November 2022 arXiv papers — page 60

Giacomo Cacciapaglia, Ammar Abdalgabar, Corentin Cot, Alan S. Cornell

We present a new grand unification paradigm, where gauge couplings do not need to be equal at any given scale, instead they run towards the same fixed point in the deep ultraviolet. We provide a concrete example based on SU(5) with a compactified extra space dimension. By construction, fermions are embedded in different SU(5) bulk fields, hence baryon number

Alexandru Chirvasitu, Jun Peng

Consider a compact group $G$ acting on a real or complex Banach Lie group $U$, by automorphisms in the relevant category, and leaving a central subgroup $K\le U$ invariant. We define the spaces ${}_KZ^n(G,U)$ of $K$-relative continuous cocycles as those maps ${G^n\to U}$ whose coboundary is a $K$-valued $(n+1)$-cocycle; this applies to possibly non-abelian $

Yvain Bruned, Usama Nadeem

In this work, we translate at the level of decorated trees some of the crucial arguments which have been used in arXiv:2112.10739 for proposing a diagram-free approach for the convergence of the model in Regularity Structures. This allows us to broaden the perspective and enlarge the scope of singular SPDEs covered by this approach. It also sheds new light o

Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can n

Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek

Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or bounding box. In this work, we address the need for more informative explanations for these predictors by proposing

Tuomas Varanka, Yante Li, Wei Peng, Guoying Zhao

Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limit

Yingxue Xu, Guihua Wen, Yang Hu, Pei Yang

Unsupervised domain adaptation (UDA) aims to estimate a transferable model for unlabeled target domains by exploiting labeled source data. Optimal Transport (OT) based methods have recently been proven to be a promising solution for UDA with a solid theoretical foundation and competitive performance. However, most of these methods solely focus on domain-leve

Zhihang Zhong, Mingdeng Cao, Xiang Ji, Yinqiang Zheng

This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution. The challenges are twofold: 1) the current methods still leave considerable room for improvement in terms of visual quality even on the synthetic dataset, and 2) poor generalization to real-world data.

Takano Taira

The spontaneous symmetry breaking of a continuous symmetry in complex field theory at the exceptional point of the parameter space is known to exhibit interesting phenomena, such as the breakdown of a Higgs mechanism. In this work, we derive the complex Ginzburg-Landau model from a non-Hermitian two-band BCS model via path integral and investigate its sponta

Takashi Nagao, Ferdinando Patat, Keiichi Maeda, Dietrich Baade

Investigating interstellar (IS) dust properties in external galaxies is important not only to infer the intrinsic properties of astronomical objects but also to understand the star/planet formation in the galaxies. From the non-Milky-Way-like extinction and interstellar polarization (ISP) observed in reddened Type Ia supernovae (SNe), it has been suggested t

Maria Mylova, Jackson Levi Said, Emmanuel N. Saridakis

We construct the Effective Field Theory (EFT) of the teleparallel equivalent of general relativity (TEGR). Firstly, we present the necessary field redefinitions of the scalar field and the tetrads. Then we provide all the terms at next-to-leading-order, containing the torsion tensor and its derivatives, and derivatives of the scalar field, accompanied by gen

Fangyuan Wang, Bo Xu, Bo Xu

Currently, the chunk-wise schemes are often used to make Automatic Speech Recognition (ASR) models to support streaming deployment. However, existing approaches are unable to capture the global context, lack support for parallel training, or exhibit quadratic complexity for the computation of multi-head self-attention (MHSA). On the other side, the causal co

Ulf Lindström, Özgür Sarıoğlu

We consider the sum of the Einstein-Hilbert action and a Pontryagin density (PD) in arbitrary even dimension $D$. All curvatures are functions of independent affine (torsionless) connections only. In arbitrary dimension, not only in $D=4n$, these first order PD terms are shown to be covariant divergences of "Chern-Simons" currents. The field equation for the

Raviraj Joshi

The monolingual Hindi BERT models currently available on the model hub do not perform better than the multi-lingual models on downstream tasks. We present L3Cube-HindBERT, a Hindi BERT model pre-trained on Hindi monolingual corpus. Further, since Indic languages, Hindi and Marathi share the Devanagari script, we train a single model for both languages. We re

Ehsan Pajouheshgar, Yitao Xu, Tong Zhang, Sabine Süsstrunk

Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable

Yini Jiang, Hongwei Lin

The fairing curves and surfaces are used extensively in geometric design, modeling, and industrial manufacturing. However, the majority of conventional fairing approaches, which lack sufficient parameters to improve fairness, are based on energy minimization problems. In this study, we develop a novel progressive-iterative approximation method for fairing cu

Jamie Vovrosh, Katie Wilkinson, Sam Hedges, Kieran McGovern

Borehole gravity sensing can be used in a number of applications to measure features around a well including rock-type change mapping and determination of reservoir porosity. Quantum technology gravity sensors based on atom interferometry have the ability to offer increased survey speeds and reduced need for calibration. While surface sensors have been demon

Aleksi Kurkela

In this contribution, I discuss the utility that perturbative QCD offers in studying the matter in the cores of neutron stars. I discuss the reasons why perturbative QCD can constrain the equation of state at densities far below the densities where we can perform controlled calculations. I discuss how perturbative QCD can inform nuclear modelling of neutron

Gregory Nordahl, Lewys Jones, Emil Frang Christiansen, Kasper Aas Hunnestad

Precession electron diffraction has in the past few decades become a powerful technique for structure solving, strain analysis, and orientation mapping, to name a few. One of the benefits of precessing the electron beam, is increased reciprocal space resolution, albeit at a loss of spatial resolution due to an effect referred to as 'probe wandering'. Here, a

Kaiyi Chi, Qianqian Yang, Zhaohui Yang, Yiping Duan

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this pap

Chiranjib Mukherjee, Konstantin Recke

Let $\Gamma$ be a countable discrete group. Given any sequence $(f_n)_{n\geq 1}$ of $\ell^p$-normalized functions ($p\in [1,2)$), consider the associated positive definite matrix coefficients $\langle f_n, \rho(\cdot) f_n\rangle$ of the right regular representation $\rho$. We construct an orthogonal decomposition of the corresponding {\it Schur multipliers}

Marcin Briański, Gwenaël Joret, Konrad Majewski, Piotr Micek

The circumference of a graph $G$ is the length of a longest cycle in $G$, or $+\infty$ if $G$ has no cycle. Birmel\'e (2003) showed that the treewidth of a graph $G$ is at most its circumference minus $1$. We strengthen this result for $2$-connected graphs as follows: If $G$ is $2$-connected, then its treedepth is at most its circumference. The bound is best

Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi

Simulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated tests do not strongly contribute to identification of SDC faults, hence do not contribute towards increasing the quality of

X. Y. Gao, W. Reich, X. H. Sun, H. Zhao

A 5$^{\circ}$ $\times$ 7$^{\circ}$ sky area containing two large radio structures of G203.1+6.6 and G206.7+5.9 with a size of about 2.5$^{\circ}$ and 3.5$^{\circ}$ respectively is scanned by using the L-band 19-beam receiver of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST L-band receiver covers a frequency range of 1.0-1.5 GHz.

Genet Asefa Gesese, Harald Sack, Mehwish Alam

Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning representations for entities not seen during training. However, to the best of our knowledge, none of the existing in

Lukas Rapp, Luca Schmid, Andrej Rode, Laurent Schmalen

We propose a novel method to optimize the structure of factor graphs for graph-based inference. As an example inference task, we consider symbol detection on linear inter-symbol interference channels. The factor graph framework has the potential to yield low-complexity symbol detectors. However, the sum-product algorithm on cyclic factor graphs is suboptimal

Hossein Movasati

There are many instances such that deformation space of the homology class of an algebraic cycle as a Hodge cycle is larger than its deformation space as algebraic cycle. This phenomena can occur for algebraic cycles inside hypersurfaces, however, we are only able to gather evidences for it by computer experiments. In this article we describe one example of

Ricarda-Samantha Götte, Julia Timmermann

A major challenge in state estimation with model-based observers are low-quality models that lack of relevant dynamics. We address this issue by simultaneously estimating the system's states and its model uncertainties by a square root UKF. Concretely, we extend the state by the parameter vector of a linear combination containing suitable functions that appr

Hongyu Yu, Boyu Liu, Yang Zhong, Liangliang Hong

This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzya

Congyi Zhang, Mohamed Elgharib, Gereon Fox, Min Gu

3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implici

Marco Cè, Antoine Gérardin, Georg von Hippel, Harvey B. Meyer

The energy dependency (running) of the strength of electromagnetic interactions $\alpha$ plays an important role in precision tests of the Standard Model. The running of the former to the $Z$ pole is an input quantity for global electroweak fits, while the running of the mixing angle is susceptible to the effects of Beyond Standard Model physics, particularl

Lasse Fischer, Marta Bofill Roig, Werner Brannath

The closure principle is fundamental in multiple testing and has been used to derive many efficient procedures with familywise error rate control. However, it is often unsuitable for modern research, which involves flexible multiple testing settings where not all hypotheses are known at the beginning of the evaluation. In this paper, we focus on online multi

B. Altschul, L. C. T. Brito, J. C. C. Felipe, S. Karki

The renormalization of quantum field theories usually assumes Lorentz and gauge symmetries, besides the general restrictions imposed by unitarity and causality. However, the set of renormalizable theories can be enlarged by relaxing some of these assumptions. In this work, we consider the particular case of a CPT-preserving but Lorentz-breaking extension of

Marco Vanadia

This article presents a review of recent results on direct top quark mass measurements performed by the ATLAS and CMS collaborations on $pp$ collisions collected during Run 2 of the LHC at $\sqrt{s}=$13 TeV.

Zezhou Zhu, Yucong Zhou, Zhao Zhong

Vector Quantization (VQ) is an appealing model compression method to obtain a tiny model with less accuracy loss. While methods to obtain better codebooks and codes under fixed clustering dimensionality have been extensively studied, optimizations of the vectors in favour of clustering performance are not carefully considered, especially via the reduction of

Marcus Münzer, Chris Bard

Physics-informed Neural Networks (PINNs) often have, in their loss functions, terms based on physical equations and derivatives. In order to evaluate these terms, the output solution is sampled using a distribution of collocation points. However, density-based strategies, in which the number of collocation points over the domain increases throughout the trai

Prashant Arote, Manish Mishra

Let $G$ be a connected reductive group defined over a finite field $\mathbb{F}_q$ with corresponding Frobenius $F$. Let $\iota_G$ denote the duality involution defined by D. Prasad under the hypothesis $2\mathrm{H}^1(F,Z(G))=0$, where $Z(G)$ denotes the center of $G$. We show that for each irreducible character $\rho$ of $G^F$, the involution $\iota_G$ takes

Arul Selvam Periyasamy, Luis Denninger, Sven Behnke

Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing methods for object pose estimation output a single 6D pose. Thus, they lack the ability to reason about symmetries. Lately, modeling object orientation as a non-parametric probabil

Yilan Zhang, Fengying Xie, Jianqi Chen

Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by modern computer-aided diagnosis (CAD) technology based on deep convolutions. However, the information aggregation across modalities in MSLD remains challenging due to severity unaligned spatial resolution (e.g., dermoscopic image and clinical image) and heterogeneous data (e.g., derm

Antonio Alcántara, Carlos Ruiz

The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodolog

Stephen McIlvanna, Nhat Nguyen Minh, Yuzhu Sun, Mien Van

In this paper we present the implementation of a Control Barrier Function (CBF) using a quadratic program (QP) formulation that provides obstacle avoidance for a robotic manipulator arm system. CBF is a control technique that has emerged and developed over the past decade and has been extensively explored in the literature on its mathematical foundations, pr

Mojtaba Hosseini, Diego Rodriguez, Sven Behnke

Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotio

Héctor Sainz-Cruz, Pierre A. Pantaleón, Vo Tien Phong, Alejandro Jimeno-Pozo

Junctions provide a wealth of information on the symmetry of the order parameter of superconductors. We analyze junctions between a scanning tunneling microscope (STM) tip and superconducting twisted bilayer graphene (TBG) and TBG Josephson junctions (JJs). We compare superconducting phases that are even or odd under valley exchange (s- or f-wave). The criti

S. Moretti, S. Semlali, C. H. Shepherd-Themistocleous

We show the reinterpretation of existing searches for exotic decays of the Standard Model (SM)-like Higgs, $H \to aa (hh)$, in various final states, in the framework of the 2-Higgs Doublet Model (2HDM) Type-I. We then explore a new search for such light Higgses, $a$ and $h$, at the Large Hadron Collider (LHC) Run 3 for an integrated luminosity of 300 $\text{

Chengjie Fu, Chao Chen

We investigate the amplification of curvature perturbations in a two-field inflation model featuring a Gaussian potential bump. When the inflaton encounters a potential bump along the inflationary trajectory, its rolling speed is generally reduced, potentially causing a violation of the slow-roll condition. Consequently, the original decaying modes of comovi

Satoshi Ikehata

Existing deep calibrated photometric stereo networks basically aggregate observations under different lights based on the pre-defined operations such as linear projection and max pooling. While they are effective with the dense capture, simple first-order operations often fail to capture the high-order interactions among observations under small number of di

Chen Ma, Chushun Tian

We study the chaos exponent of some variants of the Sachdev-Ye-Kitaev (SYK) model, namely, the $\Ns=1$ supersymmetry (SUSY)-SYK model and its sibling, the $(N|M)$-SYK model which is not supersymmetric in general, for arbitrary interaction strength. We find that for large $q$ the chaos exponent of these variants, as well as the SYK and the $\Ns=2$ SUSY-SYK mo

Arnold Filtser, Michael Kapralov, Mikhail Makarov

In this paper we initiate the study of expander decompositions of a graph $G=(V, E)$ in the streaming model of computation. The goal is to find a partitioning $\mathcal{C}$ of vertices $V$ such that the subgraphs of $G$ induced by the clusters $C \in \mathcal{C}$ are good expanders, while the number of intercluster edges is small. Expander decompositions are

Lin Guoqiang

Variable selection is a classic problem in statistics. In this paper, we consider a Bayes variable selection problem based on spike-and-slab prior with mixed normal distribution proposed by Ro\v{c}kov\'a and George (2014). Motivated by Ormerod and You (2017, 2023), we use the variational inference and collapsed variational inference method to solve the Bayes

Eric L. Wisotzky, Charul Daudkhane, Anna Hilsmann, Peter Eisert

Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection

Sebastian Allmeier, Nicolas Gast

Mean field approximation is a powerful technique which has been used in many settings to study large-scale stochastic systems. In the case of two-timescale systems, the approximation is obtained by a combination of scaling arguments and the use of the averaging principle. This paper analyzes the approximation error of this `average' mean field model for a tw

Seung Hyun Lee, Chanyoung Kim, Wonmin Byeon, Sang Ho Yoon

We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization. Sound often provides information about the specific context of the scene and is closely related to a certain part of the scene or object. However, existing image stylization works have focused on stylizing the entire image usin

Yuhao Wang, Kai Wang, Xiaohong Liu, Tianrun Gao

Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas. Most existing works pay attention to the holistic impression of medical images, failing to utilize important anatomy information. However

Alberto Racca, Nguyen Anh Khoa Doan, Luca Magri

The spatiotemporal dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate and stable reduced-order models challenging. The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state for a reduced-order representation of the dynamics. We divide the turbulent flow into a spatia

Yuval Meir, Itamar Ben-Noam, Yarden Tzach, Shiri Hodassman

Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, currently extended to hundreds, are far from their biological realization. Their implausible biological dynamics relies on changing a weight in a non-local manner, as the number of routes between an output unit and a weight is typically large, using the b

Ning-An Lai, Nico Michele Schiavone

In this paper we are interested in the upper bound of the lifespan estimate for the compressible Euler system with time dependent damping and small initial perturbations. We employ some techniques from the blow-up study of nonlinear wave equations. The novelty consists in the introduction of tools from the Orlicz spaces theory to handle the nonlinear term em

Yanan Sun, Jin Lyu, Jian-Ping Lv

There has been much recent progress on exotic surface critical behavior, yet the classical-quantum correspondence of special and extraordinary-log criticality remains largely unclear. Employing worm Monte Carlo simulations, we explore the surface criticality at an emergent superfluid-Mott insulator critical point in the Villain representation, which is belie

Quan Zheng

Inspired by J. Novak's works on the asymptotic behavior of the BGW and the HCIZ matrix integrals \cite{[N0]} and by the algebraic and geometric properties of the Hurwitz numbers \cite{[IP]}, \cite{[LZZ]}, \cite{[LR]}, \cite{[OP]}, \cite{[Z1]}, and by the symplectic surgery theory of the relative GW-invariants \cite{[IP]}, \cite{[LR]}, using the elements of t

Arjun Ashoka, Adam J. Clancy, Naitik A. Panjwani, Adam Cronin

Nanoribbons - nanometer wide strips of a two-dimensional material - are a unique system in condensed matter physics. They combine the exotic electronic structures of low-dimensional materials with an enhanced number of exposed edges, where phenomena including ultralong spin coherence times, quantum confinement and topologically protected states can emerge. A

Isabel Fernández

We prove that any uniformly elliptic Weingarten (topological) sphere in S2xR must be congruent to the canonical example associated to the Weingarten equation. The result is obtained by proving that rotational uniformly elliptic Weingarten surfaces in S2xR have bounded second fundamental form together with a Hopf type result by J. A. G\'alvez and P. Mira.

Xin-Wei Yi, Zheng-Wei Liao, Jing-Yang You, Bo Gu

Topological superconductors (TSC) become a focus of research due to the accompanying Majorana fermions. However, the experimentally reported TSC are extremely rare. The recent experiments reported the kagome TSC AV$_{3}$Sb$_{5}$ (A=K, Rb, Cs), which exhibit unique superconductivity, topological surface states (TSS), and Majorana bound states. More recently,

Fabio Enrique Brochero Martínez, Daniela Alves de Oliveira

Let $\mathbb F_{q^n}$ denote the finite field with $q^n$ elements. In this paper we determine the number of $\mathbb F_{q^n}$-rational points of the affine Artin-Schreier curve given by $y^q-y = x(x^{q^i}-x)-\lambda$ and of the Artin-Schreier hypersurface $y^q-y=\sum_{j=1}^r a_jx_j(x_j^{q^{i_j}}-x_j)-\lambda.$ Moreover in both cases, we show that the Weil bo

Zsuzsanna Dancso, Tamara Hogan, Marcy Robertson

Homomorphic expansions are combinatorial invariants of knotted objects, which are universal in the sense that all finite-type (Vassiliev) invariants factor through them. Homomorphic expansions are also important as bridging objects between low-dimensional topology and quantum algebra. For example, homomorphic expansions of parenthesised braids are in one-to-

Peter Hillmann, Diana Schnell, Harald Hagel, Andreas Karcher

The knowledge of the world is passed on through libraries. Accordingly, domain expertise and experiences should also be transferred within an enterprise by a knowledge base. Therefore, models are an established medium to describe good practices for complex systems, processes, and interconnections. However, there is no structured and detailed approach for a d

Yihan Zhang, Marco Mondelli, Ramji Venkataramanan

In a mixed generalized linear model, the goal is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular

Jasmin Bogatinovski, Qiao Yu, Jorge Cardoso, Odej Kao

Dynamic random access memory failures are a threat to the reliability of data centres as they lead to data loss and system crashes. Timely predictions of memory failures allow for taking preventive measures such as server migration and memory replacement. Thereby, memory failure prediction prevents failures from externalizing, and it is a vital task to impro

Jean Pachebat, Sergei Ivanov

Gradient Boosted Decision Trees (GBDTs) are dominant machine learning algorithms for modeling discrete or tabular data. Unlike neural networks with millions of trainable parameters, GBDTs optimize loss function in an additive manner and have a single trainable parameter per leaf, which makes it easy to apply high-order optimization of the loss function. In t

Gareth Walley, Aditi Shenvi, Peter Strong, Katarzyna Kobalczyk

Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to embed, within its graph and its statistical model, asymmetries exhibited by a process. These asymmetries might be in the conditional independence relationships or in the structure

Sören Schlichting, Sayantan Sharma

We present a first principles study of chiral plasma instabilities and axial charge transfer in non-Abelian plasmas with a strong gauge-matter coupling $g^2N_f=64$, by performing $3+1$ D real-time classical-statistical lattice simulation with dynamical fermions. We explicitly demonstrate for the first time that -- unlike in an Abelian plasma -- the transfer

Shahab Eghbali, Yves-Marie Ducimetiere, Edouard Boujo, Francois Gallaire

We study numerically and theoretically the gravity-driven flow of a viscous liquid film coating the inner side of a horizontal cylindrical tube and surrounding a shear-free dynamically inert gaseous core. The liquid-gas interface is prone to the Rayleigh-Plateau and Rayleigh-Taylor instabilities. Here, we focus on the limit of low and intermediate Bond numbe

Yongyu Yan, Kui Xue, Xiaoming Shi, Qi Ye

Continual pretraining is a popular way of building a domain-specific pretrained language model from a general-domain language model. In spite of its high efficiency, continual pretraining suffers from catastrophic forgetting, which may harm the model's performance in downstream tasks. To alleviate the issue, in this paper, we propose a continual pretraining

Jie Chen, Zilong Li, Yin Zhu, Junping Zhang

Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the broad application of GNNs in large-scale industrial settings s

Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal

This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real

Alessandro Di Marco, Amit Dutta Banik, Anish Ghoshal, Gianfranco Pradisi

We discuss how a post inflationary reheating phase characterized by a nonstandard multiple scalar field cosmology can change the thermal history of the universe, affecting minimal high scale leptogenesis. In particular, we explore a class of models where a set of scalar fields in a brane-inspired dynamical scenario modifies the Boltzmann equations concerning

Ali Hürriyetoğlu, Osman Mutlu, Fırat Duruşan, Onur Uca

We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classification, iii) event sentence coreference identification, and iv) event extraction. The CASE 2022 extension consists of expanding the test dat

Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Reyyan Yeniterzi

We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022. The workshop consists of regular papers, two keynotes, working papers of shared task participants, and task overview papers. This workshop has been bringing together all aspects of event information collection across technical and social science fields. In

Rajesh Biswas, Sukanya Mitra, Victor Roy

The recently proposed connection between the Lorentz invariance of stability and the speed of signal propagation has been tested for a first-order relativistic dissipative hydrodynamic theory. The fact that the stability situation in different reference frames agrees with each other only as long as the signal propagation respects causality, has been explicit

Masudul Hasan Masud Bhuiyan, Cristian-Alexandru Staicu

Algorithmic complexity vulnerabilities are a class of security problems that enables attackers to trigger the worst-case complexity of certain algorithms. Such vulnerabilities can be leveraged to deploy low-volume, asymmetric, CPU-based denial-of-service (DoS) attacks. Previous work speculates that these vulnerabilities are more dangerous in certain web serv

Xintong Liu, Jianyu Wang, Leping Xiao, Xing Fu

The non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. For most existing methods, dense points on the relay surface are raster scanned to obtain high-quality reconstructions, which requires a long acquisition time. In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides

Chandrachur Chakraborty

Black holes (BHs) formed by collapsing and/or merging of magnetized progenitors, have magnetic fields penetrating the event horizon, and there are several possible scenarios. Thus, the no-hair theorem that assumes the outside medium is a vacuum, is not applicable in this case. Bearing this in mind and considering a Schwarzschild BH of mass $M$ immersed in a

Timo Kaiser, Lukas Ehmann, Christoph Reinders, Bodo Rosenhahn

Optimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the early training stages and overfit to specific details of noisy samples in the latter ones. We introduce Blind Knowledge Distillation - a novel teacher-student approach for learnin

Marcoen J. T. F. Cabbolet

The PBR theorem has been hailed as one of the most important theorems in the foundations of quantum mechanics (QM), , cf. E. Samuel Reich, "Quantum theorem shakes foundations", Nature (2011). Here we argue that the special measurement, used by Pusey et al. to derive the theorem, is nonexisting from the Einsteinian view on QM.

Julian Hau, Simon Bultmann, Sven Behnke

Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. The scene understanding can further be improved by including object-level information in the map. In this work, we extend a multi-view 3D semantic mapping system consisting of a network of distributed smart e

Kanehisa Takasaki

The intermediate long wave (ILW) hierarchy and its generalization, labelled by a positive integer $N$, can be formulated as reductions of the lattice KP hierarchy. The integrability of the lattice KP hierarchy is inherited by these reduced systems. In particular, all solutions can be captured by a factorization problem of difference operators. A special solu

Antoine El-Hayek, Monika Henzinger, Stefan Schmid

We study the broadcast problem on dynamic networks with $n$ processes. The processes communicate in synchronous rounds along an arbitrary rooted tree. The sequence of trees is given by an adversary whose goal is to maximize the number of rounds until at least one process reaches all other processes. Previous research has shown a $\lceil{\frac{3n-1}{2}}\rceil

Damianos Galanopoulos, Vasileios Mezaris

In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn thes

Oleg Smirnov, Aditya Tewari

In this paper, we introduce a new dataset of room interior pictures with overlaying and scene text, totalling to 4836 annotated images in 25 product categories. We provide details on the collection and annotation process of our dataset, and analyze its statistics. Furthermore, we propose a baseline method for overlaying text detection, that leverages the cha

Han Qi, Yi Su, Aviral Kumar, Sergey Levine

The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active interaction. These problems appear in many forms: offline reinforcement learning (RL), where we must produce actions that optimize the long-term reward, bandits from logged data, where

Nhat Nguyen Minh, Stephen McIlvanna, Yuzhu Sun, Yan Jin

In this paper, a safety-critical control strategy for a nonholonomic robot is developed to generate control signals that result in optimal, obstacle-free paths through dynamic environments. We formulate the control synthesis problem as an Optimal Control Problem (OCP) that enforces Control Lyapunov Function (CLF) constraints for system stability as well as s

Giovanni Amelino-Camelia, Vittorio D'Esposito, Giuseppe Fabiano, Domenico Frattulillo

Quantum gravity is expected to introduce quantum aspects into the description of reference frames. Here we set the stage for exploring how quantum gravity induced deformations of classical symmetries could modify the transformation laws among reference frames in an effective regime. We invoke the quantum group $SU_q(2)$ as a description of deformed spatial r

Kazuyoshi Tsutsumi, Ernst Niebur

We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are connected with a layered internetwork that consists of a pair of forward and backward subnets composed of static neurons (wi

Eiji Konishi

In quantum mechanics without application of any superselection rule to the set of the observables, a closed quantum system temporally evolves unitarily, and this Lorentzian regime is characterized by von Neumann entropy of exactly zero. In the holographic theory in the classicalized ground state, we argue that the unitary real-time evolution of a non-relativ

Shyam Narayanan, Jakub Tětek

Estimating the support size of a distribution is a well-studied problem in statistics. Motivated by the fact that this problem is highly non-robust (as small perturbations in the distributions can drastically affect the support size) and thus hard to estimate, Goldreich [ECCC 2019] studied the query complexity of estimating the $\epsilon$-\emph{effective sup

Milutin Obradović, Nikola Tuneski

In this paper we determine the upper bounds of $|H_{2}(3)|$ for the inverse functions of functions of some classes of univalent functions, where $H_{2}(3)(f)=a_{3}a_{5}-a_{4}^{2}$ is the Hankel determinant of a special type.

Anqi Li, Congying Han, Tiande Guo

Instances generation is crucial for linear programming algorithms, which is necessary either to find the optimal pivot rules by training learning method or to evaluate and verify corresponding algorithms. This study proposes a general framework for designing linear programming instances based on the preset optimal solution. First, we give a constraint matrix

Pradeep R. Varadwaj

Twenty-five molecule-anion complex systems [I4Tt...X-] (Tt = C, Si, Ge, Sn and Pb; X = F, Cl, Br, I, At) were examined using density functional theory (wB97XD) and ab initio (MP2 and CCSD) methods to demonstrate the ability of the tetrel atoms in molecular entities, I4Tt, to recognize the halide anions when in close proximity. The tetrel bond strength for th

Jagannath Bhanja, Sayan Goswami

A family $\mathcal{F}$ of subsets of $\{1,2,\ldots,n\}$ is called a $t$-intersecting family if $|F\cap G| \geq t$ for any two members $F, G \in \mathcal{F}$ and for some positive integer $t$. If $t=1$, then we call the family $\mathcal{F}$ to be intersecting. Define the set $\mathcal{I}(\mathcal{F}) = \{F\cap G: F, G \in \mathcal{F} \text{ and } F \neq G\}$

Amjad Ali, Mohd Arif, Romesh Kumar

In this paper we prove Area theorem, Biebarbach`s Theorem, Koebe Quarter Theorem and Mergelyan`s Approximation Theorem in the bicomplex framework.

Oliver Lantwin

The DUNE experiment is a future long-baseline neutrino oscillation experiment aiming at measuring the neutrino CP-violating phase and establishing the neutrino mass hierarchy, as well as at a rich physics programme from supernovae over low-energy physics to beyond Standard Model searches. The baseline technology for the first far detector is a proven single-

Christoph Strössner, Bonan Sun, Daniel Kressner

This work proposes the extended functional tensor train (EFTT) format for compressing and working with multivariate functions on tensor product domains. Our compression algorithm combines tensorized Chebyshev interpolation with a low-rank approximation algorithm that is entirely based on function evaluations. Compared to existing methods based on the functio