June 2020 arXiv papers — page 39

Valerio Rizzi, Luigi Bonati, Narjes Ansari, Michele Parrinello

One of the main applications of atomistic computer simulations is the calculation of ligand binding energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape.

Yurii Kolomoitsev, Maria Skopina

Multivariate quasi-projection operators $Q_j(f,φ, \widetildeφ)$, associated with a function $φ$ and a distribution/function $\widetildeφ$, are considered. The function $φ$ is supposed to satisfy the Strang-Fix conditions and a compatibility condition with $\widetildeφ$. Using technique based on the Fourier multipliers, we studied approximation properties of

Giovanni Felder, David Kazhdan, Alexander Polishchuk

We prove that the moduli of stable supercurves with punctures is a smooth proper DM stack and study an analog of the Mumford's isomorphism for its canonical line bundle.

E. Martioli, G. Hebrard, C. Moutou, J. -F. Donati

We present high resolution near-infrared spectropolarimetric observations using the SPIRou instrument at CFHT during a transit of the recently detected young planet AU Mic b, with supporting spectroscopic data from iSHELL at IRTF. We detect Zeeman signatures in the Stokes V profiles, and measure a mean longitudinal magnetic field of $\overline{B}_\ell=46.3\p

Nina Shvetsova, Bart Bakker, Irina Fedulova, Heinrich Schulz

Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex medical images, such as barely visible abnormalities in chest X-rays and metastases in lymph nodes. To address this problem,

Indrajit Banerjee, Atul Kumar, Rupam Bhattacharyya

Since March 25, 2020, India had been under a nation-wide lockdown announced as a response to the spread of SARS-CoV-2 and COVID-19 and has resorted to a process of 'unlocking' the lockdown over the past couple of months. This work attempts to examine the effect of novel coronavirus 2019 (COVID-19) and its resulting disease, the COVID-19, on the forei

Rossella Gaffoglio, Marco Righero, Giorgio Giordanengo, Marcello Zucchi

Microwave hyperthermia aims at selectively heating cancer cells to a supra-physiological temperature. For non-superficial tumors, this can be achieved by means of an antenna array equipped with a proper cooling system (the water bolus) to avoid overheating of the skin. In patient-specific treatment planning, antenna feedings are optimized to maximize the spe

Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau

Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle

Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version, EPIC-KIT

Jose J. Blanco-Pillado, Daniel Jiménez-Aguilar, Jon Urrestilla

Many solitonic configurations in field theory have localized bound states in their spectrum of linear perturbations. This opens up the possibility of having long lived excitations of these solitons that could affect their dynamics. We start the study of these effects in the simplest configuration of a domain wall kink solution in the $λϕ^4$ theory in $1+1$ d

Aidan Boyd, Zhaoyuan Fang, Adam Czajka, Kevin W. Bowyer

As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount. This work presents an overview of the most important advances in the area of iris presentation attack detection published in recent two years. Newly-released, publicly-available datasets for development and ev

CMS Collaboration

A search for standard model Higgs bosons (H) produced with transverse momentum ($p_\mathrm{T}$) greater than 450 GeV and decaying to bottom quark-antiquark pairs ($\mathrm{b\bar{b}}$) is performed using proton-proton collision data collected by the CMS experiment at the LHC at $\sqrt{s} =$ 13 TeV. The data sample corresponds to an integrated luminosity of 13

Gaetana Spedalieri, Lolita Piersimoni, Omar Laurino, Samuel L. Braunstein

The field of quantum sensing aims at improving the detection and estimation of classical parameters that are encoded in physical systems by resorting to quantum sources of light and quantum detection strategies. The same approach can be used to improve the current classical measurements that are performed on biological systems. Here we consider the scenario

Soumangsu Chakraborty, Amit Giveon, David Kutasov

String theory on $AdS_3$ has a solvable single-trace irrelevant deformation that is closely related to $T\bar T$. For one sign of the coupling, it leads to an asymptotically linear dilaton spacetime, and a corresponding Hagedorn spectrum. For the other, the resulting spacetime has a curvature singularity at a finite radial location, and an upper bound on the

Peter Plavchan, Thomas Barclay, Jonathan Gagné, Peter Gao

AU Microscopii (AU Mic) is the second closest pre main sequence star, at a distance of 9.79 parsecs and with an age of 22 million years. AU Mic possesses a relatively rare and spatially resolved3 edge-on debris disk extending from about 35 to 210 astronomical units from the star, and with clumps exhibiting non-Keplerian motion. Detection of newly formed plan

Kyrylo Snizhko, Parveen Kumar, Nihal Rao, Yuval Gefen

Geometrical dephasing is distinct from dynamical dephasing in that it depends on the trajectory traversed, hence it reverses its sign upon flipping the direction in which the path is traced. Here we study sequences of generalized (weak) measurements that steer a system in a closed trajectory. The readout process is marked by fluctuations, giving rise to deph

Teruyuki Hirano, Vigneshwaran Krishnamurthy, Eric Gaidos, Heather Flewelling

We obtained spectra of the pre-main sequence star AU Microscopii during a transit of its Neptune-sized planet to investigate its orbit and atmosphere. We used the high-dispersion near-infrared spectrograph IRD on the Subaru telescope to detect the Doppler "shadow" from the planet and constrain the projected stellar obliquity. Modeling of the observed

Wladimir A. Benalcazar, Jiho Noh, Mohan Wang, Sheng Huang

The discovery of the quantization of particle transport in adiabatic pumping cycles of periodic structures by Thouless [Phys. Rev. B 27, 6083 (1983)] linked the Chern number, a topological invariant characterizing the quantum Hall effect in two-dimensional electron gases, with the topology of dynamical periodic systems in one dimension. Here, we demonstrate

Aljaž Božič, Pablo Palafox, Michael Zollhöfer, Angela Dai

We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization. Given two input RGB-D frames of a non-rigidly moving object, we employ a convolutional neural network to predict dense correspondences and their confidences. These correspondences are used as co

Zhenjiu Wang, Yuhai Liu, Toshihiro Sato, Martin Hohenadler

A unique property of a dynamically generated quantum spin Hall state are Goldstone modes that correspond to the long-wavelength fluctuations of the spin-orbit coupling order parameter whose topological Skyrmion excitations carry charge 2$e$. Within the model considered here, upon varying the chemical potential, we observe two transitions: An s-wave supercond

Yuguang Chen, Charles C. Steidel, Cameron B. Hummels, Gwen C. Rudie

We present new measurements of the spatial distribution and kinematics of neutral hydrogen in the circumgalactic and intergalactic medium surrounding star-forming galaxies at z ~ 2. Using the spectra of ~ 3000 galaxies with redshifts <z> +/- 0.4 from the Keck Baryonic Structure Survey (KBSS), we assemble a sample of more than 200,000 distinct foreground-back

A. Fluetsch, R. Maiolino, S. Carniani, S. Arribas

Galactic outflows are known to consist of several gas phases, however, so far the connection between these multiple phases has been investigated little and only in a few objects. In this paper, we analyse MUSE/VLT data of 26 local (U)LIRGs and study their ionised and neutral atomic phases. We also include objects from the literature to obtain a total sample

Matthew Ho, Arya Farahi, Markus Michael Rau, Hy Trac

We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and demonstrate how variational inference techniques can be used to perform computationally tractable posterior estimation for a var

Aaron Z. Goldberg, Ilaria Gianani, Marco Barbieri, Fabio Sciarrino

Multiphase estimation is a paradigmatic example of a multiparameter problem. When measuring multiple phases embedded in interferometric networks, specially-tailored input quantum states achieve enhanced sensitivities compared with both single-parameter and classical estimation schemes. Significant attention has been devoted to defining the optimal strategies

Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu

We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters a

Kedron Silsbee, Alexei Ivlev, Paola Caselli, Olli Sipila

We argue that impact velocities between dust grains with sizes less than $\sim 0.1$ $μm$ in molecular cloud cores are dominated by drift arising from ambipolar diffusion. This effect is due to the size dependence of the dust coupling to the magnetic field and the neutral gas. Assuming perfect sticking in collisions up to $\approx 50$ m/s, we show that this e

Gordan Krnjaic

Light new vector bosons can be produced gravitationally through quantum fluctuations during inflation; if these particles are feebly coupled and cosmologically metastable, they can account for the observed dark matter abundance. However, in minimal anomaly free $U(1)$ extensions to the Standard Model, these vectors generically decay to neutrinos if at least

J. Goldstein, R. H. D. Townsend

The contour method is a new approach to calculating the non-adiabatic pulsation frequencies of stars. These frequencies can be found by solving for the complex roots of a characteristic equation constructed from the linear non-adiabatic stellar pulsation equations. A complex-root solver requires an initial trial frequency for each non adiabatic root. A stand

Andrey Kardashin, Alexey Uvarov, Dmitry Yudin, Jacob Biamonte

Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quantum-mechanical states of a given Hamiltonian using available numerical methods, in particular those based on variational Monte Carlo simulations, become exponentially more challeng

Jonathan J. Davies, Robert A. Crain, Andrew Pontzen

We examine the influence of dark matter halo assembly on the evolution of a simulated $\sim L^\star$ galaxy. Starting from a zoom-in simulation of a star-forming galaxy evolved with the EAGLE galaxy formation model, we use the genetic modification technique to create a pair of complementary assembly histories: one in which the halo assembles later than in th

Andreea Bobu, Marius Wiggert, Claire Tomlin, Anca D. Dragan

When a person is not satisfied with how a robot performs a task, they can intervene to correct it. Reward learning methods enable the robot to adapt its reward function online based on such human input, but they rely on handcrafted features. When the correction cannot be explained by these features, recent work in deep Inverse Reinforcement Learning (IRL) su

Christopher J. Turner, Jean-Yves Desaules, Kieran Bull, Zlatko Papić

The theory of quantum scarring -- a remarkable violation of quantum unique ergodicity -- rests on two complementary pillars: the existence of unstable classical periodic orbits and the so-called quasimodes, i.e., the non-ergodic states that strongly overlap with a small number of the system&#39;s eigenstates. Recently, interest in quantum scars has been revi

Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn

The ability to predict and plan into the future is fundamental for agents acting in the world. To reach a faraway goal, we predict trajectories at multiple timescales, first devising a coarse plan towards the goal and then gradually filling in details. In contrast, current learning approaches for visual prediction and planning fail on long-horizon tasks as t

Oleh Rybkin, Kostas Daniilidis, Sergey Levine

Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the uncertainty of the decoding distribution

Erik Burman, Mihai Nechita, Lauri Oksanen

We consider the numerical approximation of the ill-posed data assimilation problem for stationary convection-diffusion equations and extend our previous analysis in [Numer. Math. 144, 451--477, 2020] to the convection-dominated regime. Slightly adjusting the stabilized finite element method proposed for dominant diffusion, we draw upon a local error analysis

Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan

Generalization beyond a training dataset is a main goal of machine learning, but theoretical understanding of generalization remains an open problem for many models. The need for a new theory is exacerbated by recent observations in deep neural networks where overparameterization leads to better performance, contradicting the conventional wisdom from classic

L. T. Giorgini, W. Moon, J. S. Wettlaufer

We use asymptotic methods from the theory of differential equations to obtain an analytical expression for the survival probability of an Ornstein-Uhlenbeck process with a potential defined over a broad domain. We form a uniformly continuous analytical solution covering the entire domain by asymptotically matching approximate solutions in an interior region,

Shaojie Wang, Tong Wu, Ayan Chakrabarti, Yevgeniy Vorobeychik

We experimentally study the robustness of deep camera-LiDAR fusion architectures for 2D object detection in autonomous driving. First, we find that the fusion model is usually both more accurate, and more robust against single-source attacks than single-sensor deep neural networks. Furthermore, we show that without adversarial training, early fusion is more

Connor Anderson, Matt Gwilliam, Adam Teuscher, Andrew Merrill

In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance of recent SOTA methods, quantitatively, but more importantly, qualitatively. We show that these models universally struggle with certain &#34;hard&#34; images, while also making co

Aaron Sonabend-W, Junwei Lu, Leo A. Celi, Tianxi Cai

Offline Reinforcement Learning (RL) is a promising approach for learning optimal policies in environments where direct exploration is expensive or unfeasible. However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and

Thomas W. Mitchel, Benedict Brown, David Koller, Tim Weyrich

Fast methods for convolution and correlation underlie a variety of applications in computer vision and graphics, including efficient filtering, analysis, and simulation. However, standard convolution and correlation are inherently limited to fixed filters: spatial adaptation is impossible without sacrificing efficient computation. In early work, Freeman and

Juliana Gonzalez-Astudillo, Tiziana Cattai, Giulia Bassignana, Marie-Constance Corsi

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends o

F. Castillo, A. Reisenegger, J. A. Valdivia

In a previous paper, we reported simulations of the evolution of the magnetic field in neutron star cores through ambipolar diffusion, taking the neutrons as a motionless uniform background. However, in real neutron stars, neutrons are free to move, and a strong composition gradient leads to stable stratification (stability against convective motions) both o

Hyun Min Lee

Motivated by the recent excess in the electron recoil from XENON1T experiment, we consider the possibility of exothermic dark matter, which is composed of two states with mass splitting. The heavier state down-scatters off the electron into the lighter state, making an appropriate recoil energy required for the Xenon excess even for the standard Maxwellian v

The MAJORANA Collaboration, I. J. Arnquist, F. T. Avignone, A. S. Barabash

P-type point contact (PPC) HPGe detectors are a leading technology for rare event searches due to their excellent energy resolution, low thresholds, and multi-site event rejection capabilities. We have characterized a PPC detector&#39;s response to $α$ particles incident on the sensitive passivated and p+ surfaces, a previously poorly-understood source of ba

Maya Fishbach, Reed Essick, Daniel E. Holz

Gravitational-wave detectors have opened a new window through which we can observe black holes (BHs) and neutron stars (NSs). Analyzing the 11 detections from LIGO/Virgo&#39;s first gravitational-wave catalog, GWTC-1, we investigate whether the power-law fit to the BH mass spectrum can also accommodate the binary neutron star (BNS) event GW170817, or whether

Johannes Weis, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann

The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is carried out in the synapse

Marcin Bury, Andreas van Hameren, Piotr Kotko, Krzysztof Kutak

We calculate various azimuthal angle distributions for three jets produced in the forward rapidity region with transverse momenta $p_T>20\,\mathrm{GeV}$ in proton-proton (p-p) and proton-lead (p-Pb) collisions at center of mass energy $5.02\,\,\mathrm{TeV}$. We use the multi-parton extension of the so-called small-$x$ Improved Transverse Momentum Dependent f

Charles E. Thornton, Mark A. Kozy, R. Michael Buehrer, Anthony F. Martone

In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance. The radar learns to vary the bandwidth and center frequency of its linear frequency

Santi Cassisi, Maurizio Salaris

The existence of star-to-star light-element abundance variations in massive Galactic and extragalactic star clusters has fairly recently superseded the traditional paradigm of individual clusters hosting stars with the same age, and uniform chemical composition. Several scenarios have been put forward to explain the origin of this multiple stellar population

Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets

We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair,

Luigi Cigarini, Michal Novotný, František Karlický

Stacking mismatches in hexagonal boron nitride (h-BN) nanostructures affect their photonic, mechanical, and thermal properties. To access information about the stacked configuration of layered ensembles, highly sophisticated techniques like X-ray photoemission spectroscopy or electron microscopy are necessary. Here, instead, by taking advantage of the geomet

Amir Dembo, Reza Gheissari

Consider $(X_{i}(t))$ solving a system of $N$ stochastic differential equations interacting through a random matrix $\mathbf J = (J_{ij})$ with independent (not necessarily identically distributed) random coefficients. We show that the trajectories of averaged observables of $(X_i(t))$, initialized from some $μ$ independent of $\mathbf J$, are universal, i.e

Ronaldo Garcia, Dan Reznik, Hellmuth Stachel, Mark Helman

The Negative Pedal Curve (NPC) of the Ellipse with respect to a boundary point M is a 3-cusp closed-curve which is the affine image of the Steiner Deltoid. Over all M the family has invariant area and displays an array of interesting properties.

Dongruo Zhou, Jiafan He, Quanquan Gu

Modern tasks in reinforcement learning have large state and action spaces. To deal with them efficiently, one often uses predefined feature mapping to represent states and actions in a low-dimensional space. In this paper, we study reinforcement learning for discounted Markov Decision Processes (MDPs), where the transition kernel can be parameterized as a li

Yongxin Wang, Kris Kitani, Xinshuo Weng

Object detection and data association are critical components in multi-object tracking (MOT) systems. Despite the fact that the two components are dependent on each other, prior works often design detection and data association modules separately which are trained with separate objectives. As a result, one cannot back-propagate the gradients and optimize the

Mauricio Neira, Catalina Gómez, John F. Suárez-Pérez, Diego A. Gómez

We introduce MANTRA, an annotated dataset of 4869 transient and 71207 non-transient object lightcurves built from the Catalina Real Time Transient Survey. We provide public access to this dataset as a plain text file to facilitate standardized quantitative comparison of astronomical transient event recognition algorithms. Some of the classes included in the

R. Primulando, J. Julio, P. Uttayarat

In light of the excess in the low-energy electron recoil events reported by XENON1T, many new physics scenarios have been proposed as a possible origin of the excess. One possible explanation is that the excess is a result of a fast moving dark matter (DM), with velocity $v\sim0.05-0.20$ and mass between 1 MeV and 10 GeV, scattering off an electron. Assuming

Kazunori Nakayama, Yong Tang

Recently, the XENON1T experiment has reported an excess in the electronic recoil events. The excess is consistent with the interpretation of absorption of 3keV bosonic dark matter, for example, hidden photon dark matter with kinetic mixing of the order of $10^{-15}$. We point out that the minimally gravitational production provides a viable mechanism for obt

George Stepaniants, Bingni W. Brunton, J. Nathan Kutz

Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given data. We explore in depth a strategy to disambiguate between possible underlying causal networks by perturbing the network, where the actuations are either targeted or applied at rand

Francois Petit, Pierre Schapira

Given a topological space $X$, a thickening kernel is a monoidal presheaf on $(\mathbb{R}_{\geq0},+)$ with values in the monoidal category of derived kernels on $X$. A bi-thickening kernel is defined on $(\mathbb{R},+)$. To such a thickening kernel, one naturally associates an interleaving distance on the derived category of sheaves on $X$. We prove that a t

Saviz Mowlavi, Ken Kamrin

We introduce a contact law for the normal force generated between two contacting, elastically anisotropic bodies of arbitrary geometry. The only requirement is that their surfaces be smooth and frictionless. This anisotropic contact law is obtained from a simplification of the exact solution to the continuum elasticity problem and takes the familiar form of

Kyungkeun Kang, Hideyuki Miura, Tai-Peng Tsai

We prove an $ε$-regularity criterion for the 3D Navier-Stokes equations in terms of initial data. It shows that if a scaled local $L^2$ norm of initial data is sufficiently small around the origin, a suitable weak solution is regular in a set enclosed by a paraboloid started from the origin. The result is applied to the estimate of the regular set for local

Elias Kassapis, Georgi Dikov, Deepak K. Gupta, Cedric Nugteren

In semantic segmentation tasks, input images can often have more than one plausible interpretation, thus allowing for multiple valid labels. To capture such ambiguities, recent work has explored the use of probabilistic networks that can learn a distribution over predictions. However, these do not necessarily represent the empirical distribution accurately.

Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried

We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine learning framework using its extension interface. In particular, we provide accelerator support for vector-matrix multipli

Bo Chen, Qiyu Zeng, Han Wang, Shen Zhang

A detailed understanding of the material response to rapid compression is challenging and demanding. For instance, the element gold under dynamic compression exhibits complex phase transformations where there exist some large discrepancies between experimental and theoretical studies. Here, we combined large-scale molecular dynamics simulations with a deep p

Sebastian Pölsterl, Christian Wachinger

Studying the relationship between neuroanatomy and cognitive decline due to Alzheimer&#39;s has been a major research focus in the last decade. However, to infer cause-effect relationships rather than simple associations from observational data, we need to (i) express the causal relationships leading to cognitive decline in a graphical model, and (ii) ensure

Zijia Zhong, Mark Nejad, Earl E. Lee

Intersection is a major source of traffic delays and accidents within modern transportation systems. Compared to signalized intersection management, autonomous intersection management (AIM) coordinates the intersection crossing at an individual vehicle level with additional flexibility. AIM can potentially eliminate stopping in intersection crossing due to t

Vartika Agrahari, Sridhar Chimalakonda

With the ever-increasing use of games, game developers are expected to write efficient code supporting several qualities such as security, maintainability, and performance. However, the continuous need to update the features of games in less duration might compel the developers to use anti-patterns, code smells and quick-fix solutions that may affect the fun

Patrick Concha, Lucrezia Ravera, Evelyn Rodríguez, Gustavo Rubio

In the present work we find novel Newtonian gravity models in three spacetime dimensions. We first present a Maxwellian version of the extended Newtonian gravity, which is obtained as the non-relativistic limit of a particular $U(1)$-enlargement of an enhanced Maxwell Chern-Simons gravity. We show that the extended Newtonian gravity appears as a particular s

Andrew D Burbanks, Andrew H Osbaldestin, Judi A Thurlby

We gain tight rigorous bounds on the renormalisation fixed point for period doubling in families of unimodal maps with degree $4$ critical point. We use a contraction mapping argument to bound essential eigenfunctions and eigenvalues for the linearisation of the operator and for the operator controlling the scaling of added noise. Multi-precision arithmetic

Vivek F. Farias, Andrew A. Li, Tianyi Peng

Inaccurate records of inventory occur frequently, and by some measures cost retailers approximately 4% in annual sales. Detecting inventory inaccuracies manually is cost-prohibitive, and existing algorithmic solutions rely almost exclusively on learning from longitudinal data, which is insufficient in the dynamic environment induced by modern retail operatio

Tianyi Li, Mai Xu, Runzhi Tang, Ying Chen

Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its ancestor standard High Efficiency Video Coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the

Gaoping Long, Norbert Bodendorfer

A comprehensive study of the application of SO$(D+1)$ coherent states of Perelomov type to loop quantum gravity in general spacetime dimensions $D+1\geq 3$ is given in this paper. We focus on so-called simple representations of SO$(D+1)$ which solve the simplicity constraint and the associated homogeneous harmonic function spaces. With the harmonic function

M. Horio, K. P. Kramer, Q. Wang, A. Zaidan

We present a combined soft x-ray and high-resolution vacuum-ultraviolet angle-resolved photoemission spectroscopy study of the electron-overdoped cuprate Pr$_{1.3-x}$La$_{0.7}$Ce$_{x}$CuO$_4$ (PLCCO). Demonstration of its highly two-dimensional band structure enabled precise determination of the in-plane self-energy dominated by electron-electron scattering.

Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns. However, in practice factors like privacy and regulation often preclude the collection of protected features, or their use for training or inference, severely limit

Emanuel Billeter, Andrea Sterzi, Olga Sambalova, René Wick-Joliat

The measurement of hydrogen induced changes on the electronic structure of transition metal oxides by X-ray photoelectron spectroscopy is a challenging endeavor, since the origin of the photoelectron cannot be unambiguously assigned to hydrogen. The H-induced electronic structure changes in tungsten trioxide have been known for more than 100 years, but are s

Vitor M. de Oliveira, Priscilla A. Sousa-Silva, Iberê L. Caldas

In this work, we investigate the Earth-Moon system, as modeled by the planar circular restricted three-body problem, and relate its dynamical properties to the underlying structure associated with specific invariant manifolds. We consider a range of Jacobi constant values for which the neck around the Lagrangian point $L_1$ is always open but the orbits are

Blaž Bortolato, Jernej F. Kamenik, Nejc Košnik, Aleks Smolkovič

We use machine learning (ML) and non-ML techniques to study optimized $CP$-odd observables, directly and maximally sensitive to the $CP$-odd $i \tilde κ\bar t γ^5 t h$ interaction at the LHC and prospective future hadron colliders using the final state with a Higgs boson and a top quark pair, $pp\to t\bar t h$, followed by semileptonic $t$ decays. We perform

Daniel R. Kowal

Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often diffic

Andrew Svesko

Here I develop the connection between thermodynamics, entanglement, and gravity. I begin by showing that the classical null energy condition (NEC) can arise as a consequence of the second law of thermodynamics applied to local holographic screens. This is accomplished by essentially reversing the steps of Hawking&#39;s area theorem, leading to the Ricci conv

Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller

Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discrete-continuous optimization problem, where the segmentation is the discrete part and all other model parameters are continuous. A number of estimation algorithms have been developed that are highly sp

Siyi Hu, Xiaojun Chang

In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag. To solve this problem, most existing methods adopt metric loss with an weighted classification block to force the generation of comm

Nikolai A. Stepanov, Mikhail A. Skvortsov

We study stochastic dynamics of an inverted pendulum subject to a random force in the horizontal direction (Whitney&#39;s problem). Considered on the entire time axis, the problem admits a unique solution that always remains in the upper half plane. We formulate the problem of statistical description of this never-falling trajectory and solve it by a field-t

Felipe Taha Sant&#39;Ana

Bosonic atoms confined in optical lattices are described by the Bose-Hubbard model and can exist in two different phases, Mott insulator or superfluid, depending on the strength of the system parameters. In the vicinity of the phase boundary, there are degeneracies that occur between every two adjacent Mott lobes. Because of this, nondegenerate perturbation

Alexander Giessing, Jianqing Fan

This paper considers a new bootstrap procedure to estimate the distribution of high-dimensional $\ell_p$-statistics, i.e. the $\ell_p$-norms of the sum of $n$ independent $d$-dimensional random vectors with $d \gg n$ and $p \in [1, \infty]$. We provide a non-asymptotic characterization of the sampling distribution of $\ell_p$-statistics based on Gaussian app

Eugenia Rosu

We prove a new formula for the central value of the $L$-function $L(E_{D, α}, 1)$ corresponding to the family of sextic twists over $\mathbb{Q}[\sqrt{-3}]$ of elliptic curves $E_{D, α}: y^2=x^3+16D^2α^3$ for $D$ an integer and $α\in \mathbb{Q}[\sqrt{-3}]$. The formula generalizes the result of cubic twists over $\mathbb{Q}$ of Rodriguez-Villegas and Zagier f

Guillaume Godefroy, Bastien Arnal, Emmanuel Bossy

Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental approach to build the training and the test sets using photog

Liliane Maia, Gabrielle Nornberg, Filomena Pacella

In this paper we study existence, nonexistence and classification of radial positive solutions of some weighted fully nonlinear equations involving Pucci extremal operators. Our results are entirely based on the analysis of the dynamics induced by an autonomous quadratic system which is obtained after a suitable transformation. This method allows to treat bo

Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer

Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods often fail to preserve classification accuracy. When classes have small prior p

Jiho Lee

We define joint exchangeability on arrays indexed by a vector of natural numbers with coordinates being the vertices of directed acyclic graphs (DAGs) using local isomorphisms. The notion provides a new version of exchangeability, which is a joint version of hierarchical exchangeability defined in Jung, L., Staton, Yang (2020). We also prove the existence of

Jordan Holland, Ross Teixeira, Paul Schmitt, Kevin Borgolte

In this paper, we develop a method to create a large, labeled dataset of visible network device vendors across the Internet by mapping network-visible IP addresses to device vendors. We use Internet-wide scanning, banner grabs of network-visible devices across the IPv4 address space, and clustering techniques to assign labels to more than 160,000 devices. We

Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso

Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems. We consider a general sum partially observable Markov game where agents of different types share a single policy network, conditioned on agent-specific information. This paper aims at i) formally understanding equilibria reach

Gang Yang, Jialun Ping, Lianyi He, Qing Wang

The fully-heavy tetraquark states $QQ\bar{Q}\bar{Q}$ ($Q=c, b$) are systematically investigated by means of a nonrelativistic potential model. The model is based on the lattice QCD study of the two-body $Q\bar{Q}$ interaction, which exhibits a spin-independent Cornell potential along with a spin-spin interaction. The four-body problem is implemented with a h

Axel Brandenburg

The ohmic decay of magnetic fields in the crusts of neutron stars is generally believed to be governed by Hall drift which leads to what is known as a Hall cascade. Here we show that helical and fractionally helical magnetic fields undergo strong inverse cascading like in magnetohydrodynamics (MHD), but the magnetic energy decays more slowly with time $t$: $

Giovanni Canepa, Alberto S. Cattaneo, Michele Schiavina

In this note the AKSZ construction is applied to the BFV description of the reduced phase space of the Einstein-Hilbert and of the Palatini--Cartan theories in every space-time dimension greater than two. In the former case one obtains a BV theory for the first-order formulation of Einstein--Hilbert theory, in the latter a BV theory for Palatini--Cartan theo

ATLAS Collaboration

The $t\bar{t}$ production cross-section is measured in the lepton+jets channel using proton$-$proton collision data at a centre-of-mass energy of $\sqrt{s}=13$ TeV collected with the ATLAS detector at the LHC. The dataset corresponds to an integrated luminosity of 139 fb$^{-1}$. Events with exactly one charged lepton and four or more jets in the final state,

Thorsten Holm, Andrzej Skowroński, Adam Skowyrski

The finite-dimensional symmetric algebras over an algebraically closed field, based on surface triangulations, motivated by the theory of cluster algebras, have been extensively investigated and applied. In particular, the weighted surface algebras and their deformations were introduced and studied in [16]-[20], and it was shown that all these algebras, exce

Ronen Eldan, Dana Moshkovitz

We reduce the problem of proving a &#34;Boolean Unique Games Conjecture&#34; (with gap 1-delta vs. 1-C*delta, for any C> 1, and sufficiently small delta>0) to the problem of proving a PCP Theorem for a certain non-unique game. In a previous work, Khot and Moshkovitz suggested an inefficient candidate reduction (i.e., without a proof of soundness). The curren

Marcel Braukhoff, Ilaria Perugia, Paul Stocker

Cross-diffusion systems are systems of nonlinear parabolic partial differential equations that are used to describe dynamical processes in several application, including chemical concentrations and cell biology. We present a space-time approach to the proof of existence of bounded weak solutions of cross-diffusion systems, making use of the system entropy to