November 2022 arXiv papers — page 58

Steven Wilkins-Reeves, Tyler McCormick

Network data, commonly used throughout the physical, social, and biological sciences, consist of nodes (individuals) and the edges (interactions) between them. One way to represent network data's complex, high-dimensional structure is to embed the graph into a low-dimensional geometric space. The curvature of this space, in particular, provides insights abou

Mehrdad Mirbabayi, Flavio Riccardi

In a QFT on de Sitter background, one can study correlators between fields pushed to the future and past horizons of a comoving observer. This is a neat probe of the physics in the observer's causal diamond (known as the static patch). We use this observable to give a generalization of the quasinormal spectrum in interacting theories, and to connect it to th

David W. Farmer

We examine published arguments which suggest that the Riemann Hypothesis may not be true. In each case we provide evidence to explain why the claimed argument does not provide a good reason to doubt the Riemann Hypothesis. The evidence we cite involves a mixture of theorems in analytic number theory, theorems in random matrix theory, and illustrative example

Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou, Horst Stoecker

Bayesian methods are used to constrain the density dependence of the QCD Equation of State (EoS) for dense nuclear matter using the data of mean transverse kinetic energy and elliptic flow of protons from heavy ion collisions (HIC), in the beam energy range $\sqrt{s_{\mathrm{NN}}}=2-10 GeV$. The analysis yields tight constraints on the density dependent EoS

Emma Lepri

This paper contains an elementary proof of the existence of the classical model structure on the category of unbounded DG-Lie algebras over a field of characteristic zero, with an emphasis on the properties of free and semifree extensions, which are particularly nice cofibrations. The cobar construction of a locally conilpotent cocommutative coalgebra is sho

Ron Ziv, Anatoly Patsyk, Yaakov Lumer, Yoav Sagi

We propose and demonstrate numerically a measurement scheme for complete reconstruction of the 2D quantum wave function of a Bose-Einstein condensate, amplitude and phase, from a time-of-flight measurement. We identify a fundamental ambiguity present in the measurement of phase structures of high-symmetry excitations (e.g., vortices) and show how to overcome

Meghendra Singh, Darshan Singh, Kanhaiya Lal Pandey, Dinkar Verma

Gamma Ray Bursts (GRB) are among the brightest objects in the Universe and hence can be observed up to a very high redshift. Properly calibrated empirical correlations between intensity and spectral correlations of GRBs can be used to estimate the cosmological parameters. However, the possibility of the evolution of GRBs with the redshift is a long-standing

Eimear Byrne, Andrew Fulcher

We describe a construction of the Tutte polynomial for both matroids and $q$-matroids based on an appropriate partition of the underlying support lattice into intervals that correspond to prime-free minors, which we call a Tutte partition. We show that such partitions in the matroid case include the class of partitions arising in Crapo's definition of the Tu

Lyndon R. Duong, Jingyang Zhou, Josue Nassar, Jules Berman

Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these

Claudio Bonanno

We consider the problem of showing that 1 is an eigenvalue for a family of generalised transfer operators of the Farey map. This problem is related to the spectral theory of the modular surface via the Selberg Zeta function and the theory of dynamical zeta functions of maps. After briefly recalling these connections, we show that the problem can be formulate

Salah Eddine Ennadifi

Motivated by the growing interest in the existence of new massive gauge bosons, we suggest that massive dark photons A0 can be a consequence of a broken new abelian symmetry U(1)'X. Such a dark symmetry U(1)'D is afterwards supposed to be associated with the conservation of the weak number W belonging to the Weakling Interacting Slim Particles U(1)'W, being

Yaochen Zhu, Zhenzhong Chen

Recently, user-oriented auto-encoders (UAEs) have been widely used in recommender systems to learn semantic representations of users based on their historical ratings. However, since latent item variables are not modeled in UAE, it is difficult to utilize the widely available item content information when ratings are sparse. In addition, whenever new items a

Ioan Manolescu, Leonardo V. Santoro

We answer the following question: if the occupied (or vacant) set of a planar Poisson Boolean percolation model does contain a crossing of an $n\times n$ square, how wide is this crossing? The answer depends on the whether we consider the critical, sub- or super-critical regime, and is different for the occupied and vacant sets.

K. A. Brown, M. T. Yakimov

The two main approaches to the study of irreducible representations of orders (via traces and Poisson orders) have so far been applied in a completely independent fashion. We define and study a natural compatibility relation between the two approaches leading to the notion of Poisson trace orders. It is proved that all regular and reduced traces are always c

Anshuman Mohan, Yunhe Liu, Nate Foster, Tobias Kappé

Early programming models for software-defined networking (SDN) focused on basic features for controlling network-wide forwarding paths, but more recent work has considered richer features, such as packet scheduling and queueing, that affect performance. In particular, PIFO trees, proposed by Sivaraman et al., offer a flexible and efficient primitive for prog

Zuo Ye, Ohad Elishco

We first give a construction of binary $t_1$-deletion-$t_2$-insertion-burst correcting codes with redundancy at most $\log(n)+(t_1-t_2-1)\log\log(n)+O(1)$, where $t_1\ge 2t_2$. Then we give an improved construction of binary codes capable of correcting a burst of $4$ non-consecutive deletions, whose redundancy is reduced from $7\log(n)+2\log\log(n)+O(1)$ to

Tejinder Kumar, Chaman Kumar

The coefficients of the stochastic differential equations with Markovian switching (SDEwMS) additionally depend on a Markov chain and there is no notion of differentiating such functions with respect to the Markov chain. In particular, this implies that the It\^o-Taylor expansion for SDEwMS is not a straightforward extension of the It\^o-Taylor expansion for

Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal

Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. While several FU methods have been proposed, we currently lac

Massimiliano Guarneri, Ilaria Gianani, Marco Barbieri, Andrea Chiuri

Characterization of quantum objects, being them states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real-life components. To this end, Machine Learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating spec

Dayana Alejandra Barrera Buitrago, Nidia Danigza Lugo López, Freddy Alexander Torres Payoma, Diana Carolina Herrera Muñoz

This research is about to analyze the effects of the implementation of a virtual learning space in the homogeneous second-order differential equations applied to the modeling of the phenomenon of simple pendulum physics in the framework of the alliance with the international project World Pendulum Alliance WPA. The collection of information was carried out w

Gabriel Behrendt, Alexander Soderlund, Matthew Hale, Sean Phillips

This paper presents a time-constrained model predictive control strategy for the 6 degree-of-freedom (6DOF) autonomous rendezvous and docking problem between a controllable "deputy" spacecraft and an uncontrollable "chief" spacecraft. The control strategy accounts for computational time constraints due to limited onboard processing speed. The translational d

LHCb Collaboration

The first measurement of $J/\psi$ and $D^0$ production in PbNe collisions by the LHCb experiment in its fixed-target configuration is reported. The production of $J/\psi$ and $D^0$ mesons is studied with a beam of lead ions with an energy of 2.5 TeV per nucleon colliding on gaseous neon targets at rest, corresponding to a nucleon-nucleon centre-of-mass energ

Marouane Assal, Setsuro Fujiié, Kenta Higuchi

This paper is concerned with the asymptotics of resonances in the semiclassical limit $h\to 0^+$ for $2\times 2$ matrix Schr\"odinger operators in one dimension. We study the case where the two underlying classical Hamiltonian trajectories cross tangentially in the phase space. In the setting that one of the classical trajectories is a simple closed curve wh

Zihan Ye, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

Deep neural learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. One ability that seems crucial to this kind of general intelligence is meta-reasoning, i.e., our ability to reason about reasoning. To

Shiv Shankar, Vihari Piratla

Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of standard losses. When the objective aligns well with the evaluation metric, this is not a major issue. However when dealing with complex structured outputs, the ideal objective can be

José L. Cereceda

By applying the Newton-Gregory expansion to the polynomial associated with the sum of powers of integers $S_k(n) = 1^k + 2^k + \cdots + n^k$, we derive a couple of infinite families of explicit formulas for $S_k(n)$. One of the families involves the $r$-Stirling numbers of the second kind $\genfrac{\{}{\}}{0pt}{}{k}{j}_r$, $j=0,1,\ldots,k$, while the other i

Sandro Costa Magalhães, Filipe Neves Santos, Pedro Machado, António Paulo Moreira

Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous pl

Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai

This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to regress the

LHCb Collaboration

The measurement of charmonium states produced in proton-neon ($p$Ne) collisions by the LHCb experiment in its fixed-target configuration is presented. The production of $J/\psi$ and $\psi(2S)$ mesons is studied with a beam of 2.5 TeV protons colliding on gaseous neon targets at rest, corresponding to a nucleon-nucleon centre-of-mass energy $\sqrt{s_{\rm NN}}

Patrick M. Wensing, Michael Posa, Yue Hu, Adrien Escande

In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to impact emerging robotics applications from logistics, to agriculture, to home assistance. The goal of this survey is to cover the recent progress toward these applications that has been driven by model-based optimization for the real-time generation and control of movemen

Alice Le Brigant, Jules Deschamps, Antoine Collas, Nina Miolane

We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of probability distributions, such as normal, gamma, beta, Dirichlet distributions, and more. The module further gives the Fisher-Rao Riemannian geometry of any parametric family of distr

Dariusz Kosz, Guillermo Rey, Luz Roncal

We prove necessary and sufficient conditions for the weak-$L^p$ boundedness, for $p \in (1,\infty)$, of a maximal operator on the infinite-dimensional torus. In the endpoint case $p=1$ we obtain the same weak-type inequality enjoyed by the strong maximal function in dimension two. Our results are quantitatively sharp.

Arjun Bagchi, Aritra Banerjee, Rudranil Basu, Minhajul Islam

The Carroll algebra is constructed as the $c\to0$ limit of the Poincare algebra and is associated to symmetries on generic null surfaces. In this paper, we begin investigations of Carrollian fermions or fermions defined on generic null surfaces. Due to the availability of two different (degenerate) metrics on Carroll spacetimes, there is the possibility of t

Aritra Banerjee, Sudipta Dutta, Saikat Mondal

Carroll symmetry is a very powerful characteristic of generic null surfaces, as it replaces the usual Poincar\'e algebra with a vanishing speed of light version thereof. These symmetries have found universal applications in the physics of null manifolds as they arise in diverse situations ranging from black hole horizons to condensed matter systems with vani

Hanze Dong, Shizhe Diao, Weizhong Zhang, Tong Zhang

Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions. However, the standard approach, which maps the observed data to a normal distribution, has difficulty in handling data distributions with multiple relatively isolated modes. To overcome this issue, we

STAR Collaboration, B. E. Aboona, J. Adam, J. R. Adams

The linear and mode-coupled contributions to higher-order anisotropic flow are presented for Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 27, 39, 54.4, and 200 GeV and compared to similar measurements for Pb+Pb collisions at the Large Hadron Collider (LHC). The coefficients and the flow harmonics' correlations, which characterize the linear and mode-couple

Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang

We revisit and advance visual prompting (VP), an input prompting technique for vision tasks. VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the target domain by simply incorporating universal prompts (in terms of input perturbation patterns) into downstream data points. Yet, it remains elusive why VP stays effective even

Davide Bolognini, Paolo Sentinelli

We introduce immanant varieties, associated to simple characters of a finite group. They include well-studied classes of varieties, as Segre embeddings, Grassmannians and certain other classes of Chow varieties. For a one-dimensional character $\chi$, we define $\chi$-matroids by a maximality property. For trivial characters, by exploring the combinatorics o

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A measurement of $D^0$ meson production by the LHCb experiment in its fixed-target configuration is presented. The production of $D^0$ mesons is studied with a beam of 2.5 TeV protons colliding on a gaseous neon target at rest, corresponding to a nucleon-nucleon centre-of-mass energy of $\sqrt{s_{\rm NN}}$ = 68.5 GeV. The sum of the $D^0$ and ${\overline D^0

Matthew Houtput, Jacques Tempere

A large polaron is a quasiparticle that consists of a nearly free electron interacting with the phonons of a material, whose lattice parameters are much smaller than the polaron scale. The electron-phonon interaction also leads to an attractive interaction between electrons, which can allow two polarons to pair up and form a bipolaron. It has been shown that

Matteo Dalla Riva, Paolo Luzzini, Paolo Musolino

We consider a Dirichlet problem for the Poisson equation in a periodically perforated domain. The geometry of the domain is controlled by two parameters: a real number $\epsilon>0$ proportional to the radius of the holes and a map $\phi$, which models the shape of the holes. So, if $g$ denotes the Dirichlet boundary datum and $f$ the Poisson datum, we have a

Petro Franchuk, Tetiana Yezerska

Recently, remote respiratory rate measurement techniques gained much attention as they were developed to overcome the limitations of device-based classical methods and manual counting. Many approaches for RR extraction from the video stream of the visible light camera were proposed, including the pixel intensity changes method. In this paper, we propose a ne

Buse Sibel Korkmaz, Marta Zagórowska, Mehmet Mercangöz

Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In this paper, we demonstrate the application of an adaptive and explorative real-time optimization framework to

Bowen Li, Ziyuan Huang, Junjie Ye, Yiming Li

Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicles (UAVs), where robust tracking is more challenging and onboard computation is limited, the latency issue can be fatal. In this wo

Owe Philipsen, Leonid Ya. Glozman, Peter Lowdon, Robert D. Pisarski

Recently, an approximate $SU(4)$ chiral spin-flavour symmetry was observed in multiplet patterns of QCD meson correlation functions, in a temperature range above the chiral crossover. This symmetry is larger than the chiral symmetry of massless QCD, and can only arise effectively when colour-electric quark-gluon interactions dynamically dominate the quantum

Maria Grazia Di Luca, Flavio Mercati

We discuss the construction of a free scalar quantum field theory on $\kappa$-Minkowski noncommutative spacetime. We do so in terms of $\kappa$-Poincar\'e-invariant $N$-point functions, i.e. multilocal functions which respect the deformed symmetries of the spacetime. As shown in a previous paper by some of us, this is only possible for a lightlike version of

Heide Gluesing-Luerssen, Benjamin Jany

While there are many parallels between matroid theory and $q$-matroid theory, most notably on the level of cryptomorphisms, there are substantial differences when it comes to the direct sum. The direct sum of $q$-matroids has been introduced in the literature only recently. In this short note we show that the direct sum of representable $q$-matroids may not

Thibault Vieu, Brian Reville

We demonstrate that supernova remnant (SNR) shocks embedded within massive star clusters can reproduce both the cosmic-ray proton and all-particle spectra measured in the vicinity of the Earth up to hundreds of peta-electronvolts (PeV). We model two classes of massive star clusters. The first population are "loose clusters" which do not power a collective wi

Elham Yousefi, Luc Pronzato, Markus Hainy, Werner G. Müller

The paper covers the design and analysis of experiments to discriminate between two Gaussian process models, such as those widely used in computer experiments, kriging, sensor location and machine learning. Two frameworks are considered. First, we study sequential constructions, where successive design (observation) points are selected, either as additional

Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang, Zhiwei Bai

Models with nonlinear architectures/parameterizations such as deep neural networks (DNNs) are well known for their mysteriously good generalization performance at overparameterization. In this work, we tackle this mystery from a novel perspective focusing on the transition of the target recovery/fitting accuracy as a function of the training data size. We pr

Dan Mao, Kevin Zhang, Eun-Ah Kim

Fractionalization without time-reversal symmetry breaking is a long-sought-after goal in the study of correlated phenomena. The earlier proposal of correlated insulating states at $n \pm 1/3$ filling in twisted bilayer graphene and recent experimental observations of insulating states at those fillings strongly suggest that moir\'e graphene systems provide a

Rafael Celestre, Thomas Roth, Carsten Detlefs, Peng Qi

In this work, we measure and model tilted x-ray refractive lenses to investigate their effects on an x-ray beam. The modelling is benchmarked against at-wavelength metrology obtained with x-ray speckle vector tracking experiments (XSVT) at the BM05 beamline at the ESRF-EBS light source, showing very good agreement. This validation permits us to explore possi

Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli

In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control opportunities, along with some drawbacks, such as possible inefficient exploration and vanishing of the action advantages. The repetition of the actions, i.e., action persistence, come

S. M. Wang, W. Nazarewicz, A. Volya, Y. G. Ma

The most important law of radioactivity is that of the exponential decay. In the realm of quantum mechanics, however, this decay law is neither rigorous nor fundamental. The deviations from the exponential decay have been observed experimentally at the early stage of a decay process, but there is little evidence for non-exponential behavior at long times. Ye

Xiaohan Wan, Siddhartha Sarkar, Shi-Zeng Lin, Kai Sun

We study flat bands and their topology in 2D materials with quadratic band crossing points (QBCPs) under periodic strain. In contrast to Dirac points in graphene, where strain acts as a vector potential, strain for QBCPs serves as a director potential with angular momentum $\ell=2$. We prove that when the strengths of the strain fields hit certain ``magic" v

Yinpei Dai, Wanwei He, Bowen Li, Yuchuan Wu

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, cov

Marco Majland, Rasmus Berg Jensen, Mads Greisen Højlund, Nikolaj Thomas Zinner

One of the primary challenges prohibiting demonstrations of practical quantum advantages for near-term devices amounts to excessive measurement overheads for estimating relevant physical quantities such as ground state energies. However, with major differences between the electronic and vibrational structure of molecules, the question of how the resource req

A. M. Gavrilik, A. V. Nazarenko

By improving the Bose-Einstein condensate model of dark matter through the repulsive three-particle interaction to better reproduce observables such as rotation curves, both different thermodynamic phases and few-particle correlations are revealed. Using the numerically found solutions of the Gross-Pitaevskii equation for averaging the products of local dens

Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat, Thibault Faney

This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn t

Philippe Gagnon, Florian Maire, Giacomo Zanella

Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several candidates for the next state of the Markov chain and randomly selects one of them based on a weight function. The canonical weight function is proportional to the target density.

Chenhongyi Yang, Lichao Huang, Elliot J. Crowley

Annotating datasets for object detection is an expensive and time-consuming endeavor. To minimize this burden, active learning (AL) techniques are employed to select the most informative samples for annotation within a constrained "annotation budget". Traditional AL strategies typically rely on model uncertainty or sample diversity for query sampling, while

Giorgio Chiarelli

INFN is recognized as an Italian excellence in science. Born in 1951, over time it created a world-wide network of activities spanning from high-energy physics at the most powerful accelerators, to the search for Dark Matter and rare events in deep underground laboratories, flanking the operations of four national laboratories in Italy. However, until a coup

Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst

Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital trace of the actions of a user through the graphical elements of an application, website or software. While readily available, click data is often missing a case notion: an attribut

Ruizhi Shao, Zerong Zheng, Hanzhang Tu, Boning Liu

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three ti

Raphaël Groscot, Laurent D. Cohen

We present Deformable Voxel Grids (DVGs) for 3D shapes comparison and processing. It consists of a voxel grid which is deformed to approximate the silhouette of a shape, via energy-minimization. By interpreting the DVG as a local coordinates system, it provides a better embedding space than a regular voxel grid, since it is adapted to the geometry of the sha

Haleh Hayati, Nathan van de Wouw, Carlos Murguia

We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup. A remote station seeks to identify anomalies based on system input-output signals transmitted over communication networks. However, it is not desired to disclose true data of the s

Lutz M. K. Krause, Emily Manderfeld, Patricia Gnutt, Louisa Vogler

Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g., salt water). The

Krishnendu Gongopadhyay, Tejbir Lohan, Chandan Maity

An element $g$ in a group $G$ is called reversible if $g$ is conjugate to $g^{-1}$ in $ G $. An element $g$ in $G$ is strongly reversible if $ g $ is conjugate to $g^{-1}$ by an involution in $G$. The group of affine transformations of $\mathbb{D}^n$ may be identified with the semi-direct product $\mathrm{GL}(n, \mathbb{D}) \ltimes \mathbb{D}^n $, where $\ma

Bastien Jean

Let $X$ be a compact Riemann surface, $\Sigma$ a finite set of points and $M = X\setminus \Sigma$. We study the $L^2$ cohomology of a polarized complex variation of Hodge structure on a Galois covering of the Riemann surface of finite type $M$. In this article we treat the case when the covering comes from a branched covering of $X$, and where $M$ is endowed

Jacob B Khurgin

Optical isolators are indispensable components of optical networks. Magneto-optic isolators have excellent operating characteristics, including low-to-no power consumption, but are not well suited for on-chip integration. The technique of temporal modulation of dielectric constant offers an alternative way to achieve isolation without magnetic field but is n

Charles A. Hepburn, Giovanni Montana

In many real-world applications, collecting large and high-quality datasets may be too costly or impractical. Offline reinforcement learning (RL) aims to infer an optimal decision-making policy from a fixed set of data. Getting the most information from historical data is then vital for good performance once the policy is deployed. We propose a model-based d

Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev

An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforcement learning from human feedback (RLHF) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning. First, we collected data of huma

Haiying Cai

The radion in the Randall-Sundrum model is stabilized by the back reaction of a bulk scalar field with its VEV depending on the fifth dimensional coordinate. We studied the radion dynamics in an extended scenario, where intermediate branes exist between the UV and IR branes. Our analysis proves that the formalism of EFT delivers the same equations of motion

Shahram Janbaz, Corentin Coulais

Mechanical waves that travel without inertia are often encountered in nature -- e.g. motion of plants -- yet such waves remain rare in synthetic materials. Here, we discover the emergence of slow kinks in overdamped metamaterials and we show that they can be used for applications such as sensing, dynamic pattern morphing and transport of objects. To do this,

Tomáš Husek

We discuss radiative transitions of a charged kaon to a pion and two photons. In particular, we have a closer look at radiative corrections for the $K^+\to\pi^+\ell^+\ell^-$ decays and present the branching ratio of the $K^+\to\pi^+e^+e^-e^+e^-$ process calculated for the first time at leading order in the Standard Model.

Hao Sun, Yi-Ning Wang, Jiang-Hao Yu

We present a systematic procedure for determining the Hilbert series that counts the number of independent operators in the Higgs effective field theory. After removing the redundancies from equation-of-motion and integration-by-part, we provide an algorithm of treating the redundancy from the operators involving in spurion fields parametrizing the custodial

Jonathan Massey, Bharathram Ganapathisubramani, Gabriel Weymouth

This study examines the effects of surface topography on the flow and performance of a Self-Propelled Swimming (SPS) body. We consider a thin flat plate with an egg-carton roughness texture undergoing prescribed undulatory swimming kinematics at a Strouhal number of $0.3$ and tail amplitude to length ratio of $0.1$; we use plate Reynolds numbers of $\mathit{

Andreas Roth, Thomas Liebig

Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors. But complete spatial coverage of sensors is generally unavailable and would practically be infeasible due to limitations in budget and other resources during deployment and maintenance. Currently existin

Alexey Vishnyakov, Daniil Kuts, Vlada Logunova, Darya Parygina

Nowadays automated dynamic analysis frameworks for continuous testing are in high demand to ensure software safety and satisfy the security development lifecycle (SDL) requirements. The security bug hunting efficiency of cutting-edge hybrid fuzzing techniques outperforms widely utilized coverage-guided fuzzing. We propose an enhanced dynamic analysis pipelin

Mario Falsaperna, Johnathan M. Bulled, Gavin B. G. Stenning, Andrew L. Goodwin

We investigate the effects of diamagnetic doping in the solid-solution series $Tb_{1-x}Y_{x}(HCO_{2})_{3}$, in which the parent $Tb(HCO_{2})_{3}$ phase has previously been shown to host a combination of frustrated and quasi-1D physics, giving rise to a triangular Ising antiferromagnetic ground state that lacks long range 3D order. Heat capacity measurements

Bert Herteleer, Anastasios Kladas, Gofran Chowdhury, Francky Catthoor

This paper presents a range of methods to improve the accuracy of equation-based thermal models of PV modules at second-to-minute timescales. We present an RC-equivalent conceptual model for PV modules, where wind effects are captured. We show how the thermal time constant $\tau$ of PV modules can be determined from measured data, and subsequently used to ma

Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler

Performing super-resolution of a depth image using the guidance from an RGB image is a problem that concerns several fields, such as robotics, medical imaging, and remote sensing. While deep learning methods have achieved good results in this problem, recent work highlighted the value of combining modern methods with more formal frameworks. In this work, we

Bjarne Pfitzner, Bert Arnrich

Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare. Instead of directly training classification models on these datasets, recent works have considered training data generators capable of synthesising a new dataset which is not protected by any privacy restrictions. Thus,

Saeid Alikhani, Davood Bakhshesh, Hamidreza Golmohammadi

Let $G$ be a graph with vertex set $V$. Two disjoint sets $V_1, V_2\subseteq V$ are called a total coalition in $G$, if neither $V_1$ and $V_2$ is a total dominating set of $G$ but $V_1\cup V_2$ is a total dominating set. A total coalition partition of $G$ is a vertex partition $\pi=\{V_1,V_2,\ldots, V_k\}$ such that no set of $\pi$ is a total dominating set

Paul Roetzer, Zorah Lähner, Florian Bernard

We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a shortest path in the product graph between both shapes, existing solutions heavily rely on unrealistic prior assumptions to avoid degenerate solutions (e.g. knowledge to which region of

Jordan Budhu, Nicholas Ventresca, Anthony Grbic

A technique is presented for the design of printed unit cells in aperiodic metasurface environments. The method begins with a solved matrix equation governing electromagnetic scattering from a homogenized metasurface design. The matrix equation is used to find the local, inhomogeneous electric field exciting a printed-circuit unit cell geometry. The local fi

William W. Howard, Charles E. Thornton, R. Michael Buehrer

We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period. Th

M. Cadoni, M. De Laurentis, I. De Martino, R. Della Monica

We propose a novel nonsingular black-hole spacetime representing a strong deformation of the Schwarzschild solution with mass $M$ by an additional hair $\ell$, which may be hierarchically larger than the Planck scale. Our black-hole model presents a de Sitter core and $\mathcal{O}(\ell^2/r^2)$ slow-decaying corrections to the Schwarzschild solution. Our blac

Florian Grötschla, Joël Mathys

Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data. We propose an approach using Graph Neural Networks that directly works on the road graph topology which was extracted from OpenStreetMap data. Our architecture can incorporate a hierarchical graph representation to improve the information flow between key inter

Yu-Feng Li, Shuo-yu Xia

The theoretical framework of the neutrino electron excitation at low energies including the screening effect in semiconductor detectors is developed for the first time, both in the Standard Model and in the presence of the neutrino magnetic moment. We explore the contribution of the screening effect of semiconductors to the neutrino electron excitation based

Carlos Granero-Belinchon

We define and study a fully-convolutional neural network stochastic model, NN-Turb, which generates a 1-dimensional field with some turbulent velocity statistics. In particular, the generated process satisfies the Kolmogorov 2/3 law for second order structure function. It also presents negative skewness across scales (i.e. Kolmogorov 4/5 law) and exhibits in

Thomas Gilray, Arash Sahebolamri, Sidharth Kumar, Kristopher Micinski

State-of-the-art Datalog engines include expressive features such as ADTs (structured heap values), stratified aggregation and negation, various primitive operations, and the opportunity for further extension using FFIs. Current parallelization approaches for state-of-art Datalogs target shared-memory locking data-structures using conventional multi-threadin

Mingye Ju, Chuheng Chen, Charles A. Guo, Jinshan Pan

How to effectively explore semantic feature is vital for low-light image enhancement (LLE). Existing methods usually utilize the semantic feature that is only drawn from the output produced by high-level semantic segmentation (SS) network. However, if the output is not accurately estimated, it would affect the high-level semantic feature (HSF) extraction, wh

I. Katsioulas, P. Knights, I. Manthos, J. Matthews

The spherical proportional counter is a versatile gaseous detector with physics applications ranging from rare event searches to fast neutron spectroscopy. In its simplest form, the detector operates with a single channel read-out, and uses pulse-shape information to reconstruct the interaction radius, which is used for background discrimination and target v

Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt

The ability of deep neural networks to generalise well even when they interpolate their training data has been explained using various "simplicity biases". These theories postulate that neural networks avoid overfitting by first learning simple functions, say a linear classifier, before learning more complex, non-linear functions. Meanwhile, data structure i

Rebecka Sax, Alberto Boaron, Gianluca Boso, Simone Atzeni

Quantum key distribution (QKD) is nowadays a well established method for generating secret keys at a distance in an information-theoretic secure way, as the secrecy of QKD relies on the laws of quantum physics and not computational complexity. In order to industrialize QKD, low-cost, mass-manufactured and practical QKD setups are required. Hence, photonic an

Jorge Gil-Rostra, Javier Castillo-Seoane, Qian Guo, Ana Jorge Sobrido

A well-established procedure for the photoelectrochemical (PEC) splitting of water relies on using porous electrodes of WO3 sensitized with BiVO4 as a visible scavenger photoanode semiconductor. In this work, we propose an evolved photoelectrode fabricated by a soft-template approach consisting of supported multishell nanotubes (NTs). These NTs are formed by

Mengjiao Hu, Xudong Jiang, Kang Sim, Juan Helen Zhou

Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizophrenia and depression that have no visible alteration in specific slices. In this study, we propose to process the 3D data by a 2+1D framework s

Adrien Vandenbroucque, Ezequiel Ignacio Rodríguez Chiacchio, Ewan Munro

The study of spin systems with disorder and frustration is known to be a computationally hard task. Standard heuristics developed for optimizing and sampling from general Ising Hamiltonians tend to produce correlated solutions due to their locality, resulting in a suboptimal exploration of the search space. To mitigate these effects, cluster Monte-Carlo meth

Ri-Qing Qian, Zhan-Wei Liu, Xu Cao, Xiang Liu

To understand the oscillatory behavior exhibited in the timelike electromagnetic form factors of nucleons, we propose a toy model based on the Jost function of the $N\bar N$ pair into the timelike form factors with the help of the distorted-wave Born approximation. By constructing a simple square-well potential reflecting the final-state interaction of $N\ba