February 2024 arXiv papers — page 121

Alexandros Sarantopoulos, Kristof Lange, Francisco Rivadulla, Stephan Menzel

Enhancing the switching speed of oxide-based memristive devices at a low voltage level is crucial for their use as non-volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to accelerate the switching speed often result in an energy tradeoff, leading to an increase of the minimum working voltage. In o

Lorenzo Lyons, Thijs Niesten, Laura Ferranti

This paper presents the design of a research platform for autonomous driving applications, the Delft's Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an e

Long Teng, Yanhao Wang, Zhe Lin, Fei Yu

Influential community search (ICS) finds a set of densely connected and high-impact vertices from a social network. Although great effort has been devoted to ICS problems, most existing methods do not consider how relevant the influential community found is to specific topics. A few attempts at topic-aware ICS problems cannot capture the stochastic nature of

Ethan Davies, Darren Banfield, Vlad Carare, Ben Weaver

A challenge for scalability of demand-responsive, elastic optical Dense Wavelength Division Multiplexing (DWDM) and Flexgrid networks is the computational complexity of allocating many optical routes on large networks. We demonstrate that demand satisfaction problems in communication networks can be formulated as quadratic unconstrained binary optimisation (

Kavya Ranjan Saxena, Vipul Arora

Extraction of predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large amount of labeled audio data is required to train the model. However, a classical model pre-trained on data from one domain (source), e.g., song

Dalibor Javůrek

The theory of Gravitomagnetism and spinor quantum mechanics describing the interaction between the Dirac spinor field, the electromagnetic field, and a weak gravitational field is extended by including the Lagrangian density of the free electromagnetic field. It is shown that the newly added term in the Lagrangian density is necessary to restore a symmetric

Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle

We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), resolving an open question of Zhang et al. (2023). Our analysis provides new theoretical results on categorical approaches to distributional RL,

Wei Bo Li, Hans Rabus, Carmen Villagrasa, Jan Schuemann

Computational approaches, such as Monte Carlo (MC) radiation transport simulations, are used to estimate the dosimetric effects of GNPs, where results differing by orders of magnitudes have been reported by different investigators. This has motivated an intercomparison exercise, which was conducted as a joint activity of EURADOS Working Groups 6 "Computation

Cansu Korkmaz, Ege Cirakman, A. Murat Tekalp, Zafer Dogan

Super-resolution (SR) is an ill-posed inverse problem with a large set of feasible solutions that are consistent with a given low-resolution image. Various deterministic algorithms aim to find a single solution that balances fidelity and perceptual quality; however, this trade-off often causes visual artifacts that bring ambiguity in information-centric appl

Antonio Ríos-Vila, Jorge Calvo-Zaragoza, Thierry Paquet

State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations. Despite their efficacy, these approaches imply challenges related to scalability and limitations. This

Azar Taheri Tayebi, Julia S. Schmid, Sean Simmons, Mark S. Poesch

Reliable angler activity data inform fisheries management. Traditionally, such data are gathered through surveys, but an innovative cost-effective approach involves utilizing online platforms and smartphone applications. These citizen-sourced data were reported to correlate with conventional survey information. However, the nature of this correlation--whethe

Yuning Huang, Jingchen Zou, Lanxi Meng, Xin Yue

Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of foundational models like the DINOv2, which uses the vision transformer architecture, has opened new opportunities in the field and gathered significant interest. Ho

Patrick Seifner, Kostadin Cvejoski, Antonia Körner, Ramsés J. Sánchez

Dynamical systems governed by ordinary differential equations (ODEs) serve as models for a vast number of natural and social phenomena. In this work, we offer a fresh perspective on the classical problem of imputing missing time series data, whose underlying dynamics are assumed to be determined by ODEs. Specifically, we revisit ideas from amortized inferenc

Cristhian Montoya, Ignacio Brevis, David Bolivar

This paper is devoted to the study of source reconstruction algorithms for coupled systems of heat equations, with either constant or spatially dependent coupling terms, where internal measurements are available from a reduced number of observed states. Two classes of systems are considered. The first comprises parabolic equations with constant zero-order co

Maksim V. Shibalov, Ilya M. Asharchuk, Evgeniy O. Epifanov, Igor V. Trofimov

This paper presents the development of a superconducting nanowire single-photon detector (SNSPD) integrated into a distributed Bragg reflector (DBR) with a design center wavelength of 830 nm and a width of 200 nm. This SNSPD is made of a superconducting niobium nitride (NbN) thin film that is produced using plasma-enhanced atomic layer deposition (PEALD). Th

Alexia M. Lopez, Roger G. Clowes, Gerard M. Williger

We present the discovery of `A Big Ring on the Sky' (BR), the second ultra-large large-scale structure (uLSS) found in MgII-absorber catalogues, following the previously reported Giant Arc (GA). In cosmological terms the BR is close to the GA - at the same redshift $z \sim 0.8$ and with a separation on the sky of only $\sim 12^\circ$. Two extraordinary uLSSs

Rakesh Kumar Malakar, Asim Kumar Ghosh

Magnetic and topological properties along with quantum correlations in terms of several entanglement measures have been investigated for an antiferromagnetic spin-1/2 XY model in the presence of transverse magnetic field and XZX$-$YZY type of three-spin interactions. Symmetries of the spin Hamiltonian have been identified. Under the Jordan-Wigner transformat

Ryotaku Suzuki, Shinya Tomizawa

We present an exact solution for a non-BPS charged rotating black ring endowed with a dipole charge in the bosonic sector of five-dimensional minimal supergravity. Utilizing the electric Harrison transformation, we derive this solution by converting a five-dimensional vacuum solution into a charged solution within the realm of five-dimensional minimal superg

Tinashe Handina, Eric Mazumdar

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over$\unicode{x2013}$and the more data one has access to$\unicode{x2013}$the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments. In th

Huai-Min Chen, Xiao-Wei Li, Cheng-Jun Xia, Jing-Tao Wang

We study the magnetized strangelets in the baryon density-dependent quark mass model, including the effects of both confinement and lead-order perturbation interactions. The properties of magnetized strangelets are investigated under the the field strength 2*10^17 G, where the anisotropy caused by the strong magnetic field is insignificant can be treated app

Teresa Salazar, João Gama, Helder Araújo, Pedro Henriques Abreu

In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensitive attributes such as gender or race, en

Francisco Durán, Silverio Martínez-Fernández, Matias Martinez, Patricia Lago

The growing use of large machine learning models highlights concerns about their increasing computational demands. While the energy consumption of their training phase has received attention, fewer works have considered the inference phase. For ML inference, the binding of ML models to the ML system for user access, known as ML serving, is a critical yet und

Akito Yamamoto, Tetsuo Shibuya

With the increasing amount of data in society, privacy concerns in data sharing have become widely recognized. Particularly, protecting personal attribute information is essential for a wide range of aims from crowdsourcing to realizing personalized medicine. Although various differentially private methods based on randomized response have been proposed for

David Ramírez, Ignacio Santamaria, Louis L. Scharf

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood rati

Sagar Silva Pratapsi, Sebastian Deffner, Stefano Gherardini

What is the minimal time until a quantum system can exhibit genuine quantum features? To answer this question we derive quantum speed limits for two-time correlation functions arising from statistics of measurements. Generally, these two-time correlators are described by quasiprobabilities, if the initial quantum state of the system does not commute with the

Gerhard Hilgers, Miriam Schwarze, Hans Rabus

At the Heidelberg Ion-Beam Therapy Center, the track structure of carbon ions of therapeutic energy after penetrating layers of simulated tissue was investigated for the first time. Measurements were conducted with carbon ion beams of different energies and polymethyl methacrylate (PMMA) absorbers of different thicknesses to realize different depths in the p

R. K. Kushwaha, P. K. Meena, S. Jangid, P. Manna

Rhenium-based superconductors have recently attracted significant interest due to their unconventional superconducting properties. In this work, we report the synthesis and properties of new superconducting Re$_{7}$X$_{3}$ (X = Nb, Ta, Ti, Zr, Hf) binary alloys which maintain a fixed composition of rhenium while crystallizing in centrosymmetric to non-centro

Pietro Colangelo, Floriana Giannuzzi, Stefano Nicotri

We compute the light axial-vector and tensor meson two-photon transition form factors in the soft-wall holographic model of QCD in the flavor-symmetric case. They are used to evaluate the axial-vector and tensor meson contributions to the anomalous magnetic moment of the muon via the hadronic light-by-light scattering process. As expected, these contribution

Adrián García-García, Juan Carlos Sáez, Fernando Castro, Manuel Prieto-Matías

Multicore processors constitute the main architecture choice for modern computing systems in different market segments. Despite their benefits, the contention that naturally appears when multiple applications compete for the use of shared resources among cores, such as the last-level cache (LLC), may lead to substantial performance degradation. This may have

Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy T Nguyen

Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-level mutual information, such as word ratio

Alauadinov A. K., Yusupov B. B

This paper studies local derivations on the Schr{\"o}dinger algebra $\ms_n$ in $(n+1)$-dimensional space-time of Schr{\"o}dinger Lie groups for any integer $n$. The purpose of this work is to prove that every local derivation on $\ms_n$ is a derivation.

Juan Daniel Torres Luna, A. Mert Bozkurt, Michael Wimmer, Chun-Xiao Liu

Connecting quantum dots through Andreev bound states in a semiconductor-superconductor hybrid provides a platform to create a Kitaev chain. Interestingly, in a double quantum dot, a pair of poor man's Majorana zero modes can emerge when the system is fine-tuned to a sweet spot, where superconducting and normal couplings are equal in magnitude. Control of the

Panagiotis Charalambous

The static Love numbers of four-dimensional asymptotically flat, isolated, general-relativistic black holes are known to be identically vanishing. The Love symmetry proposal suggests that such vanishings are addressed by selection rules following from the emergence of an enhanced $\text{SL}(2,\mathbb{R})$ ("Love") symmetry in the near-zone region; more speci

Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore, Adnan Aijaz

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and in

Adrian Mena, Sarah K. Mann, Angus Cowley-Semple, Emma Bryan

Benefiting from both molecular tunability and versatile methods for deployment, optically interfaced molecular spins are a promising platform for quantum technologies such as sensing and imaging. Room-temperature optically detected coherent spin control is a key enabler for many applications, combining sensitive readout, versatile spin manipulation, and ambi

Monica Colpi, Karsten Danzmann, Martin Hewitson, Kelly Holley-Bockelmann

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to dist

Cheng Feng, Long Huang, Denis Krompass

We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve sha

Aruna Mohan, Danne Elbers, Or Zilbershot, Fatemeh Afghah

Remote patient monitoring based on wearable single-lead electrocardiogram (ECG) devices has significant potential for enabling the early detection of heart disease, especially in combination with artificial intelligence (AI) approaches for automated heart disease detection. There have been prior studies applying AI approaches based on deep learning for heart

Xiaoling Dou, Satoshi Kuriki, Gwo Dong Lin, Donald Richards

The B-spline copula function is defined by a linear combination of elements of the normalized B-spline basis. We develop a modified EM algorithm, to maximize the penalized pseudo-likelihood function, wherein we use the smoothly clipped absolute deviation (SCAD) penalty function for the penalization term. We conduct simulation studies to demonstrate the stabi

Billy J. Franks, Christopher Morris, Ameya Velingker, Floris Geerts

The Weisfeiler-Leman algorithm ($1$-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expressive power of message-passing graph neural networks (MPNNs) and being effective as a graph kernel. Despite its success, $1$-WL faces challenges in distinguishing non-isomorphic g

Huaiyu Li, Andrew Hofstrand, Michael I. Weinstein

We study a semilinear hyperbolic system of PDEs which arises as a continuum approximation of the discrete nonlinear dimer array model introduced by Hadad, Vitelli and Alu (HVA) in \cite{HVA17}. We classify the system's traveling waves, and study their stability properties. We focus on traveling pulse solutions (``solitons'') on a nontrivial background and mo

Andrea Zani

The DarkSide-20k experiment represents the present goal of the Global Argon Dark Matter Collaboration program. Bringing together the experience from previous argon-based detectors, as well as the knowledge gained on large volume membrane cryostats developed within the DUNE program, the community is now building a dual-phase LAr-TPC equipped with SiPM arrays

Prakash Sarkar, Hemant Nanavati

Sharp tip nanoindentation of glassy polymers is a constrained, localized viscoelastoplastic deformation. We interpret this complexity, in terms of the well-understood uniaxial deformation. From the uniaxial compression data in the literature, for PMMA, PC and crosslinked SU-8, we obtain their universal, yield-normalized recovery curves, with eps*=eps/eps_y,

Gaurav Waratkar, Varun Bhalerao, Dipankar Bhattacharya

GW150914 marked the start of the gravitational wave (GW) era with the direct detection of binary black hole (BBH) merger by the LIGO-Virgo GW detectors. The event was temporally coincident with a weak signal detected by Fermi-GBM, which hinted towards the possibility of electromagnetic emission associated with the compact object coalescence. The detection of

Santosh Kumar Singh, Satyabrata Sahu, Ayushi Thawait, Prasanna Chaporkar

We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized. We prove that this problem is NP-complete. We propose two algorithms -- Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG

Shengfang Zhai, Weilong Wang, Jiajun Li, Yinpeng Dong

Recently text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such models and their potential ethical issues have not been fully explored. In this paper, we introduce Universal Semantic Trigger, a meaningless token sequence that can be added at any loc

S. Sgroi, G. Zicari, A. Imparato, M. Paternostro

We propose a bottom-up approach, based on Reinforcement Learning, to the design of a chain achieving efficient excitation-transfer performances. We assume distance-dependent interactions among particles arranged in a chain under tight-binding conditions. Starting from two particles and a localised excitation, we gradually increase the number of constitutents

Hoai-Minh Nguyen

This paper is devoted to the stabilization of a linear control system $y' = A y + B u$ and its suitable non-linear variants where $(A, \cD(A))$ is an infinitesimal generator of a strongly continuous {\it group} in a Hilbert space $\mH$, and $B$ defined in a Hilbert space $\mU$ is an admissible control operator with respect to the semigroup generated by $A$.

Joanna Janczura

In this paper we propose a new method for probabilistic forecasting of electricity prices. It is based on averaging point forecasts from different models combined with expectile regression. We show that deriving the predicted distribution in terms of expectiles, might be in some cases advantageous to the commonly used quantiles. We apply the proposed method

C. -M. Michael Wong, Sarah Zampa

These are the notes for a lecture series on Heegaard Floer homology, given by the first author at the R\'enyi Institute in January 2023, as part of a special semester titled ``Singularities and Low Dimensional Topology''. Familiarity with Heegaard diagrams and Morse theory is assumed. We first illustrate the relevant algebraic structures via grid homology, a

Raul M. Falcon, K. Abirami, N. Mohanapriya, Dafik

Now-a-days, ensuring data security has become an increasingly formidable challenge in safeguarding individuals' sensitive information. Secret-sharing scheme has evolved as a most successful cryptographic technique that allows a secret to be divided or distributed among a group of participants in such a way that only a subset of those participants can reconst

Himanshu Gaur

In this work, we study the universal total and symmetry-resolved entanglement spectra for a single interval of some $2$d Fermionic CFTs using the Boundary Conformal Field theory (BCFT) approach. In this approach, the partition of Hilbert space is achieved by cutting out discs around the entangling boundary points and imposing boundary conditions preserving t

Charles Abdulrazak

Drones, also known as unmanned air vehicles (UAVs), have revolutionised various industries, from farming to national security. (Wexler., Lesley. 2016) However, their broad use has revealed a severe weakness in cybersecurity. (Jean-Paul Yaacoub 2020) The urgent necessity to defend UAV networks from new cyber threats is explored in-depth in this research, maki

Favour O. Adetunji, Niamh Ellis, Maria Koskinopoulou, Ignacio Carlucho

Subsea exploration, inspection, and intervention operations heavily rely on remotely operated vehicles (ROVs). However, the inherent complexity of the underwater environment presents significant challenges to the operators of these vehicles. This paper delves into the challenges associated with navigation and maneuvering tasks in the teleoperation of ROVs, s

Mario Kunzemann, Leonhard K. Doppelbauer, Rene Preuer, Astrid Pechstein

The present article is concerned with modelling the viscoelastic behavior of Polydimethylsiloxane (PDMS) in large-strain regime. Starting from the basic principles of thermodynamics, an incremental variational formulation is derived. Within this model, the free energy density and dissipation function determine elastic and viscous properties of the solid. The

Feliks Rączka

We investigate when the filtration induced by Beilinson's spectral sequence splits non-canonically into a direct sum decomposition. We conclude that for any vector bundle $\mathcal{E}$ on a projective space over an algebraically closed field of characteristic $p>0$ there exists $r_{0}$ such that for $r\geq r_{0}$ the Frobenius pushforward $\mathsf{F}^{r}_{*}

Federico Castellani

We analyze the resonance contributions to the generalized Baldin sum rule, namely the sum of the generalized electric and magnetic nucleon polarizabilities $\alpha_E(Q^2)$ and $\beta_M(Q^2)$, within the Holographic QCD model by Witten, Sakai, and Sugimoto (WSS). In particular, we account for the contributions from the first low-lying nucleon resonances with

Bashaiah Elaganuru, Resmi M, Ramachandrarao Yalla

We report numerically the efficient channeling of single photons from a single quantum emitter into guided modes of optical nanocapillary fibers (NCFs). The NCF is formed of a liquid core optical nanofiber with inner and outer diameters. We optimize the inner and outer diameters of the NCF filled with water medium by placing a single dipole source (SDS) insi

Sergio L. Cacciatori, Fabrizio Canfora, Federica Muscolino

The first analytic solutions representing baryonic layers living at finite baryon density within a constant magnetic field in the gauged Skyrme model are constructed. A remarkable feature of these configurations is that, if the Skyrme term is neglected, then these baryonic layers in the constant magnetic background cannot be found analytically and their ener

Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz

For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to

Matthew V Macfarlane, Edan Toledo, Donal Byrne, Paul Duckworth

Leveraging planning during learning and decision-making is central to the long-term development of intelligent agents. Recent works have successfully combined tree-based search methods and self-play learning mechanisms to this end. However, these methods typically face scaling challenges due to the sequential nature of their search. While practical engineeri

Elena Ferro, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo

Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is required to deal with circuit mismatches and non-idealities associated with the memory devices. Efficient near-memory digital logic is critical to retain the high area/energy efficienc

Alexander D. Fuchs, Johannes A. F. Lehmeyer, Heinz Junkes, Heiko B. Weber

NOMAD CAMELS (short: CAMELS) is a configurable, open-source measurement software that records fully self-describing experimental data. It has its origins in the field of experimental physics where a wide variety of measurement instruments are used in frequently changing experimental setups and measurement protocols. CAMELS provides a graphical user interface

Stefania Costantini

Autonomous Intelligent Agents are employed in many applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. A priori certification techniques (i.e., techniques applied prior to system's deployment) can be useful, but are not sufficient for agents that evolve, and thus modify

Baptiste Maucourt

We delve into the interactions between a prey-predator and a vector-borne epidemic system, driven by agro-ecological motivations. This system involves an ODE, two reaction--diffusion PDEs and one reaction--diffusion--advection PDE. It has no complete variational or monotonic structure and features spatially heterogeneous coefficients. Our initial focus is to

Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris, Jörg Henkel

Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convolutional Neural Networks (CNNs). However, the high computational requirements of these models limit their practical applicability especially on low-power devices. Current state-of-the-

David Corlin Marchand, David Coupier, Benoît Henry

In this work, percolation properties of device-to-device (D2D) networks in urban environments are investigated. The street system is modeled by a Poisson-Delaunay triangulation (PDT). Users are of two types: given either by a Cox process supported by the edges of the PDT or by a Bernoulli process on the vertices of the PDT (i.e. on streets and at crossroads)

Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kuehnberger

Our research demonstrates the significant benefits of using fine-tuning with explanations to enhance the performance of language models. Unlike prompting, which maintains the model's parameters, fine-tuning allows the model to learn and update its parameters during a training phase. In this study, we applied fine-tuning to various sized language models using

Sebastian Nielebock, Paul Blockhaus, Jacob Krüger, Frank Ortmeier

Modern software development relies on the reuse of code via Application Programming Interfaces (APIs). Such reuse relieves developers from learning and developing established algorithms and data structures anew, enabling them to focus on their problem at hand. However, there is also the risk of misusing an API due to a lack of understanding or proper documen

Qi Zhou, Ben-Wei Zhang

The effect of finite coupling corrections to the Langevin diffusion coefficients on a moving heavy quark in the Super Yang-Mills plasma is investigated. These corrections are related to curvature squared corrections in the corresponding gravity sector. We compare the results of both longitudinal and perpendicular Langevin diffusion coefficients with those in

Nolwenn Bernard, Ivica Kostric, Weronika Łajewska, Krisztian Balog

Personal knowledge graphs (PKGs) offer individuals a way to store and consolidate their fragmented personal data in a central place, improving service personalization while maintaining full user control. Despite their potential, practical PKG implementations with user-friendly interfaces remain scarce. This work addresses this gap by proposing a complete sol

Sayo Horigome, Kazuhiro Ichihara

We show that a two-bridge ribbon knot $K(m^2 , m k \pm 1)$ with $m > k >0$ and $(m,k)=1$ admits a symmetric union presentation with partial knot which is a two-bridge knot $K(m,k)$. Similar descriptions for all the other two-bridge ribbon knots are also given.

Gregory Horndeski, Alessandra Silvestri

An essay on Horndeski gravity, how it was formulated in the early 1970s and how it was 're-discovered' and widely adopted by Cosmologists more than thirty years later.

Ahmed Radwan, Ali Tourani, Hriday Bavle, Holger Voos

Aerial robots play a vital role in various applications where the situational awareness of the robots concerning the environment is a fundamental demand. As one such use case, drones in GPS-denied environments require equipping with different sensors (e.g., vision sensors) that provide reliable sensing results while performing pose estimation and localizatio

Ziyuan Ma, Conor Ryan, Jim Buckley, Muslim Chochlov

Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user's financial background on sentiment-based forecasting of the stock market using artificial neural networks. In this work, a novel combination of neural networks is used for the assessment of sentiment-based stock market prediction, base

Kang Zhang, Osamu Yoshie, Lichao Sun, Weiran Huang

Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require la

Davide Giraudo

In this paper, we investigate the law of large numbers for strictly stationary random fields, that is, we provide sufficient conditions on the moments and the dependence of the random field in order to guarantee the almost sure convergence to $0$ and the convergence in $\mathbb L^p$ of partials sums over squares or rectangles of $\mathbb Z^d$. Approximation

Pierre Gilles Lemarié-Rieusset

We construct non-trivial steady solutions in $H^{-1}$ for the 2D Navier-Stokes equations on the torus. In particular, the solutions are not square integrable, so that we have to redefine the notion of solutions.

Thomas Naimer, Martin Gmitra, Jaroslav Fabian

We investigate the effect of the twist angle on the proximity spin-orbit coupling (SOC) in graphene/NbSe$_2$ heterostructures from first principles. The low-energy Dirac bands of several different commensurate twisted supercells are fitted to a model Hamiltonian, allowing us to study the twist-angle dependency of the SOC in detail. We predict that the magnit

Marc Escudier, Ikram Abdelkefi, Clément Fernandes, Wojciech Pieczynski

Pairwise Markov Models (PMMs) extend the wellknown Hidden Markov Models (HMMs). Being significantly more general, PMMs enable several types of processing, like Bayesian filtering or smoothing, similar to those used in HMMs. In this paper, we deal with Bayesian forecasting. The aim is to show analytically in the simple stationary Gaussian case that the extent

Francesco Beretta

Given the challenge of giant knowledge graphs created by major eco-nomic actors, which could virtually replace research in the Humani-ties and Social Sciences (HSS) in responding to public concerns, thequestion arises of how to increase the value of research data throughtheir publication and networking, applying the FAIR principles. Bothan epistemological an

Benjamin A. Antunes, David R. C. Hill

Reproducibility is widely acknowledged as a fundamental principle in scientific research. Currently, the scientific community grapples with numerous challenges associated with reproducibility, often referred to as the ''reproducibility crisis.'' This crisis permeated numerous scientific disciplines. In this study, we examined the factors in scientific practi

Haoyu Li, Yuchen Xu, Jiayi Chen, Rohit Dwivedula

As deep neural networks (DNNs) grow in complexity and size, the resultant increase in communication overhead during distributed training has become a significant bottleneck, challenging the scalability of distributed training systems. Existing solutions, while aiming to mitigate this bottleneck through worker-level compression and in-network aggregation, fal

Carmen Delgado, José María Sanz, Chris Blondia, Jeroen Famaey

Billions of IoT devices are deployed worldwide and batteries are their main power source. However, these batteries are bulky, short-lived and full of hazardous chemicals that damage our environment. Relying on batteries is not a sustainable solution for the future IoT. As an alternative, battery-less devices run on long-lived capacitors charged using energy

Julien Legendre, Pierre-Olivier Chapuis

We propose a unified description of dual radiative heat engines (RHEs), consisting of two facing optoelectronic components (diodes) and capable of generating electrical power from heat. They can operate in three regimes depending on the applied biases, namely in thermoradiative-negative electroluminescent (TRNEL), thermoradiative-photovoltaic (TRPV) or therm

Gilles Bertrand

We introduce the notion of a Morse sequence, which provides a simple and effective approach to discrete Morse theory. A Morse sequence is a sequence composed solely of two elementary operations, that is, expansions (the inverse of a collapse), and fillings (the inverse of a perforation). We show that a Morse sequence may be seen as an alternative way to repr

Dario Compagno

The aim of this paper is to extend the framework of causal inference, in particular as it has been developed by Judea Pearl, in order to model actions and identify their intended effects, in the direction opened by Elisabeth Anscombe. We show how intentions can be inferred from a causal model and its implied correlations observable in data. The paper defines

Lucas Weber, Ana Bušić, Jiamin Zhu

Utilities have introduced demand charges to encourage customers to reduce their demand peaks, since a high peak may cause very high costs for both the utility and the consumer. We herein study the bill minimization problem for customers equipped with an energy storage device and a self-owned renewable energy production. A model-free reinforcement learning al

Tobias Kaiser

Given a power series in finitely many variables that is algebraic over the corresponding polynomial ring over a subfield of the reals, we show that its convergence domain is semialgebraic over the real closure of the subfield. This gives in particular that the convergence radius of a univariate Puiseux series that is algebraic in the above sense belongs to t

Gul Aftab Ahmed, James Vincent Patten, Yuanhua Han, Guoxian Lu

Large-scale source-code clone detection is a challenging task. In our previous work, we proposed an approach (SSCD) that leverages artificial neural networks and approximates nearest neighbour search to effectively and efficiently locate clones in large-scale bodies of code, in a time-efficient manner. However, our literature review suggests that the relativ

Yves Aubry, Marc Perret

An upper bound for the maximum number of rational points on an hypersurface in a projective space over a finite field has been conjectured by Tsfasman and proved by Serre in 1989. The analogue question for hypersurfaces on weighted projective spaces has been considered by Castryck, Ghorpade, Lachaud, O'Sullivan, Ram and the first author in 2017. A conjecture

Tomasz Żądło, Adam Chwila

The usage of machine learning methods in traditional surveys including official statistics, is still very limited. Therefore, we propose a predictor supported by these algorithms, which can be used to predict any population or subpopulation characteristics. Machine learning methods have already been shown to be very powerful in identifying and modelling comp

Vincent Magnin, José Alves, Antoine Arnoud, Arjen Markus

Modern Fortran is a standardized language that includes object-oriented and parallel programming paradigms. The Fortran-lang community, created at the end of 2019, is actively working to modernize its ecosystem. New compilers are under development. And the fourth Fortran standard of the 21st century is due to be published in autumn 2023.

Prachi Jain, Ashutosh Sathe, Varun Gumma, Kabir Ahuja

Pretrained Language Models (PLMs) are widely used in NLP for various tasks. Recent studies have identified various biases that such models exhibit and have proposed methods to correct these biases. However, most of the works address a limited set of bias dimensions independently such as gender, race, or religion. Moreover, the methods typically involve finet

Quirijn B. van Woerkom, Evangelia Kleisioti

Though efforts to detect them have been made with a variety of methods, no technique can claim a successful, confirmed detection of a moon outside the Solar System yet. Moon detection methods are restricted in capability to detecting moons of masses beyond what formation models would suggest, or they require surface temperatures exceeding what tidal heating

Jean-Marie Malherbe

Bernard Lyot invented the monochromatic birefringent filter in 1933 in order to investigate the coronal emissions of solar structures above the limb with the coronagraph installed at the Pic du Midi observatory. The filter was improved later and he made the first observations of the chromosphere above the solar disk in 1948, at Meudon. After his death, Grena

Rownak Kundu, Ujjal Debnath, Himanshu Chaudhary, G. Mustafa

This paper investigates the accelerated cosmic expansion in the late Universe by examining two dark energy models, viscous modified Chaplygin gas (VsMCG) and variable modified Chaplygin gas (VMCG), within loop quantum cosmology alongside the $\Lambda$CDM model. The objective is to constrain cosmic parameters using the $\Lambda$CDM model and 30 of the latest

Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer

Physics-informed machine learning combines the expressiveness of data-based approaches with the interpretability of physical models. In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency. We prove that for linear differential priors, the

Ajinkya Kulkarni, Anna Tokareva, Rameez Qureshi, Miguel Couceiro

In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances. This study is dedicated to a comprehensive exploration of the Whisper and MMS systems, with a focus on assessing biases in automatic speech recognition (ASR) inherent to casual conversation speech specific to the

Daniela Pugliese, Hernando Quevedo

In this chapter, we study special photon orbits defined by means of Killing vectors and present a framework based on the properties of such null orbits. For concreteness, we restrict ourselves to the case of axially symmetric spacetimes describing either black holes with Killing horizons or naked singularities. The null-orbits framework is then applied to an