May 2022 arXiv papers — page 2

Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, MoonKyung Ryu

Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL app

Yaniv Kerzhner, Ariel Epstein

We present an analytical scheme for designing metagrating-enhanced sparse antenna arrays. Unlike previous work, the proposed method does not involve time-consuming cost function optimizations, complex structural manipulations on the active array or demanding computational capabilities. Instead, it merely requires the integration of a passive metagrating (MG)

Zheng Chai, Guangji Bai, Liang Zhao, Yue Cheng

Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many graph-based applications. Traditional sampling-based methods accelerate GNN training by dropping edges and nodes, which impairs the graph integrity and model performance. Differen

John Waldo

The de facto algorithm for training the back pass of a feedforward neural network is backpropagation (BP). The use of almost-everywhere differentiable activation functions made it efficient and effective to propagate the gradient backwards through layers of deep neural networks. However, in recent years, there has been much research in alternatives to backpr

Natanael Alpay, Kamal Diki

We introduce the Segal-Bargmann transform associated to the Mittag Leffler Fock space and study how it will be connected to the Fourier transform. We will discuss also the counterpart of the creation and annihilation operator in this setting using the Caputo and Liouville operators. Finally, we give an extension of these results to the case of quaternions, i

Ramkishor Sharma, Axel Brandenburg

Hydrodynamic and magnetohydrodynamic turbulence in the early Universe can drive gravitational waves (GWs) and imprint their spectrum onto that of GWs, which might still be observable today. We study the production of the GW background from freely decaying magnetohydrodynamic turbulence from helical and nonhelical initial magnetic fields. To understand the pr

Xing Wang, Yijun Wang

Federated Learning is a rapidly growing area of research and with various benefits and industry applications. Typical federated patterns have some intrinsic issues such as heavy server traffic, long periods of convergence, and unreliable accuracy. In this paper, we address these issues by proposing asynchronous hierarchical federated learning, in which the c

Armaan Goyal, Songhu Wang

It has been demonstrated that planets belonging to the same close-in, compact multiple-planet system tend to exhibit a striking degree of uniformity in their sizes. A similar trend has also been found to hold for the masses of such planets, but considerations of such intra-system mass uniformity have generally been limited to statistical samples wherein a ma

Akshita Jha, Chandan K. Reddy

Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., they are primarily concerned with the human understanding of the code. They are not robust

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois

We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous methods for learning augmentations have typically assumed independence between the original input and the transformation applied to that input. This can be highly restrictive, as the invariances we hope our augmentation will capture are themselves ofte

Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei

The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a model ensemble. In a naive implementation, t

F. Onori, G. Cannizzaro, P. G. Jonker, M. Kim

We present the results from a dense multi-wavelength (optical/UV, near-infrared (IR), and X-ray) follow-up campaign of the nuclear transient AT2017gge, covering a total of 1698 days from the transient's discovery. The bolometric lightcurve, the black body temperature and radius, the broad H and He I $\lambda$5876 emission lines and their evolution with time,

James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis A. Nicolaou

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering gl

William Wei Wang, Gezheng Xu, Ruizhi Pu, Jiaqi Li

Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalization methods ignore the relationship between tasks, implicitly assuming that all the tasks are sampled from a stationary environment. Therefo

Jie Yu, Shourya Khanna, Nathalie Themessl, Saskia Hekker

Asteroseismology has become widely accepted as a benchmark for accurate and precise fundamental stellar properties. It can therefore be used to validate and calibrate stellar parameters derived from other approaches. Meanwhile, one can leverage large-volume surveys in photometry, spectroscopy, and astrometry to infer stellar parameters over a wide range of e

Bryanne McDonough, Tereasa Brainerd

We investigate the spatial distribution of the satellites of isolated host galaxies in the TNG100 simulation. In agreement with a previous, similar analysis of the Illustris-1 simulation, the satellites are typically poor tracers of the mean host mass density. Unlike the Illustris-1 satellites, here the spatial distribution of the complete satellite sample i

Maxim Dvornikov

We study neutrinos gravitationally scattered off a rotating supermassive black hole which is surrounded by a thin accretion disk with a realistic magnetic field. Neutrinos are supposed to be Dirac particles having a nonzero magnetic moment. Neutrinos move along arbitrary trajectories, with the incoming flux being parallel to the equatorial plane. We exactly

Artemisa Villalobos-Ramirez, Oswaldo Gallardo-Rivera, Alfredo Herrera-Aguilar, Ulises Nucamendi

In this work we make use of a general relativistic method to estimate the mass-to-distance ratio M/D = 3.54^{+0.2}_{-0.2} X 10^4 M_{sun}/Mpc of the black hole hosted at the core of the active galactic nucleus of TXS 2226-184, along with its Right Ascension offset and the recession redshift (velocity) of the galaxy. Our statistical fit is based on the frequen

Edwin S. Kite, Michael A. Mischna, Bowen Fan, Alexander M. Morgan

Early Mars had rivers, but the cause of Mars' wet-to-dry transition remains unknown. Past climate on Mars can be probed using the spatial distribution of climate-sensitive landforms. We analyzed global databases of water-worked landforms and identified changes in the spatial distribution of rivers over time. These changes are simply explained by comparison t

Sayash Kapoor, Matthew Sun, Mona Wang, Klaudia Jaźwińska

We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is situated at the intersection of two pivotal shifts in workplace dynamics: (a) the increase in online workplace communications due to remote work, and (b) the resurgence of the labo

Manuel Ettengruber

In this paper, the general features of the neutrino sector in TeV scale quantum gravity theories, such as ADD and Many Species Theory, is investigated. This class of theories has an inherent way to generate small neutrino masses. After reviewing this mechanism it is generalized to a realistic three-flavour case. Furthermore, a procedure is presented how to d

Finn Jaekel, Klaus Desch, Jochen Kaminski, Michael Lupberger

We present the development of an automated testing system for the VMM hybrid of the RD51 collaboration. The VMM hybrid is a new front-end board for the RD51 common readout system, the Scalable Readout System, and will become the workhorse for the next decade to read out Micro-Pattern Gaseous Detectors. It uses the VMM chip developed for the ATLAS New Small W

Juan J. Lastra-Díaz, M. Teresa Ortuño

The irregular strip-packing problem, also known as nesting or marker making, is defined as the automatic computation of a non-overlapping placement of a set of non-convex polygons onto a rectangular strip of fixed width and unbounded length, such that the strip length is minimized. Nesting methods based on heuristics are a mature technology, and currently, t

Minjia Shi, Denis S. Krotov, Patrick Solé

We show that no more new distance-regular graphs in the tables of the book of (Brouwer, Cohen, Neumaier, 1989) can be produced by using the coset graph of additive completely regular codes over finite fields.

Jacob Haqq-Misra, Edward W. Schwieterman, Hector Socas-Navarro, Ravi Kopparapu

Technosignatures refer to observational manifestations of technology that could be detected through astronomical means. Most previous searches for technosignatures have focused on searches for radio signals, but many current and future observing facilities could also constrain the prevalence of some non-radio technosignatures. This search could thus benefit

Viktoria Kabel, Wolfgang Wieland

In general relativity, it is difficult to localise observables such as energy, angular momentum, or centre of mass in a bounded region. The difficulty is that there is dissipation. A self-gravitating system, confined by its own gravity to a bounded region, radiates some of the charges away into the environment. At a formal level, dissipation implies that som

Ryan C. Felton, Sandra T. Bastelberger, Kathleen E. Mandt, Adrienn Luspay-Kuti

Saturn's Moon Titan receives volatiles into the top of its atmosphere-including atomic oxygen-sourced from cryovolcanoes on Enceladus. Similar types of atmosphere exchange from one body to another, such as O2 and O3 sourced from TRAPPIST-1 d, could be introduced into the upper atmosphere of TRAPPIST-1 e and might be interpreted as biosignatures. We simulate

Yijian Zou, Bowen Shi, Jonathan Sorce, Ian T. Lim

The modular commutator is a recently discovered multipartite entanglement measure that quantifies the chirality of the underlying many-body quantum state. In this Letter, we derive a universal expression for the modular commutator in conformal field theories in $1+1$ dimensions and discuss its salient features. We show that the modular commutator depends onl

James Bagrow, Yong-Yeol Ahn

The deluge of network datasets demands a standard way to effectively and succinctly summarize network datasets. Building on similar efforts to standardize the documentation of models and datasets in machine learning, here we propose network cards, short summaries of network datasets that can capture not only the basic statistics of the network but also infor

Tyler M. Heintz, J. J. Hermes, Kareem El-Badry, Charlie Walsh

White dwarf (WD) stars evolve simply and predictably, making them reliable age indicators. However, self-consistent validation of the methods for determining WD total ages has yet to be widely performed. This work uses 1565 wide ( > 100 au) WD+WD binaries and 24 new triples containing at least two WDs to test the accuracy and validity of WD total age determi

Maxime Haddouche, Benjamin Guedj

Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected and the algorithms must dynamically adjust. We prove new PAC-Bayesian bounds in this online learning framework, leveraging an updated def

Shintaro Takayoshi, Thierry Giamarchi

We study the transport properties of a one dimensional quantum system with disorder. We numerically compute the frequency dependence of the conductivity of a fermionic chain with nearest neighbor interaction and a random chemical potential by using the Chebyshev matrix product state (CheMPS) method. As a benchmark, we investigate the noninteracting case firs

Lyne Van de Vyvere, Dominique Sluse, Matthew R. Gomer, Sampath Mukherjee

Massive elliptical galaxies can display structures that deviate from a pure elliptical shape, such as a twist of the principal axis or variations in the axis ratio with galactocentric distance. Although satisfactory lens modeling is generally achieved without accounting for these azimuthal structures, the question about their impact on inferred lens paramete

Vitor Cardoso, Taishi Ikeda, Zhen Zhong, Miguel Zilhão

New light fundamental fields are natural candidates for all or a fraction of dark matter. Self-gravitating structures of such fields might be common objects in the universe, and could comprise even galactic halos. These structures would interact gravitationally with black holes, a process of the utmost importance since it dictates their lifetime, the black h

Adolfo Holguin, Shannon Wang

We find generating functions for half BPS correlators in $\mathcal{N}=4$ SYM theories with gauge groups $Sp(2N)$, $SO(2N+1)$, and $SO(2N)$ by computing the norms of a class of BPS coherent states. These coherent states are built from operators involving Harish-Chandra integrals. Such operators have an interpretation as localized giant gravitons in the bulk o

Roman Stricker, Michael Meth, Lukas Postler, Claire Edmunds

Quantum computers solve ever more complex tasks using steadily growing system sizes. Characterizing these quantum systems is vital, yet becoming increasingly challenging. The gold-standard is quantum state tomography (QST), capable of fully reconstructing a quantum state without prior knowledge. Measurement and classical computing costs, however, increase ex

Ryan Endsley, Daniel P. Stark, Jianwei Lyu, Feige Wang

We present band 6 ALMA observations of a heavily-obscured radio-loud ($L_{1.4\ \mathrm{GHz}}=10^{25.4}$ W Hz$^{-1}$) AGN candidate at $z_\mathrm{phot}=6.83\pm0.06$ found in the 1.5 deg$^2$ COSMOS field. The ALMA data reveal detections of exceptionally strong [CII]158$\mu$m ($z_\mathrm{[CII]}=6.8532$) and underlying dust continuum emission from this object (C

Nikolay K. Nikolov, David K. Sing, Jessica J. Spake, Barry Smalley

We present new analysis of infrared transmission spectroscopy of the cloud-free hot-Saturn WASP-96b performed with the Hubble and Spitzer Space Telescopes (HST and Spitzer). The WASP-96b spectrum exhibits the absorption feature from water in excellent agreement with synthetic spectra computed assuming a cloud-free atmosphere. The HST-Spitzer spectrum is coup

Kira Riedl, Danila Amoroso, Steffen Backes, Aleksandar Razpopov

Motivated by the recent wealth of exotic magnetic phases emerging in two-dimensional frustrated lattices, we investigate the origin of possible magnetism in the monolayer family of triangular lattice materials $MX_2$ ($M$={V, Mn, Ni}, $X$={Cl, Br, I}). We first show that consideration of general properties such as filling and hybridization enables to formula

Eyal Leviatan, David F. Mross

Strong interactions between electrons in two dimensions can realize phases where their spins and charges separate. We capture this phenomenon within a dual formulation. Focusing on square lattices, we analyze the long-wavelength structure of vortices when the microscopic particles -- electrons or spinful bosons -- are near half-filling. These conditions lead

Nicola Franchini, Miguel Bezares, Enrico Barausse, Luis Lehner

One of the major obstacles to testing alternative theories of gravity with gravitational-wave data from merging binaries of compact objects is the formulation of their field equations, which is often mathematically ill-suited for time evolutions. A possible way to address these delicate shortcomings is the fixing-the-equations approach, which was developed t

Tzu-Chi Hsieh, Han Ma, Leo Radzihovsky

We study a "helical" superfluid, a nonzero-momentum condensate in a frustrated bosonic model. At mean-field Bogoliubov level, such a novel state exhibits "smectic" fluctuation that are qualitatively stronger than that of a conventional superfluid. We develop a phase diagram and compute a variety of its physical properties, including the spectrum, structure f

Uri Malamud, Wolf A. Landeck, Dorothea Bischoff, Christopher Kreuzig

We show that if comets (or any small icy planetesimals such as Kuiper belt objects) are composed of pebble piles, their internal radiogenic as well as geochemical heating results in considerably different evolutionary outcomes compared to similar past studies. We utilize a 1D thermo-physical evolution code, modified to include state-of-the-art empirical meas

Adam S. Jermyn, Evan H. Anders, Daniel Lecoanet, Matteo Cantiello

Convection is ubiquitous in stars and occurs under many different conditions. Here we explore convection in main-sequence stars through two lenses: dimensionless parameters arising from stellar structure and parameters which emerge from the application of mixing length theory. We first define each quantity in terms familiar both to the 1D stellar evolution c

Nathan A. Kaib, Kathryn Volk

The Oort cloud and the scattered disk are the two primary reservoirs for long-period and short-period comets, respectively. In this review, we assess the known observational constraints on these reservoirs' properties and their formation. In addition, we discuss how the early orbital evolution of the giant planets generated the modern scattered disk from the

Jahan Claes, Shruti Puri

Recent work has demonstrated that high-threshold quantum error correction is possible for biased-noise qubits, provided one can implement a controlled-not (CX) gate that preserves the bias. Bias-preserving CX gates have been proposed for several biased-noise qubit platforms, most notably Kerr cats. However, experimentally measuring the noise bias is challeng

Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

In 3D printing, stiff fibers (e.g., carbon fiber) can reinforce thermoplastic polymers with limited stiffness. However, existing commercial digital manufacturing software only provides a few simple fiber layout algorithms, which solely use the geometry of the shape. In this work, we build an automated fiber path planning algorithm that maximizes the stiffnes

Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen

Vector quantized diffusion (VQ-Diffusion) is a powerful generative model for text-to-image synthesis, but sometimes can still generate low-quality samples or weakly correlated images with text input. We find these issues are mainly due to the flawed sampling strategy. In this paper, we propose two important techniques to further improve the sample quality of

Andrew J. Sargent, Megan C. Johnson, Amy E. Reines, Nathan J. Secrest

Thirteen dwarf galaxies have recently been found to host radio-selected accreting massive black hole (MBH) candidates, some of which are ``wandering" in the outskirts of their hosts. We present 9 GHz Very Long Baseline Array (VLBA) observations of these sources at milliarcsecond resolution. Our observations have beam solid angles ${\sim}10^4$ times smaller t

Man Luo

Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks such as open domain question answering and dialogue systems, where external knowledge is needed. In the past, searching algorithms based on term matching have been widely used. Recent

Utkarsh Ojha, Yuheng Li, Anirudh Sundara Rajan, Yingyu Liang

Knowledge distillation aims to transfer useful information from a teacher network to a student network, with the primary goal of improving the student's performance for the task at hand. Over the years, there has a been a deluge of novel techniques and use cases of knowledge distillation. Yet, despite the various improvements, there seems to be a glaring gap

Sitan Chen, Jerry Li, Yuanzhi Li

Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural network $F:\mathbb{R}^d\to\mathbb{R}^{d'}$, let $D$ be the distribution over $\mathbb{R}^{d'}$ given by pushing the standard Gaussian $\mathcal{N}(0,\textrm{Id}_d)$ through $F$. Giv

Sarah E. Caddy, Lee R. Spitler, Simon C. Ellis

The sky observed by space telescopes in Low Earth Orbit (LEO) can be dominated by stray light from multiple sources including the Earth, Sun and Moon. This stray light presents a significant challenge to missions that aim to make a secure measurement of the Extragalactic Background Light (EBL). In this work we quantify the impact of stray light on sky observ

Tiago Pimentel, Clara Meister, Ryan Cotterell

A good automatic evaluation metric for language generation ideally correlates highly with human judgements of text quality. Yet, there is a dearth of such metrics, which inhibits the rapid and efficient progress of language generators. One exception is the recently proposed Mauve. In theory, Mauve measures an information-theoretic divergence between two prob

Mourad Choulli

We establish a Lipschitz stability inequality for the problem of determining the nonlinear term in a quasilinear elliptic equation by boundary measurements. We give a proof based on a linearization procedure together with special solutions constructed from the fundamental solution of the linearized problem.

Hailong Ma, Sibo Feng, Xi Xiao, Chenyu Dong

Photo retouching aims to adjust the luminance, contrast, and saturation of the image to make it more human aesthetically desirable. However, artists' actions in photo retouching are difficult to quantitatively analyze. By investigating their retouching behaviors, we propose a two-stage network that brightens images first and then enriches them in the chromin

Seth Davidovits, Christoph Federrath, Romain Teyssier, Kumar S. Raman

An initially planar shock wave propagating into a medium of non-uniform density will be perturbed, leading to the generation of post-shock velocity perturbations. Using numerical simulations we study this phenomenon in the case of highly-non-uniform density (order-unity normalized variance, $\sigma_{\rho}/\overline{\rho} \sim 1$) and strong shocks (shock Mac

Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu

How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g. object detection, motion forecasting). However, in the context of end-to-end driving, we find that imitation learning based on existing sensor fusion methods underperforms in complex driving scenarios with a

Yuming Jiang, Shuai Yang, Haonan Qiu, Wayne Wu

Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation process is even desired to be intuitively controllable for layman users. In this work, we present a text-driven

Joseph J. Cuozzo, Wenlong Yu, Paul Davids, Tina M. Nenoff

In recent years experimentalists have been able to clearly show that several materials, such as MgB2, iron-based superconductors3, monolayer NbSe2, are multiband superconductors. Superconducting pairing in multiple bands can give rise to novel and very interesting phenomena. Leggett modes are exemplary of the unusual effects that can be present in multiband

Himanshu Grover, Lokesh Panwar, Ashu Verma, B. K. Panigrahi

In recent times, non-intrusive load monitoring (NILM) has emerged as an important tool for distribution-level energy management systems owing to its potential for energy conservation and management. However, load monitoring in smart building environments is challenging due to high variability of real-time load and varied load composition. Furthermore, as the

José L. Mancilla-Aguilar, José E. Rojas-Ruiz, Hernan Haimovich

For large classes of infinite-dimensional time-varying control systems, the equivalence between integral input-to-state stability (iISS) and the combination of global uniform asymptotic stability under zero input (0-GUAS) and uniformly bounded-energy input/bounded state (UBEBS) is established under a reasonable assumption of continuity of the trajectories wi

Sajani Vithana, Sennur Ulukus

We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification. In FSL, a machine learning model is divided into multiple submodels, where each user updates only the submodel that is relevant to the user's local data. PRUW is the process of privately performing FSL by reading from and writing to the re

Samuel N. Cohen, Christoph Reisinger, Sheng Wang

We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their performance when applied to various option portfolios using real-world data. Through backtesting analysis over typic

Nathan Haut, Wolfgang Banzhaf, Bill Punch

The use of correlation as a fitness function is explored in symbolic regression tasks and the performance is compared against the typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Using correlation as a fitness function led to solutions being

Edgar Alonso Guzmán, Alejandro V. Arzola

Near-field patterns of light provide a way to optically trap, deliver and sort single nanoscopic particles in a wide variety of applications in nanophotonics, microbiology and nanotechnology. Using rigorous electromagnetic theory, we investigate the forces and trapping performance of near-field optical tweezers carrying spin and orbital angular momenta. The

Sandra Robles, Jonathan S. Gómez, Adín Ramírez Rivera, Nelson D. Padilla

A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of high-resolution, computationally intensive N-body simulations. We show that machine learning (ML) techniques, in particular Gener

Wenjie Li, Qiaolin Xia, Junfeng Deng, Hao Cheng

As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the li

Rong Du, Jian-Chong Xing, Bo Xiong, Jun-Hui Zheng

We investigate the nonequilibrium dynamics of two-dimensional Bose-Einstein condensates in boxlike traps with power-law potential boundaries by quenching the interatomic interactions. For both concave and convex potentials, we show that ring dark solitons can be excited during the quench dynamics. The modulation strength of the quench and the steepness of th

Damiano F. G. Fiorillo, Mauricio Bustamante, Victor B. Valera

Upcoming neutrino telescopes may discover ultra-high-energy (UHE) cosmic neutrinos, with energies beyond 100 PeV, in the next 10-20 years. Finding their sources would identify guaranteed sites of interaction of UHE cosmic rays, whose origin is unknown. We search for sources by looking for multiplets of UHE neutrinos arriving from similar directions. Our fore

Gonzalo Contreras, Yuriria Estrada

We proof the homogenization of the Hamilton-Jacobi equation on arbitrary compact manifolds using Evans perturbed test function method.

You Zhao, Xiaofeng Liao, Xing He, Chaojie Li

This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical approach (APDMD) based on accelerated mirror descent and primal-dual framework is proposed and accelerated converg

Nasir U. Eisty, Jeffrey C. Carver

Background: Research software plays an important role in solving real-life problems, empowering scientific innovations, and handling emergency situations. Therefore, the correctness and trustworthiness of research software are of absolute importance. Software testing is an important activity for identifying problematic code and helping to produce high-qualit

Sergey S. Pershoguba, Victor M. Yakovenko

We study how the energy dispersion of bosonic atoms loaded into an optical lattice becomes modified due to periodic circular stirring of the lattice to the second order in the strength of stirring. If the lattice breaks mirror symmetry, the bosonic atoms may acquire a nonzero group velocity at the center of the Brillouin zone and produce a nonzero direct cur

Daniel D. Carpintero, Nicolás P. Maffione, Facundo A. Gómez

Modern astronomical potentials modeling galaxies or stellar systems can be rather involved, and deriving their first derivatives (accelerations) and second derivatives (variational equations) in order to compute orbits and their chaoticity may be a formidable task. We present here a fully automated routine, dubbed Smart, with which the accelerations and the

Johannes Betz, Tobias Betz, Felix Fent, Maximilian Geisslinger

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new tec

Brendan Guilfoyle, Morgan Robson

The evolution of a rotationally symmetric surface by a linear combination of its radii of curvature equation is considered. It is known that if the coefficients form certain integer ratios the flow is smooth and can be integrated explicitly. In this paper the non-integer case is considered for certain values of the coefficients and with mild analytic restric

Federico Moretti

In this paper we compute the gonality and the dimension of the Brill-Noether loci $W^1_d(C)$ for curves in a non primitive linear system of a simple abelian surface, adapting vector bundles techniques \`a la Lazarsfeld originally introduced with $K3$ surfaces. As a corollary, we obtain general Green's conjecture for curves on abelian surfaces.

Yoshinori Matsuo

A fluid model of self-gravitating strings is proposed. It is expected that black holes turn into strings around the end of black hole evaporation. The transition will occur near the Hagedorn temperature. After the transition, strings would form a bound state by the self-gravitation. Horowitz and Polchinski formulated a model of self-gravitating strings by us

S. Appel, Z. Bagdasarian, D. Basilico, G. Bellini

We present an improved measurement of the CNO solar neutrino interaction rate at Earth obtained with the complete Borexino Phase-III dataset. The measured rate R$_{\rm CNO}$ = $6.7^{+2.0}_{-0.8}$ counts/(day$ \cdot$ 100 tonnes), allows us to exclude the absence of the CNO signal with about 7$\sigma$ C.L. The correspondent CNO neutrino flux is $6.6^{+2.0}_{-0

Danilo Forastiere, Riccardo Rao, Massimiliano Esposito

We study the thermodynamics of open systems weakly driven out-of-equilibrium by nonconservative and time-dependent forces using the linear regime of stochastic thermodynamics. We make use of conservation laws to identify the potential and nonconservative components of the forces. This allows us to formulate a unified near-equilibrium thermodynamics. For none

Daniel Katz, Prashanth Sridhar

We study the Cohen-Macaulay property of a particular class of radical extensions of an unramified regular local ring having mixed characteristic.

Hao Yang, Yang Xu, Yong Li, Hyun-Deok Choi

After a developer submits code, corresponding test cases arise to ensure the quality of software delivery. Test failures would occur during this period, such as crash, error, and timeout. Since it takes time for developers to resolve them, many duplicate failures will happen during this period. In the delivery practice of SAP HANA, crash triage is considered

Aditya Rotti, Andrea Ravenni, Jens Chluba

Primordial non-Gaussianity can source $\mu$-distortion anisotropies that are correlated with the large-scale temperature and polarization signals of the cosmic microwave background (CMB). A measurement of $\mu T$ and $\mu E$ correlations can therefore be used to constrain it on wavelengths of perturbations not directly probed by the standard CMB anisotropies

Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete

The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an increase in undesirable non-biological variance due to scanner and acquisition differences - the harmonisation probl

Alex Rodrigo dos Santos Sousa

In this paper we consider aggregated functional data composed by a linear combination of component curves and the problem of estimating these component curves. We propose the application of a bayesian wavelet shrinkage rule based on a mixture of a point mass function at zero and the logistic distribution as prior to wavelet coefficients to estimate mean curv

S. Chatterjee, B. S. Sanjeev

Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially SARS-CoV-2. COVID-19 caused up to 33-55\% death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways which ar

Keiran Paster, Sheila McIlraith, Jimmy Ba

Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks. However, simply conditioning a probabilistic model on a desired return and taking the

Subhendu Das, Sridhar Tripathy, Sandip Sarkar, Nayana Majumdar

This work reports a cost-effective, multi-parameter readout and data-acquisition system for a muon scattering tomography system based on Resistive Plate Chambers (RPCs). Initial test measurements with a prototype Resistive Plate Chamber were performed using a low-cost FPGA coupled to the NINO ASIC for the event selection and handling data. The Time over-Thre

Ritwik Sinha, David Arbour, Aahlad Manas Puli

In a multi-channel marketing world, the purchase decision journey encounters many interactions (e.g., email, mobile notifications, display advertising, social media, and so on). These impressions have direct (main effects), as well as interactive influence on the final decision of the customer. To maximize conversions, a marketer needs to understand how each

D. Babukhin, D. Kronberg, D. Sych

Distinguishability of photons in non-operational degrees of freedom compromises unconditional security of quantum key distribution since an eavesdropper can improve attack strategies by exploiting this distinguishability. However, the optimal eavesdropping strategies in the presence of light source side channels are not known. Here we provide several explici

Alessandro Gallo, Manh Duong Phung

A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used t

Rong-Lan Li, Ben-Yang Zhu, Yun-Feng Liang

We use 10 years of publicly available IceCube data to investigate the correlations between high-energy neutrinos and various Fermi-LAT gamma-ray samples. This work considers the following gamma-ray samples: the third Fermi-LAT catalog of high-energy sources(3FHL), >100GeV Fermi-LAT events, LAT 12-year source catalog(4FGL), the fourth catalog of active galact

Tongzhou Zhao, Koji Kudo, W. N. Faugno, Ajit C. Balram

Recent systematic measurements of the quantum well width dependence of the excitation gaps of fractional quantum Hall states in high mobility samples [Villegas Rosales {\it et al.}, Phys. Rev. Lett. {\bf 127}, 056801 (2021)] open the possibility of a better quantitative understanding of this important issue. We present what we believe to be accurate theoreti

Samuel H. Moody, Matthew T. Littlehales, Jonathan S. White, Daniel Mayoh

Magnetic skyrmions are vortex-like, swirls of magnetisation whose topological protection and particle-like nature have suggested them to be suitable for a number of novel spintronic devices. One such application is skyrmionic computing, which has the advantage over conventional schemes due to the amalgamation of logic calculations and data storage. Using sma

Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra

Natural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource languages such as English and Chinese, while it remains inaccessible to many languages due to the unavailability of data resources and benchmarks.

Alessandra Cappati, Roberto Covarelli, Paolo Torrielli, Marco Zaro

We analyse the sensitivity to beyond-the-Standard-Model effects of hadron-collider processes involving the interaction of two electroweak and two Higgs bosons, VVHH, with V being either a W or a Z boson. We examine current experimental results by the CMS collaboration in the context of a dimension-8 extension of the Standard Model in an effective-field-theor

Sijia Lu, Feng Xu

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. Previous works have shown that converting Artificial Neural Networks (ANNs) into SNNs is a practical and efficient approach for implementing an SNN. However, the basic principle and theoretical ground

Carlo Michele Valensise, Matteo Cinelli, Walter Quattrociocchi

Users online tend to join polarized groups of like-minded peers around shared narratives, forming echo chambers. The echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic m

Yonadav Barry Ginat, Hagai Perets

Chaotic three-body interactions may lead to the formation of gravitational-wave sources. Here, by modelling the encounter as a series of close, non-hierarchical, triple approaches, interspersed with hierarchical phases, in which the system consists of an inner binary and a star that orbits it, we compute the pericentre probability distribution, and thereby t