October 2022 arXiv papers — page 86

Dmitry Kleinbock, Anurag Rao

Given a norm $\nu$ on $\mathbb{R}^2$, the set of $\nu$-Dirichlet improvable numbers $\mathbf{DI}_\nu$ was defined and studied in the papers of Andersen-Duke (Acta Arith. 2021) and Kleinbock-Rao (Internat. Math. Res. Notices 2022). When $\nu$ is the supremum norm, $\mathbf{DI}_\nu = \mathbf{BA}\cup \mathbb{Q}$, where $\mathbf{BA}$ is the set of badly approxim

Yuhong Li, Tianle Cai, Yi Zhang, Deming Chen

Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution, making the model unable to handle long-range dependency efficiently. Attention overcomes this problem by aggregating global information but also makes the computational complexity quadratic to the sequence length. Recently, Gu et al. [202

Carolina Higuera, Siyuan Dong, Byron Boots, Mustafa Mukadam

We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing e

Anup Poudel

We prove a conjecture of L\^{e} and Sikora by providing a comparison between various existing $SL_n$ skein theories. While doing so, we show that the full subcategory of the spider category, $\mathcal{S}p(SL_n)$, defined by Cautis-Kamnitzer-Morrison, whose objects are monoidally generated by the standard representation and its dual, is equivalent as a spheri

Ta Tang, Brian Moritz, Cheng Peng, Z. X. Shen

The appearance of certain spectral features in one-dimensional (1D) cuprate materials has been attributed to a strong, extended attractive coupling between electrons. Here, using time-dependent density matrix renormalization group methods on a Hubbard-extended Holstein model, we show that extended electron-phonon ({\it e-ph}) coupling presents an obvious cho

Brian Batell, Tathagata Ghosh, Tao Han, Keping Xie

The future Electron-Ion Collider (EIC) at Brookhaven National Laboratory, along with its primary capacity to elucidate the nuclear structure, will offer new opportunities to probe physics beyond the Standard Model coupled to the electroweak sector. Among the best motivated examples of such new physics are new heavy neutral leptons (HNLs), which are likely to

Matthew Powell

It is known that the Lyapunov exponent of analytic 1-frequency quasiperiodic cocycles is continuous in cocycle and, when the frequency is irrational, jointly in cocycle and frequency. In this paper, we extend a result of Bourgain to show the same continuity result for multifrequency quasiperiodic $M(2,\mathbb{C})$ cocycles. Our corollaries include applicatio

Alex Burgin, Samuel Goldberg, Tamás Keleti, Connor MacMahon

We study some variants of the Erd\H{o}s similarity problem. We pose the question if every measurable subset of the real line with positive measure contains a similar copy of an infinite geometric progression. We construct a compact subset $E$ of the real line such that $0$ is a Lebesgue density point of $E$, but $E$ does not contain any (non-constant) infini

Fei Dai, Kento Masuda, Corey Beard, Paul Robertson

Convergent disk migration has long been suspected to be responsible for forming planetary systems with a chain of mean-motion resonances (MMR). Dynamical evolution over time could disrupt the delicate resonant configuration. We present TOI-1136, a 700-Myr-old G star hosting at least 6 transiting planets between $\sim$2 and 5 $R_\oplus$. The orbital period ra

Yuri D. Lensky, Kostyantyn Kechedzhi, Igor Aleiner, Eun-Ah Kim

Stabilizer codes allow for non-local encoding and processing of quantum information. Deformations of stabilizer surface codes introduce new and non-trivial geometry, in particular leading to emergence of long sought after objects known as projective Ising non-Abelian anyons. Braiding of such anyons is a key ingredient of topological quantum computation. We s

Marc Wagner, Constantia Alexandrou, Jacob Finkenrath, Theodoros Leontiou

We present first results of a recently started lattice QCD investigation of antiheavy-antiheavy-light-light tetraquark systems including scattering interpolating operators in correlation functions both at the source and at the sink. In particular, we discuss the importance of such scattering interpolating operators for a precise computation of the low-lying

Dzmitry Dudko, Mikhail Lyubich

We prove uniform ``pseudo-Siegel'' a priori bounds for Siegel disks of bounded type that give a uniform control of oscillations of their boundaries in all scales. As a consequence, we construct the Mother Hedgehog controlling the postcritical set for any quadratic polynomial with a neutral periodic point and show that this hedgehog has a star-like structure.

Shounak Chattopadhyay, Stephanie M. Engel, David Dunson

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amp

Valter Moretti, Simone Murro, Daniele Volpe

This paper deals with several issues concerning the algebraic quantization of the real Proca field in a globally hyperbolic spacetime and the definition and existence of Hadamard states for that field. In particular, extending previous work, we construct the so-called M\o ller $*$-isomorphism between the algebras of Proca observables on paracausally related

Damian Owerko, Fernando Gama, Alejandro Ribeiro

Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we propose to leverage graph signal processing and machine learning. More specifically, we use a graph neural network

Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov

Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to

Mahsa Karimi, Ali Javadi-Abhari, Christoph Simon, Roohollah Ghobadi

This paper explores the potential benefits of quantum coherence and quantum discord in the non-universal quantum computing model called deterministic quantum computing with one qubit (DQC1) in supervised machine learning. We show that the DQC1 model can be leveraged to develop an efficient method for estimating complex kernel functions. We demonstrate a simp

Sergei A. Avdonin, Aleksander S. Mikhaylov, Victor S. Mikhaylov, Abdon E. Choque-Rivero

The question of what conditions should be set at the nodes of a discrete graph for the wave equation with discrete time is investigated. The variational method for the derivation of these conditions is used. A parallel with the continuous case is also drawn. As an example the problem of shape controllability from the boundary is studied.

Masanori Adachi, Séverine Biard, Judith Brinkschulte

We discuss residue formulae that localize the first Chern class of a line bundle to the singular locus of a given holomorphic connection. As an application, we explain a proof for Brunella's conjecture about exceptional minimal sets of codimension one holomorphic foliations with ample normal bundle and for a nonexistence theorem of Levi flat hypersurfaces wi

Wan-Li Ju, Marek Schönherr

The transverse momentum distribution of the $t\bar{t}$ system is of both experimental and theoretical interest. In the presence of the azimuthally asymmetric divergences, pursuing the resummation at the high logarithmic precision is rather demanding in general. In this paper, we propose the projected transverse momentum spectrum $\mathrm{d}\sigma_{t\bar{t}}/

P. M. Lavrov, I. L. Shapiro

The current understanding of renormalization in quantum gravity (QG) is based on the fact that UV divergences of effective actions in the covariant QG models are covariant local expressions. This fundamental statement plays a central role in QG and, therefore, it is important to prove it for the widest possible range of the QG theories. Using the Batalin-Vil

Kaveh Eftekharinasab

We prove multiplicity theorems for Keller $ C_c^1 $-functionals on Frechet spaces and Finsler manifolds which are invariant under the action of a discrete subgroup. For such functionals, we evaluate the minimal number of critical points by applying the Lusternik-Schnirelmann category.

Yi Liu, Ke Sun, Linglong Kong, Bei Jiang

Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of sensitive individual information. Despite the extra interpretability and tighter bounds under composition GDP provides, many widely used mechanisms (e.g., the Laplace mechanism) inherently provide GDP guarantees but o

Arpan Bhattacharyya, Tanvir Hanif, S. Shajidul Haque, Arpon Paul

In this paper, we compare the saturation time scales for complexity, linear entropy and entanglement negativity for two open quantum systems. Our first model is a coupled harmonic oscillator, where we treat one of the oscillators as the bath. The second one is a type of Caldeira Leggett model, where we consider a one-dimensional free scalar field as the bath

Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela, Sean Rafferty

Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different environmental conditions. Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to c

Maurizio Titz, Franz Kaiser, Johannes Kruse, Dirk Witthaut

A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article we propose a

Shi-Xian Sun, Yong-Qiang Wang, Li Zhao

In this paper, we re-investigate the stationary, soliton-like solutions in the model of the Einstein gravity coupled to a free and complex scalar field, which have been known as mini-boson stars. With numerical method, we find that in addition to the usual single mini-boson star solution, there exist a novel family of solutions interpreted as chains of boson

Raphael Chetrite, Frederic Patras

The present survey results from the will to reconcile two approaches to quantum probabilities: one rather physical and coming directly from quantum mechanics, the other more algebraic. The second leading idea is to provide a unified picture introducing jointly to several fields of applications, many of which are probably not all familiar (at leat at the same

Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang

This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, s

Alberto Olmo, Ahmed Zamzam, Andrew Glaws, Ryan King

With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approache

Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann

BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65% of the BIG-Bench tasks via few-shot p

E. Di Salvo, Z. J. Ajaltouni

We propose to investigate the effects of new physics in the semi-leptonic sequential decay $\Lambda_b \to \Lambda_c (\to \Lambda \pi) \tau{\bar \nu}_{\tau}$. Firstly, we write the general, model independent, non-covariant expression of the differential decay width of the process. Then, we calculate that observable according to three different types of new ph

Anna Puecher, Tim Dietrich, Ka Wa Tsang, Chinmay Kalaghatgi

Gravitational waves provide us with an extraordinary tool to study the matter inside neutron stars. In particular, the postmerger signal probes an extreme temperature and density regime and will help reveal information about the equation of state of supranuclear-dense matter. Although current detectors are most sensitive to the signal emitted by binary neutr

Yury S. Krivosenko, Ivan V. Iorsh, Ivan A. Shelykh

We present a quantitative microscopic analysis of the formation of exciton-polaritons, the composite particles possessing light and material components, polariton-polariton interactions, and resonant pumping dynamics in cylindrical semiconductor micropillars. We discuss how the redistribution effect can be used in devices generating photons with non zero orb

Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti

Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunately, solving a normal-form game could take prohibitive or non-deterministic time to converge, and could fail. We introduce the Neural Equilibrium Solver which utilizes a special eq

Philipp Becker, Gerhard Neumann

Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their empirical success, many of the underlying design choices are not well understood. We show that RSSMs use a suboptimal inference scheme and that models trained using this inference ov

Christoph Dann, Chen-Yu Wei, Julian Zimmert

We study reinforcement learning in stochastic path (SP) problems. The goal in these problems is to maximize the expected sum of rewards until the agent reaches a terminal state. We provide the first regret guarantees in this general problem by analyzing a simple optimistic algorithm. Our regret bound matches the best known results for the well-studied specia

Jamie Bamber, Josu C. Aurrekoetxea, Katy Clough, Pedro G. Ferreira

The interaction of binary black hole mergers with their environments can be studied using numerical relativity simulations. These start only a short finite time before merger, at which point appropriate initial conditions must be imposed. A key task is therefore to identify the configuration that is appropriate for the binary and its environment at this stag

Ankan Ganguly, Kavita Ramanan

Consider a system of interacting particles indexed by the nodes of a graph whose vertices are equipped with marks representing parameters of the model such as the environment or initial data. Each particle takes values in a countable state space and evolves according to a (possibly non-Markovian) continuous-time pure jump process whose jump intensities depen

Andrew Pocklington, Yu-Xin Wang, Aashish A. Clerk

We analyze an unusual class of bosonic dynamical instabilities that arise from dissipative (or non-Hermitian) pairing interactions. We show that, surprisingly, a completely stable dissipative pairing interaction can be combined with simple hopping or beam-splitter interactions (also stable) to generate instabilities. Further, we find that the dissipative ste

Felix Zilk, Korbinian Staudacher, Tobias Guggemos, Karl Fürlinger

Photons are a natural resource in quantum information, and the last decade showed significant progress in high-quality single photon generation and detection. Furthermore, photonic qubits are easy to manipulate and do not require particularly strongly sealed environments, making them an appealing platform for quantum computing. With the one-way model, the vi

S. V. Troitsky

Recent astrophysical transient Swift J1913.1+1946 is possibly associated with the gamma-ray burst GRB 221009A at the redshift z=0.151. The transient was accompanied by very high-energy gamma rays up to 18 TeV observed by LHAASO and a photon-like air shower of 251 TeV detected by Carpet-2. These energetic gamma rays cannot reach us from the claimed distance o

Niklas Freymuth, Nicolas Schreiber, Philipp Becker, Aleksandar Taranovic

Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt to new and changing environments. Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting, making it diffic

Tufan Akba, Derek K. Baker, M. Pinar Menguc

Concentrating solar thermal power is an emerging renewable technology with accessible storage options to generate electricity when required. Central receiver systems or solar towers have the highest commercial potential in large-scale power plants because of reaching the highest temperature. With the increasing solar chemistry applications and new solar ther

Jean-Jacques Godeme, Jalal Fadili, Xavier Buet, Myriam Zerrad

In this paper, we consider the problem of phase retrieval, which consists of recovering an $n$-dimensional real vector from the magnitude of its $m$ linear measurements. We propose a mirror descent (or Bregman gradient descent) algorithm based on a wisely chosen Bregman divergence, hence allowing to remove the classical global Lipschitz continuity requiremen

Bernd Kolar, Johannes Diwold, Conrad Gstöttner, Markus Schöberl

The paper studies the relation between a nonlinear time-varying flat discrete-time system and the corresponding linear time-varying systems which are obtained by a linearization along trajectories. It is motivated by the continuous-time case, where it is well-known that the linearization of flat systems along trajectories results in linear time-varying syste

Mattias Jonsson, Nicholas McCleerey, Sanal Shivaprasad

We give new proofs of two implications in the Donaldson--Uhlenbeck--Yau theorem. Our proofs are based on geodesic rays of Hermitian metrics, inspired by recent work on the Yau--Tian--Donaldson conjecture.

Haoming Li, Xinzhuo Lin, Yang Zhou, Xiang Li

3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smoot

D Bharti, H Srinivas, F Shobeiry, K R Hamilton

We report a joint experimental and theoretical study of a three-sideband (3-SB) modification of the "reconstruction of attosecond beating by interference of two-photon transitions" (RABBIT) setup. The 3-SB RABBIT scheme makes it possible to investigate phases resulting from interference between transitions of different orders in the continuum. Furthermore, t

E. R. Bezerra de Mello, M. B. Cruz

In this work, we consider a theoretical model that presents violation of the Lorentz symmetry in the approach of Quantum Field Theory. The theoretical model adopted consists of a real massive scalar quantum field confined in the region between two large parallel plates. The Lorentz symmetry violation is introduced by CPT-even, aether-like approach, consideri

C. Ordenes-Huanca, M. Zoccali, A. Bayo, J. Cuadra

T Tauri stars are low-mass pre-main sequence stars that are intrinsically variable. Due to the intense magnetic fields they possess, they develop dark spots on their surface that, because of rotation, introduce a periodic variation of brightness.In addition, the presence of surrounding disks could generate flux variations by variable extinction or accretion.

Yang Yue, Bingyi Kang, Xiao Ma, Zhongwen Xu

Offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. To address this problem, existing works mainly focus on designing sophisticated algorithms to explicitly or implicitly constrain the learned policy to be close to the behavior policy. The constraint applies not only to well-performing actions

Maxence Lefèvre, Emmanuel Marcq, Franck Lefèvre

Venus clouds host a convective layer between roughly 50 and 60 km that mixes heat, momentum, and chemical species. Observations and numerical modelling have helped to understand the complexity of this region. However, the impact on chemistry is still not known. Here, we use for the first time a three-dimensional convection-resolving model with passive tracer

Yunfei Qin

Various topological concepts are often involved in the research of mathematical logic, and almost all of these concepts can be regarded as developing from the Stone representation theorem. In the Stone representation theorem, a Boolean algebra is represented as the algebra of the clopen sets of a Stone space. And based on this, a natural connection is establ

Sergio R Fenley

The main result is that if an Anosov flow in a closed hyperbolic three manifold is not R-covered, then the flow is a quasigeodesic flow. We also prove that if a hyperbolic three manifold supports an Anosov flow, then up to a double cover it supports a quasigeodesic flow. We prove the continuous extension property for the stable and unstable foliations of any

Gaurav N. Gadbail, Sanjay Mandal, P. K. Sahoo

In this manuscript, we present a number of fascinating explicit reconstructions for the $f(Q)$ gravity from the background of Friedmann-La\^imatre-Robertson-Walker (FLRW) evolution history. We find the more general functions of non-metricity scalar $Q$ that admit exact $\Lambda$CDM expansion history. Adding extra degrees of freedom to the matter sector is th

Qishi Dong, Awais Muhammad, Fengwei Zhou, Chuanlong Xie

Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalization, for which the goal is to perform well on possible unseen domains after fine-tuning on multiple training domains. However, maximally exploiting a zoo of PTMs is challenging since

Maxence Corman, Justin L. Ripley, William E. East

We study the nonlinear dynamics of binary black hole systems with scalar charge by numerically evolving the full equations of motion for shift-symmetric Einstein scalar Gauss-Bonnet gravity. We consider quasi-circular binaries with different mass-ratios, varying the Gauss-Bonnet coupling and quantifying its impact on the emitted scalar and gravitational wave

Isaac Ronald Ward, Charles Moore, Kai Pak, Jingdao Chen

Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are ap

Alphonse C. Sterling, Conrad Schwanitz, Louise K. Harra, Nour E. Raouafi

We examine in greater detail five events previously identified as being sources of strong transient coronal outflows in a solar polar region in Hinode/EUV Imaging Spectrometer (EIS) Doppler data. Although relatively compact or faint and inconspicuous in Hinode/Soft X-ray Telescope (XRT) soft-X-ray (SXR) images and in Solar Dynamics Observatory (SDO)/Atmosphe

Sami Hamdan, Bradley C. Love, Georg G. von Polier, Susanne Weis

Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be removed as is commonly done by featurewise removal of their variance by linear regression before applying ML. Here, we show this common approach to confound removal biases ML models, l

Miguel S. Concha-Aracena, Leonardo Barrios-Blanco, David Elal-Olivero, Paulo Henrique Silva

This article presents an important theorem, which shows that from the moments of the standard normal distribution one can generate density functions originating a family of models. Additionally, we discussed that different random variable domains are achieved with transformations. For instance, we adopted the moment of order two, from the proposed theorem, a

Houda Ferradi, Jiannong Cao, Shan Jiang, Yinfeng Cao

Big Data Sharing (BDS) refers to the act of the data owners to share data so that users can find, access and use data according to the agreement. In recent years, BDS has been an emerging topic due to its wide applications, such as big data trading and cross-domain data analytics. However, as the multiple parties are involved in a BDS platform, the issue of

Kévin Belkacem, Charly Pinçon, Gaël Buldgen

Solar gravity modes are considered as the {\it Rosetta Stone} for probing and subsequently deciphering the physical properties of the solar inner-most layers. Recent claims of positive detection therefore shed some new light on the long-standing issue of estimating solar gravity mode amplitudes. In this article, our objective is to review the theoretical eff

Chad A Melton, Brianna M White, Robert L Davis, Robert A Bednarczyk

This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter, harvested from January 1, 2020, to March 1, 2022. To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9.5 million Tweets and 70 thousand Reddit comments

Kui Ren, Lu Zhang

The task of simultaneously reconstructing multiple physical coefficients in partial differential equations (PDEs) from observed data is ubiquitous in applications. In this work, we propose an integrated data-driven and model-based iterative reconstruction framework for such joint inversion problems where additional data on the unknown coefficients are supple

António Girão, Gal Kronenberg, Alex Scott

Ramsey theory is a central and active branch of combinatorics. Although Ramsey numbers for graphs have been extensively investigated since Ramsey's work in the 1930s, there is still an exponential gap between the best known lower and upper bounds. For $k$-uniform hypergraphs, the bounds are of tower-type, where the height grows with $k$. Here, we give a mult

Enrique García-Sánchez, Pedro Tradacete

The relation between the free Banach lattice generated by a Banach space and free dual spaces is clarified. In particular, it is shown that for every Banach space $E$ the free $p$-convex Banach lattice generated by $E^{**}$, denoted $FBL^p[E^{**}]$, admits a canonical isometric lattice embedding into $FBL^p[E]^{**}$ and $FBL^p[E^{**}]$ is lattice finitely re

Franz Scherr, Qinghai Guo, Timoleon Moraitis

Self-supervised learning (SSL) methods aim to exploit the abundance of unlabelled data for machine learning (ML), however the underlying principles are often method-specific. An SSL framework derived from biological first principles of embodied learning could unify the various SSL methods, help elucidate learning in the brain, and possibly improve ML. SSL co

Willem. T. Pye, David. A. Sinclair

This paper presents the novel combination of a visual transformer style patch classifier with saccaded local attention. A novel optimisation paradigm for training object models is also presented, rather than the optimisation function minimising class membership probability error the network is trained to estimate the normalised distance to the centroid of la

Maxence Lefèvre

Few spacecraft have studied the dynamics of Venus' deep atmosphere, which is needed to understand the interactions between the surface and atmosphere. Recent global simulations suggest a strong effect of the diurnal cycle of surface winds on the depth of the planetary boundary layer. We propose to use a turbulent-resolving model to characterize the Venus bou

Yu-Chia Lin, Huaiyu Duan

We investigate the collision-induced flavor instability in homogeneous, isotropic, dense neutrino gases in the two-flavor mixing scenario with energy-dependent scattering. We uncover a simple expression of the growth rate of this instability in terms of the flavor-decohering collision rates and the electron lepton number distribution of the neutrino. This gr

Hongtu Zhu, Tengfei Li, Bingxin Zhao

The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We deli

Kevin Kleinbeck, Hannes Busche, Nina Stiesdal, Sebastian Hofferberth

Creating non-classical states of light from simple quantum systems together with classical resources is a challenging problem. We show how chiral emitters under a coherent drive can generate non-classical photon states. For our analysis, we select a specific temporal mode in the transmitted light field, resulting in a coupled master equation for the relevant

M. Turner, S. S. Bulanov, C. Benedetti, A. J. Gonsalves

The Petawatt (PW) laser facility of the Berkeley Lab Laser Accelerator (BELLA) Center has recently commissioned its second laser pulse transport line. This new beamline can be operated in parallel with the first beamline and enables strong-field quantum electrodynamics (SF-QED) experiments at BELLA. In this paper, we present an overview of the upgraded BELLA

Meri Harutyunyan, Frederic Holweck, Dominique Sugny, Stephane Guerin

The ability of pulse-shaping devices to generate accurately quantum optimal control is a strong limitation to the development of quantum technologies. We propose and demonstrate a systematic procedure to design robust digital control processes adapted to such experimental constraints. We show to what extent this digital pulse can be obtained from its continu

Miguel Garcia-Ortegon, Andreas Bender, Sergio Bacallado

Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs). They are adept at modelling data consisting of few observations of many related functions on the same input space and are trained by minimizing a variational objective, which is computationally much less expensive than the Bayesian updating requir

Mara Strungaru, Mathias Augustin, Elton J. G. Santos

Ultrafast laser excitations provide an efficient and low-power consumption alternative since different magnetic properties and topological spin states can be triggered and manipulated at the femtosecond (fs) regime. However, it is largely unknown whether laser excitations already used in data information platforms can manipulate the magnetic properties of re

Sayan Chakraborty, Siegfried Echterhoff, Julian Kranz, Shintaro Nishikawa

For a large class of C*-algebras $A$, we calculate the $K$-theory of reduced crossed products $A^{\otimes G}\rtimes_rG$ of Bernoulli shifts by groups satisfying the Baum--Connes conjecture. In particular, we give explicit formulas for finite-dimensional C*-algebras, UHF-algebras, rotation algebras, and several other examples. As an application, we obtain a f

Pei-Yuan Zhao, Ke Ding, Shuo Yang

The electron-phonon ($e$-ph) coupling system often has a large number of phonon degrees of freedom, whose spectral functions are numerically difficult to compute using matrix product state (MPS) formalisms. To solve this problem, we propose a new and practical method that combines the Chebyshev MPS and the pseudosite density matrix renormalization group (DMR

Ariel Barton, Michael Duffy

We establish the Caccioppoli inequality, a reverse H\"older inequality in the spirit of the classic estimate of Meyers, and construct the fundamental solution for linear elliptic differential equations of order $2m$ with certain lower order terms.

Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong

In this paper, we leverage the rapid advances in imitation learning, a topic of intense recent focus in the Reinforcement Learning (RL) literature, to develop new sample complexity results and performance guarantees for data-driven Model Predictive Control (MPC) for constrained linear systems. In its simplest form, imitation learning is an approach that trie

Peng-Xiong Ma, Zhi-Hui Xu, Qiang Yuan, Xiao-Jun Bi

Precise measurements of the boron-to-carbon and boron-to-oxygen ratios by DAMPE show clear hardenings around $100$ GeV/n, which provide important implications on the production, propagation, and interaction of Galactic cosmic rays. In this work we investigate a number of models proposed in literature in light of the DAMPE findings. These models can roughly b

Fabio Sanchez, Jorge Arroyo-Esquivel, Juan Gabriel Calvo

We constructed a Susceptible-Addicted-Reformed model and explored the dynamics of nonlinear relapse in the Reformed population. The transition from susceptible considered {\it at-risk} is modeled using a strictly decreasing general function, mimicking an influential factor that reduces the flow into the addicted class. The {\it basic reproductive number} is

Aline Sindel, Andreas Maier, Vincent Christlein

Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmar

Roman Golovko

In this short note we discuss certain examples of Legendrian submanifolds, whose linearized Legendrian contact (co)homology groups over integers have non-vanishing algebraic torsion. More precisely, for a given arbitrary finitely generated abelian group $G$ and a positive integer $n\geq 3$, $n\neq 4$, we construct examples of Legendrian submanifolds of the s

Hanqing Wu, Shan Jiang, Jiannong Cao

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumu

Jonathan Franceschi, Andrea Medaglia, Mattia Zanella

It is recognized that social heterogeneities in terms of the contact distribution have a strong influence on the spread of infectious diseases. Nevertheless, few data are available on the group composition of social contacts, and their statistical description does not possess universal patterns and may vary spatially and temporally. It is therefore essential

Shambhavi Dikshit, Shradha Mishra

We model the active polar fluid as a collection of orientable objects supplied with active stresses and momentum damping coming from the viscosity of bulk fluid medium. The growth kinetics of local orientation field is studied. The effect of active fluid is contractile or extensile depending upon the sign of the active stress. We explore the growth kinetics

Shijian Jiang, Guwen Han, Danhang Tang, Yang Zhou

Non-parametric mesh reconstruction has recently shown significant progress in 3D hand and body applications. In these methods, mesh vertices and edges are visible to neural networks, enabling the possibility to establish a direct mapping between 2D image pixels and 3D mesh vertices. In this paper, we seek to establish and exploit this mapping with a simple a

Zhixue Zhao, George Chrysostomou, Kalina Bontcheva, Nikolaos Aletras

Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i.e. synchronous settings). While model performance often deteriorates due to temporal variation (i.e. temporal concept drift), it is currently unknown how explanation faithfulness is i

Marco Bertuletti, Yichao Zhang, Alessandro Vanelli-Coralli, Luca Benini

5G Radio access network disaggregation and softwarization pose challenges in terms of computational performance to the processing units. At the physical layer level, the baseband processing computational effort is typically offloaded to specialized hardware accelerators. However, the trend toward software-defined radio-access networks demands flexible, progr

Andrzej Derdzinski, Ivo Terek

Pseudo-Riemannian manifolds with parallel Weyl tensor that are not conformally flat or locally symmetric, also known as ECS manifolds, have a natural local invariant, the rank, which equals 1 or 2, and is the dimension of a certain distinguished null parallel distribution $\,\mathcal{D}$. All known examples of compact ECS manifolds are of rank one and have d

Khoa D. Doan, Yingjie Lao, Ping Li

In recent years, machine learning models have been shown to be vulnerable to backdoor attacks. Under such attacks, an adversary embeds a stealthy backdoor into the trained model such that the compromised models will behave normally on clean inputs but will misclassify according to the adversary's control on maliciously constructed input with a trigger. While

Joakim Færgeman

In this paper, we introduce the category of quasi-tempered automorphic D-modules, which is a rather natural class of D-modules from the point of view of geometric Langlands. We provide a characterization of this category in terms of singular support, and as a consequence, we obtain certain microlocal categorical K\"unneth formulas.

Amruta Mishra, S. P. Misra

We study the partial decay widths of charmonium (bottomonium) states to ${\rm D\bar D \; (B\bar B)}$ mesons in magnetized (nuclear) matter using a field theoretical model of composite hadrons with quark (and antiquark) constituents. These are computed from the mass modifications of the decaying and produced mesons within a chiral effective model, including t

Niall F. Robertson, Albert Akhriev, Jiri Vala, Sergiy Zhuk

Quantum compilation provides a method to translate quantum algorithms at a high level of abstraction into their implementations as quantum circuits on real hardware. One approach to quantum compiling is to design a parameterised circuit and to use techniques from optimisation to find the parameters that minimise the distance between the parameterised circuit

Benedikt Lienkamp, Maximilian Schiffer

In light of the need for design and analysis of intermodal transportation systems, we propose an algorithmic framework to determine the system optimum of an intermodal transportation system. To this end, we model an intermodal transportation system by combining two core principles of network optimization - layered-graph structures and (partially) time-expand

Tran-Trung Nghiem

Using pluripotential theory on degenerate Sasakian manifolds, we show that a locally bounded conical Calabi-Yau potential on a Fano cone is actually smooth on the regular locus. This work is motivated by a similar result obtained by R. Berman in the case where the cone is toric. Our proof is purely pluripotential and independent of any extra symmetry imposed

Kai Zhen, Martin Radfar, Hieu Duy Nguyen, Grant P. Strimel

For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency. Among existing QAT methods, one major drawback is that the quantization centroids have to be predetermined and fixed. To overcome this limitation, we introduce a regularization-free, "