May 2022 arXiv papers — page 95

Tatiana V. Demidova, Vladimir P. Grinin

The development of perturbations in the circumstellar disks of pre-main-sequence stars caused by clumpy accretion was investigated. Here we perform 3D hydrodynamical smoothed particle hydrodynamics simulations of disks perturbed by a recent clump accretion event. These simulations are further explored by radiative transfer calculations to quantify the observ

Yuqiang Zeng, Buyi Zhang, Yanbao Fu, Fengyu Shen

The long charge time of electric vehicles compared with the refueling time of gasoline vehicles, has been a major barrier to the mass adoption of EVs. Currently, the charge time to 80% state of charge in electric vehicles such as Tesla with fast charging capabilities is >30 minutes. For a comparable recharging experience as gasoline vehicles, governments and

Junyan He, Shashank Kushwaha, Diab Abueidda, Iwona Jasiuk

This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the interactions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design sy

Michael Bradley Johanson, Edward Hughes, Finbarr Timbers, Joel Z. Leibo

Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one an

Seiji Tomita, Oliver Couto

Different authors have done analysis regarding sums of powers References number 1,2 and 3, but systematic approach for solving Diophantine equations having sums of many biquadratics equal to a quartic has not been done before. In this paper we give methods for finding numerical solutions to equation A given above in section one. Next in section two, we give

Nicos Makris, Gholamreza Moghimi, Eric Godat, Tue Vu

Motivated from the increasing need to develop a quantitative, science-based, predictive understanding of the dynamics and response of cities when subjected to hazards, in this paper we apply concepts from statistical mechanics and microrheology to develop mechanical analogs for cities with predictive capabilities. We envision a city to be a matrix where peop

Germán García-Jara, Pavlos Protopapas, Pablo A. Estévez

Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine learning algorithms whose performance is limited by the number of existing annotations of astronomical object

Hanno Rein, Nick Choksi

We describe the implementation of a new module which can be used to simulate physical systems in which the motion of particles is affected by stochastic forces. Such forces are expected to be present in turbulent circumstellar disks or remnant planetesimal disks. Our implementation offers a convenient way to generate correlated noise with a user-specified am

Shuangqi Li, Diego Antognini, Boi Faltings

Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction and are meaningful to humans. A lot of research on rationalization has been based on the selective rationalization framework, which has recently been shown to be problematic due to

Marco Gaido, Matteo Negri, Marco Turchi

Recent work has shown that systems for speech translation (ST) -- similarly to automatic speech recognition (ASR) -- poorly handle person names. This shortcoming does not only lead to errors that can seriously distort the meaning of the input, but also hinders the adoption of such systems in application scenarios (like computer-assisted interpreting) where t

Zhaocheng Liu, Luis Herranz, Fei Yang, Saiping Zhang

Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeof

S. C. ten Kate, M. F. Ritter, A. Fuhrer, J. Jung

PbTe is a semiconductor with promising properties for topological quantum computing applications. Here we characterize quantum dots in PbTe nanowires selectively grown on InP. Charge stability diagrams at zero magnetic field reveal large even-odd spacing between Coulomb blockade peaks, charging energies below 140$~\mathrm{\mu eV}$ and Kondo peaks in odd Coul

Jun Li, Chengjie Zhu, Yaping Yang

We propose that the squeezed light accompanied by hyperradiance is induced by quantum interference in a linear system consisting of a high quality optical cavity and two coherently driven two-level qubits. When two qubits are placed at the crest and trough of the standing wave in the cavity respectively (i.e., they have the opposite coupling coefficient to t

Ziv Ran

For a general birational projection of a smooth nondegenerate projective $n$-fold from $\mathbb P^{n+c}$ to $\mathbb P^m$, $n<m\leq(n+c)/2$, all fibres have total length asymptotically bounded by $2^{\sqrt{n}+1} $ and the fibres are locally defined by linear and quadratic equations.

Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks. However, vanilla RL and most safe RL approaches do not guarantee safety. In recent years, several methods have been proposed to provide hard safety guarantees for RL, which is essential for applications where unsafe actions could have

Marcel Dengler, Jonathan J. Bevan

In this paper we consider the problem of minimizing functionals of the form $E(u)=\int_B f(x,\nabla u) \,dx$ in a suitably prepared class of incompressible, planar maps $u: B \rightarrow \mathbb{R}^2$. Here, $B$ is the unit disk and $f(x,\xi)$ is quadratic and convex in $\xi$. It is shown that if $u$ is a stationary point of $E$ in a sense that is made clear

Monique Dauge, Patrick Dular, Laurent Krähenbühl, Victor Péron

In this paper, we describe the magnetic potential in the vicinity of a corner of a conducting body embedded in a dielectric medium in a bidimensional setting. We make explicit the corner asymptotic expansion for this potential as the distance to the corner goes to zero. This expansion involves singular functions and singular coefficients. We introduce a meth

Ipek Goktan, Karen Ly, Thomas R. Groechel, Maja J. Mataric

In order to address the limitations of gestural capabilities in physical robots, researchers in Virtual, Augmented, Mixed Reality Human-Robot Interaction (VAM-HRI) have been using augmented-reality visualizations that increase robot expressivity and improve user perception (e.g., social presence). While a multitude of virtual robot deictic gestures (e.g., po

Hsiang-nan Li

We derive the dependence of the leading-twist pion light-cone distribution amplitude (LCDA) on a parton momentum fraction $x$ by directly solving the dispersion relations for the moments with inputs from the operator product expansion (OPE) of the corresponding correlation function. It is noticed that these dispersion relations must be organized into those f

Gray Putnam, David W. Schmitz

Liquid Argon Time Projection Chamber (LArTPC) detectors observe ionization electrons to measure charged particle trajectories and energy. In a LArTPC, the long time ($\sim$ms) between when the ionization is produced and when it is collected means that diffusion can smear the charge by an amount comparable to the spatial resolution of the detector, given by t

Petri P. Karenlampi

Two strategies for boreal forestry with goodwill in estate capitalization are introduced. A strategy focusing on Real Estate (RE) is financially superior to Timber Sales (TS). The feasibility of the RE requires the presence of forest land end users in the real estate market, like insurance companies or investment trusts, and the periodic boundary condition d

Yisheng Song, Ting Wang, Subrota K Mondal, Jyoti Prakash Sahoo

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three ye

Colin J. Trout, Kevin Schultz, Paraj Titum, Leigh Norris

We provide a technique to obtain provably optimal control sequences for quantum systems under the influence of time-correlated multiplicative control noise. Utilizing the circuit-level noise model introduced in [Phys. Rev. Research 3, 033229(2021)], we show that we can map the problem of finding such a sequence to a convex optimization problem with guarantee

Minesh Mathew, Ajoy Mondal, CV Jawahar

Recognition of text on word or line images, without the need for sub-word segmentation has become the mainstream of research and development of text recognition for Indian languages. Modelling unsegmented sequences using Connectionist Temporal Classification (CTC) is the most commonly used approach for segmentation-free OCR. In this work we present a compreh

Venkatesan Guruswami, Pravesh K. Kothari, Peter Manohar

Let $\mathcal{H}(k,n,p)$ be the distribution on $k$-uniform hypergraphs where every subset of $[n]$ of size $k$ is included as an hyperedge with probability $p$ independently. In this work, we design and analyze a simple spectral algorithm that certifies a bound on the size of the largest clique, $\omega(H)$, in hypergraphs $H \sim \mathcal{H}(k,n,p)$. For e

Venkatesan Guruswami, Peter Manohar, Jonathan Mosheiff

A matrix $A$ is said to have the $\ell_p$-Restricted Isometry Property ($\ell_p$-RIP) if for all vectors $x$ of up to some sparsity $k$, $\|{Ax}\|_p$ is roughly proportional to $\|{x}\|_p$. We study this property for $m \times n$ matrices of rank proportional to $n$ and $k = \Theta(n)$. In this parameter regime, $\ell_p$-RIP matrices are closely connected to

Ching-Peng Huang

Given a submersion $\phi: M \to N$, where $M$ is Riemannian, we construct a stochastic process $X$ on $M$ such that the image $Y:=\phi(X)$ is a (reversed, scaled) mean curvature flow of the fibers of the submersion. The model example is the mapping $\pi: GL(n) \to GL(n)/O(n)$, whose image is equivalent to the space of $n$-by-$n$ positive definite matrices, $

Zhaoyu Wang, Huading Song, Dong Pan, Zitong Zhang

Probing an isolated Majorana zero mode is predicted to reveal a tunneling conductance quantized at $2e^2/h$ at zero temperature. Experimentally, a zero-bias peak (ZBP) is expected and its height should remain robust against relevant parameter tuning, forming a quantized plateau. Here, we report the observation of large ZBPs in a thin InAs-Al hybrid nanowire

Alexander Gallego Cadavid, Antonio Enea Romano, Andrew R. Liddle

Purely geometrical arguments show that there exist classes of homospectral inflationary cosmologies, i.e. different expansion histories producing the same spectrum of comoving curvature perturbations. We develop a general algorithm to reconstruct the potential of minimally-coupled single scalar fields from an arbitrary expansion history. We apply it to homos

Florio M. Ciaglia, Fabio Di Cosmo, Alberto Ibort, Giuseppe Marmo

Starting from the canonical symmetroid $\mathcal{S}(G)$ associated with a groupoid $G$, the issue of describing dynamical maps in the groupoidal approach to Quantum Mechanics is addressed. After inducing a Haar measure on the canonical symmetroid $\mathcal{S}(G)$, the associated von-Neumann groupoid algebra is constructed. It is shown that the left-regular r

Dominic Petrak, Nafise Sadat Moosavi, Iryna Gurevych

State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers. Recent work suggests two main reasons for this: (1) popular tokenisation algorithms have limited expressiveness for numbers, and (2) common pretraining objectives do not target numeracy

Johannes Kraus, Philip L. Lederer, Maria Lymbery, Kevin Osthues

The quasi-static multiple network poroelastic theory (MPET) model, first introduced in the context of geomechanics, has recently found new applications in medicine. In practice, the parameters in the MPET equations can vary over several orders of magnitude which makes their stable discretization and fast solution a challenging task. Here, a new efficient par

Lori Downen, Christian Iliadis, Art Champagne, Thomas Clegg

The thermonuclear rate of the $^{29}$Si(p,$\gamma$)$^{30}$P reaction impacts the $^{29}$Si abundance in classical novae. A reliable reaction rate is essential for testing the nova paternity of presolar stardust grains. At present, the fact that no classical nova grains have been unambiguously identified in primitive meteorites among thousands of grains studi

Monica Ribero, Haris Vikalo, Gustavo De Veciana

Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be bias

H. Boehringer, G. Chon, R. S. Ellis, R. Barrena

Massive galaxy clusters are interesting astrophysical and cosmological study objects, but are relatively rare. In the redshift range z = 0.25 to 0.5 which is, for example, a favourable region for gravitational lensing studies, about 100 such systems are known. Most of them have been studied in X-rays. In this paper we study the six remaining massive clusters

Daniele Asnicar, Emanuele Penocchio, Diego Frezzato

Here, steady-state reaction networks are inspected from the viewpoint of individual tagged molecules jumping among their chemical states upon the occurrence of reactive events. Such an agent-based viewpoint is useful for selectively characterizing the behavior of functional molecules, especially in the presence of bimolecular processes. We present the tools

Jonathan Dumas, Antoine Dubois, Paolo Thiran, Pierre Jacques

Planning the defossilization of energy systems by facilitating high penetration of renewables and maintaining access to abundant and affordable primary energy resources is a nontrivial multi-objective problem. However, so far, most long-term policies to decrease the carbon footprint of our societies consider the cost of the system as the leading indicator in

Evgeny Andronov, Magdalena Kuich, Marek Gaździcki

Many new particles, mostly hadrons, are produced in high energy collisions between atomic nuclei. The most popular models describing the hadron production process are based on the creation, evolution and decay of resonances, strings or quark-gluon plasma. The validity of these models is under vivid discussion, and it seems that a common framework for this di

Florian Beier, Robert Beinert, Gabriele Steidl

Gromov-Wasserstein (GW) distances are combinations of Gromov-Hausdorff and Wasserstein distances that allow the comparison of two different metric measure spaces (mm-spaces). Due to their invariance under measure- and distance-preserving transformations, they are well suited for many applications in graph and shape analysis. In this paper, we introduce the c

Benjamin Kenwright

This article explores the value and measurable effects of hard and soft skills in academia when teaching and developing abilities for the game industry. As we discuss, each individuals engagement with the subject directly impacts their performance; which is influenced by their 'soft' skill level. Students that succeed in mastering soft skills earlier on typi

Yizhuang Liu, Maciej A. Nowak, Ismail Zahed

We derive a general formula for the replica partition function in the vacuum state, for a large class of interacting theories with fermions, with or without gauge fields, using the equal-time formulation on the light front. The result is used to analyze the spatial entanglement of interacting Dirac fermions in two-dimensional QCD. A particular attention is p

Issa Khalifeh, Marc Gorriz Blanch, Ebroul Izquierdo, Marta Mrak

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation method

Connor Oxenhorn

This paper generalizes a graph theoretic proof technique for a Fibonacci identity proposed by Lee Knisley Sanders, and explores characteristics of these generalized theorems ad infinitum.

Dapeng Jing, Yong Han, James W. Evans, Marek Kolmer

A general method was developed to intercalate metals under layered materials through a controlled density of sputtered defects. The method has been already applied to study a range of metals intercalated under graphite and different types of morphologies were realized. In the current work, we extend the method to the study of intercalation under MoS2 noting

Wenxuan Bao, Luke A. Bauer, Vincent Bindschaedler

We study a pitfall in the typical workflow for differentially private machine learning. The use of differentially private learning algorithms in a "drop-in" fashion -- without accounting for the impact of differential privacy (DP) noise when choosing what feature engineering operations to use, what features to select, or what neural network architecture to u

Victor Péron

We present equivalent conditions and asymptotic models for the diffraction problem of elastic and acoustic waves in a solid medium surrounded by a thin layer of fluid medium. Due to the thinness of the layer with respect to the wavelength, this problem is well suited for the notion of equivalent conditions and the effect of the fluid medium on the solid is a

Morteza Hasanvand

Let $G$ be a graph and let $g$, $f$, and $f'$ be three positive integer-valued functions on $V(G)$ with $g\le f$. Tokuda, Xu, and Wang (2003) showed that if $G$ contains a $(g,f)$-factor and a spanning $f'$-tree, then $G$ also contains a connected $(g,f+f'-1)$-factor. In this note, we develop their result to a tree-connected version by proving that if $G$ co

Nassir Mohammad

A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or further investigation. Anomalies are intuitively understood to be something unusual or inconsistent, whose occurrence sparks immediate attention. Mo

Jürgen Herzog, Takayuki Hibi, Somayeh Moradi

We consider the ideal of inner $2$-minors $I_{\mathcal{P}}$ of a finite set of cells $\mathcal{P}$, which we call the cell ideal of $\mathcal{P}$. A nice interpretation for the height of an unmixed ideal $I_{\mathcal{P}}$, in terms of the number of cells of $\mathcal{P}$ is given. Moreover, the coordinate rings of cell ideals with isolated singularities are

Mahdieh Kazemimoghadam, Zi Yang, Lin Ma, Mingli Chen

Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring sufficient training data is another challenge limiting models' applications. We proposed to leverage the consistency of organs' anatomi

Thomas Lips, Victor-Louis De Gusseme, Francis wyffels

Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks (CNN) can be used to detect these keypoints, but require large amounts of annotated data, which is expensive to collect.

Xavier D'Haultfœuille, Purevdorj Tuvaandorj

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then, we show that the test is asymptotically of correct level, consistent and has power against local alternatives when the independence condition is relaxed, under two main conditions. T

Thomas Rink

The detection of coherent elastic neutrino-nucleus scattering (CE$\nu$NS) opens new possibilities for neutrino physics within and beyond the Standard Model. Following the initial discovery in 2017, several experimental attempts have emerged allowing this reaction channel to be studied with the full repertoire of modern detection technologies. As one of sever

A. J. Tropiano, S. K. Bogner, R. J. Furnstahl, M. A. Hisham

The quasi-deuteron model introduced by Levinger is used to explain cross sections for knocking out high-momentum protons in photo-absorption on nuclei. This is within a framework we characterize as exhibiting high renormalization group (RG) resolution. Assuming a one-body reaction operator, the nuclear wave function must include two-body short-range correlat

Stefania Bellavia, Eugenio Fabrizi, Benedetta Morini

This paper studies the numerical solution of strictly convex unconstrained optimization problems by linesearch Newton-CG methods. We focus on methods employing inexact evaluations of the objective function and inexact and possibly random gradient and Hessian estimates. The derivative estimates are not required to satisfy suitable accuracy requirements at eac

CMS Collaboration

A search is reported for heavy resonances and quantum black holes decaying into e$\mu$, e$\tau$, and $\mu\tau$ final states in proton-proton collision data recorded by the CMS experiment at the CERN LHC during 2016-2018 at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The e$\mu$, e$\tau$, and $\mu\tau$ invariant mass spectr

Yihan Zhang, Sidharth Jaggi, Michael Langberg, Anand D. Sarwate

We characterize the capacity for the discrete-time arbitrarily varying channel with discrete inputs, outputs, and states when (a) the encoder and decoder do not share common randomness, (b) the input and state are subject to cost constraints, (c) the transition matrix of the channel is deterministic given the state, and (d) at each time step the adversary ca

Holger Gies, Dimitrios Gkiatas, Luca Zambelli

We combine a recent construction of a BRST-invariant, nonlinear massive gauge fixing with the background field formalism. The resulting generating functional preserves background-field invariance as well as BRST invariance of the quantum field manifestly. The construction features BRST-invariant mass parameters for the quantum gauge and ghost fields. The for

Wenqing He, Caihua Wan, Cuixiu Zheng, Yizhan Wang

Perpendicularly magnetized structures that are switchable using a spin current under field-free conditions can potentially be applied in spin-orbit torque magnetic random-access memory(SOT-MRAM).Several structures have been developed;however,new structures with a simple stack structure and MRAM compatibility are urgently needed.Herein,a typical structure in

L. Álvaro-Gómez, S. Ruiz-Gómez, C. Fernández-González, M. Schöbitz

We analyze the micromagnetics of short longitudinal modulations of a high-magnetization material in cylindrical nanowires made of a soft-magnetic material of lower magnetization such as permalloy, combining magnetic microscopy, analytical modeling, and micromagnetic simulations. The mismatch of magnetization induces curling of magnetization around the axis i

Paul Escapil-Inchauspé, Gonzalo A. Ruz

We consider physics-informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and co

Yahui Liu, Haoping Yang, Chen Gong, Qingrong Xia

During the past decade, neural network models have made tremendous progress on in-domain semantic role labeling (SRL). However, performance drops dramatically under the out-of-domain setting. In order to facilitate research on cross-domain SRL, this paper presents MuCPAD, a multi-domain Chinese predicate-argument dataset, which consists of 30,897 sentences a

Peng-Jie Guo, Zheng-Xin Liu, Zhong-Yi Lu

So far, experimentally realized quantum anomalous Hall (QAH) insulators all exhibit ferromagnetic order and the QAH effect only occurs at very low temperatures. On the other hand, up to now the QAH effect in antiferromagnetic (AFM) materials has never been reported. In this letter, we realize the QAH effect by proposing a four-band lattice model with static

Jing Yang, Xiatian Zhu, Adrian Bulat, Brais Martinez

Existing knowledge distillation methods mostly focus on distillation of teacher's prediction and intermediate activation. However, the structured representation, which arguably is one of the most critical ingredients of deep models, is largely overlooked. In this work, we propose a novel {\em \modelname{}} ({\bf\em \shortname{})} method dedicated for distill

Juan Ignacio Polanco, S. Arun, Aurore Naso

Geophysical flows are often turbulent and subject to rotation. This rotation modifies the structure of turbulence and is thereby expected to sensibly affect its Lagrangian properties. Here, we investigate the relative dispersion and geometry of pairs, triads and tetrads in homogeneous rotating turbulence, by using direct numerical simulations at different ro

Hamed Nili, Alexander Walther, Arjen Alink, Nikolaus Kriegeskorte

Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars

Hrishit Banerjee, Jagjit Kaur, M. K. Nazeeruddin, Sudip Chakraborty

We have witnessed a wide range of theoretical as well as experimental investigations to envisage external stimuli induced changes in electronic, optical, and magnetic properties in the metal organic complexes, while hybrid perovskites have recently joined this exciting league of explorations. The flexible organic linkers in such complexes are ideal for trigg

Alejandro Bravo-Doddoli

Given a sub-Riemannian manifold, a relevant question is: what are the metric lines (isometric embedding of the real line)? The space of $k$-jets of a real function of one real variable $x$, denoted by $J^k(\mathbb{R},\mathbb{R})$, admits the structure of a Carnot group, as every Carnot group $J^k(\mathbb{R},\mathbb{R})$ is a sub-Riemannian Manifold. This wor

J. Senthilnath, Nagaraj G, Sumanth Simha C, Sushant Kulkarni

A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted u

Jiachen Jiang, Askar B. Abdikamalov, Cosimo Bambi, Christopher S. Reynolds

We present a re-analysis of the XMM-Newton and NuSTAR observing campaign for the well-studied, X-ray-bright AGN MCG-06-30-15. In particular, we consider a disc model with finite thickness. By fitting the disc reflection spectra in the data, we obtain a black hole spin of 0.87--0.99 (90\% confidence range) after taking the thickness of the disc into considera

Muhammad Faraz Ul Abrar, Muhammad Talha, Rafay Iqbal Ansari, Syed Ali Hassan

Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged as prominent technology that exploits the transmissive property of RIS to mitigate the half-space coverage limitation of conventional RIS operating on millimeter-wave (mmWave). In this paper, we study a downlink STAR-RIS-based multi-user multiple-in

Théo Moins, Julyan Arbel, Anne Dutfoy, Stéphane Girard

Diagnosing convergence of Markov chain Monte Carlo is crucial and remains an essentially unsolved problem. Among the most popular methods, the potential scale reduction factor, commonly named $\hat{R}$, is an indicator that monitors the convergence of output chains to a target distribution, based on a comparison of the between- and within-variances. Several

Yevhen Kushnirenko, Benjamin Schrunk, Brinda Kuthanazhi, Lin-Lin Wang

Since the discovery of topological insulators a lot of research effort has been devoted to magnetic topological materials, in which non-trivial spin properties can be controlled by magnetic fields, culminating in a wealth of fundamental phenomena and possible applications. The main focus was on ferromagnetic materials that can host Weyl fermions and therefor

P. Y. Astafyeva, O. K. Kiselev

The system of equations for parametric sub-resonant growth of the amplitude of oscillations was obtained. The time of turning point from the growing of the amplitude to the bounded oscillations in the slow variable was found. The comparison between the asymptotic approximation for the turning time and numerical one is shown.

Miklós Abert, Mikolaj Fraczyk, Ben Hayes

We define the co-spectral radius of inclusions $\mathcal{S}\leq \mathcal{R}$ of discrete, probability measure-preserving equivalence relations, as the sampling exponent of a generating random walk on the ambient relation. The co-spectral radius is analogous to the spectral radius for random walks on $G/H$ for inclusion $H\leq G$ of groups. For the proof, we

Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg

We present the first shared task on semantic change discovery and detection in Spanish and create the first dataset of Spanish words manually annotated for semantic change using the DURel framework (Schlechtweg et al., 2018). The task is divided in two phases: 1) Graded Change Discovery, and 2) Binary Change Detection. In addition to introducing a new langua

Nadine Bradbury, Minh Nguyen, Justin R Caram, Daniel Neuhauser

We present a highly efficient method for the extraction of optical properties of very large molecules via the Bethe-Salpeter equation. The crutch of this approach is the calculation of the action of the effective Coulombic interaction, $W$, through a stochastic TD Hartree propagation, which uses only 10 stochastic orbitals rather than propagating the full se

Mert Gurbuzbalaban, Yuanhan Hu, Umut Simsekli, Kun Yuan

Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to `multiplicative noise', even under surprisingly simple settings, such as linear regression with Gaussian data. While these studies have uncovered several interesting phenomena, they consider conventional stochastic optimization problems, which exclude decentra

Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning frameworks. To allow for an efficient training of respective neural networks, we propose an algorithm that obtains analytical gradients of a Sinkhorn layer via implicit differentiation. In

Suppanat Supanyo, Monsit Tanasittikosol, Sikarin Yoo-Kong

The various types of the non-standard Lagrangian can be added to the standard Lagrangian with the invariant of the equation of motion in the low energy limit. In this paper, we construct the multiplicative Lagrangian of a complex scalar field giving the approximated Klein-Gordon equation from the inverse problem of the calculus of variation. Then, this multi

Vincent Pilaud

A pebble tree is an ordered tree where each node receives some colored pebbles, in such a way that each unary node receives at least one pebble, and each subtree has either one more or as many leaves as pebbles of each color. We show that the contraction poset on pebble trees is isomorphic to the face poset of a convex polytope called pebble tree polytope. B

Pieter Moree, Armand Noubissie

We describe the set of prime numbers splitting completely in the non-abelian splitting field of certain monic irreducible polynomials of degree three. As an application we establish some divisibility properties of the associated ternary recurrence sequence by primes $p$, thus greatly extending recent work of Evink and Helminck and of Faisant. We also prove s

Will Rowan, Nick Pears

Deepfakes are a form of synthetic image generation used to generate fake videos of individuals for malicious purposes. The resulting videos may be used to spread misinformation, reduce trust in media, or as a form of blackmail. These threats necessitate automated methods of deepfake video detection. This paper investigates whether temporal information can im

M. Balasubrahmaniyam, Arie Simkovich, Adina Golombek, Guy Ankonina

In recent years it has become clear that the transport of excitons and charge carriers in molecular systems can be enhanced by coherent coupling with photons, giving rise to the formation of hybrid excitations known as polaritons. Such enhancement has far-reaching technological implications, however, the enhancement mechanism and the transport nature of thes

Sarah J. Faller, Moira M. Jardine

Observations of rapidly-rotating cool stars often show coronal slingshot prominences that remove mass and angular momentum when they are ejected. The derived masses of these prominences show a scatter of some two orders of magnitude. In order to investigate if this scatter could be intrinsic, we use a full magnetic cycle of solar magnetograms to model the co

Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang

AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts. Although progress has been made in developing automatic methods to detect AI-synthesized faces, there is no open platform to study the human performance of AI-synthesized f

Roy J. Garcia, Chen Zhao, Kaifeng Bu, Arthur Jaffe

The existence of barren plateaus has recently revealed new training challenges in quantum machine learning (QML). Uncovering the mechanisms behind barren plateaus is essential in understanding the scope of problems that QML can efficiently tackle. Barren plateaus have recently been shown to exist when learning global properties of random unitaries, which is

Pradeep K. Murukannaiah, Catholijn M. Jonker

Existing protocols for multilateral negotiation require a full consensus among the negotiating parties. In contrast, we propose a protocol for multilateral negotiation that allows partial consensus, wherein only a subset of the negotiating parties can reach an agreement. We motivate problems that require such a protocol and describe the protocol formally.

Hossein Salahshoor, Michael Ortiz

We develop a Data-Driven framework for the simulation of wave propagation in viscoelastic solids directly from dynamic testing material data, including data from Dynamic Mechanical Analysis (DMA), nano-indentation, Dynamic Shear Testing (DST) and Magnetic Resonance Elastography (MRE), without the need for regression or material modeling. The problem is formu

Daniel Reisenbüchler, Sophia J. Wagner, Melanie Boxberg, Tingying Peng

Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities. On the other hand, Transformers with global self-attention modules have been proposed to model the interdependencies among all in

Debabani Chowdhury, Debesh K. Das, Bhargab B. Bhattacharya

We revisit the problem of determining the independent domination number in hypercubes for which the known upper bound is still not tight for general dimensions. We present here a constructive method to build an independent dominating set $S_n$ for the $n$-dimensional hypercube $Q_n$, where $n=2p+1$, $p$ being a positive integer $\ge 1$, provided an independe

Sushil Kumar Jain, Amit M. Joshi

A dielectric-modulated double gate bilayer electrodes organic thin-film transistor (DMDGBE-OTFT) based sensor is proposed for label-free biomolecule detection. The double gate of DMDGBE-OTFT is used for creating two symmetrical gates underlap regions on both sides of the organic semiconductor. The parallel immobilization of biomolecule in two gates underlaps

Nico Benincasa, Luigi Delle Rose, Kristjan Kannike, Luca Marzola

The inert doublet model is a well-motivated extension of the Standard Model that contains a dark matter candidate and modifies the dynamics of the electroweak symmetry breaking. In order to detail its phenomenology, we perform a comprehensive study of cosmic phase transitions and gravitational wave signals implied by the framework, accounting for the latest

Ozan Erturk, Kilian Shambaugh, Ha-Seong Park, Sang-Goo Lee

Considering evolution of rotation sensing and timing applications realized in Micro-electro-mechanical systems (MEMS), flexural mode resonant shapes are outperformed by bulk acoustic wave (BAW) counterparts by achieving higher frequencies with both electrostatic and piezoelectric transduction. Within the 1-30 MHz range, which hosts BAW gyroscopes and timing

CMS Collaboration

A search for nonresonant Higgs boson (H) pair production via gluon and vector boson (V) fusion is performed in the four-bottom-quark final state, using proton-proton collision data at 13 TeV corresponding to 138 fb$^{-1}$ collected by the CMS experiment at the LHC. The analysis targets Lorentz-boosted H pairs identified using a graph neural network. It const

Catalina Goanta, Thales Bertaglia, Adriana Iamnitchi

In the course of under a year, the European Commission has launched some of the most important regulatory proposals to date on platform governance. The Commission's goals behind cross-sectoral regulation of this sort include the protection of markets and democracies alike. While all these acts propose sophisticated rules for setting up new enforcement instit

Małgorzata Biernacka, Dariusz Biernacki, Sergueï Lenglet, Piotr Polesiuk

We present fully abstract encodings of the call-by-name and call-by-value $\lambda$-calculus into HOcore, a minimal higher-order process calculus with no name restriction. We consider several equivalences on the $\lambda$-calculus side -- normal-form bisimilarity, applicative bisimilarity, and contextual equivalence -- that we internalize into abstract machi

Lucas Hataishi, Makoto Yamashita

We establish the existence of injective envelopes for unital Yetter-Drinfeld C*-algebras, and a related class of bimodule categories over rigid C*-tensor categories. This implies monoidal invariance for boundary actions of Drinfeld doubles of compact quantum groups.

Mohammad Farsi, Christian Häger, Magnus Karlsson, Erik Agrell

In this paper, we analyze the effectiveness of polarization tracking algorithms in optical transmission systems suffering from fast state of polarization (SOP) rotations and polarization-dependent loss (PDL). While most of the gradient descent (GD)-based algorithms in the literature may require step size adjustment when the channel condition changes, we prop

Roberto Doriguzzi-Corin, Domenico Siracusa

Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have n