July 2023 arXiv papers — page 65

Paolo Franceschi, Fabio Bertini, Francesco Braghin, Loris Roveda

This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work inv

Nguyen Tong Xuan, Roberto Fernandez

We develop a novel cluster expansion for finite-spin lattice systems subject to multi-body quantum -- and, in particular, classical -- interactions. Our approach is based on the use of ``decoupling parameters", advocated by Park [34], which relates partition functions with successive additional interaction terms. Our treatment, however, leads to an explicit

Eleftheria Psatha, Dimitrios Laskos, Gerasimos Arvanitis, Konstantinos Moustakas

The increasing demand for accurate representations of 3D scenes, combined with immersive technologies has led point clouds to extensive popularity. However, quality point clouds require a large amount of data and therefore the need for compression methods is imperative. In this paper, we present a novel, geometry-based, end-to-end compression scheme, that co

Antoine Jego, Titus Lupu, Wei Qian

Consider a Brownian loop soup $\mathcal{L}_D^\theta$ with subcritical intensity $\theta \in (0,1/2]$ in some 2D bounded simply connected domain. We define and study the properties of a conformally invariant field $h_\theta$ naturally associated to $\mathcal{L}_D^\theta$. Informally, this field is a signed version of the local time of $\mathcal{L}_D^\theta$ t

Paolo Franceschi, Manuel Beschi, Nicola Pedrocchi, Anna Valente

Applications involving humans and robots working together are spreading nowadays. Alongside, modeling and control techniques that allow physical Human-Robot Interaction (pHRI) are widely investigated. To better understand its potential application in pHRI, this work investigates the Cooperative Differential Game Theory modeling of pHRI in a cooperative reach

Yuxin Shi, Zelei Liu, Zhuan Shi, Han Yu

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL client selection has become an important research problem. Existing approaches generally focus on either enhanc

Clara Lavinia Del Pio, Simone Amoroso, Mauro Chiesa, Ekaterina Lipka

The weak mixing angle is a probe of the vector-axial coupling structure of electroweak interactions. It has been measured precisely at the $Z$-pole by experiments at the LEP and SLD colliders, but its energy dependence above $M_Z$ remains unconstrained. In this contribution we propose to exploit measurements of Neutral-Current Drell Yan at large invariant di

Arman Bolatov, Maxat Tezekbayev, Igor Melnykov, Artur Pak

We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020). We demonstrate that a linear classifier cannot decrease the generalization error below a certain level in the proposed model, whereas a nonlinear classifier with a memorization capacity can. This confirms that for long-tailed distributions, ra

Simone Venturini, Barbara Pasquini, Simone Rodini

We developed a model for the pion light-front wave function (LFWF) that incorporates valence, sea and gluon degrees of freedom. Using the LFWF overlap representation, we derived parametrizations for the pion parton distribution functions and the electromagnetic form factor. These parametrizations depend on two distinct sets of parameters, enabling separate f

Rita Pucci, Vincent J. Kalkman, Dan Stowell

Fine-grained classification is challenging due to the difficulty of finding discriminatory features. This problem is exacerbated when applied to identifying species within the same taxonomical class. This is because species are often sharing morphological characteristics that make them difficult to differentiate. We consider the taxonomical class of Insecta.

Tomáš Bravenec, Joaquín Torres-Sospedra, Michael Gould, Tomas Fryza

This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on up-to-date data. We provide a month-long probe request captu

Hayoung Yu, Suhwan Song, Seungsoo Nam, Kieron Burke

Density functional theory (DFT) is widely used to predict chemical properties, but its accuracy is limited by functional approximations and their approximate self-consistent densities. Density-corrected DFT (DC-DFT) is the study of the errors due to densities and Hartree-Fock DFT (HF-DFT) uses HF densities to improve energetics. With increasing use of HF-DFT

Nijil Mankuzhiyil, Massimo Persic, Alberto Franceschini

The Extragalactic Background Light (EBL) that spans the UV-IR band originates from direct and dust-reprocessed starlight integrated over the history of the Universe. EBL measurements are very challenging due to foreground emission like the zodiacal light and interplanetary dust emission. Indeed, some optical/NIR direct measurements overpredict EBL models bas

Yannick De Koninck, Charles Caer, Didit Yudistira, Marina Baryshnikova

Silicon photonics is a rapidly developing technology that promises to revolutionize the way we communicate, compute, and sense the world. However, the lack of highly scalable, native CMOS-integrated light sources is one of the main factors hampering its widespread adoption. Despite significant progress in hybrid and heterogeneous integration of III-V light s

Zhonghao Yang, Shuhao Zhang, Binbin Chen

Managing shared mutable states in high concurrency state access operations is a persistent challenge in Network Functions Virtualization (NFV). This is particularly true when striving to meet chain output equivalence (COE) requirements. This paper presents TransNFV, an innovative NFV framework that incorporates transactional semantics to optimize NFV state m

Elias Fischer, Bulcsú Sándor, Claudius Gros

We present self-organizing control principles for simulated robots actuated by synthetic muscles. Muscles correspond to linear motors exerting force only when contracting, but not when expanding, with joints being actuated by pairs of antagonistic muscles. Individually, muscles are connected to a controller composed of a single neuron with a dynamical thresh

Cheng Zhang, Pengguang Du, Minjie Ding, Yindi Jing

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a g

Louis Paletta, Anthony Leverrier, Alain Sarlette, Mazyar Mirrahimi

Between NISQ (noisy intermediate scale quantum) approaches without any proof of robust quantum advantage and fully fault-tolerant quantum computation, we propose a scheme to achieve a provable superpolynomial quantum advantage (under some widely accepted complexity conjectures) that is robust to noise with minimal error correction requirements. We choose a c

Tiago F. T. Cerqueira, Antonio Sanna, Miguel A. L. Marques

We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predict

Christopher Popp, Beatrix C. Hiesmayr

Maximally entangled Bell states are of crucial importance for entanglement based methods in quantum information science. Typically, a standard construction of a complete orthonormal Bell-basis by Weyl-Heisenberg operators is considered. We show that the group structure of these operators has strong implication on error correction schemes and on the entanglem

Achilleas Spanos, Ioanna Kantzavelou

The development of an electronic voting system that would replace traditional election procedures is a research topic of great interest for many years. Blockchain technology could provide some guarantees and fulfill strong requirements for electronic voting platforms, such as transparency, immutability, and confidentiality. From time to time research is cond

Martin R. Bridson, Dawid Kielak, Monika Kudlinska

We give a relatively self-contained proof that if a group $G$ fibres algebraically and is part of a $\mathrm{PD}^3$-pair, then $G$ is the fundamental group of a fibred compact aspherical 3-manifold. This yields a homological proof of a classical theorem of Stallings: if $G = \pi_1(M^3)$ is the fundamental group of a compact irreducible 3-manifold $M^3$ and $

Yo Tokunaga, Hironori Sakai, Shinsaku Kambe, Petr Opletal

Our measurements of $^{125}$Te NMR relaxations reveal an enhancement of electronic spin fluctuations above $\mu_0H^*\sim15$ T, leading to their divergence in the vicinity of the metamagnetic transition at $\mu_0H_m\approx35$ T, below which field-reinforced superconductivity appears when a magnetic field ($H$) is applied along the crystallographic $b$ axis. T

Yao Lu, I. V. Tokatly, F. Sebastian Bergeret

We investigate the behavior of magnetic impurities placed on the surface of superconductor thin films with spin-orbit coupling. Our study reveals long-range interactions between the impurities, which decay according to a power law, mediated by the supercurrents. Importantly, these interactions possess a ferromagnetic component when considering the influence

B. K. Xiang, Y. S. Lin, Q. S. He, J. J. Zhu

Conventional superconductors naturally disfavor ferromagnetism because the supercurrent-carrying electrons are paired into anti-parallel spin singlets. In superconductors with strong Rashba spin-orbit coupling, impurity magnetic moments induce supercurrents through the spin-galvanic effect. As a result, long-range ferromagnetic interaction among the impurity

Wynn C. G. Ho, Nils Andersson

Previous theoretical works using the pre-merger orbital evolution of coalescing neutron stars to constrain properties of dense nuclear matter assume a gravitational wave phase uncertainty of a few radians, or about a half cycle. However, recent studies of the signal from GW170817 and next generation detector sensitivities indicate actual phase uncertainties

Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha

This paper focuses on the substantive application of multilevel LCA to the evolution of citizenship norms in a diverse array of democratic countries. To do so, we present a two-stage approach to fit multilevel latent class models: in the first stage (measurement model construction), unconditional class enumeration is done separately on both low and high leve

David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot

Large language models (LLMs) have exhibited impressive capabilities in comprehending complex instructions. However, their blind adherence to provided instructions has led to concerns regarding risks of malicious use. Existing defence mechanisms, such as model fine-tuning or output censorship using LLMs, have proven to be fallible, as LLMs can still generate

Mahsa Forouzesh, Patrick Thiran

Extracting noisy or incorrectly labeled samples from a labeled dataset with hard/difficult samples is an important yet under-explored topic. Two general and often independent lines of work exist, one focuses on addressing noisy labels, and another deals with hard samples. However, when both types of data are present, most existing methods treat them equally,

Yuhao Qin, Qinglin Niu, Dong Guo, Sheng Xiao

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung $\gamma$ rays produced in $^{86}$Kr + $^{124}$Sn at 25 MeV/u. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum above 30 MeV can be reproduc

Fabian Gabel, Albrecht Seelmann

A final-state observability result in the Banach space setting for non-autonomous observation problems is obtained that covers and extends all previously known results in this context, while providing a streamlined proof that follows the established Lebeau-Robbiano strategy.

Rasool Hafezi, Jiaqun Wei

We investigate the structure of certain almost split sequences in $\mathcal{P}(\Lambda)$, i.e., the category of morphisms between projective modules over an Artin algebra $\Lambda$. The category $\mathcal{P}(\Lambda)$ has very nice properties and is closely related to $\tau$-tilting theory, $g$-vectors, and Auslander-Reiten theory. We provide explicit constr

Tim Puphal, Julian Eggert

We consider the problem of group interactions in urban driving. State-of-the-art behavior planners for self-driving cars mostly consider each single agent-to-agent interaction separately in a cost function in order to find an optimal behavior for the ego agent, such as not colliding with any of the other agents. In this paper, we develop risk shadowing, a si

Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden

Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast quantities of data. Unfortunately, existing approaches limit themselves to the automotive domain, resulting in models incapable of generalizing to complex environments such as natural or indoor settings. To address this, we propose a large-scale SlowTV dataset curated from

Rinkal Patel, B. S. Ratanpal, D. M. Pandya

We present an entirely new class of solutions to Einstein's field equations that correspond to a static spherically symmetric anisotropic system by generalizing the Finch and Skea ansatz using the linear equation of state for the gravitational potential $g_{rr} $. Based on physical requirements, regularity condition, and stability, we make various assumption

Xiaoyu Zhang, Chunhou Gao, Denis Dochain

Complex balanced mass-action systems (CBMASs) are of great importance in the filed of biochemical reaction networks. However analyzing the persistence of these networks with high dimensions and time delays poses significant challenges. To tackle this, we propose a novel approach that combines 1-dimensional (1d) or 2d delayed CBMASs (DeCBMASs) and introduces

Jiachun Pan, Jun Hao Liew, Vincent Y. F. Tan, Jiashi Feng

Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. Since the sampling procedure of DPM

Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy that overcomes limitations inherent in the commonly-used Gaussian parameterization. To achieve this, we propose a principled framework that models the continuous RL policy as a ge

Parviz Goodarzi

In this work, we consider the gravitational baryogenesis in the framework of non-minimal derivative coupling model. A mechanism to generate the baryon asymmetry based on the coupling between the derivative of the Ricci scalar curvature and the baryon current in context of non-minimal derivative coupling model is investigated. We show that, in this model, the

Shi-Jie Gao, Xiang-Dong Li

Observations of elliptical galaxies suggest that black holes (BHs) might serve as dark energy candidates, coupled to the expansion of the Universe. According to this hypothesis, the mass of a BH could increase as the Universe expands. BH low-mass X-ray binaries (LMXBs) in the Galactic disk were born several gigayears ago, making the coupling effect potential

Ignacio Santamaria, Mohammad Soleymani, Eduard Jorswieck, Jesus Gutierrez

Reconfigurable intelligent surface (RIS) architectures not limited to diagonal phase shift matrices have recently been considered to increase their flexibility in shaping the wireless channel. One of these beyond-diagonal RIS or BD-RIS architectures leads to a unitary and symmetric RIS matrix. In this letter, we consider the problem of maximizing the signal-

N. Karjanto

This article reviews the current state of teaching and learning mathematical modeling in the context of sustainable development goals for education at the tertiary level. While ample research on mathematical modeling education and published textbooks on the topic are available, there is a lack of focus on mathematical modeling for sustainability. This review

Youssef Aziz Alaoui, Bruno Laburthe-Tolra

We study whether a generic isolated quantum system initially set out of equilibrium can be considered as localized close to its initial state. Our approach considers the time evolution in the Krylov basis, which maps the dynamics onto that of a particle moving in a one-dimensional lattice where both the energy in the lattice sites and the tunneling from one

Lauro Ferreira, Yasmine Baazizi, Simon Meunier, Tanguy Phulpin

Although a superconductor has no DC losses, a superconducting system does have significant losses, especially when it comes to power supply. Here, we study two different power supply systems. The first, a conventional one, consists of a transformer and a diode bridge operating at room temperature, plus current leads that allow the current to flow from the ro

Quang Huy Che, Dinh Phuc Nguyen, Minh Quan Pham, Duc Khai Lam

Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for e

Sharyal Zafar, Raphaël Feraud, Anne Blavette, Guy Camilleri

The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands. These issues can be mitigated by controlling the operation of electric vehicles i.e., smart charging. Centralized smart charging solutions have already been proposed in the litera

Pierre Casadebaig, Nicolas Blanchet, Nicolas Bernard Langlade

The automatic measurement of developmental and physiological responses of sunflowers to water stress represents an applied challenge for a better knowledge of the varieties available to growers, but also a fundamental one for identifying the biological, genetic and molecular bases of plant response to their environment.On INRAE Toulouse's Heliaphen high-thro

Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà, Gustau Camps-Valls

Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of s

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users

Florentina Şoiman, Mathis Mourey, Jean-Guillaume Dumas, Sonia Jimenez-Garces

This study introduces the concept of the forking effect in the cryptocurrency market,specifically focusing on the impact of forking events on bitcoin, also called parent coin.We use a modified exponential GARCH model to examine the bitcoin's response inreturns and volatility. Our findings reveal that forking events do not significantlyaffect the bitcoin's re

Jinhua Cheng

This paper explores the properties of multipliers associated with discrete analogues of fractional integrals, revealing intriguing connections with Dirichlet characters, Euler's identity, and Dedekind zeta functions of quadratic imaginary fields. Employing Fourier transform techniques, the Hardy--Littlewood circle method, and a discrete analogue of the Stein

Rajiv Movva, Sidhika Balachandar, Kenny Peng, Gabriel Agostini

Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinary shifts: LLM research increasingly cons

Yue-Wen Fang, Ðorđe Dangić, Ion Errea

The recent report of near-ambient superconductivity in nitrogen-doped lutetium hydrides (Lu-N-H) has generated a great interest. However, conflicting results have raised doubts regarding superconductivity. Here, we combine high-throughput crystal structure predictions with a fast predictor of the superconducting critical temperature ($T_c$) to shed light on

Jules Chenal

In a first time we present a version of the Poincar{\'e}-Lefschetz theorem for certain cellular cosheaves on a particular subdivision of a CW-complex K. To that end we construct a cellular sheaf on K whose cohomology with compact support is isomorphic to the homology of the initial cosheaf. In a second time we use the first result to generalise the tropical

Sahar Almahfouz Nasser, Nihar Gupte, Amit Sethi

Retinal image matching plays a crucial role in monitoring disease progression and treatment response. However, datasets with matched keypoints between temporally separated pairs of images are not available in abundance to train transformer-based model. We propose a novel approach based on reverse knowledge distillation to train large models with limited data

Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades Rubio, Josef Bigun

The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication. In this context, facial recognition technologies have emerged as a dependable method for verifying users due to the prevalence of cameras in mobile devices and their integration into everyday applications. The rapid advanceme

Zhimiao Yu, Tiancheng Lin, Yi Xu

Improving the feature representation ability is the foundation of many whole slide pathological image (WSIs) tasks. Recent works have achieved great success in pathological-specific self-supervised learning (SSL). However, most of them only focus on learning patch-level representations, thus there is still a gap between pretext and slide-level downstream tas

Jaekyun Ko, Sanghwan Lee

Recently, denoising methods based on supervised learning have exhibited promising performance. However, their reliance on external datasets containing noisy-clean image pairs restricts their applicability. To address this limitation, researchers have focused on training denoising networks using solely a set of noisy inputs. To improve the feasibility of deno

Kyungho Lee, Yoon-Jae Whang

We introduce PySDTest, a Python/Stata package for statistical tests of stochastic dominance. PySDTest implements various testing procedures such as Barrett and Donald (2003), Linton et al. (2005), Linton et al. (2010), and Donald and Hsu (2016), along with their extensions. Users can flexibly combine several resampling methods and test statistics, including

Anton Andreev, Grégoire Cattan

We present a new framework called KorraAI for conceiving and building embodied conversational agents (ECAs). Our framework models ECAs' behavior considering contextual information, for example, about environment and interaction time, and uncertain information provided by the human interaction partner. Moreover, agents built with KorraAI can show proactive be

Davide Carolillo, Gianluca Paolini

In [11] Sklinos proved that any uncountable free group is not $\aleph_1$-homogenenous. This was later generalized by Belegradek in [1] to torsion-free residually finite relatively free groups, leaving open whether the assumption of residual finiteness was necessary. In this paper we use methods arising from the classical analysis of relatively free groups in

Antoine Chatrenet, Giorgina Piccoli, Jean Michel Audebrand, Massimo Torreggiani

Background Chronic kidney disease (CKD) induces muscle wasting and a reduction in the maximum voluntary force (MVF). Little is known about the neuromuscular fatigability in CKD patients, defined as the reduction of muscle force capacities during exercise. Neuromuscular fatigability is a crucial physical parameter of the daily living. The quantification of ex

Shimian Zhang, Qiuhong Lu

As the advent of artificial general intelligence (AGI) progresses at a breathtaking pace, the application of large language models (LLMs) as AI Agents in robotics remains in its nascent stage. A significant concern that hampers the seamless integration of these AI Agents into robotics is the unpredictability of the content they generate, a phenomena known as

J. Meibohm, L. Sundberg, B. Mehlig, K. Gustavsson

Caustics in the dynamics of heavy particles in turbulence accelerate particle collisions. The rate $\mathscr{J}$ at which these singularities form depends sensitively on the Stokes number St, the non-dimensional inertia parameter. Exact results for this sensitive dependence have been obtained using Gaussian statistical models for turbulent aerosols. However,

Yuya Yamada, Mutsunori Banbara, Katsumi Inoue, Torsten Schaub

We develop an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set Programming (ASP). The general task is to study the solution spaces of source combinatorial problems and to decide whether or not there are sequences of feasible solutions that have special properties. The resulting recon

Hendrik Schricker, Benedikt Schuler, Christiane Reinert, Niklas von der Aßen

Industrial decision-makers often base decisions on mathematical optimization models to achieve cost-efficient design solutions in energy transitions. However, since a model can only approximate reality, the optimal solution is not necessarily the best real-world energy system. Exploring near-optimal design spaces, e.g., by the Modeling All Alternatives (MAA)

Shuang Wu, Xinyu Chen, Canyu Hong, Xiaofei Hou

The discovery of atomically thin van der Waals ferroelectric and magnetic materials encourages the exploration of 2D multiferroics, which holds the promise to understand fascinating magnetoelectric interactions and fabricate advanced spintronic devices. In addition to building a heterostructure consisting of ferroelectric and magnetic ingredients, thinning d

Yinghui Xing, Dexuan Kong, Shizhou Zhang, Geng Chen

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related sem

Marco Caliari, Fabio Cassini

We propose a second order exponential scheme suitable for two-component coupled systems of stiff evolutionary advection--diffusion--reaction equations in two and three space dimensions. It is based on a directional splitting of the involved matrix functions, which allows for a simple yet efficient implementation through the computation of small-sized exponen

Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effectiv

Maria Bras-Amorós, César Marín-Rodríguez

We give a graphical reinterpretation of the seeds algorithm to explore the tree of numerical semigroups. We then exploit the seeds algorithm to find all the Eliahou semigroups of genus up to 65. Since all these semigroups satisfy the Wilf conjecture, this shows that the Wilf conjecture holds up to genus 65.

Claire Schune, Marc Yonger, Mohamed Hanafi, Jürgen Thiel

Nanometer-thick supported lms of polymer melts spontaneously form and spread around sessile droplets that are deposited on oxidized silicon wafers. At steady state, the lms become dense and adopt a uniform thickness which is equal to twice the gyration radius of the free polymer. Remarkably, this law applies to a wide variety of melts and does not depend on

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better

Yuki Tokumoto, Kotaro Hamano, Sunao Nakagawa, Yasushi Kamimura

van der Waals (vdW) layered transition-metal chalcogenides are attracting significant attention owing to their fascinating physical properties. This group of materials consists of abundant members with various elements, having a variety of different structures. However, all vdW layered materials studied to date have been limited to crystalline materials, and

A. Iuliano

SND@LHC is a compact and stand-alone experiment to perform measurements with neutrinos produced at the LHC in a hitherto unexplored pseudo-rapidity region of 7.2 < {\eta} < 8.4, complementary to all the other experiments at the LHC. The experiment is located 480 m downstream of ATLAS IP1 in the unused TI18 tunnel. The detector is composed of a hybrid system

Kevin Setterstrom, Jeremy Straub

As the autonomous vehicle industry continues to grow, various companies are exploring the use of aftermarket kits to retrofit existing vehicles with semi-autonomous capabilities. However, differences in implementation of the controller area network (CAN) used by each vehicle manufacturer poses a significant challenge to achieving large-scale implementation o

Rebecca Leygonie, Sylvain Lobry, ), Laurent Wendling (LIPADE)

We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR codes (Quick Response codes) fall into this category of abstract images: one bit corresponding to one encoded character, QR codes were not designed to be decoded manually. To understa

Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen

Reliable fault detection is an essential requirement for safe and efficient operation of complex mechanical systems in various industrial applications. Despite the abundance of existing approaches and the maturity of the fault detection research field, the interdependencies between condition monitoring data have often been overlooked. Recently, graph neural

Alan E. Rask, Lee Huntington, SungYeon Kim, David Walker

Accurate solutions to the electronic Schr\"odinger equation can provide valuable insight for electron interactions within molecular systems, accelerating the molecular design and discovery processes in many different applications. However, the availability of such accurate solutions are limited to small molecular systems due to both the extremely high comput

Stefan P. Feyer, Bruno Pinaud, Stephen G. Kobourov, Nicolas Brich

Relational information between different types of entities is often modelled by a multilayer network (MLN) -- a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied t

Andrea Cappozzo, Alessandro Casa, Michael Fop

Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are becoming increasingly prevalent in various fields. Despite its widespread adoption and successful application, this approach suffers from over-parameterization issues, making it less suitable even for matrix-variate data of mod

Aram Akram Mohammed, Haidar Anwar Arkwazee, Ayub Karim Mahmood, Hemn Abdalla Mustafa

The aim of this study was to determine the germination ability and seedling growth of the apple of Sodom by soaking in water, gibberellin (GA3), naphthylacetic acid (NAA), and salicylic acid (SA), separately. The findings showed that NAA at 50 mgL-1 produced superior germination (77.78%), germination speed (1.43 seeds/time interval), hypocotyl length (1.01 c

Michal Pilipczuk, Mathieu Mari, Timothe Picavet

We study a natural geometric variant of the classic Knapsack problem called 2D-Knapsack: we are given a set of axis-parallel rectangles and a rectangular bounding box, and the goal is to pack as many of these rectangles inside the box without overlap. Naturally, this problem is NP-complete. Recently, Grandoni et al. [ESA'19] showed that it is also W[1]-hard

M. C. Calderón-Moreno, P. J. Gerlach-Mena, J. A. Prado-Bassas

In this paper we introduce the concept of infinite pointwise dense lineability (spaceability), and provide a criterion to obtain density from mere lineability. As an application, we study the linear and topological structures within the set of infinite differentiable and integrable functions, for any order $p \geq 1$, on $\mathbb{R}^N$ which are unbounded in

Yohann Le Floch, Joseph Palmer

The aim of this paper is to give new insights about families of integrable systems lifting a Hamiltonian $S^1$-space. Specifically, we study one-parameter families $(M^4,\omega,F_t=(J,H_t))_{0 \leq t \leq 1}$ of systems with a fixed Hamiltonian $S^1$-space $(M,\omega,J)$ and which are semitoric for certain values of the parameter $t$, with a focus on such fa

Guillermo B. Morales, Serena Di Santo, Miguel A. Muñoz

Deciphering the underpinnings of the dynamical processes leading to information transmission, processing, and storing in the brain is a crucial challenge in neuroscience. An inspiring but speculative theoretical idea is that such dynamics should operate at the brink of a phase transition, i.e., at the edge between different collective phases, to entail a ric

Jean-Bernard Lasserre, Yuan Xu

We extend the polynomial Pell's equation satisfied by univariate Chebyshev polynomials on [--1, 1] from one variable to several variables, using orthogonal polynomials on regular domains that include cubes, balls, and simplexes of arbitrary dimension. Moreover, we show that such an equation is strongly connected (i) to a certificate of positivity (from real

Haechang Lee, Dongwon Park, Wongi Jeong, Kijeong Kim

As the physical size of recent CMOS image sensors (CIS) gets smaller, the latest mobile cameras are adopting unique non-Bayer color filter array (CFA) patterns (e.g., Quad, Nona, QxQ), which consist of homogeneous color units with adjacent pixels. These non-Bayer sensors are superior to conventional Bayer CFA thanks to their changeable pixel-bin sizes for di

Hynek Kydlíček, Jindřich Libovický

Pre-trained models for Czech Natural Language Processing are often evaluated on purely linguistic tasks (POS tagging, parsing, NER) and relatively simple classification tasks such as sentiment classification or article classification from a single news source. As an alternative, we present CZEch~NEws~Classification~dataset (CZE-NEC), one of the largest Czech

Fabrizio Bianchi, Tien-Cuong Dinh, Karim Rakhimov

We prove that, for every invertible horizontal-like map (i.e., H{\'e}non-like map) in any dimension, the sequence of the dynamical degrees is increasing until that of maximal value, which is the main dynamical degree, and decreasing after that. Similarly, for polynomial-like maps in any dimension, the sequence of dynamical degrees is increasing until the las

Haoyuan Wang, Xiaogang Xu, Ke Xu, Rynson WH. Lau

Neural Radiance Field (NeRF) is a promising approach for synthesizing novel views, given a set of images and the corresponding camera poses of a scene. However, images photographed from a low-light scene can hardly be used to train a NeRF model to produce high-quality results, due to their low pixel intensities, heavy noise, and color distortion. Combining e

Matthieu Dussaule, Wenyuan Yang, Longmin Wang

Given a probability measure $\mu$ on a finitely generated group $\Gamma$, the Green function $G(x,y|r)$ encodes many properties of the random walk associated with $\mu$. Finding asymptotics of $G(x,y|r)$ as $y$ goes to infinity is a common thread in probability theory and is usually referred as renewal theory in literature. Endowing $\Gamma$ with a word dist

Serafino Cicerone, Gabriele Di Stefano

The concept of mutual-visibility in graphs has been recently introduced. If $X$ is a subset of vertices of a graph $G$, then vertices $u$ and $v$ are $X$-visible if there exists a shortest $u,v$-path $P$ such that $V(P)\cap X \subseteq \{u, v\}$. If every two vertices from $X$ are $X$-visible, then $X$ is a mutual-visibility set. The mutual-visibility number

Sourish Dutta

Studying intra-industry trade involves theoretical explanations and empirical methods to measure the phenomenon. Indicators have been developed to measure the intensity of intra-industry trade, leading to theoretical models explaining its determinants. It is essential to distinguish between horizontal and vertical differentiation in empirical analyses. The d

Michele Ancona, Thomas Letendre

We define a notion of multijet for functions on $\mathbb{R}^n$, which extends the classical notion of jets in the sense that the multijet of a function is defined by contact conditions at several points. For all $p \geq 1$ we build a vector bundle of $p$-multijets, defined over a well-chosen compactification of the configuration space of $p$ distinct points

Daniel Rosendo, Marta Mattoso, Alexandru Costan, Renan Souza

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key perfor

Grégory Faye, L. Miguel Rodrigues, Zhao Yang, Kevin Zumbrun

Extending work of Yang-Zumbrun for the hydrodynamically stable case of Froude number F < 2, we categorize completely the existence and convective stability of hydraulic shock profiles of the Saint Venant equations of inclined thin-film flow. Moreover, we confirm by numerical experiment that asymptotic dynamics for general Riemann data is given in the hydrody

Kaushal R Purohit, Ajay Kumar Rai, Rajendrasinh H Parmar

An approximate bound state solution of the Klein-Gordon equation is derive analytically for the 3-dimensional space with a combination framework of linear plus modified Yukawa Potential (LIMYP) using the Nikiforov-Uvarov (N-U) method for obtaining the energy eigenvalues and corresponding wave function. A detailed study of mass spectra of all combination sets

Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou

Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentr

Ronald Richman, Mario V. Wüthrich

A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (f