November 2022 arXiv papers — page 92

Yiheng Zhang, Ting Yao, Zhaofan Qiu, Tao Mei

The recent advances in deep learning predominantly construct models in their internal representations, and it is opaque to explain the rationale behind and decisions to human users. Such explainability is especially essential for domain adaptation, whose challenges require developing more adaptive models across different domains. In this paper, we ask the qu

Qi Cai, Yingwei Pan, Ting Yao, Tao Mei

Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection. However, there has not been evidence in support of building such cascade structures for 3D object detection, a challenging detection scenario with highly spars

Joseph Early, Ying-Jung Deweese, Christine Evers, Sarvapali Ramchurn

Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable

Paloma Rabaey, Cedric De Boom, Thomas Demeester

Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inability to deal with unstructured data. While neural networks do not have this limitation, they are not interpretable and are inherently unable to deal with causal structure in the inp

Yi Han

We investigate the stochastic heat equation driven by space-time white noise defined on an abstract Hilbert space, assuming that the drift and diffusion coefficients are both merely H\"older continuous. Random field SPDEs are covered as special examples. We give the first proof that there exists a unique in law mild solution when the diffusion coefficient is

Alemayehu Nana Koya, Marco Romanelli, Joel Kuttruff, Nils Henriksson

In the past twenty years, we have reached a broad understanding of many light-driven phenomena in nanoscale systems. The temporal dynamics of the excited states are instead quite challenging to explore, and, at the same time, crucial to study for understanding the origin of fundamental physical and chemical processes. In this review we examine the current st

Thomas Fernique, Victor Lutfalla

Rhombus Penrose tilings are tilings of the plane by two decorated rhombi such that the decoration match at the junction between two tiles (like in a jigsaw puzzle). In dynamical terms, they form a tiling space of finite type. If we remove the decorations, we get, by definition, a sofic tiling space that we here call geometrical Penrose tilings. Here, we show

Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang

Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term. A core problem of LJP is how to distinguish confusing legal cases, where only subtle text differences exist. Previous studies fail to distinguish different classification errors with a standard cross-entropy classi

Sergei Lepeshov, Nadine Meyer, Patrick Maurer, Oriol Romero-Isart

We propose to introduce additional control in levitated optomechanics by trapping a meta-atom, i.e. a subwavelength and high-permittivity dielectric particle supporting Mie resonances. In particular, we theoretically demonstrate that optical levitation and center-of-mass ground-state cooling of silicon nanoparticles in vacuum is not only experimentally feasi

Shanshan Wang, Michael Schreckenberg, Thomas Guhr

Vehicle velocities in neighbouring road sections are correlated with memory effects. We explore the response of the velocities in the sequence of sections to a congestion in a given section and its dynamic characteristics. To this end, we transfer the concept of response functions from previous applications in finance to traffic systems. The dynamical charac

P. Miron

We investigate the influence of the Loop Current (LC) on the connectivity of the Gulf of Mexico, with a focus on the West Florida Shelf (WFS), using in situ trajectories from satellite-tracked drifting buoys in the Gulf of Mexico (GoM). We subset the dataset into two groups, Loop Current extended and retracted phases, that are used to construct two Markov Ch

Luis Altenkort, Alexander M. Eller, Anthony Francis, Olaf Kaczmarek

We calculate shear viscosity and bulk viscosity in SU(3) gauge theory on the lattice at $1.5 \,T_c$. The viscosities are extracted via a Kubo formula from the reconstructed spectral function which we determine from the Euclidean-time dependence of the corresponding channel of the energy-momentum tensor correlators. We obtain unprecedented precision for the c

Jinghuai Zhang, Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong

Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset, which consists of images or image-text pairs. CL is vulnerable to data poisoning based backdoor attacks (DPBAs), in which an attacker injects poisoned inputs into the pre-training dataset so the encoder is backdoored. However, existing DPBAs achieve limited

Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang, Chengxiang Zhai

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models

Dominik Scheinert, Soeren Becker, Jonathan Bader, Lauritz Thamsen

Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments. Automated approaches are desirable as poor decisions can reduce performance and raise costs. The majority of existing automated approaches either build performance models from previous workload executions or conduct iterative resou

Lukas Körber, Christopher Heins, Ivan Soldatov, Rudolf Schäfer

We present an experimental and numerical study of three-magnon splitting in a micrometer-sized magnetic disk with the vortex state strongly deformed by static in-plane magnetic fields. Excited with a large enough power at frequency $f_\mathrm{RF}$, the primary radial magnon modes of a cylindrical magnetic vortex can decay into secondary azimuthal modes via s

Antonino Curcio, Antonio Gianfranco Sabato, Marc Nuñez Eroles, Juan Carlos GonzalezRosillo

Li1.5Al0.5Ge1.5(PO4)3 (LAGP) is among the most promising solid electrolytes for the next generation all-solid-state lithium batteries. However, preparing LAGP electrolytes is timeand energy-intensive. In this work, LAGP glassy powders were sintered and crystallized in 180 seconds by ultrafast high-temperature sintering (UHS) under conditions attractive for c

Jian-Zhou Zhu

We propose and demonstrate, with the one-dimensional Korteweg-de Vries-Burgers model, the scenarios of transfer loop and \textit{all-scale} statistical equilibrium, the former being associated to shock formation and the latter to Gaussian distributions as in a canonical ensemble, but with wavelength-dependent temperatures. The discussions emphasize, among th

Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan

Reconfigurable intelligent surfaces (RISs) are considered to be a promising solution to overcome the blockage issue in the millimeter-wave (mmWave) band. Energy efficiency is an important performance metric in mmWave systems with a large number of antennas. However, due to the severe path loss in mmWave systems, resource allocation algorithms tend to allocat

Martin Averseng

We construct a piecewise-polynomial interpolant $u \mapsto \Pi u$ for functions $u:\Omega \setminus \Gamma \to \mathbb{R}$, where $\Omega \subset \mathbb{R}^d$ is a Lipschitz polyhedron and $\Gamma \subset \Omega$ is a possibly non-manifold $(d-1)$-dimensional hypersurface. This interpolant enjoys approximation properties in relevant Sobolev norms, as well a

Wannapon Suraworachet, Qi Zhou, Mutlu Cukurova

Reflective writing is part of many higher education courses across the globe. It is often considered a challenging task for students as it requires self-regulated learning skills to appropriately plan, timely engage and deeply reflect on learning experiences. Despite an advance in writing analytics and the pervasiveness of human feedback aimed to support stu

Jennifer Andreoli-Fang, George Kondylis

We describe a new synchronous and distributed medium access control (MAC) protocol for multihop wireless networks that provides bandwidth guarantees to unicast connections. Our MAC protocol is based on a slotted time division multiple access (TDMA) architecture, with a multi-mini-slotted signaling phase scheduling data transmissions over slots in the followi

Seok Hyun Byun, Tri Lai

MacMahon's classical theorem on the number of boxed plane partitions has been generalized in several directions. One way to generalize the theorem is to view boxed plane partitions as lozenge tilings of a hexagonal region and then generalize it by making some holes in the region and counting its tilings. In this paper, we provide new regions whose numbers of

Sergio Cruz-Blázquez, Angela Pistoia, Giusi Vaira

We consider the classical geometric problem of prescribing the scalar and boundary mean curvatures via conformal deformation of the metric on a $n-$dimensional compact Riemannian manifold. We deal with the case of negative scalar curvature and positive boundary mean curvature. It is known that if $n=3$ all the blow-up points are isolated and simple. In this

David Curtin, Jyotirmoy Roy, Graham White

We demonstrate analytically and numerically that "optimized partial dressing" (OPD) thermal mass resummation, which uses gap equation solutions inserted into the tadpole, efficiently tames finite-temperature perturbation theory calculations of the effective thermal potential, without necessitating use of the high-temperature approximation. An analytical esti

Nikola Djukic, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan

We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts. Existing methods extract queries by feature pooling which

Roberto Muñoz, Gianluca Occhetta, Luis E. Solá Conde

In this paper we study properties of the Chow ring of rational homogeneous varieties of classical type, more concretely, effective zero divisors of low codimension, and a related invariant called effective good divisibility. This information is then used to study the question of (non)existence of nonconstant maps among these varieties, generalizing previous

Chee-Khian Sim

In the literature, besides the assumption of strict complementarity, superlinear convergence of implementable polynomial-time interior point algorithms using known search directions, namely, the HKM direction, its dual or the NT direction, to solve semi-definite programs (SDPs) is shown by (i) assuming that the given SDP is nondegenerate and making modificat

K. A. Lyakhov, A. N. Pechen

A method for solving the problem of efficient population transfer from the ground to some excited state by available technical means (varying resonator length) is proposed. We consider a mixture of similar quantum systems distributed in a stationary gas flow in the resonator with variable resonator length, which implements tailored laser field close to the o

Morgan Sandler, Arun Ross

In this work, we study the hypothesis that speaker identity embeddings extracted from speech samples may be used for detection and classification of emotion. In particular, we show that emotions can be effectively identified by learning speaker identities by use of a 1-D Triplet Convolutional Neural Network (CNN) & Global Style Token (GST) scheme (e.g., Deep

Zi-Ying Yang, Fang-Zheng Peng, Mao-Jun Yan, Mario Sánchez Sánchez

Theoretical predictions for the spectrum of heavy meson-baryon bound states are a fundamental tool for disentangling the nature of the different pentaquark states that have been observed in experimental facilities. Here we explore this spectrum in a phenomenological model that describes the heavy meson-baryon interaction in terms of a contact-range interacti

Abdelrahman Taha, Hao Luo, Ahmed Alkhateeb

Current scene depth estimation approaches mainly rely on optical sensing, which carries privacy concerns and suffers from estimation ambiguity for distant, shiny, and transparent surfaces/objects. Reconfigurable intelligent surfaces (RISs) provide a path for employing a massive number of antennas using low-cost and energy-efficient architectures. This has th

Ziwen Liu, Bonan Li, Congying Han, Tiande Guo

Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased to learn only the ability to discriminate positive and negative pairs due to the discriminative task setting. However, this bias would lead to ignoring its sufficiency for other do

Nana Cabo Bizet, Jonanthan Hidalgo Núñez, Gil Estefano Rodrígez Rivera

In this work, we discuss the SIR epidemiological model and different variations of it applied to the propagation of the COVID-19 pandemia; we employ the data of the state of Guanajuato and of Mexico. We present some considerations that can improve the predictions made by those models. We consider a time-dependent infection rate, which we adjust to the data.

Kai J. Spychala, Zeeshan H. Amber, Lukas M. Eng, Michael Rüsing

Coherent nonlinear optical micro-spectroscopy is a frequently used tool in modern material science, as it is sensitive to many different local observables, which comprise, among others, crystal symmetry and vibrational properties. The richness in information, however, may come with challenges in data interpretation, as one has to disentangle the many differe

Vittorio Lippi, Raphael Deimel

Probabilistic Movement Primitives (ProMPs) are a widely used representation of movements for human-robot interaction. They also facilitate the factorization of temporal and spatial structure of movements. In this work we investigate a method to temporally align observations so that when learning ProMPs, information in the spatial structure of the observed mo

Greta Goracci, Davide Ferrari, Simone Giannerini, Francesco ravazzolo

Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. However, neither theory nor estimation methods are currently available when the data present heavy tails or anomalous observations, which is often the case in applications. In this pape

Leanna B. Müller, Götz S. Uhrig

Establishing a comprehensive model of the rich spin dynamics in BiCu$_2$PO$_6$ has been a challenge over the last decade. Inelastic neutron scattering experiments revealed that its elementary triplons are non-degenerate showing the existence of significant anisotropic spin couplings. Evidence for triplon decay into two triplons has been found, but two promin

Francisco Valentini, Juan Cruz Sosa, Diego Fernandez Slezak, Edgar Altszyler

Recent research has shown that static word embeddings can encode word frequency information. However, little has been studied about this phenomenon and its effects on downstream tasks. In the present work, we systematically study the association between frequency and semantic similarity in several static word embeddings. We find that Skip-gram, GloVe and Fas

Simon Wietheger, Benjamin Doerr

The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the most prominent multi-objective evolutionary algorithm for real-world applications. While it performs evidently well on bi-objective optimization problems, empirical studies suggest that it is less effective when applied to problems with more than two objectives. A recent mathematical runtime ana

William Emanuelsson, Alejandro Penacho Riveiros, Yuchao Li, Karl H. Johansson

Efficiently solving path planning problems for a large number of robots is critical to the successful operation of modern warehouses. The existing approaches adopt classical shortest path algorithms to plan in environments whose cells are associated with both space and time in order to avoid collision between robots. In this work, we achieve the same goal by

Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang

When users move in a physical space (e.g., an urban space), they would have some records called mobility records (e.g., trajectories) generated by devices such as mobile phones and GPS devices. Naturally, mobility records capture essential information of how users work, live and entertain in their daily lives, and therefore, they have been used in a wide ran

Simone Rademacher

We consider the regularized Landau-Pekar equations with positive speed of sound and prove the existence of subsonic traveling waves. We provide a definition of the effective mass for the regularized Landau-Pekar equations based on the energy-velocity expansion of subsonic traveling waves. Moreover we show that this definition of the effective mass agrees wit

Antía López Galdo, Alejandro Guerrero-López, Pablo M. Olmos, María Jesús Gómez García

Railway axle maintenance is critical to avoid catastrophic failures. Nowadays, condition monitoring techniques are becoming more prominent in the industry to prevent enormous costs and damage to human lives. This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures

Erik Fransson, Petter Rosander, Fredrik Eriksson, J. Magnus Rahm

The soft modes associated with continuous-order phase transitions are associated with strong anharmonicity. This leads to the overdamped limit where the phonon quasi-particle picture can breakdown. However, this limit is commonly restricted to a narrow temperature range, making it difficult to observe its signature feature, namely the breakdown of the invers

Anne-Marie Aubert, Ahmed Moussaoui, Maarten Solleveld

We consider four classes of classical groups over a non-archimedean local field F: symplectic, (special) orthogonal, general (s)pin and unitary. These groups need not be quasi-split over F. The main goal of the paper is to obtain a local Langlands correspondence for any group G of this kind, via Hecke algebras. To each Bernstein block Rep(G)^s in the categor

Danijela Damjanovic, Ralf Spatzier, Kurt Vinhage, Disheng Xu

Zimmer's superrigidity theorems on higher rank Lie groups and their lattices launched a program of study aiming to classify actions of semisimple Lie groups and their lattices, known as the {\it Zimmer program}. When the group is too large relative to the dimension of the phase space, the Zimmer conjecture predicts that the actions are all virtually trivial.

Mingren Shen, Dina Sheyfer, Troy David Loeffler, Subramanian K. R. S. Sankaranarayanan

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the abil

Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg

We initiate an investigation of private sampling from distributions. Given a dataset with $n$ independent observations from an unknown distribution $P$, a sampling algorithm must output a single observation from a distribution that is close in total variation distance to $P$ while satisfying differential privacy. Sampling abstracts the goal of generating sma

Pieter Delobelle, Thomas Winters, Bettina Berendt

Large transformer-based language models, e.g. BERT and GPT-3, outperform previous architectures on most natural language processing tasks. Such language models are first pre-trained on gigantic corpora of text and later used as base-model for finetuning on a particular task. Since the pre-training step is usually not repeated, base models are not up-to-date

Yuying Xie, Thomas Arildsen, Zheng-Hua Tan

Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attributes. Disentanglement of these attributes is carried out by different prior settings of the corresponding latent variables. For the prior of speaker identity variable, \acrshort{f

Faran Zhou, Kyle Hwangbo, Qi Zhang, Chong Wang

The interplay between a multitude of electronic, spin, and lattice degrees of freedom underlies the complex phase diagrams of quantum materials. Layer stacking in van der Waals (vdW) heterostructures is responsible for exotic electronic and magnetic properties, which inspires stacking control of two-dimensional magnetism. Beyond the interplay between stackin

Hernan Haimovich, Rodrigo Aldana-Lopez, Richard Seeber, David Gomez-Gutierrez

According to recent results, convergence in a prespecified or prescribed finite time can be achieved under extreme model uncertainty if control is applied continuously over time. This paper shows that this extreme amount of uncertainty cannot be tolerated under sampling, not even if sampling could become infinitely frequent as the deadline is approached, unl

Pieter Schiettecatte, Zeger Hens, Pieter Geiregat

Atomically thin two-dimensional (2D) semiconductors are extensively investigated for opto-electronic applications that require strong light-matter interactions. In view of such applications, it is essential to understand how (photo)excitation alters the non-linear optical response of these materials under high carrier density conditions. Broadband transient

Ryugo Iwami, Kazutaka Kanno, Atsushi Uchida

Photonic computing is attracting increasing interest to accelerate information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor lasers is useful for solving the multi-armed bandit problem in reinforcement learning for computing applications. In this study, we numerically evaluate the chaotic mode-competiti

KM3NeT Collaboration

This article reports the first observation of the Moon and the Sun shadows in the sky distribution of cosmic-ray induced muons measured by the KM3NeT/ORCA detector. The analysed data-taking period spans from February 2020 to November 2021, when the detector had 6 Detection Units deployed at the bottom of the Mediterranean Sea, each composed of 18 Digital Opt

Rhorom Priyatikanto, Emanuel Sungging Mumpuni, Taufiq Hidayat, Muhammad Bayu Saputra

A new astronomical observatory in southeastern Indonesia is currently under construction. This Timau National Observatory will host a 3.8-metre telescope for optical and near-infrared observations. To support the operation and planning, the characterisation of the site needs to be appropriately performed. However, limited resources and access to the site hin

Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn

Given a set of $n$ points in $d$ dimensions, the Euclidean $k$-means problem (resp. the Euclidean $k$-median problem) consists of finding $k$ centers such that the sum of squared distances (resp. sum of distances) from every point to its closest center is minimized. The arguably most popular way of dealing with this problem in the big data setting is to firs

Eloi Ruiz-Gironés, Xevi Roca

We answer the questions of the high-order technology focus group (HO-TFG) about the mesh generation for the high-lift common research model of the 4-th high-lift prediction workshop. The HO-TFG seeks answers about the feasibility of generating meshes for complex geometries, and how to measure the quality of different aspects of the mesh. To answer these ques

Hrishikesh Gupta, Stefan Thalhammer, Markus Leitner, Markus Vincze

Transparent objects are common in day-to-day life and hence find many applications that require robot grasping. Many solutions toward object grasping exist for non-transparent objects. However, due to the unique visual properties of transparent objects, standard 3D sensors produce noisy or distorted measurements. Modern approaches tackle this problem by eith

Souvik Sur

Verifiable delay functions (VDF) are functions that take a specified number of sequential steps to be evaluated but can be verified efficiently. In this paper, we introduce a new complexity class that contains all the VDFs. We show that this new class $\mathbf{VDF}$ is a subclass of $\mathbf{CLS}$ (continuous local search) and Relaxed-Sink-of-Verifiable-Line

Olga Polverino, Paolo Santonastaso, John Sheekey, Ferdinando Zullo

A subspace of matrices over $\mathbb{F}_{q^e}^{m\times n}$ can be naturally embedded as a subspace of matrices in $\mathbb{F}_q^{em\times en}$ with the property that the rank of any of its matrix is a multiple of $e$. It is quite natural to ask whether or not all subspaces of matrices with such a property arise from a subspace of matrices over a larger field

Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen, H. S. Udaykumar

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and

Xiaoyou Chen, Mark L. Lewis

In this note, we prove that if $G$ is solvable and ${\rm cod}(\chi)$ is a $p$-power for every nonlinear, monomial, monolithic $\chi\in {\rm Irr}(G)$ or every nonlinear, monomial, monolithic $\chi \in {\rm IBr} (G)$, then $P$ is normal in $G$, where $p$ is a prime and $P$ is a Sylow $p$-subgroup of $G$.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these models do not leverage. In this work, we

George Onoufriou, Marc Hanheide, Georgios Leontidis

Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply. However yield forecasting is notoriously difficult, and oft-inaccurate. Premonition Net is a multi-timeline, time sequence ingesting approach towards processing the

Thomas K. Bracht, Tim Seidelmann, Yusuf Karli, Florian Kappe

To coherently control a few-level quantum emitter, typically pulses with an energy resonant to the transition energy are applied making use of the Rabi mechanism, while a single off-resonant pulse does not result in a population inversion. Surprisingly, a two-color excitation with a combination of two off-resonant pulses making use of the Swing-UP of quantum

Manfred Buchacher, Manuel Kauers

The orbit-sum method is an algebraic version of the reflection-principle that was introduced by Bousquet-M\'{e}lou and Mishna to solve functional equations that arise in the enumeration of lattice walks with small steps restricted to $\mathbb{N}^2$. It proceeds by computing a set of algebraic substitutions that can be applied to a given functional equation,

E. J. Marchesini, V. Reynaldi, F. Vieyro, J. Saponara

Context. The gamma-ray emitting source WISE J141046.00+740511.2 has been associated with a Fermi-LAT detection by crossmatching with Swift/XRT data. It has shown all the canonical observational characteristics of a BL Lac source, including a power-law, featureless optical spectrum. However, it was only recently detected at radio frequencies and its radio flu

Sharan Mourya, SaiDhiraj Amuru, Kiran Kumar Kuchi

Deep learning-based massive MIMO CSI feedback has received a lot of attention in recent years. Now, there exists a plethora of CSI feedback models mostly based on auto-encoders (AE) architecture with an encoder network at the user equipment (UE) and a decoder network at the gNB (base station). However, these models are trained for a single user in a single-c

Ravi Dwivedi

In this paper, we give the matrix version of Horn's hypergeometric function and its confluent cases. We also discuss the regions of convergence, the system of matrix differential equations of bilateral type, differential formulae and infinite summation formulae satisfied by these hypergeometric matrix functions. We also give the certain integral representati

Lucas Iwanikow, Pascal Febvre

We realized a time-domain simulator based on the electrodynamics of Cooper pairs and quasiparticles in Josephson junctions. The tool, based on the charge carriers' densities of states described by the Werthamer and Harris formalisms of Bardeen-Cooper-Schrieffer (BCS) theory, allows to analyze the behavior of current- or voltage-controlled Josephson junction-

Khiem Vinh Tran, Hao Phu Phan, Khang Nguyen Duc Quach, Ngan Luu-Thuy Nguyen

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that,

Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge

Qian Li, Jianxin Li, Lihong Wang, Cheng Ji

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems mak

Franz Gmeineder, Bogdan Raiţă, Jean Van Schaftingen

We identify necessary and sufficient conditions on $k$th order differential operators $\mathbb{A}$ in terms of a fixed halfspace $H^+\subset\mathbb{R}^n$ such that the Gagliardo--Nirenberg--Sobolev inequality $$ \|D^{k-1}u\|_{\mathrm{L}^{\frac{n}{n-1}}(H^+)}\leq c\|\mathbb{A} u\|_{\mathrm{L}^1(H^+)}\quad\text{for }u\in\mathrm{C}^\infty_c (\mathbb{R}^{n},V) $

Luca Bertolani, Andrea Idini

Cellular automata are a set of computational models in discrete space that have a discrete time evolution defined by neighbourhood rules. They are used to simulate many complex systems in physics and science in general. In this work, statistical mechanics and thermodynamics are used to analyse a large set of outer totalistic two-dimensional cellular automata

Alin Albu-Schäffer, Arne Sachtler

Traditionally, robots are regarded as universal motion generation machines. They are designed mainly by kinematics considerations while the desired dynamics is imposed by strong actuators and high-rate control loops. As an alternative, one can first consider the robot's intrinsic dynamics and optimize it in accordance with the desired tasks. Therefore, one n

Sebastián Reyes-Carocca, Pietro Speziali

In this short note, we prove the existence of infinitely many pairwise non-isomorphic non-hyperelliptic Riemann surfaces with automorphism group acting transitively on the Weierstrass points. We also found all those compact Riemann surfaces with automorphism group acting transitively on the Weierstrass points, under the assumption that they are simple.

Xin-Yi Tang, Zhe Li, Feng Xue, Pengfei Ji

By first-principles calculations, we study the magnetic and topological properties of XYBi2Te5-family (X, Y = Mn, Ni, V, Eu) compounds. The strongly coupled double magnetic atom-layers can significantly enhance the magnetic ordering temperature while keeping the topologically nontrivial properties. Particularly, NiVBi2Te5 is found to be a magnetic Weyl semim

Souvik Sur

A Verifiable Delay Function (VDF) is a function that takes a specified sequential time $T$ to be evaluated, but can be verified in $\Omega(\log{T})$-time. For meaningful security, $T$ can be at most subexponential in the security parameter $\lambda$ but has no lower bound. VDFs are useful in several applications ranging from randomness beacons to sustainable

Umberto Cappellazzo, Daniele Falavigna, Alessio Brutti

Continual learning refers to a dynamical framework in which a model receives a stream of non-stationary data over time and must adapt to new data while preserving previously acquired knowledge. Unluckily, neural networks fail to meet these two desiderata, incurring the so-called catastrophic forgetting phenomenon. Whereas a vast array of strategies have been

Seth D. Axen, Alexandra Gessner, Christian Sommer, Nils Weitzel

Paleoclimatology -- the study of past climate -- is relevant beyond climate science itself, such as in archaeology and anthropology for understanding past human dispersal. Information about the Earth's paleoclimate comes from simulations of physical and biogeochemical processes and from proxy records found in naturally occurring archives. Climate-field recon

Sahazada Aziz, Sohan Kumar Jha, Anisur Rahaman

We investigate the cosmic inflation scenario of a specific $f(R)$ model that contains more than one higher-order term in $R$. The $f(R)$ considered here has the terms $R^2$, $R^3$, and $R^4$ along with the linear term. A rigorous investigation has been carried out in the presence of these higher-order terms to figure out whether it leads to a physically sens

Yue Zhang, Zhenghua Li

Recently, Zhang et al. (2022) propose a syntax-aware grammatical error correction (GEC) approach, named SynGEC, showing that incorporating tailored dependency-based syntax of the input sentence is quite beneficial to GEC. This work considers another mainstream syntax formalism, i.e., constituent-based syntax. By drawing on the successful experience of SynGEC

Mohammed Alser, Julien Eudine, Onur Mutlu

Searching for similar genomic sequences is an essential and fundamental step in biomedical research and an overwhelming majority of genomic analyses. State-of-the-art computational methods performing such comparisons fail to cope with the exponential growth of genomic sequencing data. We introduce the concept of sparsified genomics where we systematically ex

David Meister, Frank Dürr, Frank Allgöwer

Event-triggered control has the potential to provide a similar performance level as time-triggered (periodic) control while triggering events less frequently. It therefore appears intuitive that it is also a viable approach for distributed systems to save scarce shared network resources used for inter-agent communication. While this motivation is commonly us

Maximilian Ciric, Denys I. Bondar, Ole Steuernagel

Quantum Hamiltonians containing nonseparable products of non-commuting operators, such as $\hat{\bf x}^m \hat{\bf p}^n$, are problematic for numerical studies using split-operator techniques since such products cannot be represented as a sum of separable terms, such as $T(\hat{\bf p}) + V(\hat{\bf x})$. In the case of classical physics, Chin [Phys. Rev. E $\

Sania Dad, Piotr Dziawa, Wiktoria Zajkowska, Sławomir Kret

We investigate the full and half-shells of Pb(1-x)Sn(x)Te topological crystalline insulator deposited by molecular beam epitaxy on the sidewalls of wurtzite GaAs nanowires (NWs). Due to the distinct orientation of the IV-VI shell with respect to the III-V core the lattice mismatch along the nanowire axis is less than 4%. The Pb(1-x)Sn(x)Te solid solution is

Zinuo Cai, Changliang Ren

Quantum networks, which can exceed the framework of standard bell theorem, flourish the investigation of quantum nonlocality further. Recently, a concept of full quantum network nonlocality (FNN) which is stronger than network nonlocality, has been defined and can be witnessed by Kerstjens-Gisin-Tavakoli (KGT) inequalities [Phys. Rev. Lett. 128 (2022)]. In t

Marco Crepaldi, Charanraj Mohan, Erik Garofalo, Andrew Adamatzky

Magnetic fluids are excellent candidates for important research fields including energy harvesting, biomedical applications, soft robotics and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computation capability, including the emulation of synaptic func

Wei-Hua Wang, Ming-Yu Ge, Xi Huang, Xiao-Ping Zheng

Ge et al. reported a peculiar large glitch observed from SGR 1935 recently~\citep{2022arXiv221103246G}. % Interestingly, this glitch occurred about $3.1\pm 2.5~\rm{days}$ before FRB 200428, accompanied by a delayed spin up (DSU) process with a timescale of $8\pm 1~\rm{days}$. % This DSU is the first one detected from magnetars unambiguously, previously, DSU

Jing-Bo Wang

Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing. To construct two-qubit gates in trapped ions, experimental manipulation approaches for ion chains are becoming increasingly prevalent. Given the restricted control technology, how implementing high-fidelity quantum gate operations is cru

Tianbai Xiao, Martin Frank

This paper addresses a neural network-based surrogate model that provides a structure-preserving approximation for the fivefold collision integral. The notion originates from the similarity in structure between the BGK-type relaxation model and residual neural network (ResNet) when a particle distribution function is treated as the input to the neural networ

M. Holovko, D. di Caprio

In this paper, starting from the Born-Green-Yvon (BGY) equation, we derive a general expression for the contact value of the singlet distribution function near a hard wall for anisotropic fluids. This relation includes two separate contributions. One is connected to the partial bulk pressure relative to a given orientation of the molecules. The second one is

Mentzelos Melistas

Let $E/\mathbb{Q}$ be an optimal elliptic curve of analytic rank zero. It follows from the Birch and Swinnerton-Dyer conjecture for elliptic curves of analytic rank zero that the order of the torsion subgroup of $E/\mathbb{Q}$ divides the product of the order of the Shafarevich--Tate group of $E/\mathbb{Q}$, the (global) Tamagawa number of $E/\mathbb{Q}$, an

Jiahao Cui, Ruoxin Xiao, Shiyuan Fang, Minnan Pei

Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based

Michal Doucha

A countable group $G$ has the strong topological Rokhlin property (STRP) if it admits a continuous action on the Cantor space with a comeager conjugacy class. We show that having the STRP is a symbolic dynamical property. We prove that a countable group $G$ has the STRP if and only if certain sofic subshifts over $G$ are dense in the space of subshifts. A su

Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framewo

Geoffroy Peeters, Florian Angulo

In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA). We train a deep encoder to learn features such that the Self-Similarity-Matrix (SSM) resulting from those approximates a ground-truth SSM. This is done by minimizing a loss between both SSMs. Since this loss is differentiable w.r.t. its input features we can