May 2023 arXiv papers — page 118

Xuanchen Li, Yan Niu, Bo Zhao, Haoyuan Shi

3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spectra from exchanging complementary information, resulting in spectral-inconsistent inference of the local spatial high frequency components. As a

Sonu Verma, Moon Jip Park

It has been known that the bulk-boundary correspondence (BBC) of the non-Hermitian skin effect is characterized by the topology of the complex eigenvalue spectra, while the topology of the wave function gives rise to Hermitian BBC with conventional boundary modes. In this work, we go beyond the known description of the non-Hermitian topological phase by disc

Georgios Panayiotou, Matteo Magnani, Bruno Pinaud

Real-world multilayer networks can be very large and there can be multiple choices regarding what should be modeled as a layer. Therefore, there is a need for their effective storage and manipulation. Currently, multilayer network analysis software use different data structures and manipulation operators. We aim to categorize operators in order to assess whi

Stanislav Minkov, Alexey Okunev, Ivan Shilin

We construct an open set of endomorphisms of an arbitrary two-dimensional manifold which have attractors and non-wandering sets with non-invariant interior. This is a notable contrast to the properties of diffeomorphisms, where the interior must be invariant.

Ryu Sasaki

Multivariate Krawtchouk polynomials are constructed explicitly as Birth and Death polynomials, which have the nearest neighbour interactions. They form the complete set of eigenpolynomials of a birth and death process with the birth and death rates at population $x=(x_1,\ldots,x_n)$ are $B_j(x)=\bigl(N-\sum_{i=1}^nx_i\bigr)$ and $D_j(x)=p_i^{-1}x_j$, $0<p_j$

Gustave Cortal, Alain Finkel, Patrick Paroubek, Lina Ye

Emotion regulation is a crucial element in dealing with emotional events and has positive effects on mental health. This paper aims to provide a more comprehensive understanding of emotional events by introducing a new French corpus of emotional narratives collected using a questionnaire for emotion regulation. We follow the theoretical framework of the Comp

David J. Benson, John Greenlees

Let $G$ be a finite group and $k$ a field of characteristic $p$. We conjecture that if $M$ is a $kG$-module with $H^*(G,M)$ finitely generated as a module over $H^*(G,k)$ then as an element of the stable module category $\mathsf{StMod}(kG)$, $M$ is contained in the thick subcategory generated by the finitely generated $kG$-modules and the modules $M'$ with $

Simon Koschel, Sebastian Buschjäger, Claudio Lucchese, Katharina Morik

With the ongoing integration of Machine Learning models into everyday life, e.g. in the form of the Internet of Things (IoT), the evaluation of learned models becomes more and more an important issue. Tree ensembles are one of the best black-box classifiers available and routinely outperform more complex classifiers. While the fast application of tree ensemb

Laurène Muller, Michel Libsig, Bastien Martinez, Denis Bidino

The current need of improving performance in terms of control and aerodynamic efficiency of ammunitions leads to the necessity of performing accurate flying geometry characterizations. Therefore, new investigation methods are developed in order to increase the aerodynamic knowledge. Free flight measurements experiments are the most common way to obtain dynam

K. Motla, Arushi, S. Jangid, P. Meena

Superconducting high-entropy alloys (HEAs) are a newly burgeoning field of unconventional superconductors and raise intriguing questions about the presence of superconductivity in highly disordered systems, which lack regular phonon modes. In our study, we have synthesized and investigated the superconducting characteristics of two new transition elements ba

Priya Mishra, Mitesh Kumar Behera, Papia Panda, Monojit Ghosh

Our study aims to investigate the viability of neutrino mass models that arise from discrete non-Abelian modular symmetry groups, i.e., $\Gamma_N$ with ($N=1,2,3,\dots$) in the future neutrino experiments T2HK, DUNE, and JUNO. Modular symmetry reduces the usage of flavon fields compared to the conventional discrete flavor symmetry models. Theories based on m

Alon Granot, Amir Levinson, Ehud Nakar

We compute the structure of a Newtonian, multi-ion radiation-mediated shock (RMS) for different compositions anticipated in various stellar explosions. We use a multifluid RMS model that incorporates electrostatic coupling between the different plasma constituents as well as Coulomb friction in a self-consistent manner, and approximates the effect of pair cr

Hanfeng Zhai, Jingjie Yeo

Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilization of biofilms as engineered living materials for applications in pollution alleviation, material self-healing

Federico Pichi, Beatriz Moya, Jan S. Hesthaven

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real-time and many-query evaluations of parametric Partial Differential Equations (PDEs). Linear techniques such as Proper Orthogonal Decomposition (POD) an

Julia Rozanova, Marco Valentino, Andre Freitas

Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a desirable form of analysis from both an interpretability and model evaluation perspective, that it is valuable to zone in on specific patterns of reasoning with enough structure a

Márk Kondákor, Karlo Penc

Motivated by the recent realization of an artificial quantum spin ice in an array of superconducting qubits with tunable parameters [King {\it et al.}, Science 373, 576 (2021)], we scrutinize a quantum six vertex model on the square lattice that distinguishes type-I and type-II vertices. We map the zero-temperature phase diagram using numerical (exact diagon

Brian Harvie, Ye-Kai Wang

In this paper, we study the Minkowski-type inequality for asymptotically flat static manifolds $(M^{n}, g)$ with boundary and with dimension $ n < 8$ that was establishedby McCormick. First, we show that any asymptotically flat static $(M^{n},g)$ which achieves the equality and has CMC or equipotential boundary is isometric to a rotationally symmetric region

Jiadong Yu, Ahmad Alhilal, Tailin Zhou, Pan Hui

Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user experience. In contrast to online reinforcement learning (RL) algorithms, which can incur substantial communication overheads and longer delays, the majority of existing works empl

Luca Marzola

We explore the phenomenology of quantum entanglement at collider experiments by computing the polarization density matrix of processes yielding two massive gauge bosons. After reviewing the formalism, we detail observables suitable to test the presence of entanglement and quantum correlations in the di-boson system. The implied violation of Bell inequalities

Alexandru Pascadi

We introduce a regularity method for sparse graphs, with new regularity and counting lemmas which use the Schatten-von-Neumann norms to measure uniformity. This leads to $k$-cycle removal lemmas in subgraphs of mildly-pseudorandom graphs, and also in graphs lacking a quasi-smooth family of bipartite subgraphs, extending results of Conlon, Fox, Sudakov and Zh

Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy

In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks, yet they have a weak correlation with human. Human-aligned metrics (CTC, CtrlEval,

Sushmita Saha, Deepak Sain, Alestin Mawrie

We show that the tunable gate voltage in n-doped AlGaAs/GaAs QW (quantum well) is a key in designing an efficient and ultrafast MRAM (magnetoresistive random access memory). The Rashba spin-orbit coupling in such QWs can be tuned appropriately by the gate voltage to create an intense spin-Hall field which in turns interacts with the ferromagnetic layer of th

Kai Xu, Jiali Guo, Grazia Raciti, Alejandro R. Goni

We present an innovative contactless method suitable to study in-plane thermal transport based on beam-offset frequency-domain thermoreflectance using a one-dimensional heat source with uniform power distribution. Using a one-dimensional heat source provides a number of advantages as compared to point-like heat sources, as typically used in time- and frequen

Henri Gouin

Invariance theorems in analytical mechanics, such as Noether's theorem, can be adapted to continuum mechanics. For this purpose, it is useful to give a functional representation of the motion and to interpret the groups of invariance with respect to the space of reference associated with Lagrangian variables. A convenient method of calculus uses the Lie deri

Tim Fischer, Michael Rogenmoser, Matheus Cavalcante, Frank K. Gürkaynak

Meeting the staggering bandwidth requirements of today's applications challenges the traditional narrow and serialized NoCs, which hit hard bounds on the maximum operating frequency. This paper proposes FlooNoC, an open-source, low-latency, fully AXI4-compatible NoC with wide physical channels for latency-tolerant high-bandwidth non-blocking transactions and

Shitao Li, Minjia Shi

Few-weight codes over finite chain rings are associated with combinatorial objects such as strongly regular graphs (SRGs), strongly walk-regular graphs (SWRGs) and finite geometries, and are also widely used in data storage systems and secret sharing schemes. The first objective of this paper is to characterize all possible parameters of Plotkin-optimal two-

Jorge Alvarez-Fidalgo, Jose Emilio Labra-Gayo

Shape Expressions (ShEx) are used in various fields of knowledge to define RDF graph structures. ShEx visualizations enable all kinds of users to better comprehend the underlying schemas and perceive its properties. Nevertheless, the only antecedent (RDFShape) suffers from limited scalability which impairs comprehension in large cases. In this work, a visual

Ibtihal Ferwana, Suyoung Park, Ting-Yi Wu, Lav R. Varshney

Discontinuities can be fairly arbitrary but also cause a significant impact on outcomes in larger systems. Indeed, their arbitrariness is why they have been used to infer causal relationships among variables in numerous settings. Regression discontinuity from econometrics assumes the existence of a discontinuous variable that splits the population into disti

Ahad N. Zehmakan, Charlotte Out, Sajjad Hesamipour Khelejan

We study a rumor spreading model where individuals are connected via a network structure. Initially, only a small subset of the individuals are spreading a rumor. Each individual who is connected to a spreader, starts spreading the rumor with some probability as a function of their trust in the spreader, quantified by the Jaccard similarity index. Furthermor

I. T. Habibullin, A. R. Khakimova, A. U. Sakieva

The article studies a class of integrable semidiscrete equations with one continuous and two discrete independent variables. Miura type transformations are obtained that relate the equations of the class. A new integrable chain of this type is found, for which the Lax pair is presented. Integrable in the sense of Darboux reductions of the chain are discussed

Padtarapan Banyadsin, Salvatore De Vincenzo

Consider a free Schr\"odinger particle inside an interval with walls characterized by the Dirichlet boundary condition. Choose a parabola as the normalized state of the particle that satisfies this boundary condition. To calculate the variance of the Hamiltonian in that state, one needs to calculate the mean value of the Hamiltonian and that of its square. I

David Klaška, Antonín Kučera, Vít Musil, Vojtěch Řehák

Consider oriented graph nodes requiring periodic visits by a service agent. The agent moves among the nodes and receives a payoff for each completed service task, depending on the time elapsed since the previous visit to a node. We consider the problem of finding a suitable schedule for the agent to maximize its long-run average payoff per time unit. We show

José Luis Jaramillo, Badri Krishnan, Carlos F. Sopuerta

The waveform of a binary black hole coalescence appears to be both simple and universal. In this essay we argue that the dynamics should admit a separation into 'fast and slow' degrees of freedom, such that the latter are described by an integrable system of equations, accounting for the simplicity and universality of the waveform. Given that Painlev\'e tran

Sourav Das, Guglielmo Camporese, Shaokang Cheng, Lamberto Ballan

Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics. One fundamental difficulty stands in the evolution of the trajectory that becomes more and more uncertain and unpredictable as the time horizon grows, subsequently increasing the complexity of the problem. To overcome thi

Hemanth Saratchandran, Shin-Fang Chng, Sameera Ramasinghe, Lachlan MacDonald

Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities. However, training these networks with first-order optimizers can be slow, hindering their use in real-time applications. Recent works have opted for shallow voxel-based representations to achieve faster training, but this sacri

Ibrahim Batuhan Akkaya, Senthilkumar S. Kathiresan, Elahe Arani, Bahram Zonooz

Vision transformers (ViTs) achieve remarkable performance on large datasets, but tend to perform worse than convolutional neural networks (CNNs) when trained from scratch on smaller datasets, possibly due to a lack of local inductive bias in the architecture. Recent studies have therefore added locality to the architecture and demonstrated that it can help V

German F. R. Sborlini

The calculation of higher-order corrections in Quantum Field Theories is a challenging task. In particular, dealing with multiloop and multileg Feynman amplitudes leads to severe bottlenecks and a very fast scaling of the computational resources required to perform the calculation. With the purpose of overcoming these limitations, we discuss efficient strate

Muhammad Awais Altaf, Takashi Mukaiyama, Muhammad Waseem

We study the $p$-wave elastic collision rates in a two-dimensional spin-polarized ultracold Fermi gas in the presence of a $p$-wave Feshbach resonance. We derive the analytical relation of the elastic collision rate coefficient in the close vicinity of resonance when the effective range is dominant. The elastic collision rate is enhanced by an exponential sc

Geonhwi Hwang, Hideaki Obuse

The bulk-edge correspondence is one of the most important ingredients in the theory of topological phases of matter. While the bulk-edge correspondence is applicable for Hermitian junction systems where two subsystems with independent topological invariants are connected to each other, it has not been discussed for junction systems with non-Hermitian point-g

M. Fernández-Torreiro, R. T. Génova-Santos, J. A. Rubiño-Martín, C. H. López-Caraballo

The Andromeda Galaxy (M31) is the Local Group galaxy that is most similar to the Milky Way (MW). The similarities between the two galaxies make M31 useful for studying integrated properties common to spiral galaxies. We use the data from the recent QUIJOTE-MFI Wide Survey, together with new raster observations focused on M31, to study its integrated emission

Yuhang Lu, Zewei Xu, Touradj Ebrahimi

In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition

Enrique Velasco, Yuri Martinez-Raton

We have used an extended Scaled-Particle Theory that incorporates four-body correlations through the fourth-order virial coefficient to analyse the orientational properties of a fluid of hard right-angle triangles. This fluid has been analysed by computer simulation studies, with clear indications of strong octatic correlations present in the liquid-crystal

Min-Gang Zhou, Zhi-Ping Liu, Hua-Lei Yin, Chen-Long Li

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically-controlled) single-qubit operations and measurements on real-world quan

Antonio J. Guirao, Vicente Montesinos, Andrés Quilis

We study projectional skeletons and the Plichko property in Lipschitz-free spaces, relating these concepts to the geometry of the underlying metric space. Specifically, we identify a metric property that characterizes the Plichko property witnessed by Dirac measures in the associated Lipschitz-free space. We also show that the Lipschitz-free space of all $\m

Yexin Pan, Yong Xu, Bo Ma, Chuanhuang Li

In recent years, with the increasing maturity of UAV technology, the application of UAV in the civilian field has seen explosive growth due to their low cost, high flexibility, and wide adaptability. In order to address the drawbacks of current tethered UAV lighting, which necessitates manual operation and coordination with tethered cables, this paper presen

Qiquan Zhang, Hongxu Zhu, Qi Song, Xinyuan Qian

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study pre

Chuanxin Song, Xin Ma

Despite the remarkable success of convolutional neural networks in various computer vision tasks, recognizing indoor scenes still presents a significant challenge due to their complex composition. Consequently, effectively leveraging semantic information in the scene has been a key issue in advancing indoor scene recognition. Unfortunately, the accuracy of s

Verena Bögelein, Frank Duzaar, Ugo Gianazza, Naian Liao

This paper is devoted to studying the local behavior of non-negative weak solutions to the doubly non-linear parabolic equation \begin{equation*} \partial_t u^q - \text{div}\big(|D u|^{p-2}D u\big) = 0 \end{equation*} in a space-time cylinder. H\"older estimates are established for the gradient of its weak solutions in the super-critical fast diffusion regim

Kang Xiang, Shi Huang, Hongyuan Song, Vasilii Bazhenov

Ultrasonic liquid phase exfoliation is a promising method for the production of two-dimensional (2D) layered materials. A large number of studies have been made in investigating the underlying ultrasound exfoliation mechanisms. However, due to the experimental challenges for capturing the highly transient and dynamic phenomena in real-time at sub-microsecond

I. I. Gontchar, M. V. Chushnyakova

Although the study of collisions of heavy ions resulting in formation of dinuclear systems has a long history this process still is a subject of experimental and scientific activities, partly because nowadays the heavy-ion fusion process is the only practical way for extending further the Periodic Table. Yet the heavy-ion capture cross sections are calculate

Tong Guanchun, Michael Muehlebach

We discuss a dynamical systems perspective on discrete optimization. Departing from the fact that many combinatorial optimization problems can be reformulated as finding low energy spin configurations in corresponding Ising models, we derive a penalized rank-two relaxation of the Ising formulation. It turns out that the associated gradient flow dynamics exac

Xuyang Wu, Changxin Liu, Sindri Magnusson, Mikael Johansson

We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as special cases. We prove that DEGAS converges under both bounded and unbounded delays under delay-free parameter con

Michael I. Tribelsky

The phenomenological theory revealing the generic effects of the problem symmetry, its violation, and energy conservation law on the singularities of the Poynting vector field is presented. The bifurcation scenario of their formation (annihilation) under variations of the problem parameters is elucidated. The results describe the singularities in scattering

Tim Hobson, Lydia France, Sam Greenbury, Luke Hare

The sharing of public key information is central to the digital credential security model, but the existing Web PKI with its opaque Certification Authorities and synthetic attestations serves a very different purpose. We propose a new approach to decentralised public key infrastructure, designed for digital identity, in which connections between legal entiti

Paola Torrico Morón, Sahar Salimpour, Lei Fu, Xianjia Yu

Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based

Mark Pankov, Krzysztof Petelczyc, Mariusz Zynel

We investigate point-line geometries whose singular subspaces correspond to binary equidistant codes. The main result is a description of automorphisms of these geometries. In some important cases, automorphisms induced by non-monomial linear automorphisms surprisingly arise.

Maxime Markov, Vladimir Markov

In this paper, we revisit the relationship between investors' utility functions and portfolio allocation rules. We derive portfolio allocation rules for asymmetric Laplace distributed $ALD(\mu,\sigma,\kappa)$ returns and compare them with the mean-variance approach, which is based on Gaussian returns. We reveal that in the limit of small $\frac{\mu}{\sigma}$

Zhaolu Liu, Robert L. Peach, Felix Laumann, Sara Vallejo Mengod

Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint i

Matthias Kerzel, Philipp Allgeuer, Erik Strahl, Nicolas Frick

Robotic platforms that can efficiently collaborate with humans in physical tasks constitute a major goal in robotics. However, many existing robotic platforms are either designed for social interaction or industrial object manipulation tasks. The design of collaborative robots seldom emphasizes both their social interaction and physical collaboration abiliti

Jinlei Xu, Zhengyu Zhu, Zheng Chu, Hehao Niu

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenn

Jae-Hoon Sim, D. D. Sarma, Jean-Marie Tarascon, Silke Biermann

Motivated by experimental suggestions of anionic redox processes helping to design higher energy lithium ion-battery cathode materials, we investigate this effect using first-principles electronic structure calculations for Li-rich layered sulfides. We identify the determination of the energetic contribution of intra-atomic Hund's exchange coupling as a majo

Sumit, Najmul Haque, Binoy Krishna Patra

The screening masses of mesons provide a gauge invariant and definite order parameter of chiral symmetry restoration. Different mesonic correlation lengths for flavor non-singlets, at least up to NLO, are well-defined gauge invariant physical quantities calculated earlier using the perturbative resummation techniques. The NLO perturbative results match the a

Linyi Yang, Yingpeng Ma, Yue Zhang

Financial forecasting has been an important and active area of machine learning research, as even the most modest advantage in predictive accuracy can be parlayed into significant financial gains. Recent advances in natural language processing (NLP) bring the opportunity to leverage textual data, such as earnings reports of publicly traded companies, to pred

Tianxiang Zhan, Yuanpeng He, Yong Deng, Zhen Li

Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system which leads to loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model Differential Fuzzy Convolutional Neural Netwo

Jiyang He, Benjamin Favier, Michel Rieutord, Stéphane Le Dizès

Following our previous work on periodic ray paths (He et al, 2022), we study asymptotically and numerically the structure of internal shear layers for very small Ekman numbers in a three-dimensional (3D) spherical shell and in a two-dimensional (2D) cylindrical annulus when the rays converge towards an attractor. We first show that the asymptotic solution ob

Jingyi Wang, Jinfa Huang, Can Zhang, Zhidong Deng

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particular

Sayantan Chakraborty, Upendra Kapshikar

We introduce a new operational technique for deriving chain rules for general information theoretic quantities. This technique is very different from the popular (and in some cases fairly involved) methods like SDP formulation and operator algebra or norm interpolation. Instead, our framework considers a simple information transmission task and obtains lower

Surendra Nepal, Magnus Ogren, Yosief Wondmagegne, Adrian Muntean

For certain materials science scenarios arising in rubber technology, one-dimensional moving boundary problems (MBPs) with kinetic boundary conditions are capable of unveiling the large-time behavior of the diffusants penetration front, giving a direct estimate on the service life of the material. In this paper, we propose a random walk algorithm able to lea

G. Beretta, A. Torcinovich, M. Pelillo

In 1965, T. S. Motzkin and E. G. Straus established an elegant connection between the clique number of a graph and the global maxima of a quadratic program defined on the standard simplex. Over the years, this seminal finding has inspired a number of studies aimed at characterizing the properties of the (local and global) solutions of the Motzkin-Straus prog

Derguene Mbaye, Moussa Diallo

The progress of Natural Language Processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. This is parti

Xiaojing Chen, Xingbo Lu, Shixin Zhu, Wan Jiang

The theory of entanglement-assisted quantum error-correcting codes (EAQECCs) is a generalization of the standard stabilizer quantum error-correcting codes, which can be possibly constructed from any classical codes by relaxing the duality condition and utilizing preshared entanglement between the sender and receiver. In this paper, a new family of EAQECCs is

Miguel Brozos-Vázquez, Diego Mojón-Álvarez

We study the geometric structure of weighted Einstein smooth metric measure spaces with weighted harmonic Weyl tensor. A complete local classification is provided, showing that either the underlying manifold is Einstein, or decomposes as a warped products in a specific way. Moreover, if the manifold is complete, then it either is a weighted analogue of a spa

Simon Zacharias Lahme, Pascal Klein, Antti Lehtinen, Andreas Müller

Physics lab courses permanently undergo transformations, in recent times especially to adapt to the emergence of new digital technologies and the Covid-19 pandemic in which digital technologies facilitated distance learning. Since these transformations often occur within individual institutions, it is useful to get an overview of these developments by captur

Martin Hermann Paul Fuchs, Akshara Preethy Byju, Alisa Walda, Behnood Rasti

The development of deep learning-based models for the compression of hyperspectral images (HSIs) has recently attracted great attention in remote sensing due to the sharp growing of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression, and do not jointly consider the spatio-spectral redundancies present in H

Bouzid Mosbahi, Sania Asif, Ahmed Zahari

The classification of algebraic structures and their derivations is an important and ongoing research area in mathematics and physics, and various results have been obtained in this field. This article presents the classification of tridendriform algebras that was first studied by Loday and Ronco, including an analysis of structure constant equations using c

Koji Fujiwara, Panos Papasoglu

We give a quasi-isometric characterization of cacti, which is similar to Manning's characterization of quasi-trees by the bottleneck property. We also give another quasi-isometric characterization of cacti using fat theta curves.

Balder ten Cate, Maurice Funk, Jean Christoph Jung, Carsten Lutz

We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies. A main advantage is that the resulting learning algorithms come with theoretical guarantees regarding their generalization to unseen examples in the sense of PAC learning. We prove that, in contrast, several other natural learning algorithms fail to

Jonas Frey, Matias Mattamala, Nived Chebrolu, Cesar Cadena

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN), an online self-supervised learning system for traversability estimation which uses only vision. The system is able to continuous

Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang

Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world settings. To this end, in this paper, we prop

Mihály Petreczky, Roland Tóth, Guillaume Mercère

The paper makes the first steps towards a behavioral theory of LPV state-space representations with an affine dependency on scheduling, by characterizing minimality of such state-space representations. It is shown that minimality is equivalent to observability, and that minimal realizations of the same behavior are isomorphic.Finally, we establish a formal r

Julia Cantisán, Jesús M. Seoane, Miguel A. F. Sanjuán

Rotating clusters or vortices are formations of agents that rotate around a common center. These patterns may be found in very different contexts: from swirling fish to surveillance drones. Here, we propose a minimal model for self-propelled chiral particles with inertia, which shows different types of vortices. We consider an attractive interaction for shor

Yushan Liu, Bailan He, Marcel Hildebrandt, Maximilian Buchner

Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is

Tao Wang, P. F. Wang, J. L. Han, Yi Yan

Previous studies have identified two emission modes in PSR B1859+07: a normal mode that has three prominent components in the average profile, with the trailing one being the brightest, and an anomalous mode (i.e. the A mode) where emissions seem to be shifted to an earlier phase. Within the normal mode, further analysis has revealed the presence of two sub-

Theo Chow, Usman Raza, Ioannis Mavromatis, Aftab Khan

Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is transmitted to a central server for training and inference purposes. On the other hand, Federated Learning migrates both tasks

Gianluca Demartini, Kevin Roitero, Stefano Mizzaro

Due to the widespread use of data-powered systems in our everyday lives, concepts like bias and fairness gained significant attention among researchers and practitioners, in both industry and academia. Such issues typically emerge from the data, which comes with varying levels of quality, used to train supervised machine learning systems. With the commercial

Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You

Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-document summarization (MDS), where the handling of cross-document information is more complex. Previous works either design new MDS architectures or apply PLMs bluntly with concate

Reut Apel, Tom Braude, Amir Kantor, Eyal Kolman

We present MeeQA, a dataset for natural-language question answering over meeting transcripts. It includes real questions asked during meetings by its participants. The dataset contains 48K question-answer pairs, extracted from 422 meeting transcripts, spanning multiple domains. Questions in transcripts pose a special challenge as they are not always clear, a

Ryoya Yamasaki, Toshiyuki Tanaka

Label smoothing (LS) adopts smoothed targets in classification tasks. For example, in binary classification, instead of the one-hot target $(1,0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing level $\alpha\in(0,1)$, which causes squeezing of values

Sander C. Hille, Esmee S. Theewis

On spaces of finite signed Borel measures on a metric space one has introduced the Fortet-Mourier and Dudley norms, by embedding the measures into the dual space of the Banach space of bounded Lipschitz functions, equipped with different -- but equivalent -- norms: the FM-norm and the BL-nor, respectively. The norm of such a measure is then obtained by maxim

Lorenzo Rapetti, Carlotta Sartore, Mohamed Elobaid, Yeshasvi Tirupachuri

Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modell

Manon Costa, Pascal Maillard, Anthony Muraro

We consider a discrete-time version of a Hawkes process defined as a Poisson auto-regressive process whose parameters depend on the past of the trajectory. We allow these parameters to take on negative values, modelling inhibition. More precisely, the model is the stochastic process $(X_n)_{n\ge0}$ with parameters $a_1,\ldots,a_p \in \mathbb{R}$, $p\in\mathb

Francesco C. De Vecchi, Luca Fresta, Maria Gordina, Massimiliano Gubinelli

We introduce a theory of non-commutative $L^{p}$ spaces suitable for non-commutative probability in a non-tracial setting and use it to develop stochastic analysis of Grassmann-valued processes, including martingale inequalities, stochastic integrals with respect to Grassmann It\^o processes, Girsanov's formula and a weak formulation of Grassmann SDEs. We ap

David Richter, Timon Böhler, Pascal Weisenburger, Mira Mezini

Modeling sequential and parallel composition of effectful computations has been investigated in a variety of languages for a long time. In particular, the popular do-notation provides a lightweight effect embedding for any instance of a monad. Idiom bracket notation, on the other hand, provides an embedding for applicatives. First, while monads force effects

Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano

Arguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks. We present a new unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that selects contextually relevant kn

M. Iskin

In a multiband Hubbard model the self-consistency relations for the two-body bound-state bands are in the form of a nonlinear eigenvalue problem. Assuming that the resultant eigenvectors form an orthonormal set, e.g., in the strong-binding regime, here we reformulate their Berry curvatures and the associated Chern numbers. As an illustration we solve the two

Alain LACROUX

"Data is the new oil", in short, data would be the essential source of the ongoing fourth industrial revolution, which has led some commentators to assimilate too quickly the quantity of data to a source of wealth in itself, and consider the development of big data as an quasi direct cause of profit. Human resources management is not escaping this trend, and

Nicolas Scharowski, Michaela Benk, Swen J. Kühne, Léane Wettstein

Auditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open

Mohamad Elzohbi, Richard Zhao

The rapid advancement in machine learning has led to a surge in automatic data generation, making it increasingly challenging to differentiate between naturally or human-generated data and machine-generated data. Despite these advancements, the generation of creative data remains a challenge. This paper aims to investigate and comprehend the essence of creat

Ricardo Lopes, Vinh Thong Ta, Ioannis Korkontzelos

With the rapid development of online technologies and the widespread usage of mobile phones among children, it is crucial to protect their online safety. Some studies reported that online abuse and incidents negatively affect children's mental health and development. In this paper, we examine how Android applications follow the rules related to children's da

Fangwen Wu, Jingxuan He, Yufei Yin, Yanbin Hao

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws inspiration from masked image modeling and contrastive learning to devise a novel framework that induces keys to contract toward semantic regions. Unlike prevalent techniques that dir