mat2qubit, a Python package for encoding several classes of classical and quantum

Lorenzo Bianchi, Davide Bonomi

We derive a dispersion relation for two-point correlation functions in defect conformal field theories. The correlator is expressed as an integral over a (single) discontinuity that is controlled by the bulk channel operator product expansion (OPE). This very simple relation is particularly useful in perturbative settings where the discontinuity is determine

Ulrich P. Steinwandel, Greg L. Bryan, Rachel S. Somerville, Christopher C. Hayward

About ten to 20 percent of massive stars may be kicked out of their natal clusters before exploding as supernovae. These "runaway stars" might play a crucial role in driving galactic outflows and enriching the circumgalactic medium with metals. To study this effect, we carry out high resolution dwarf galaxy simulations that include velocity kicks to massive

Luc Darmé

Cosmic ray atmospheric showers provide an effective environment for the production of MeV-scale dark sector particles. We show that, when available, the resonant annihilation of positrons from the shower on atmospheric electrons is the dominant production mechanism by more than an order of magnitude. We provide a quantitative example based on dark photon pro

Bjoern Hassfeld, Arthur Hebecker, Manfred Salmhofer, Jonah Cedric Strauss

According to the `Cosmological Central Dogma', de Sitter space can be viewed as a quantum mechanical system with a finite number of degrees of freedom, set by the horizon area. We use this assumption together with the Wheeler-DeWitt (WDW) equation to approach the measure problem of eternal inflation. Thus, our goal is to find a time-independent wave function

Wouter Buijsman, Talía L. M. Lezama, Tamar Leiser, Lea F. Santos

Extreme-value distributions are studied in the context of a broad range of problems, from the equilibrium properties of low-temperature disordered systems to the occurrence of natural disasters. Our focus here is on the ground-state energy distribution of disordered many-body quantum systems. We derive an analytical expression that, upon tuning a parameter,

Paul La Plante, Jordan Mirocha, Adélie Gorce, Adam Lidz

The cross-correlation between the 21 cm field and the galaxy distribution is a potential probe of the Epoch of Reionization (EoR). The 21 cm signal traces neutral gas in the intergalactic medium and, on large spatial scales, this should be anti-correlated with the high-redshift galaxy distribution which partly sources and tracks the ionized gas. In the near

Marta Losada, Yosef Nir, Gilad Perez, Inbar Savoray

If ultra-light dark matter (ULDM) exists and couples to neutrinos, the neutrino oscillation probability might be significantly altered by a parametric resonance. This resonance can occur if the typical frequency of neutrino flavor-oscillations $\Delta m^2/(2E)$, where $\Delta m^2$ is the mass-squared difference of the neutrinos and $E$ is the neutrino energy

L. Wright, F. Barratt, J. Dborin, V. Wimalaweera

We present tensor networks for feature extraction and refinement of classifier performance. These networks can be initialised deterministically and have the potential for implementation on near-term intermediate-scale quantum (NISQ) devices. Feature extraction proceeds through a direct combination and compression of images amplitude-encoded over just $\log N

Simon Lieu, Yu-Jie Liu, Alexey V. Gorshkov

An interesting problem in the field of quantum error correction involves finding a physical system that hosts a ``passively protected quantum memory,'' defined as an encoded qubit coupled to an environment that naturally wants to correct errors. To date, a quantum memory stable against finite-temperature effects is only known in four spatial dimensions or hi

Chenan Wei, Tigran A. Sedrakyan

We use stochastic expansion and exact diagonalization to study the magic-angle twisted bilayer graphene (TBG) on a disordered substrate. We show that the substrate-induced strong Coulomb disorder in TBG with the chemical potential at the level of the flatbands drives the system to a network of weakly coupled Sachdev-Ye-Kitaev (SYK) bundles, stabilizing an em

Julien Barrat, Aleix Gimenez-Grau, Pedro Liendo

We present a dispersion relation for defect CFT that reconstructs two-point functions in the presence of a defect as an integral of a single discontinuity. The main virtue of this formula is that it streamlines explicit bootstrap calculations, bypassing the resummation of conformal blocks. As applications we reproduce known results for monodromy defects in t

Bobby Samir Acharya, Gerardo Aldazabal, Anamaría Font, Kumar Narain

We first describe the low energy dynamics of ten dimensional heterotic supergravity compactified on the smooth, flat 3-manifold ${\mathbb T^3}/{\mathbb Z_2}$, without supersymmetry, and explain how it arises from flat heterotic gauge fields. The semi-classical theory has both Coulomb and Higgs branches of non-supersymmetric vacua. We then give an exact world

Ivan Esteban, Steven Prohira, John F. Beacom

The ultrahigh energy range of neutrino physics (above $\sim 10^{7} \, \mathrm{GeV}$), as yet devoid of detections, is an open landscape with challenges to be met and discoveries to be made. Neutrino-nucleon cross sections in that range - with center-of-momentum energies $\sqrt{s} \gtrsim 4 \, \mathrm{TeV}$ - are powerful probes of unexplored phenomena. We pr

Paul Hilaire, Leonid Vidro, Hagai S. Eisenberg, Sophia E. Economou

Since linear-optical two-photon gates are inherently probabilistic, measurement-based implementations are particularly well suited for photonic platforms: a large highly-entangled photonic resource state, called a graph state, is consumed through measurements to perform a computation. The challenge is thus to produce these graph states. Several generation pr

Miguel Gonzalez-Bolivar, Orsola De Marco, Mike Y. M. Lau, Ryosuke Hirai

At least one in five of all planetary nebulae are the product of a common envelope (CE) interaction, where the companion in-spirals into the envelope of an asymptotic giant branch (AGB) star ejecting the nebula and leaving behind a compact binary. In this work we carry out 3D smoothed particle hydrodynamics simulations of the CE interaction between a 1.7 $M_

Nicolas Englebert, Carlos Mas Arabí, Simon-Pierre Gorza, François Leo

We demonstrate that the peak-to-background ratio of driven solitons can be greatly improved by harnessing the cavity detuning. We use a driven fiber laser pumped below the lasing threshold to increase the finesse and excite solitons in a very wide region of detunings. When driving a 50 m long fiber cavity close to the anti-resonance condition, we excite sub-

Rico G. Visser, Marc G. Brouwers

Asteroids, planets, stars in some open clusters, as well as molecular clouds appear to possess a preferential spin-orbit alignment, pointing to shared processes that tie their rotation at birth to larger parent structures. We present a new mechanism that describes how collections of particles or 'clouds' gain a prograde rotational component when they collaps

Yu-Wei Chao, Chris Paxton, Yu Xiang, Wei Yang

We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasping of objects. We create training and evaluation environments for the receiver with standardized protocols and metrics. We analyze the performance of a set of baselines and show a

Theodore A. Corcovilos

Geometric optics is often described as tracing the paths of non-diffracting rays through an optical system. In the paraxial limit, ray traces can be calculated using ray transfer matrices (colloquially, ABCD matrices), which are 2x2 matrices acting on the height and slope of the rays. A known limitation of ray transfer matrices is that they only work for opt

Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi

Deep learning experiments by Cohen et al. [2021] using deterministic Gradient Descent (GD) revealed an Edge of Stability (EoS) phase when learning rate (LR) and sharpness (i.e., the largest eigenvalue of Hessian) no longer behave as in traditional optimization. Sharpness stabilizes around $2/$LR and loss goes up and down across iterations, yet still with an

Gaurav Verma, Rohit Mujumdar, Zijie J. Wang, Munmun De Choudhury

Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. However, the development of advanced computational techniques and resources is disproportionate

Yunpeng Zhang, Zheng Zhu, Wenzhao Zheng, Junjie Huang

In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems. Unlike existing studies focusing on the improvement of single-task approaches, BEVerse features in producing spatio-temporal Birds-Eye-View (BEV) representations from multi-camera videos and jointly reasoning about multiple tasks for vision-

Guido Martinelli, Manuel Naviglio, Silvano Simula, Ludovico Vittorio

We present some applications of the unitarity-based Dispersion Matrix (DM) approach to the extraction of the CKM matrix element $|V_{cb}|$ from the experimental data on the exclusive $B_{(s)} \to D_{(s)}^{(*)} \ell \nu_\ell$ decays. The DM method allows to achieve a non-perturbative, model-independent determination of the momentum dependence of the semilepto

Luke Oeding

We give a translation of the article by L. V. Antonyan, "Classification of four-vectors of an eight-dimensional space," Trudy Sem. Vektor. Tenzor. Anal. 20 (1981), 144-161. MR622013. We include an appendix providing normal forms for each nilpotent orbit.

Willem O. Tromp, Tjerk Benschop, Jian-Feng Ge, Irene Battisti

The cuprate high-temperature superconductors exhibit many unexplained electronic phases, but it was often thought that the superconductivity at sufficiently high doping is governed by conventional mean-field Bardeen-Cooper-Schrieffer (BCS) theory[1]. However, recent measurements show that the number of paired electrons (the superfluid density) vanishes when

Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy

Standard neural networks struggle to generalize under distribution shifts in computer vision. Fortunately, combining multiple networks can consistently improve out-of-distribution generalization. In particular, weight averaging (WA) strategies were shown to perform best on the competitive DomainBed benchmark; they directly average the weights of multiple net

Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso

Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) that lays down the necessary components to enable artificial agents to learn, use and generate transferable representations. Unlike machine representation learning, which relies exclusively on raw sensory data, biological representations incorporate relational

Aghil Alaee, Hari K. Kunduri

We establish a Penrose-type inequality with angular momenta for four dimensional, biaxially symmetric, maximal, asymptotically flat initial data sets $(M,g,k)$ for the Einstein equations with fixed angular momenta and horizon inner boundary associated to a 3-sphere outermost minimal surface. Moreover, equality holds if and only if the initial data set is iso

Ambarish Chattopadhyay, Carl N. Morris, Jose R. Zubizarreta

The Finite Selection Model (FSM) was developed by Carl Morris in the 1970s for the design of the RAND Health Insurance Experiment (HIE) (Morris 1979, Newhouse et al. 1993), one of the largest and most comprehensive social science experiments conducted in the U.S. The idea behind the FSM is that each treatment group takes its turns selecting units in a fair a

Mike Wu, Noah Goodman

Probabilistic programs provide an expressive representation language for generative models. Given a probabilistic program, we are interested in the task of posterior inference: estimating a latent variable given a set of observed variables. Existing techniques for inference in probabilistic programs often require choosing many hyper-parameters, are computati

Michał Oszmaniec, Marcin Kotowski, Michał Horodecki, Nicholas Hunter-Jones

Quantum complexity is a measure of the minimal number of elementary operations required to approximately prepare a given state or unitary channel. Recently, this concept has found applications beyond quantum computing -- in studying the dynamics of quantum many-body systems and the long-time properties of AdS black holes. In this context Brown and Susskind \

Michael Damron, Julian Gold, Wai-Kit Lam, Xiao Shen

First-passage percolation is a random growth model defined on $\mathbb{Z}^d$ using i.i.d. nonnegative weights $(\tau_e)$ on the edges. Letting $T(x,y)$ be the distance between vertices $x$ and $y$ induced by the weights, we study the random ball of radius $t$ centered at the origin, $B(t) = \{x \in \mathbb{Z}^d : T(0,x) \leq t\}$. It is known that for all su

Shruthi Hariharan, Vignesh Kumar Krishnamurthy, Utkarsh, Jayantha Gowda Sarapanahalli

Recent joint intent detection and slot tagging models have seen improved performance when compared to individual models. In many real-world datasets, the slot labels and values have a strong correlation with their intent labels. In such cases, the intent label information may act as a useful feature to the slot tagging model. In this paper, we examine the ef

Patrick Esser, Peter Michael, Soumyadip Sengupta

Many video editing tasks such as rotoscoping or object removal require the propagation of context across frames. While transformers and other attention-based approaches that aggregate features globally have demonstrated great success at propagating object masks from keyframes to the whole video, they struggle to propagate high-frequency details such as textu

Carlos Pedroso, Aldri Santos

The IoT has made possible the development of increasingly driven services, like industrial IIoT services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection attacks (FDI) stand out as being one of the most aggressive. The majority of current solutions to handle this attack do

Eric Chalmers, Artur Luczak

Developments in reinforcement learning (RL) have allowed algorithms to achieve impressive performance in highly complex, but largely static problems. In contrast, biological learning seems to value efficiency of adaptation to a constantly-changing world. Here we build on a recently-proposed neuronal learning rule that assumes each neuron can optimize its ene

Samuel W. Yee, Joshua N. Winn, Joel D. Hartman, Joseph E. Rodriguez

We report the discovery of ten short-period giant planets (TOI-2193A b, TOI-2207 b, TOI-2236 b, TOI-2421 b, TOI-2567 b, TOI-2570 b, TOI-3331 b, TOI-3540A b, TOI-3693 b, TOI-4137 b). All of the planets were identified as planet candidates based on periodic flux dips observed by NASA's Transiting Exoplanet Survey Satellite (TESS). The signals were confirmed to

Afonso S. Bandeira, Ahmed El Alaoui, Samuel B. Hopkins, Tselil Schramm

Many high-dimensional statistical inference problems are believed to possess inherent computational hardness. Various frameworks have been proposed to give rigorous evidence for such hardness, including lower bounds against restricted models of computation (such as low-degree functions), as well as methods rooted in statistical physics that are based on free

Eric Chalmers, Artur Luczak

Random walks can reveal communities or clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms uses random walks to measure distance between pairs of nodes in various ways, and then applies K-Means or other generic clustering methods to these distances. Interestingly, infor

Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer

Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues we present RankGen, a 1.2B parameter encoder model for English that scores model generations given a p

Abdelkader El Makouri, Abdallah Slaoui, Mohammed Daoud

We start with a revision study of two coupled spin-$1/2$ under the influence of Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interaction and a magnetic field. We first show the role of idle levels, i.e., levels that do not couple to the external magnetic field, when the system is working as a heat engine as well as when it is a refrigerator. Then we extend

Nestor Anaya, Manuel Falconi, Guilmer González

In this paper we study the question of the survival of a predator which in a static scenario vanishes. we analyze the role of migration on the coexistence of three species interacting through a intraguild relationship.

Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa

Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical settings different from the training environment. A common mitigation strategy is to develop separate systems for eac

Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt

With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT) became highly desirable. Semantic Segmentation of CT is one of many research fields of automatic detection of COVID-19 and has been widely explored since the COVID-19 outbreak. In

Eli Chien, Puoya Tabaghi, Olgica Milenkovic

The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applications in natural language processing, phylogeny, cancer genomics and a myriad of problem areas that involve hierarchical clustering. Despite the existence of several provably exact

Ariyan Javanpeykar, Siddharth Mathur

Let $A$ be an abelian scheme of dimension at least four over a $\mathbb{Z}$-finitely generated integral domain $R$ of characteristic zero, and let $L$ be an ample line bundle on $A$. We prove that the set of smooth hypersurfaces $D$ in $A$ representing $L$ is finite by showing that the moduli stack of such hypersurfaces has only finitely many $R$-points. We

Mladen Dimitrov, Alexandre Maksoud

We compute Benois $\mathscr{L}$-invariants of weight $1$ cuspforms and of their adjoint representations and show how this extends Gross' $p$-adic regulator to Artin motives which are not critical in the sense of Deligne. Benois' construction depends on the choice of a regular submodule which is well understood when the representation is $p$-regular, as it th

Mauricio Che, Fernando Galaz-García, Luis Guijarro, Ingrid Membrillo Solis

Given a metric pair $(X,A)$, i.e. a metric space $X$ and a distinguished closed set $A \subset X$, one may construct in a functorial way a pointed pseudometric space $\mathcal{D}_\infty(X,A)$ of persistence diagrams equipped with the bottleneck distance. We investigate the basic metric properties of the spaces $\mathcal{D}_\infty(X,A)$ and obtain characteriz

Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder

Decision tree ensembles are widely used and competitive learning models. Despite their success, popular toolkits for learning tree ensembles have limited modeling capabilities. For instance, these toolkits support a limited number of loss functions and are restricted to single task learning. We propose a flexible framework for learning tree ensembles, which

Débora Aguiar Gomes, Jose Beltrán Jiménez, Tomi S. Koivisto

All energy is gravitational energy. That is the consequence of the equivalence principle, according to which gravity is the universal interaction. The physical charges of this interaction have remained undisclosed, but the Adventof the Geometrical Trinity opened a new approach to this foundational problem. Here it is shown to provide a background-independent

Morteza Hasanvand

Let $G$ be a bipartite graph with bipartition $(X,Y)$, let $k$ be a positive integer, and let $f:V(G)\rightarrow Z_k$ be a mapping with $\sum_{v\in X}f(v) \stackrel{k}{\equiv}\sum_{v\in Y}f(v)$. In this paper, we show that if $G$ is $(2m+2m_0+4k-4)$-edge-connected and $m+m_0>0$, then $G$ has an $m$-tree-connected factor $H$ such that its complement is $m_0$-

Luccas Campos, Jason Murphy

We consider the focusing inhomogeneous nonlinear Schr\"odinger equation in $H^1(\mathbb{R}^3)$, \begin{equation} i\partial_t u + \Delta u + |x|^{-b}|u|^{2}u=0,{equation} where $0 < b <\tfrac{1}{2}$. Previous works have established a blowup/scattering dichotomy below a mass-energy threshold determined by the ground state solution $Q$. In this work, we study s

B. Kaan Karamete, Eli Glaser

This paper discusses a graph based route solving algorithm to find the optimal path for an electric vehicle picking the best charging locations among thousands to minimize the total cumulative driving distance between the end points of the trip. To this end, we have devised a combinatorial optimization algorithm and a fixed storage graph topology constructio

W. A. van Wijngaarden, W. Happer

We use 2n streams, where n is an integer, of axially symmetric radiation to solve the equation of transfer for a layered medium. This is a generalization of Schuster's classic 2 stream model. As is well known, using only the first 2n Legendre polynomials to describe the angular dependence of radiation reduces the equation of transfer to a first order differe

Antonia Creswell, Murray Shanahan, Irina Higgins

Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reasoning problems. Here we carry out a comprehensive evaluation of LLMs on 50 tasks that probe different aspects of logical reasoning. We show that language models tend to perform fai

Changchun Zhong, Changhun Oh, Liang Jiang

Quantum capacity, as the key figure of merit for a given quantum channel, upper bounds the channel's ability in transmitting quantum information. Identifying different type of channels, evaluating the corresponding quantum capacity and finding the capacity-approaching coding scheme are the major tasks in quantum communication theory. Quantum channel in discr

Rodolfo Corona, Shizhan Zhu, Dan Klein, Trevor Darrell

Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG significantly improves grounding accuracy

Gilles Cohen-Tannoudji, Jean-Pierre Gazeau, Célestin Habonimana, Juma Shabani

As an extension of Gabor signal processing, the covariant Weyl-Heisenberg integral quantization is implemented to transform functions on the eight-dimensional phase space $\left(x,k\right)$ into Hilbertian operators. The $x=\left(x^{\mu}\right)$'s are space-time variables and the $k=\left(k^{\mu}\right)$'s are As an extension of Gabor signal processing, the

Siddharth S. Nijhawan, Homayoon Beigi

Majority of speech signals across different scenarios are never available with well-defined audio segments containing only a single speaker. A typical conversation between two speakers consists of segments where their voices overlap, interrupt each other or halt their speech in between multiple sentences. Recent advancements in diarization technology leverag

Ahmed A. Tawfik, Mohamed F. Abdelkader, Sherif M. Abuelenin

As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT secondary users through utilizing spectrum holes resulting from the underutilization of frequency spectrum. A CR needs to frequently sense the spe

Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia

Pre-trained language models are increasingly important components across multiple information retrieval (IR) paradigms. Late interaction, introduced with the ColBERT model and recently refined in ColBERTv2, is a popular paradigm that holds state-of-the-art status across many benchmarks. To dramatically speed up the search latency of late interaction, we intr

Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold

Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data.

Yoshinari Motokawa, Toshiharu Sugawara

In multi-agent systems, noise reduction techniques are important for improving the overall system reliability as agents are required to rely on limited environmental information to develop cooperative and coordinated behaviors with the surrounding agents. However, previous studies have often applied centralized noise reduction methods to build robust and ver

P. V. Buividovich

We use exact diagonalization to study energy level statistics and out-of-time-order correlators (OTOCs) for the simplest supersymmetric extension $\hat{H}_S = \hat{H}_B \otimes I + \hat{x}_1 \otimes \sigma_1 + \hat{x}_2 \otimes \sigma_3$ of the bosonic Hamiltonian $\hat{H}_B = \hat{p}_1^2 + \hat{p}_2^2 + \hat{x}_1^2 \, \hat{x}_2^2$. For a long time, this bos

Jeeyung Kim, Mengtian Jin, Youkow Homma, Alex Sim

In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dynamic information about the current state of a large scientific workflow, which could be generalized to other types of applications. The specific task to be modeled is the time neede

Maciej Besta, Torsten Hoefler

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processi

Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary Lipton

As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily -- do pe

Xiaodong Sun, Huijiong Yang, Nan Wu, T. C. Scott

Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is appli

Robin Corgier, Marco Malitesta, Augusto Smerzi, Luca Pezzè

Thanks to common-mode noise rejection, differential configurations are crucial for realistic applications of phase and frequency estimation with atom interferometers. Currently, differential protocols with uncorrelated particles and mode-separable settings reach a sensitivity bounded by the standard quantum limit (SQL). Here we show that differential interfe

Laura Ares, Alfredo Luis

The aim of this work is to answer the question of how much quantum coherence a beam splitter is able to produce. To this end we consider as the variables under study both the amount of coherence of the input states as well as the beam splitter characteristics. We conclude that there is an optimal combination of these factors making the gain of coherence maxi

Riccardo Fogliato, Shreya Chappidi, Matthew Lungren, Michael Fitzke

Details of the designs and mechanisms in support of human-AI collaboration must be considered in the real-world fielding of AI technologies. A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making workflows. We have a poor understanding of the i

Karthik Adimurthi, Harsh Prasad, Vivek Tewary

We prove local H\"older regularity for a nonlocal parabolic equations of the form \begin{align*} \partial_t u + \text{P.V.}\int_{\mathbb{R}^N} \frac{|u(x,t)-u(y,t)|^{p-2}(u(x,t)-u(y,t))}{|x-y|^{N+sp}}\,dy=0, \end{align*} for $p\in (1,\infty)$ and $s \in (0,1)$.

Liliana Moscardi, Stefano Varas, Alessandro Chiasera, Francesco Scotognella

Photonic crystals can integrate plasmonic materials such as indium tin oxide (ITO) in their structure. Exploiting ITO plasmonic properties it is possible to tune the photonic band gap of the photonic crystal upon the application of an external stimuli. In this work, we have fabricated a one-dimensional multilayer photonic crystal via radiofrequency sputterin

Weiwei Zhang, Goran Muric, Emilio Ferrara

How does the team formation relates to team performance in professional video game playing? This study examined one aspect of group dynamics - team switching - and aims to answer how changing a team affects individual and collective performance in eSports tournaments. In this study we test the hypothesis that switching teams can be detrimental to individual

Karim El Haff, Mustafa Jarrar, Tymaa Hammouda, Fadi Zaraket

The processing of the Arabic language is a complex field of research. This is due to many factors, including the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics. When its dialects are taken into account, this la

Victor Chernozhukov, Denis Chetverikov, Kengo Kato, Yuta Koike

This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap: cons

Hedi Zisling, Andrei Sharf

Learning 3D point sets with rotational invariance is an important and challenging problem in machine learning. Through rotational invariant architectures, 3D point cloud neural networks are relieved from requiring a canonical global pose and from exhaustive data augmentation with all possible rotations. In this work, we introduce a rotational invariant neura

N. Fraija, B. Betancourt Kamenetskaia, A. Galvan-Gamez, M. G. Dainotti

Sub-relativistic materials launched during the merger of binary compact objects and the core-collapse of massive stars acquire velocity structures when expanding in a stratified environment. The remnant (either a spinning magnetized neutron star (NS) or a central black hole) from the compact-object or core-collapse could additionally inject energy into the a

Cesar L. Pastrana, Luyi Qiu, Shahaf Armon, Ulrich Gerland

Many bacterial species are helical in form, including the widespread pathogen H. pylori. Motivated by recent experiments on H. pylori showing that cell wall synthesis is not uniform, we investigate the possible formation of helical cell shape induced by elastic heterogeneity. We show, experimentally and theoretically, that helical morphogenesis can be produc

Tania Robens

In this manuscript, I give a short summary on scenarios with new physics scalars that could be investigated at future $e^+e^-$ colliders. I concentrate on cases where at least one of the additional scalar has a mass below 125 GeV, and discuss both models where this could be realized, as well as studies which focus on such scenarios. This work is based on sev

Kassie Archer, Christina Gravies

A 3-dimensional Catalan word is a word on three letters so that the subword on any two letters is a Dyck path. For a given Dyck path $D$, a recently defined statistic counts the number of Catalan words with the property that any subword on two letters is exactly $D$. In this paper, we enumerate Dyck paths with this statistic equal to certain values, includin

Moustafa Al-Hajj, Mustafa Jarrar

Using pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to fine-tune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair binary classification task. First, we constructed a dataset of labeled Arabic context-gloss pairs (~167k pairs) we extracte

Stevan Gajović

We answer a question of Samir Siksek, asked at the open problems session of the conference ``Rational Points 2022'', which, in a broader sense, can be viewed as a reverse engineering of Diophantine equations. For any finite set $S$ of perfect integer powers, using Mih\u{a}ilescu's theorem, we construct a polynomial $f_S\in \Z[x]$ such that the set $f_S(\Z)$

Sangita Majumdar, Neetik Mukherjee, Amlan K. Roy

Ground and excited states of a confined negative Hydrogen ion has been pursued under Kohn-Sham density functional approach by invoking a physically motivated work-function-based exchange potential. The exchange-only results are of near Hartree-Fock quality. Local parameterised Wigner-type, and gradient- and Laplacian-dependent non-local Lee-Yang-Parr functio

Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orie

Emily A. Belli, Jeff Candy, Igor Sfiligoi, Frank Würthwein

Fusion simulations have traditionally required the use of leadership scale High Performance Computing (HPC) resources in order to produce advances in physics. The impressive improvements in compute and memory capacity of many-GPU compute nodes are now allowing for some problems that once required a multi-node setup to be also solvable on a single node. When

Rihan Hai, Christos Koutras, Andra Ionescu, Ziyu Li

The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a major challenge: the integration and transformation of data demand a lot of manual work and computational resources. With data privacy and security constraints, data often cannot leave

Imanol Arrieta-Ibarra, Paman Gujral, Jonathan Tannen, Mark Tygert

Predictions are often probabilities; e.g., a prediction could be for precipitation tomorrow, but with only a 30% chance. Given such probabilistic predictions together with the actual outcomes, "reliability diagrams" help detect and diagnose statistically significant discrepancies -- so-called "miscalibration" -- between the predictions and the outcomes. The

J. Massey Cashore, Peter I. Frazier, Eva Tardos

Ridesharing markets are complex: drivers are strategic, rider demand and driver availability are stochastic, and complex city-scale phenomena like weather induce large scale correlation across space and time. At the same time, past work has focused on a subset of these challenges. We propose a model of ridesharing networks with strategic drivers, spatiotempo