January 2022 arXiv papers — page 15

Kevin G. Hare

Let $\mu$ be a self-similar measure satisfying the finite type condition. It is known that the set of attainable local dimensions for such a measure is a union of disjoint intervals, where some intervals may be degenerate points. Despite this, it has not been shown if this full complexity of attainable local dimensions is achievable. In this paper we give tw

Larry Zamick, Castaly Fan

In 1967 Akito Arima spent a year as a visiting professor in the physics department of Rutgers University. In this work we pay tribute to him by discussing topics that we worked on that were directly influenced by his works or were closely related to his interests. These include nuclear Symmetries, magnetic and other moments, analytic expressions in the singl

Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron

This paper considers the problem of measure estimation under the barycentric coding model (BCM), in which an unknown measure is assumed to belong to the set of Wasserstein-2 barycenters of a finite set of known measures. Estimating a measure under this model is equivalent to estimating the unknown barycentric coordinates. We provide novel geometrical, statis

Ananya Appan, Anirudh Chandramouli, Ashish Choudhury

Secure multi-party computation (MPC) is a fundamental problem in secure distributed computing. An MPC protocol allows a set of $n$ mutually distrusting parties to carry out any joint computation of their private inputs, without disclosing any additional information about their inputs. MPC with information-theoretic security provides the strongest security gu

Jiayin Li, Chi-Wang Shu, Jianxian Qiu

In this paper, a high-order moment-based multi-resolution Hermite weighted essentially non-oscillatory (HWENO) scheme is designed for hyperbolic conservation laws. The main idea of this scheme is derived from our previous work [J. Comput. Phys., 446 (2021) 110653], in which the integral averages of the function and its first order derivative are used to reco

Ruida Zhou, Chao Tian, Tie Liu

We propose a new approach to apply the chaining technique in conjunction with information-theoretic measures to bound the generalization error of machine learning algorithms. Different from the deterministic chaining approach based on hierarchical partitions of a metric space, previously proposed by Asadi et al., we propose a stochastic chaining approach, wh

Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell

The representation space of neural models for textual data emerges in an unsupervised manner during training. Understanding how those representations encode human-interpretable concepts is a fundamental problem. One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the pr

Jan N. Fuhg, Lloyd van Wees, Mark Obstalecki, Paul Shade

The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials characterized by elastic-plastic deformation responses, predictive computational models to characterize crystal-plasticity (CP) have been developed. However, due to their large de

Babette de Wolff

A characteristic matrix function captures the spectral information of a bounded linear operator in a matrix-valued function. In this article, we consider a delay differential equation with one discrete time delay and assume this equation is equivariant with respect to a compact symmetry group. Under this assumption, the delay differential equation can have d

Andronikos Paliathanasis, Genly Leon

We investigate exact solutions and the asymptotic dynamics for the Friedmann-Lema\^{\i}tre-Robertson-Walker universe with nonzero spatial curvature in the fourth-order modified teleparallel gravitational theory known as $f\left( T, B\right) $ theory. We show that the field equations admit a minisuperspace description, and they can reproduce any exact form of

Simon Badger, Matteo Becchetti, Ekta Chaubey, Robin Marzucca

We compute helicity amplitudes for the one-loop QCD corrections to top-quark pair production analytically in terms of a set of uniformly transcendental master integrals. We provide corrections up to $O(\epsilon^2)$ in the dimensional regulator for the first time which are relevant at NNLO. Four independent pentagon integral topologies appear in the complete

Robert Byrne, Morgan Fraser

The absence of Type IIP core-collapse supernovae arising from progenitors above 17 solar masses suggests the existence of another evolutionary path by which massive stars end their lives. The direct collapse of a stellar core to a black hole without the production of a bright, explosive transient is expected to produce a long-lived, dim, red transient known

George Wei-Shu Hou

Electroweak baryogenesis can be driven by the top quark in a general two Higgs doublet model with extra Yukawa couplings. Higgs quartics provide the first order phase transition, while extra top Yukawa coupling $\rho_{tt}$ can fuel the cosmic baryon asymmetry through the $\lambda_t\,{\rm Im}\,\rho_{tt}$ product, with flavor-changing $\rho_{tc}$ coupling as b

Jing Chen, Madhavan Manivannan, Mustafa Abduljabbar, Miquel Pericàs

Parallel applications often rely on work stealing schedulers in combination with fine-grained tasking to achieve high performance and scalability. However, reducing the total energy consumption in the context of work stealing runtimes is still challenging, particularly when using asymmetric architectures with different types of CPU cores. A common approach f

Mikhail Stolpovskiy, Xin Wu, Andrii Tykhonov, Maksym Deliyergiyev

DAMPE is a space-borne experiment for the measurement of the cosmic-ray fluxes at energies up to around 100 TeV per nucleon. At energies above several tens of TeV, the electronics of DAMPE calorimeter would saturate, leaving certain bars with no energy recorded. In the present work we discuss the application of machine learning techniques for the treatment o

Mathé T. Zeegers, Tristan van Leeuwen, Daniël M. Pelt, Sophia Bethany Coban

Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images)

Dimitrios Chalkiadakis, Johanne Hizanidis

Neuromorphic computing exploits the dynamical analogy between many physical systems and neuron biophysics. Superconductor systems, in particular, are excellent candidates for neuromorphic devices due to their capacity to operate in great speeds and with low energy dissipation compared to their silicon counterparts. In this study we revisit a prior work on Jo

Matteo Castiglioni, Giulia Romano, Alberto Marchesi, Nicola Gatti

We study single-item single-unit Bayesian posted price auctions, where buyers arrive sequentially and their valuations for the item being sold depend on a random, unknown state of nature. The seller has complete knowledge of the actual state and can send signals to the buyers so as to disclose information about it. For instance, the state of nature may refle

Sourav Chakraborty, Sandip Halder, Kalpataru Pradhan

We investigate the itinerant ferromagnetism using a diluted spin-fermion model, derived from a repulsive Hubbard model, where itinerant fermions are coupled antiferromagnetically to auxiliary fields in a three-dimensional simple cubic lattice. We focus, in particular, on understanding the spin-dependent transport properties of the itinerant fermions in the i

Harikrishnan N B, Aditi Kathpalia, Nithin Nagaraj

Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classification of cause-effect from simulated data. The data instances used are generated from coupled AR processes, cou

Peter Lowdon

In this work we outline the general analytic characteristics satisfied by scalar correlation functions at finite temperature in local quantum field theory. We demonstrate that the locality of the fields in particular imposes significant constraints on the spectral structure of the theory, and that this enables the non-perturbative effects experienced by ther

Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correia, Antonia Adler

Model inversion attacks (MIAs) aim to create synthetic images that reflect the class-wise characteristics from a target classifier's private training data by exploiting the model's learned knowledge. Previous research has developed generative MIAs that use generative adversarial networks (GANs) as image priors tailored to a specific target model. This makes

Takeshi D. Itoh, Takatomi Kubo, Kazushi Ikeda

Graphs are a highly expressive data structure, but it is often difficult for humans to find patterns from a complex graph. Hence, generating human-interpretable sequences from graphs have gained interest, called graph2seq learning. It is expected that the compositionality in a graph can be associated to the compositionality in the output sequence in many gra

Ipek Ozkaya, Zachary Kurtz, Robert L. Nord, Raghvinder S. Sangwan

Technical debt (TD) refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although developers often informally discuss TD, the concept has not yet crystalized into a consistently applied label when describing issues in most repositories. We apply machine learning to understand developer in

Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i.e., restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspire

Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft

Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy. Building on SPI with Soft Baseline Bootstrapping (Soft-SPIBB) by Nadjahi et al., we identify theoretical issues in their approach, provide a corrected theory, and derive a new algorithm that is provably safe on finite

Delphine Dessaux, Jérôme Mathé, Rosa Ramirez, Nathalie Basdevant

In order to understand the physical processes of nanopore experiments at the molecular level, microscopic information from molecular dynamics is greatly needed. Coarse-grained models are a good alternative to classical all-atom models since they allow longer simulations and application of lower electric potentials, closer to the experimental ones. We perform

João V. T. de Lima, Sérgio Luiz E. F. da Silva, João M. de Araújo, Gilberto Corso

The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associated with generalized Gaussian distributions in the context of R\'enyi, Tsallis and Kaniadakis statistics. In

Aline dos S. Almeida, D. A. Bahamon, Nuno M. R. Peres, Christiano J. S. de Matos

The use of graphene in surface plasmon resonance sensors, covering a metallic (plasmonic) film, has a number of demonstrated advantages, such protecting the film against corrosion/oxidation and facilitating the introduction of functional groups for selective sensing. Recently, a number of works have claimed that few-layer graphene can also increase the sensi

Gennaro Tucci, Édgar Roldán, Andrea Gambassi, Roman Belousov

Modelling noisy oscillations of active systems is one of the current challenges in physics and biology. Because the physical mechanisms of such processes are often difficult to identify, we propose a linear stochastic model driven by a non-Markovian bistable noise that is capable of generating self-sustained periodic oscillation. We derive analytical predict

Christoph Angermann, Matthias Schwab, Markus Haltmeier, Christian Laubichler

Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synt

Jade Freeman, Michael Rawson

In today's technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the distinction between whether the information adds substantial value toward one's goal or not. Contextual multi-armed bandit has been widely used for learning to filter contents and prioritize a

Udit Khanna, Moshe Goldstein, Yuval Gefen

This paper comprises a review of our recent works on fractional chiral modes that emerge due to edge reconstruction in integer and fractional quantum Hall (QH) phases. The new part added is an analysis of edge reconstruction of the $\nu = 2/5$ phase. QH states are topological phases of matter featuring chiral gapless modes at the edge. These edge modes may p

Maximilian Neidhardt, Stefan Gerlach, Robin Mieling, Max-Heinrich Laves

In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment strategies during a pandemic such as COVID-19. However, a conventional autopsy carries the risk of disease transmission and may be rejected by relatives. We propose minimally invasive

Romina M. Arroyo, Marina Nicolini

The aim of this article is to study the existence of invariant SKT structures on nilmanifolds. More precisely, we give a negative answer to the question of whether there exist a $k$-step ($k>2$) complex nilmanifold admitting an invariant SKT metric. We also provide a construction which serves as a tool to generate examples of invariant SKT structures on $2$-

Thomas Aird

We construct minimal and irredundant generating sets for a family of submonoids of the monoid of $n \times n$ upper triangular matrices over a commutative semiring. We show that the monoid of $n \times n$ matrices over the tropical integers, $M_n(\mathbb{Z}_\mathrm{max})$, is finitely generated if and only if $n \leq 2$, and finitely presented if and only if

Adam Małkowski, Jakub Grzechociński, Paweł Wawrzyński

Invertible transformation of large graphs into fixed dimensional vectors (embeddings) remains a challenge. Its overcoming would reduce any operation on graphs to an operation in a vector space. However, most existing methods are limited to graphs with tens of vertices. In this paper we address the above challenge with recursive neural networks - the encoder

Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan

We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances. Our study focuses on optimal control without centralized precomputed policies, but rather with adaptive control policies for the different agents that are only equipped with a stabilizing controller. We give a reduction from any (standard) regr

Samuel M. Greene, Robert J. Webber, James E. T. Smith, Jonathan Weare

We present a stable and systematically improvable quantum Monte Carlo (QMC) approach to calculating excited-state energies, which we implement using our fast randomized iteration method for the full configuration interaction problem (FCI-FRI). Unlike previous excited-state quantum Monte Carlo methods, our approach, which is an asymmetric variant of subspace

Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami

We are interested in in silico evaluation methodology for molecular optimization methods. Given a sample of molecules and their properties of our interest, we wish not only to train an agent that can find molecules optimized with respect to the target property but also to evaluate its performance. A common practice is to train a predictor of the target prope

Sourav Das, Arijit Ganguly

We study the problem of improving Dirichlet's theorem of metric Diophantine approximation in the $S$-adic setting. Our approach is based on translation of the problem related to Dirichlet improvability into a dynamical one, and the main technique of our proof is the $S$-adic version of quantitative nondivergence estimate due to D. Y. Kleinbock and G. Tomanov

Edouard Lesne, Yildiz G. Saǧlam, Raffaele Battilomo, Maria Teresa Mercaldo

Quantum materials can display physical phenomena rooted in the geometry of electronic wavefunctions. The corresponding geometric tensor is characterized by an emergent field known as Berry curvature (BC). Large BCs typically arise when electronic states with different spin, orbital or sublattice quantum numbers hybridize at finite crystal momentum. In all ma

M. B. Peters, G. P. Katsoulis, A. Emmanouilidou

We formulate a three-dimensional semi-classical model to address triple and double ionization in three-electron atoms driven by intense infrared laser pulses. During time propagation, our model fully accounts for the Coulomb singularities, the magnetic field of the laser pulse and for the motion of the nucleus at the same time as for the motion of the three

A. V. Alpatov, E. A. Peters, D. A. Pasechnyuk, A. M. Raigorodskii

In this paper, we test the performance of some modern stochastic optimization methods and practices in application to digital pre-distortion problem, that is a valuable part of processing signal on base stations providing wireless communication. In first part of our study, we focus on search of the best performing method and its proper modifications. In the

Benjamin Doerr, Amirhossein Rajabi

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution ("fast mutation", Doerr et al. (2017)) and increasing the mutation strength based on stagnation detection (Rajabi and Witt (2020)). Whereas the latter can obtai

Hao Jia, Zhe Sun, Feng Duan, Yu Zhang

In non-invasive brain-computer interface systems, pre-movement decoding plays an important role in the detection of movement before limbs actually move. Movement-related cortical potential is a kind of brain activity associated with pre-movement decoding. In current studies, patterns decoded from movement are mainly applied to the binary classification betwe

Bastian Hilder, Björn de Rijk, Guido Schneider

The real Ginzburg-Landau equation arises as a universal amplitude equation for the description of pattern-forming systems exhibiting a Turing bifurcation. It possesses spatially periodic roll solutions which are known to be stable against localized perturbations. It is the purpose of this paper to prove their stability against bounded perturbations, which ar

Shuai Zhang, Jiangyan Yi, Zhengkun Tian, Jianhua Tao

Code-switching deals with alternative languages in communication process. Training end-to-end (E2E) automatic speech recognition (ASR) systems for code-switching is especially challenging as code-switching training data are always insufficient to combat the increased multilingual context confusion due to the presence of more than one language. We propose a l

Yishay Manassen, Moamen Jbara, Michael Averbukh, Zion Hazan

We report the detection of electron spin resonance (ESR) in individual dimers of the stable free radical 2,2,6,6-tetramethyl-piperidine-1-oxyl (TEMPO). ESR is measured by the current fluctuations in a scanning tunnelling microscope (ESR-STM method). The multi-peak power spectra, distinct from macroscopic data, are assigned to dimers having exchange and Dzyal

Hao Jia, Zhe Sun, Feng Duan, Yu Zhang

Pre-movement decoding plays an important role in movement detection and is able to detect movement onset with low-frequency electroencephalogram (EEG) signals before the limb moves. In related studies, pre-movement decoding with standard task-related component analysis (STRCA) has been demonstrated to be efficient for classification between movement state an

Nolann Lainé, Guillaume Zahnd, Herv é Liebgott, Maciej Orkisz

The objective of this study is the segmentation of the intima-media complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised region-based deep-learning approach based on a dilated U-net network. It was trained and evaluated using a

Julián Tachella, Dongdong Chen, Mike Davies

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be o

Felix Mohr, Jan N. van Rijn

Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in several machine learning contexts, most not

I. Pillitteri, G. Micela, A. Maggio, S. Sciortino

The characterization of exoplanets, their formation, evolution, and chemical changes is tightly linked to our knowledge of their host stars. In particular, stellar X-rays and UV emission have a strong impact on the dynamical and chemical evolution of planetary atmospheres. We analyzed 25 XMM-Newton observations encompassing about eight years and totaling abo

Ky Ho, Inbo Sim

In this paper, we study the existence of multiple solutions to a generalized $p(\cdot)$-Laplace equation with two parameters involving critical growth. More precisely, we give sufficient "local" conditions, which mean that growths between the main operator and nonlinear term are locally assumed for the cases $p(\cdot)$-sublinear, $p(\cdot)$-superlinear, and

Morgan André

We consider a stochastic system of spiking neurons which was previously proven to present a metastable behavior for a suitable choice of the parameter, in the sense that the time of extinction is asymptotically memory-less when the number of components in the system goes to $\infty$. In the present article we complete this work by showing that, previous to e

Ronaldo Rodrigues Pela, Claudia Draxl

Ehrenfest Dynamics combined with real-time time-dependent density functional theory has proven to be a reliable tool to study non-adiabatic molecular dynamics with a reasonable computational cost. Among other possibilities, it allows for assessing in real time electronic excitations generated by ultra-fast laser pulses, as e.g., in pump-probe spectroscopy, a

E. Teichmann, H. J. Lewandowski, M. Alemani

There is a large variety of goals instructors have for laboratory courses, with different courses focusing on different subsets of goals. An often implicit, but crucial, goal is to develop students` attitudes, views, and expectations about experimental physics to align with practicing experimental physicists. The assessment of laboratory courses upon this on

Mariano Quintero, Bernardo Sievers, Joaquin Sacanell

Several phase separated manganites present a peculiar effect each time they go through a phase transition within the range characterized by phase separation. The effect is known as the thermal cycling effect(TCE) and is characterized by monotonous changes in the relative content of the coexisting phases. In this work, we analyze a way to control the effects

Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja, Vijay Arya

Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity explanations that are also stable and intuitive. In this work, we provide a novel perspective by proposing a mo

Dongsheng Chen

Leveraging unmanned aerial vehicle (UAV) is convenient to collect data from ground sensor. However, in the presence of unknown urban environment, the data collection is subject to the blockage of urban buildings. In this paper, considering the urban environment during flight, we propose dynamic adaptive modulation and height control for UAV-sensor data harve

J. Fernando Barbero G., Juan Margalef-Bentabol, Valle Varo, Eduardo J. S. Villaseñor

We study a generalization of the Holst action where we admit nonmetricity and torsion in manifolds with timelike boundaries (both in the metric and tetrad formalism). We prove that its space of solutions is equal to the one of the Palatini action. Therefore, we conclude that the metric sector is in fact identical to GR, which is defined by the Einstein-Hilbe

Petr Krautwurm, Ondřej Sokol, Vladimír Holý

This paper introduces product relation correlation, a measure of product relatedness that assesses the extent to which products may function as substitutes or complements through analysis of shared purchasing patterns. Product relation correlation can be used for tasks such as product clustering and shelf space optimization, enabling retailers to arrange ite

Yanhong Fei, Yingjie Liu, Xian Wei, Mingsong Chen

Inspired by the tremendous success of the self-attention mechanism in natural language processing, the Vision Transformer (ViT) creatively applies it to image patch sequences and achieves incredible performance. However, the scaled dot-product self-attention of ViT brings about scale ambiguity to the structure of the original feature space. To address this p

Alan Pinoy

In this paper, we build a compactification by a strictly pseudoconvex CR structure for complete and non-compact K\"ahler manifolds whose curvature tensor is asymptotic to that of the complex hyperbolic space.

Zhian Jia, Dagomir Kaszlikowski, Sheng Tan

Kitaev's quantum double model is a lattice gauge theoretic realization of Dijkgraaf-Witten topological quantum field theory (TQFT), its topologically protected ground state space has broad applications for topological quantum computation and topological quantum memory. We investigate the $\mathbb{Z}_2$ symmetry enriched generalization of the model for the cy

N. Graham, H. Weigel

We review recent progress in the computation of leading quantum corrections to the energies of classical solitons with topological structure, including multi-soliton models in one space dimension and string configurations in three space dimensions. Taking advantage of analytic continuation techniques to efficiently organize the calculations, we show how quan

E. Buchanan, K. Akiba, M. van Beuzekom, P. Collins

A comprehensive study of the spatial resolution and detection efficiency of sensor prototypes developed for the LHCb VELO upgrade is presented. Data samples were collected at the CERN SPS H8 beam line using a hadron mixture of protons and pions with momenta of approximately 180 GeV/c. The sensor performance was characterised using both irradiated and non-irr

Zaid Abdullah, Anastasios Papazafeiropoulos, Steven Kisseleff, Symeon Chatzinotas

We study the performance of a phase-noise impaired double reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input single-output (MISO) system under spatial correlation at both RISs and base-station (BS). The downlink achievable rate is derived in closed-form under maximum ratio transmission (MRT) precoding. In addition, we obtain the opt

Rouven Koch, Jose L. Lado

The computation of dynamical correlators of quantum many-body systems represents an open critical challenge in condensed matter physics. While powerful methodologies have risen in recent years, covering the full parameter space remains unfeasible for most many-body systems with a complex configuration space. Here we demonstrate that conditional Generative Ad

Niklas Höpner, Ilaria Tiddi, Herke van Hoof

Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in open-source knowledge graphs to abstract

Alexander Neb, Iyed Briki, Raoul Schoenhof

Focus of this work is to recognize standards and further features directly from 3D CAD models. For this reason, a neural network was trained to recognize nine classes of machine elements. After the system identified a part as a standard, like a hexagon head screw after the DIN EN ISO 8676, it accesses the geometrical information of the CAD system via the App

Nuno Luzia

The Hausdorff dimension of general Sierpinski carpets, [4] and [20], and the generalization on Lalley-Gatzouras carpets, [10], are today well known results, the formulas being obtain via the variational principle for the dimension. We call the multidimensional versions of these carpets Sierpinski sponges and self-affine sponges, respectively,. In this paper

Yue-Hong Feng, Ming Mei, Guojing Zhang

For unipolar hydrodynamic model of semiconductor device represented by Euler-Poisson equations, when the doping profile is supersonic, the existence of steady transonic shock solutions and C-smooth steady transonic solutions for Euler-Poisson Equations were established in [27] and [41], respectively. In this paper we further study the nonlinear structural st

Huayuan Sun

The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization. Examining the method of adaptive optimizer, we product an example

Camille Gontier, Jakob Jordan, Mihai A. Petrovici

Image datasets are commonly used in psychophysical experiments and in machine learning research. Most publicly available datasets are comprised of images of realistic and natural objects. However, while typical machine learning models lack any domain specific knowledge about natural objects, humans can leverage prior experience for such data, making comparis

Machel Reid, Yutaro Yamada, Shixiang Shane Gu

Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer arc

Run-Ze Chi, Yang Liu, Yuan Wan, Hai-Jun Liao

Investigation of dynamical excitations is difficult but crucial to the understanding of many exotic quantum phenomena discovered in quantum materials. This is particularly true for highly frustrated quantum antiferromagnets whose dynamical properties deviate strongly from theoretical predictions made based on the spin-wave or other approximations. Here we pr

Ioannis Krikidis, Constantinos Psomas

The efficiency of wireless information and power transfer (WIPT) systems requires an essential reevaluation and rethinking of the entire transceiver chain, which is characterized by a bottom-up multi-layer design approach. In this paper, we introduce and describe the key design layers: i) "Mathematical modeling", associated with the investigation of mathemat

Raoul Schönhof, Manuel Fechter

Aiming for a higher economic efficiency in manufacturing, an increased degree of automation is a key enabler. However, assessing the technical feasibility of an automated assembly solution for a dedicated process is difficult and often determined by the geometry of the given product parts. Among others, decisive criterions of the automation feasibility are t

Jeonghyeon Suh, Sunghoon Kim, E. H. Hwang, Hongki Min

We explicitly calculate the density-density response function with conserving vertex corrections for anisotropic multiband systems in the presence of impurities including long-range disorder. The direction-dependence of the vertex corrections is correctly considered to obtain the diffusion constant which is given by the combination of the componentwise trans

K. Yavuz Ekşi, Sinem Şaşmaz

We propose a precessing transient magnetar model for the recently discovered radio source GLEAM-X J162759.5-523504.3. We identify the observed period of $\sim 1$ ks as the precession period of the magnetar deformed due to its strong ($B_{\phi} \sim 10^{16}$ G) toroidal field. The resulting deformation of order $10^{-4}$ implies a spin period of $P_{\rm s} =

Jouni Parkkonen, Frédéric Paulin

We prove an abstract result on the correlations of pairs of elements in an exponentially growing discrete subset $\mathcal E$ of $[0,+\infty[\,$ endowed with a weight function. Assume that there exist $\alpha\in\mathbb R$, $c,\delta>0$ such that, as $t\to+\infty$, the weighted number $\widetilde\omega(t)$ of elements of $\mathcal E$ that are not greater than

Mark Lyubarov, Yaakov Lumer, Alex Dikopoltsev, Eran Lustig

Photonic Time Crystals (PTCs) - dielectric media with their refractive index modulated periodically in time, offer new opportunities in photonics arising from time reflections and momentum bandgaps. Here, we study the emission of light from a radiation source inside a PTC. We solve the general classical and quantum mechanical models of emission in a temporal

Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong

Attention mechanisms are dominating the explainability of deep models. They produce probability distributions over the input, which are widely deemed as feature-importance indicators. However, in this paper, we find one critical limitation in attention explanations: weakness in identifying the polarity of feature impact. This would be somehow misleading -- f

Dobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis

Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hypergraph-based representations can bette

Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng

By folding into particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the global backbone topology but the local fine-grained orientational relations between amino acids should also be considered. In this work, we propose the Orientation-Aware Graph Neur

Vladimir Garanzha, Igor Kaporin, Liudmila Kudryavtseva, François Protais

Construction of optimal deformations is one of the long standing problems of computational mathematics. We consider the problem of computing quasi-isometric deformations with minimal possible quasi-isometry constant (global estimate for relative length change).We build our technique upon [Garanzha et al. 2021a], a recently proposed numerical optimization sch

Puqing Jiang, Dihui Wang, Zeyu Xiang, Ronggui Yang

In-plane thermal conductivities of small-scale samples are hard to measure, especially for the lowly conductive ones and those lacking in-plane symmetry (i.e., transversely anisotropic materials). State-of-the-art pump-probe techniques including both the time-domain and the frequency-domain thermoreflectance (TDTR and FDTR) are advantageous in measuring the

Castaly Fan, Larry Zamick

With shell model codes being able to encompassing larger and larger spaces we find that the percentage occupancy of the leading spaces becomes smaller and smaller. How can the shell model survive in such circumstances? We will not solve this puzzle here but rather will show examples where, with some explanations, the shell model holds fast. We will use nucle

Chung-Yun Hsieh

Energy transfer and information transmission are two fundamental aspects of nature. They are seemingly unrelated, while recent findings suggest that a deep connection between them is to be discovered. This amounts to asking: Can we phrase the processes of transmitting classical bits equivalently as specific energy-transmitting tasks, thereby uncovering found

Pan He, Yuxi Chen, Yan Wang, Yanru Zhang

Recently, prompt tuning \cite{lester2021power} has gradually become a new paradigm for NLP, which only depends on the representation of the words by freezing the parameters of pre-trained language models (PLMs) to obtain remarkable performance on downstream tasks. It maintains the consistency of Masked Language Model (MLM) \cite{devlin2018bert} task in the p

R. Franz, G. Picogna, B. Ercolano, S. Casassus

X-ray- and extreme-ultraviolet- (XEUV-) driven photoevaporative winds acting on protoplanetary disks around young T-Tauri stars may strongly impact disk evolution, affecting both gas and dust distributions. We compute dust densities for the wind regions of XEUV-irradiated transition disks with gap sizes of 20 and 30 AU, and determine whether they can be obse

Raoul Schönhof, Artem Werner, Jannes Elstner, Boldizsar Zopcsak

Not only automation of manufacturing processes but also automation of automation procedures itself become increasingly relevant to automation research. In this context, automated capability assessment, mainly leveraged by deep learning systems driven from 3D CAD data, have been presented. Current assessment systems may be able to assess CAD data with regards

Yaqing Jin, Ye Yang, Huibo Hong, Xiao Xiang

With the rapid development of microwave photonics, which has expanded to numerous applications of commercial importance, eliminating the emerging bottlenecks becomes of vital importance. For example, as the main branch of microwave photonics, radio-over-fiber technology provides high bandwidth, low-loss, and long-distance propagation capability, facilitating

Hong-Kwang J. Kuo, Zoltan Tuske, Samuel Thomas, Brian Kingsbury

The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts. Advances in end-to-end (E2E) speech modeling have made it possible to train solely on semantic entities, which are far cheaper to collect than verbatim transcripts. We focus on t

Matteo Capoferri, Simone Murro

We propose a geometric approach to construct the Cauchy evolution operator for the Lorentzian Dirac operator on Cauchy-compact globally hyperbolic 4-manifolds. We realise the Cauchy evolution operator as the sum of two invariantly defined oscillatory integrals -- the positive and negative Dirac propagators -- global in space and in time, with distinguished c

Mattia Segatto, Riccardo Fontanini, Francesco Driussi, Daniel Lizzit

An accurate estimate of the ferroelectric polarization in ferroelectric-dielectric stacks is important from a materials science perspective, and it is also crucial for the development of ferroelectric based electron devices. This paper revisits the theory and application of the PUND technique in Metal-Ferroelectric-Dielectric-Metal (MFDM) structures by using

G. Abbiendi, A. Arbuzov, Sw. Banerjee, D. Biswas

The mini-proceedings of the STRONG2020 Virtual Workshop "Space-like and Time-like determination of the Hadronic Leading Order contribution to the Muon $g-2$", November 24--26 2021, are presented. This is the first workshop of the STRONG2020 WP21: JRA3-PrecisionSM: Precision Tests of the Standard Model (http://www.strong-2020.eu/joint-research-activity/jra3-p

Emilien Macault

We propose a stochastic model of opinion exchange in networks. A finite set of agents is organized in a fixed network structure. There is a binary state of the world and each agent receives a private signal on the state. We model beliefs as urns where red balls represent one possible value of the state and blue balls the other value. The model revolves purel