October 2022 arXiv papers — page 109

Keisuke Fujii

Nonthermal velocity distributions with much greater tails than the Maxwellian have been observed for radical atoms in plasmas for a long time. Historically, such velocity distributions have been modeled by a two-temperature Maxwell distribution. In this paper, I propose a model based on collisional energy cascade, which has been studied in the field of granu

Aycil Cesmelioglu, Sander Rhebergen

We present and analyze a strongly conservative hybridizable discontinuous Galerkin finite element method for the coupled incompressible Navier-Stokes and Darcy problem with Beavers-Joseph-Saffman interface condition. An a priori error analysis shows that the velocity error does not depend on the pressure, and that velocity and pressure converge with optimal

Haoyu Wang, Wei-Qiang Zhang, Hongbin Suo, Yulong Wan

Labeled audio data is insufficient to build satisfying speech recognition systems for most of the languages in the world. There have been some zero-resource methods trying to perform phoneme or word-level speech recognition without labeled audio data of the target language, but the error rate of these methods is usually too high to be applied in real-world s

Jérémie Bigot, Paul Freulon, Boris P. Hejblum, Arthur Leclaire

This paper is focused on the study of entropic regularization in optimal transport as a smoothing method for Wasserstein estimators, through the prism of the classical tradeoff between approximation and estimation errors in statistics. Wasserstein estimators are defined as solutions of variational problems whose objective function involves the use of an opti

Kiyuob Jung, Jehan Oh

In this paper, we construct the transport equation and the wave equation with specular derivatives and solve these equations in one-dimension. To solve these equations, we introduce new function spaces, which we term specular spaces, consisting of certain specularly differentiable functions.

Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu

The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and low generality. After gaining a deeper understanding of the recent normalization and normalizer-free research works from a sample's perspective, we reveal the fact that the problem

Ingrid Boem

The paper concerns the modelling of masonry elements strengthened through Textile Reinforced Mortar (TRM), a near surface system made of fiber-based grids or textiles embedded in mortar layers. Recently, the author, focusing on the mechanical characterization of TRM composites, developed a detailed level modelling approach by using the free, open-source Fini

Matthew S. G. Feuer, Alejandro R. -P. Montblanch, Mohammed Sayyad, Carola M. Purser

Janus transition-metal dichalcogenide monolayers are fully artificial materials, where one plane of chalcogen atoms is replaced by chalcogen atoms of a different type. Theory predicts an in-built out-of-plane electric field, giving rise to long-lived, dipolar excitons, while preserving direct-bandgap optical transitions in a uniform potential landscape. Prev

Julia El Zini, Mariette Awad

While there has been a recent explosion of work on ExplainableAI ExAI on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the difficulty of t

Sunit Bhattacharya, Vilém Zouhar, Ondřej Bojar

It is unclear whether, how and where large pre-trained language models capture subtle linguistic traits like ambiguity, grammaticality and sentence complexity. We present results of automatic classification of these traits and compare their viability and patterns across representation types. We demonstrate that template-based datasets with surface-level arti

Maximilian Scholz, Paul-Christian Bürkner

Bayesian modeling provides a principled approach to quantifying uncertainty in model parameters and model structure and has seen a surge of applications in recent years. Within the context of a Bayesian workflow, we are concerned with model selection for the purpose of finding models that best explain the data, that is, help us understand the underlying data

Aleksey Buzmakov, Tatiana Makhalova, Sergei O. Kuznetsov, Amedeo Napoli

In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators in equivalence classes robust to noise. We introduce $\Delta$-closedness, a generalization of the closure operator, where $\Delta$ measures how a closed set differs from its upper

Patrik Wahlberg

We show a result on propagation of the anisotropic Gelfand--Shilov wave front set for linear operators with Schwartz kernel which is a Gelfand--Shilov ultradistribution of Beurling type. This anisotropic wave front set is parametrized by two positive parameters relating the space and frequency variables. The anisotropic Gelfand--Shilov wave front set of the

Rui Qin, Bin Wang, Yu-Wing Tai

Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However, due to the complex degradation in natural scenes, recovering high-resolution texts from the low-resolution inputs is ambiguous and challenging. Existing methods mainly leverage dee

Manikandan Kondappan, Manish Chaudhary, Ebubechukwu O. Ilo-Okeke, Valentin Ivannikov

We show that quantum nondemolition (QND) measurements can be used to realize measurement-based imaginary time evolution. In our proposed scheme, repeated weak QND measurements are used to estimate the energy of a given Hamiltonian. Based on this estimated energy, adaptive unitary operations are applied such that only the targeted energy eigenstates are fixed

Zhang-Qi Wu, Pan-Pan Wang, Wei-Liang Qian, Cheng-Gang Shao

We generalize the combinatorial algebraic approach first proposed by Dhurandhar et al. to construct various classes of modified second-generation time-delay interferometry (TDI) solutions. The main idea behind the algorithm is to enumerate, in a given order, a specific type of commutator between two monomials defined by the products of particular time-displa

Hongde Wu

Nowadays, the Internet of Things (IoT) has become one of the most important technologies which enables a variety of connected and intelligent applications in smart cities. The smart decision making process of IoT devices not only relies on the large volume of data collected from their sensors, but also depends on advanced optimization theories and novel mach

Bibhuti Kumar Jha, Manjunath Hegde, Aditya Priyadarshi, Sudip Mandal

Kodaikanal Solar Observatory (KoSO) possesses one of world's longest and homogeneous records of sunspot observations that span more than a century (1904-2017). Interestingly, these observations (originally recorded in photographic plates/films) were taken with the same setup over this entire time period which makes this data unique and best suitable for long

Youngsoo Baek, Wilkins Aquino, Sayan Mukherjee

We propose a general framework for obtaining probabilistic solutions to PDE-based inverse problems. Bayesian methods are attractive for uncertainty quantification but assume knowledge of the likelihood model or data generation process. This assumption is difficult to justify in many inverse problems, where the specification of the data generation process is

Viacheslav V. Saenko

The problem of calculating the probability density and distribution function of a strictly stable law is considered at $x\to0$. The expansions of these values into power series were obtained to solve this problem. It was shown that in the case $\alpha<1$ the obtained series were asymptotic at $x\to0$, in the case $\alpha>1$ they were convergent and in the ca

Yu Qiao, Ziqi Wei, Yuhao Liu, Yuxin Wang

This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting. Many approaches achieve alpha mattes with complex encoders to extract robust semantics, then resort to the U-net-like decoder to concatenate or fuse encoder features. However, image matting is essentially a pixel-wise regression, an

John Pavlopoulos, Alv Romell, Jacob Curman, Olof Steinert

Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, AI-based prognostics and health management in the automotive industry ignore the textual descriptions of the experienced problems or symptoms. With this study, however, we show that a multilingual pre-trained Transformer can e

Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo

Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL. Recent works that try to solve OORL tasks without additional feature engineering mainly focus on learning the object representations and then solving tasks via reasoning based on these object representations. However,

Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng

We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class that AIXI agents can be approximated over to complex history-dependent and structured environments. The state representation and reasoning framework is based on higher-order logic,

Julia El Zini, Mohamad Mansour, Basel Mousi, Mariette Awad

Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a common evaluation framework is hindering the progress tracking of such methods and their wider adoption. In this work, inspired by offline information retrieval, we propose different m

Ana Carrasco

This work proposes a framework that incorporates trust in an ad hoc teamwork scenario with human-agent teams, where an agent must collaborate with a human to perform a task. During the task, the agent must infer, through interactions and observations, how much the human trusts it and adapt its behaviour to maximize the team's performance. To achieve this, we

Luciano Gottardi, Kenichiro Nagayoshi

The state-of-the-art technology of X-ray microcalorimeters based on superconducting transition edge sensors (TESs), for applications in astrophysics and particle physics, is reviewed. We will show the advance in understanding the detector physics and describe the recent breakthroughs in the TES design that are opening the way towards the fabrication and the

Alireza Olama, Eduardo Camponogara, Paulo R. C. Mendes

This paper presents the Distributed Primal Outer Approximation (DiPOA) algorithm for solving Sparse Convex Programming (SCP) problems with separable structures, efficiently, and in a decentralized manner. The DiPOA algorithm development consists of embedding the recently proposed Relaxed Hybrid Alternating Direction Method of Multipliers (RH-ADMM) algorithm

V. D. Esin, O. O. Shvetsov, A. V. Timonina, N. N. Kolesnikov

We experimentally investigate charge transport through a single planar junction between a NiTe$_2$ Dirac semimetal and a normal gold lead. At millikelvin temperatures we observe non-Ohmic $dV/dI(V)$ behavior resembling Andreev reflection at a superconductor -- normal metal interface, while NiTe$_2$ bulk remains non-superconducting. The conclusion on supercon

Jiemin Li, Yanhong Gu, Yoshihiro Takahashi, Keisuke Higashi

Understanding the spin dynamics in antiferromagnetic (AFM) thin films is fundamental for designing novel devices based on AFM magnon transport. Here, we study the magnon dynamics in thin films of AFM $S=5/2$ $\alpha$-Fe$_2$O$_3$ by combining resonant inelastic x-ray scattering, Anderson impurity model plus dynamical mean-field theory, and Heisenberg spin mod

Weichen Yu, Hongyuan Yu, Yan Huang, Chunshui Cao

Gait recognition aims to identify individuals by recognizing their walking patterns. However, an observation is made that most of the previous gait recognition methods degenerate significantly due to two memorization effects, namely appearance memorization and label noise memorization. To address the problem, for the first time noisy gait recognition is stud

Georg Wölflein, In Hwa Um, David J Harrison, Ognjen Arandjelović

The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widesprea

Jian-Wei Zhang, Yifan Sun, Yi Yang, Wei Chen

Few-shot segmentation (FSS) aims at performing semantic segmentation on novel classes given a few annotated support samples. With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intricate decoder to perform sophisticated pix

Lai Tian, Anthony Man-Cho So

We consider the computation of an approximately stationary point for a Lipschitz and semialgebraic function $f$ with a local oracle. If $f$ is smooth, simple deterministic methods have dimension-free finite oracle complexities. For the general Lipschitz setting, only recently, Zhang et al. [47] introduced a randomized algorithm that computes Goldstein's appr

Yu Qiao, Yuhao Liu, Ziqi Wei, Yuxin Wang

Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details. However, we argue that appearance-agnostic integration can only provide biased foreground details and alpha mattes require different-level feature aggregation for better pixel-wi

Mengyuan Zhao, Yazheng Tao, Kevin Weber, Tim Haase

In the context of simulating precision laser interferometers, we compare via several examples two wavefront decomposition methods: the Mode Expansion Method (MEM) and the Gaussian beam decomposition (GBD) for their precision and applicability. To judge the performance of these methods, we define different types of errors and study their properties. We specif

Chen Jiang

The advent of ultra-precise photometry space missions enable the possibility of investigating stellar interior with mixed modes. The structural variations induced by the discontinuity of the chemical composition left behind during the first dredge--up is an important feature in the stellar mid-layers located between the hydrogen-burning shell and the base of

J. Gomes da Silva, A. Bensabat, T. Monteiro, N. C. Santos

In this work we investigate the effect of the H$\alpha$ bandpass width in the correlation between the CaII H&K and H$\alpha$ indices with the aim of improving the H$\alpha$ index to better identify and model the signals coming from activity variability. We used a sample of 152 FGK dwarfs observed with HARPS for more than 13 years with enough cadence to be ab

Maria Vittoria Mazziotti, Antonio Bianconi, Roberto Raimondi, Gaetano Campi

While it is known that a resonant amplification of Tc in two-gap superconductors can be driven by using the Fano-Feshbach resonance tuning the chemical potential near a Lifshitz transition, little is known on tuning the Tc resonance by cooperative interplay of the Rashba spin-orbit coupling (RSOC) joint with phonon mediated (e-ph) pairing at selected k-space

Sanjeet Swaroop Panda, P. A. Ameen Yasir, C. M. Chandrashekar

The ability of quantum walks to evolve in a superposition of distinct quantum states has been used as a resource in quantum communication protocols. Under certain settings, the $k$-cycle discrete-time quantum walks\,(DTQW) are known to recur to its initial state after every $t_r$ steps. We first present a scheme to optically realize any $k$-cycle DTQW using

Gaetano Campi, Maria Vittoria Mazziotti, Thomas Jarlborg, Antonio Bianconi

Novel nanoscale probes are opening new venues for understanding unconventional electronic and magnetic functionalities driven by multiscale lattice complexity in doped high temperature superconducting perovskites. In this work we focus on the multiscale texture at supramolecular level of atomic oxygen interstitials (O-i) stripes in HgBa$_2$CuO$_{4+y}$ at opt

Andrei Velichko, Maksim Belyaev, Matthias P. Wagner, Alireza Taravat

Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity of the short time series by approximating dissimilar entropy techniques such as Singular value decomposition entropy (SvdEn), Permutation entropy (PermEn), Sample entropy (SampEn)

Masanobu Kunitomo, Tristan Guillot, Gaël Buldgen

Solar evolutionary models are thus far unable to reproduce spectroscopic, helioseismic, and neutrino constraints consistently, resulting in the so-called solar modeling problem. In parallel, planet formation models predict that the evolving composition of the protosolar disk and, thus, of the gas accreted by the proto-Sun must have been variable. We show tha

Alessandro Codenotti, Aleksandra Kwiatkowska

We continue the study of projective Fra\"{i}ss\'{e} limits of trees initiated by Charatonik and Roe and we construct many generalized Wa\.{z}ewski dendrites as the topological realization of a projective Fra\"{i}ss\'{e} limit of families of finite trees with (weakly) coherent epimorphisms. Moreover we use the categorical approach to Fra\"{i}ss\'{e} limits de

Jeppe R. Andersen, Hitham Hassan, Sebastian Jaskiewicz

We present a method of merging the exclusive LO-matched high energy resummation of High Energy Jets (HEJ) with the parton shower of Pythia which preserves the accuracy of the LO cross sections and the logarithmic accuracy of both resummation schemes across all of phase space. Predictions produced with this merging prescription are presented with comparisons

Yusen Wu, Zigeng Huang, Jinzhao Sun, Xiao Yuan

In the noisy-intermediate-scale-quantum era, Variational Quantum Eigensolver (VQE) is a promising method to study ground state properties in quantum chemistry, materials science, and condensed physics. However, general quantum eigensolvers are lack of systematical improvability, and achieve rigorous convergence is generally hard in practice, especially in so

Hamzeh Keshavarzi, Fanglei Wu

In this paper, for $1\leq p<\infty$, we provide several descriptions of Schatten $p$-class Hankel operators $H_f$ and $H_{\overline{f}}$ on the weight Bergman space $A^2_\omega$, in terms of a certain global and local mean oscillation of the symbol $f\in L^2_\omega$, provided $\omega$ is a class of regular weights. The approaches applied to rely on several c

Zhiyuan Zhang, Ruixuan Luo, Qi Su, Xu Sun

Recently, Sharpness-Aware Minimization (SAM) algorithm has shown state-of-the-art generalization abilities in vision tasks. It demonstrates that flat minima tend to imply better generalization abilities. However, it has some difficulty implying SAM to some natural language tasks, especially to models with drastic gradient changes, such as RNNs. In this work,

Zhiyuan Zhang, Qi Su, Xu Sun

Despite the potential of federated learning, it is known to be vulnerable to backdoor attacks. Many robust federated aggregation methods are proposed to reduce the potential backdoor risk. However, they are mainly validated in the CV field. In this paper, we find that NLP backdoors are hard to defend against than CV, and we provide a theoretical analysis tha

Taijara Loiola de Santana, Paulo Anselmo da Mota Silveira Neto, Eduardo Santana de Almeida, Iftekhar Ahmed

Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, there has been no thorough study to understand Jupyter development challenges from the practitioners' point of view. This paper presents a systematic study of bugs and challenge

T. Azevedo Silva, O. D. S. Demangeon, N. C. Santos, R. Allart

High-resolution spectroscopy studies of ultra-hot Jupiters have been key in our understanding of exoplanet atmospheres. Observing into the atmospheres of these giant planets allows for direct constraints on their atmospheric compositions and dynamics while laying the groundwork for new research regarding their formation and evolution environments. Two of the

Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander

This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current approaches in experimental design focus on model-parameter estimation and require specification of a particular model, whereas in imaging, other tasks may drive the design. Fur

Mengyuan Ma, Nhan Thanh Nguyen, Markku Juntti

Hybrid beamforming (HBF) transceiver architectures based on frequency-independent phase shifters (PS-HBF) are sensitive to the phases and physical directions with limited capability to compensate for the detrimental effects of the beam squint. Motivated by the fact that switches are phase-independent and more power/cost efficient than PSs, we consider the sw

Vitaly Roman'kov

Let $S$ be a pool of $s$ parties and Alice be the dealer. In this paper, we propose a scheme that allows the dealer to encrypt messages in such a way that only one authorized coalition of parties (which the dealer chooses depending on the message) can decrypt. At the setup stage, each of the parties involved in the process receives an individual key from the

Elisabeth Steffen, Helena Mihaljević, Milena Pustet, Nyco Bischoff

Over the course of the COVID-19 pandemic, existing conspiracy theories were refreshed and new ones were created, often interwoven with antisemitic narratives, stereotypes and codes. The sheer volume of antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential for anti-discrimination organizations and researc

Farzad Nikfam, Alberto Marchisio, Maurizio Martina, Muhammad Shafique

Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human being. For example, an external attack can modify an image adding noises invisible for a human eye, but a DNN model misclassified the image. A

Christopher E. Mower, Theodoros Stouraitis, João Moura, Christian Rauch

Reliable contact simulation plays a key role in the development of (semi-)autonomous robots, especially when dealing with contact-rich manipulation scenarios, an active robotics research topic. Besides simulation, components such as sensing, perception, data collection, robot hardware control, human interfaces, etc. are all key enablers towards applying mach

Minheng Ni, Zitong Huang, Kailai Feng, Wangmeng Zuo

Without the demand of training in reality, humans can easily detect a known concept simply based on its language description. Empowering deep learning with this ability undoubtedly enables the neural network to handle complex vision tasks, e.g., object detection, without collecting and annotating real images. To this end, this paper introduces a novel challe

Thomas Lang, Tomas Sauer

Segmentation, i.e., the partitioning of volumetric data into components, is a crucial task in many image processing applications ever since such data could be generated. Most existing applications nowadays, specifically CNNs, make use of voxelwise classification systems which need to be trained on a large number of annotated training volumes. However, in man

Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell

Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are designed for context-free grammars (CFGs), algorithms for PDAs often resort to a PDA-to-CFG conversion. In this paper, we d

Leonardo Possenti, Marina Barbiroli, Enrico M. Vitucci, Franco Fuschini

mm-waves are envisaged as key enabler for 5G and 6G wireless communications, thanks to the wider bandwidth and to the possibility of implementing large-scale antenna arrays and new advanced transmission techniques, such as massive MIMO and beamforming, that can take advantage of the multidimensional properties of the wireless channel. In order to further stu

Fabien Dufoulon, Yuval Emek, Ran Gelles

Constructing a shortest path between two network nodes is a fundamental task in distributed computing. This work develops schemes for the construction of shortest paths in randomized beeping networks between a predetermined source node and an arbitrary set of destination nodes. Our first scheme constructs a (single) shortest path to an arbitrary destination

Xing Wu, Chaochen Gao, Zijia Lin, Zhongyuan Wang

Video language pre-training methods have mainly adopted sparse sampling techniques to alleviate the temporal redundancy of videos. Though effective, sparse sampling still suffers inter-modal redundancy: visual redundancy and textual redundancy. Compared with highly generalized text, sparsely sampled frames usually contain text-independent portions, called vi

Yanqiao Ding, Qinhao He

In recent years, monotone double Hurwitz numbers were introduced as a naturally combinatorial modification of double Hurwitz numbers. Monotone double Hurwitz numbers share many structural properties with their classical counterparts, such as piecewise polynomaility, while the quantitative properties of these two numbers are quite different. We consider real

Sven Jandura, Jeff D Thompson, Guido Pupillo

Robust gate sequences are widely used to reduce the sensitivity of gate operations to experimental imperfections. Typically, the optimization minimizes the average gate error, however, recent work in quantum error correction has demonstrated that the performance of encoded logical qubits is sensitive to not only the average error rate, but also the type of e

Terry Ruas, Jan Philip Wahle, Lennart Küll, Saif M. Mohammad

This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives. The dedicated interfaces allow its users to identify trends in research activity, productivity, accessibility, author's productivity, venues' statistics, topics of interest, and the impact of computer science resea

Aolan Sun, Xulong Zhang, Tiandong Ling, Jianzong Wang

Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools. The previous applications aim to save the commuting cost with real-time interactions. However, our application is going to lower the production and reproduction costs when preparing the communication materials. This paper proposes a system calle

Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li

Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. However, they still encounter several challenges: 1) Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization and should be incorporated into the model design. Existing simulators either consider insufficient symmetry, or enf

Gert Aarts

Out of the many exciting results obtained with the lattice approach to QCD under extreme conditions, I discuss a few selected items related to chiral symmetry: the chiral condensate as an approximate order parameter, meson screening masses, and masses of baryons and mesons, including D(s) mesons, when approaching the crossover from the hadronic side.

Johann Ostmeyer

The Hubbard model is an important tool to understand the electrical properties of various materials. More specifically, on the honeycomb lattice it is used to describe graphene predicting a quantum phase transition from a semimetal to a Mott insulating state. In this work two different numerical techniques are presented that have been employed for simulation

Kleanthis Malialis, Dimitris Papatheodoulou, Stylianos Filippou, Christos G. Panayiotou

There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another signific

Ioar Casado, Aritz Pérez

In recent years, we have seen a handful of work on inference algorithms over non-stationary data streams. Given their flexibility, Bayesian non-parametric models are a good candidate for these scenarios. However, reliable streaming inference under the concept drift phenomenon is still an open problem for these models. In this work, we propose a variational i

Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding

Deep learning models have shown their vulnerability when dealing with adversarial attacks. Existing attacks almost perform on low-level instances, such as pixels and super-pixels, and rarely exploit semantic clues. For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, however, resulting in low attack transfer

Hyang Keun Yoo, Daniel Schwarz, Soren Ulstrup, Woojin Kim

We investigated how SrOx segregates on a Nb doped SrTiO3 (100) surface by in air annealing. Using atomic force and photoemission electron microscopes, we can directly visualize the morphology and the electronic phase changes with SrOx segregation. SrOx islands less than 2 micron meter in size and 1-5 unit cells thick nucleate first and grow in a labyrinth do

L. Gottardi, M. de Wit, E. Taralli, K. Nagayashi

We present a detailed analysis of the fundamental noise sources in superconducting transition-edge sensors (TESs), ac voltage biased at MHz frequencies and treated as superconducting weak links. We have studied the noise in the resistive transition as a function of bath temperature of several detectors with different normal resistances and geometries. We sho

Ayush Kumar Tewari

Metric trees and metric tree arrangements index cones in the polyhedral fan structure in the Dressian $Dr(2,n)$ and $Dr(3,n)$ respectively. We introduce the notion of generalized metric tree arrangements which parameterize points in $Dr(k,n)$ and extend previously known results to $Dr(k,n)$ along with providing explicit examples of these generalized metric t

Nolene F. Naidu, Sante Carloni, Peter Dunsby

We apply the 1+1+2 covariant semi-tetrad approach to describe a general static and spherically symmetric relativistic stellar object which contains two fluids with anisotropic pressure. The corresponding Tolman-Oppenheimer-Volkoff equations are then obtained in covariant form for the anisotropic case. These equations are used to obtain new exact solutions us

Xiaotie Deng, Ningyuan Li, Weian Li, Qi Qi

We investigate the model of multiple contests held in parallel, where each contestant selects one contest to join and each contest designer decides the prize structure to compete for the participation of contestants. We first analyze the strategic behaviors of contestants and completely characterize the symmetric Bayesian Nash equilibrium. As for the strateg

Przemysław Stawczyk, Robert Nowak

Reducing the cost of sequencing genomes provided by next-generation sequencing technologies has greatly increased the number of genomic projects. As a result, there is a growing need for better assembly and assembly validation methods. One promising idea is to use heterogeneous data in assembly projects. Optical Mapping (OM) is beneficial in validating genom

S. Zarrin, S. Dadfar

We provide compact formulas for the heavy quarks structure functions $F^{q\bar{q}}_{2}(x, Q^{2})$ and $F^{q\bar{q}}_{L}(x, Q^{2})$, with $q = c, b$ and $t$, in $e^{-}p$ interaction with respect to the behavior of the gluon density at the next-to-leading order (NLO) approximation and present ratios $R ^{q\bar{q}} = F^{q\bar{q}}_{L} /F^{q\bar{q}}_{2}$ for thes

Oliver Bär, Alexander Broll, Rainer Sommer

Using Heavy Meson Chiral Perturbation Theory (HMChPT), the $B^* \pi$ excited states contamination of the $B \to \pi$ vector form factors is computed to NLO in the chiral expansion and in the static limit. The results suggest that the excited states for $h_\parallel$ are of the order of a few percent whereas $h_\perp$ receives large negative contributions and

V. O. Manturov, I. M. Nikonov

Virtual knot theory has experienced a lot of nice features that did not appear in classical knot theory, e.g., parity and picture-valued invariants. In the present paper we use virtual knot theory effects to construct new representations of classical (pure) braids.

Mohsen Khodadi, Reza Pourkhodabakhshi

Does circumventing the curvature singularity of the Kerr black hole affects the timescale of the scalar cloud formation around it? By definition, the scalar cloud, forms a gravitational atom with hydrogen-like bound states, lying on the threshold of a massive scalar field's superradiant instability regime (time-growing quasi-bound states) and beyond (time-de

Maximilian Ammer, Stephan Durr

Wilson-like Dirac operators can be written in the form $D=\gamma_\mu\nabla_\mu-\frac {ar}{2} \Delta$. For Wilson fermions the standard two-point derivative $\nabla_\mu^{(\mathrm{std})}$ and 9-point Laplacian $\Delta^{(\mathrm{std})}$ are used. For Brillouin fermions these are replaced by improved discretizations $\nabla_\mu^{(\mathrm{iso})}$ and $\Delta^{(\m

Ariel Francis Perez-Mellor, Pascal Parneix, Florent Calvo, Cyril Falvo

The effects of a finite temperature on the equilibrium structures of hydrocarbon molecules are computationally explored as a function of size and relative chemical composition in hydrogen and carbon. Using parallel tempering Monte Carlo simulations employing a reactive force field, we find that in addition to the phases already known for pure carbon, namely

Jonas Fritzsch, Justus Bogner, Markus Haug, Ana Cristina Franco da Silva

The domain of cyber-physical systems (CPS) has recently seen strong growth, e.g., due to the rise of the Internet of Things (IoT) in industrial domains, commonly referred to as "Industry 4.0". However, CPS challenges like the strong hardware focus can impact modern software development practices, especially in the context of modernizing legacy systems. While

Oliver Bar, Alexander Broll, Rainer Sommer

Multi-particle states with additional pions are expected to result in a non-negligible excited-state contamination in lattice simulations. We show that heavy meson chiral perturbation theory can be employed to calculate the contamination due to two-particle $B\pi$ states in various $B$-meson observables like the $B$-meson decay constant and the $BB^*\pi$ cou

Han Wu, Zilong Zhao, Lydia Y. Chen, Aad van Moorsel

Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning methodology, since it avoids exchanging data between participants, but instead exchanges model parameters. FL has traditionally been applied to image, voice and similar data, but recently it has started to draw attention from domains including financial services

Anchen Shao, Lina Hu, Lin Song, Minghao Hua

In this paper, the Wulff cluster model combined phonon calculation is used to investigate the relationship between the structure of metallic melts (Pb/Al) and the abnormal viscosity change. Although absolute value of the surface energy does not change significantly with temperature, the Wulff shape changed evidently. When temperature raise to 975K, Pb(321) s

Axel Hübner, Santiago Rigamonti, Claudia Draxl

The widely used linear-response (LR) theory of thermal conduction in the quantum regime rests on the yet unproven assumption, that the thermal conductivity is invariant with respect to the gauge of the energy density of the system. This assumption manifests itself clearly in, e.g., Hardy's formulation of the heat-flux operator [Phys. Rev. 132, 168 (1963)]. I

Yusen Wang, Zongcheng Li, Yu Jiang, Kaixuan Zhou

We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations u

Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara

This paper presents an overview of the tenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2022. BioASQ is an ongoing series of challenges that promotes advances in the domain of large-scale biomedical semantic indexing and question answering. In this edition, the challenge was composed of the three

Zhihuan Zhou, Yuhao Mu, Gang Liu, Lixin Xu

The Hubble horizon at matter-radiation equality ($k^{-1}_{\rm{eq}}$) and the sound horizon at the last scattering surface ($r_s(z_*)$) provides interesting consistency check for the $\Lambda$CDM model and its extensions. It is well known that the reduction of $r_s$ can be compensated by the increase of $H_0$, while the same is true for the standard rulers $k

Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its objective function, is popular for black-box optimization. However, due to the limitations of GPs, BO underperforms in some problems such as those with categorical, high-dimensional or image inputs. To this end, recent works have used the highly expressive neural netwo

Matt Deitke, Dhruv Batra, Yonatan Bisk, Tommaso Campari

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the cha

Hua Shao

In this paper, we focus on some properties, calculations and estimations of topological entropy for a nonautonomous dynamical system $(X,f_{0,\infty})$ generated by a sequence of continuous self-maps $f_{0,\infty}=\{f_n\}_{n=0}^{\infty}$ on a compact uniform space $X$. We obtain the relations of topological entropy among $(X, f_{0,\infty})$, its $k$-th produ

Zheng Zhu, Qianqian Chen

We study the prerequisites for realizing superconductivity in doped triangular-lattice Mott insulators by considering three distinct parent spin backgrounds, i.e., $120^{\circ}$ antiferromagnets, quantum spin liquid, and stripy antiferromagnets, and all possible sign combinations $(\tau_1, \tau_2)$ of nearest-neighbor hopping and next-nearest-neighbor hoppin

Yossi Azar, Amos Fiat, Federico Fusco

We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budge

Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa

The generic homomorphism problem, which asks whether an input graph $G$ admits a homomorphism into a fixed target graph $H$, has been widely studied in the literature. In this article, we provide a fine-grained complexity classification of the running time of the homomorphism problem with respect to the clique-width of $G$ (denoted $\operatorname{cw}$) for v

Bruno Mera, Tomoki Ozawa

We introduce a class of singular connections as an alternative to the Berry connection for any family of quantum states defined over a parameter space. We find a natural application of the singular connection in the context of transition dipoles between two bands. We find that the shift vector is nothing but the difference between the singular connection and