April 2023 arXiv papers — page 97

Nico Catalano, Matteo Matteucci

Semantic segmentation, vital for applications ranging from autonomous driving to robotics, faces significant challenges in domains where collecting large annotated datasets is difficult or prohibitively expensive. In such contexts, such as medicine and agriculture, the scarcity of training images hampers progress. Introducing Few-Shot Semantic Segmentation,

Swan Dubois, Laurent Feuilloley, Franck Petit, Mikaël Rabie

Consider a dynamic network and a given distributed problem. At any point in time, there might exist several solutions that are equally good with respect to the problem specification, but that are different from an algorithmic perspective, because some could be easier to update than others when the network changes. In other words, one would prefer to have a s

Giuseppe Ortolano, Carmine Napoli, Cillian Harney, Stefano Pirandola

The challenge of pattern recognition is to invoke a strategy that can accurately extract features of a dataset and classify its samples. In realistic scenarios this dataset may be a physical system from which we want to retrieve information, such as in the readout of optical classical memories. The theoretical and experimental development of quantum reading

ShanLin Guan, Zhen Guo

Let $(M^{n}, g)$ be a closed connected Einstein space, $n=dim M ,$ and $\kappa_{0} $ be the lower bound of the sectional curvature. In this paper, we prove Udo Simon's conjecture: on closed Einstein spaces, $n\geq 3,$ there is no eigenvalue $\lambda$ such that $$n\kappa_{0} < \lambda < 2(n + 1)\kappa_{0},$$ and both bounds are the best possible. Furthermore,

Luis J. Alias, Giulio Colombo, Marco Rigoli

In this paper we prove some integral estimates on the minimal growth of the positive part $u_+$ of subsolutions of quasilinear equations \[ \mathrm{div} A(x,u,\nabla u) = V|u|^{p-2}u \] on complete Riemannian manifolds $M$, in the non-trivial case $u_+\not\equiv 0$. Here $A$ satisfies the structural assumption $|A(x,u,\nabla u)|^{p/(p-1)} \leq k \langle A(x,

Yuchen Zhao, Choo Kean Khaw, Yifan Wang

Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformation with nearly infinite degree-of-freedom are necessary. Soft sensor networks can address this problem, however, measuring all sensor values throughout the b

Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein, Silvia Chiappa

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on observational data. Recent advances in ne

Chao Wang, Ke Wang

Context. Massive stars are generally believed to form in supersonic turbulent environment. However, recent observations have challenged this traditional view. High spatial and spectral resolution observations of the Orion Molecular Cloud and an infrared dark cloud G35.39 show a resolution-dependent turbulence, and that high-mass stars are forming exclusively

Stefan Grushko, Aleš Vysocký, Jakub Chlebek, Petr Prokop

This study uses domain randomization to generate a synthetic RGB-D dataset for training multimodal instance segmentation models, aiming to achieve colour-agnostic hand localization in cluttered industrial environments. Domain randomization is a simple technique for addressing the "reality gap" by randomly rendering unrealistic features in a simulation scene

Sheng Jiang, Sunjae Chung, Martina Ahlberg, Anreas Frisk

Magnetic droplets are nanoscale, non-topological, magnetodynamical solitons that can be nucleated in spin torque nano-oscillators (STNOs) or spin Hall nano-oscillators (SHNOs). All theoretical, numerical, and experimental droplet studies have so far focused on the free layer (FL), and any additional dynamics in the reference layer (RL) have been entirely ign

Xujing Li, Min Liu, Sheng Sun, Yuwei Wang

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local mo

Deyu An, Qiang Zhang, Jianshu Chao, Ting Li

Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm e

Onur Erbay, Iskender Atilla Reyhancan

A new Monte Carlo-Library Least Squares (MCLLS) method was developed for the neutron inelastic-scattering and thermal-capture analysis (NITA) technique in real-time online analysis systems to determine boron content in Tinkal ore samples. The Geant4 toolkit was used to simulate the Prompt Gamma-ray Neutron Activation Analysis (PGNAA) system for the NITA tech

Vindya Vashishth, Bidya Binay Karak, Leonid Kitchatinov

Like the solar cycle, stellar activity cycles are also irregular. Observations reveal that rapidly rotating (young) Sun-like stars exhibit a high level of activity with no Maunder-like grand minima and rarely display smooth regular activity cycles. On the other hand, slowly rotating old stars like the Sun have low activity levels and smooth cycles with occas

Zhengcong Fei, Mingyuan Fan, Junshi Huang

Recent works on personalized text-to-image generation usually learn to bind a special token with specific subjects or styles of a few given images by tuning its embedding through gradient descent. It is natural to question whether we can optimize the textual inversions by only accessing the process of model inference. As only requiring the forward computatio

Feng-Feng Wei, Wei-Neng Chen, Xiao-Qi Guo, Bowen Zhao

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crow

Willem van der Maden, Derek Lomas, Malak Sadek, Paul Hekkert

Artificial Intelligence (AI) is a double-edged sword: on one hand, AI promises to provide great advances that could benefit humanity, but on the other hand, AI poses substantial (even existential) risks. With advancements happening daily, many people are increasingly worried about AI's impact on their lives. To ensure AI progresses beneficially, some researc

Filippo Dell'Oro, Lorenzo Liverani, Vittorino Pata

The exponential decay rate of the semigroup $S(t)=e^{t\mathbb{A}}$ generated by the abstract damped wave equation $$\ddot u + 2f(A) \dot u +A u=0 $$ is here addressed, where $A$ is a strictly positive operator. The continuous function $f$, defined on the spectrum of $A$, is subject to the constraints $$\inf f(s)>0\qquad\text{and}\qquad \sup f(s)/s <\infty$$

Luke Anastassiou, Jason F. Ralph, Simon Maskell, Pieter Kok

This paper explores the effect of three-dimensional rotations on two-qubit Bell states and proposes a Bayesian method for the estimation of the parameters of the rotation. We use a particle filter to estimate the parameters of the rotation from a sequence of Bell state measurements and we demonstrate that the resultant improvement over the optimal single qub

Irina Espejo, Sinclert Pérez, Kenyi Hurtado, Lukas Heinrich

MadMiner is a Python package that implements a powerful family of multivariate inference techniques that leverage matrix element information and machine learning. This multivariate approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper, we ad

Rafael I. J. Mostert, Raffaella Morganti, Marisa Brienza, Kenneth J. Duncan

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified in radio sky surveys based on their spectral index, or, complementary, through visual inspection based on their radio m

Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga

Attack trees (ATs) are a widely deployed modelling technique to categorize potential attacks on a system. An attacker of such a system aims at doing as much damage as possible, but might be limited by a cost budget. The maximum possible damage for a given cost budget is an important security metric of a system. In this paper, we find the maximum damage given

Wei-Neng Chen, Feng-Feng Wei, Tian-Fang Zhao, Kay Chen Tan

The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly

Smaranika Banerjee, Masaomi Tanaka, Daiji Kato, Gediminas Gaigalas

We investigate the early (t < 1 day) kilonova from the neutron star merger by deriving atomic opacities for all the elements from La to Ra (Z = 57 - 88) ionized to the states V - XI. The opacities at high temperatures for the elements with open f-shells (e.g., lanthanides) are exceptionally high, reaching kappa_{exp} ~ 10^4 cm2/g at lambda < 1000 A at T ~ 70

Martin Möhle

A multi-type neutral Cannings population model with mutation and fixed subpopulation sizes is analyzed. Under appropriate conditions, as all subpopulation sizes tend to infinity, the ancestral process, properly time-scaled, converges to a multi-type exchangeable coalescent with mutation sharing the exchangeability and consistency property. The proof gains fr

Janne Nurminen

In this work we study an inverse problem for the minimal surface equation on a Riemannian manifold $(\mathbb{R}^{n},g)$ where the metric is of the form $g(x)=c(x)(\hat{g}\oplus e)$. Here $\hat{g}$ is a simple Riemannian metric on $\mathbb{R}^{n-1}$, $e$ is the Euclidean metric on $\mathbb{R}$ and $c$ a smooth positive function. We show that if we know the as

Dan Wang, M. Koussour, Adnan Malik, N. Myrzakulov

We propose a logarithmic parametrization form of energy density for the scalar field dark energy in the framework of the standard theory of gravity, which supports the necessary transition from the decelerated to the accelerated behavior of the Universe. The model under consideration is constrained by available observational data, including cosmic chronomete

Roman Kuzmin, Nitish Mehta, Nicholas Grabon, Raymond A. Mencia

Although quantum mechanics applies to many macroscopic superconducting devices, one basic prediction remained controversial for decades. Namely, a Josephson junction connected to a resistor must undergo a dissipation-induced quantum phase transition from superconductor to insulator once the resistor's value exceeds $h/4e^2 \approx 6.5~\textrm{k}\Omega$ ($h$

Kristóf Németh, Dániel Hadházi

We apply artificial neural networks (ANNs) to nowcast quarterly GDP growth for the U.S. economy. Using the monthly FRED-MD database, we compare the nowcasting performance of five different ANN architectures: the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the Elman recurrent neural network (RNN), the long short-ter

Yuchen Zhao, Yifan Wang

Soft grippers have excellent adaptability for a variety of objects and tasks. Jamming-based variable stiffness materials can further increase soft grippers' gripping force and capacity. Previous universal grippers enabled by granular jamming have shown great capability of handling objects with various shapes and weight. However, they require a large pushing

Ryo Hanai, Yukiyasu Domae, Ixchel G. Ramirez-Alpizar, Bruno Leme

When humans see a scene, they can roughly imagine the forces applied to objects based on their experience and use them to handle the objects properly. This paper considers transferring this "force-visualization" ability to robots. We hypothesize that a rough force distribution (named "force map") can be utilized for object manipulation strategies even if acc

Mariia Filianina, Maddalena Bin, Sharon Berkowicz, Mario Reiser

Studying protein interactions at low temperatures has important implications for optimizing cryostorage processes of biological tissue, food, and protein-based drugs. One of the major challenges is related to the formation of ice nanocrystals which can occur even in the presence of cryoprotectants and can lead to protein denaturation. Here, using a combinati

MaryBeth Defrance, Tijl De Bie

Fairness in AI has garnered quite some attention in research, and increasingly also in society. The so-called "Impossibility Theorem" has been one of the more striking research results with both theoretical and practical consequences, as it states that satisfying a certain combination of fairness measures is impossible. To date, this negative result has not

Carlo Piccardi

Identifying networks with similar characteristics in a given ensemble, or detecting pattern discontinuities in a temporal sequence of networks, are two examples of tasks that require an effective metric capable of quantifying network (dis)similarity. Here we propose a method based on a global portrait of graph properties built by processing local nodes featu

Matthieu Herrmann, Chang Wei Tan, Mahsa Salehi, Geoffrey I. Webb

Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, including similarity-based, features and interv

Chong Liu, Shao-Chun Chen, Nail Akhmediev

We developed an exact theory of the super-regular (SR) breathers of Manakov equations. We have shown that the vector SR breathers do exist both in the cases of focusing and defocusing Manakov systems. The theory is based on the eigenvalue analysis and on finding the exact links between the SR breathers and modulation instability. We have shown that in the fo

Shao-Chun Chen, Chong Liu, Nail Akhmediev

We study higher-order modulation instability phenomena in the frame of Manakov equations. Evolution that starts with a single pair of sidebands expands over several higher harmonics. The choice of initial pair of sidebands influences the structure of unstable frequency components and changes drastically the wave evolution leading, in some cases, to jumps acr

Wen-Juan Che, Chong Liu, Nail Akhmediev

We present exact multi-parameter families of soliton solutions for two- and three-component Manakov equations in the \emph{defocusing} regime. Existence diagrams for such solutions in the space of parameters are presented. Fundamental soliton solutions exist only in finite areas on the plane of parameters. Within these areas, the solutions demonstrate rich s

Andrea Blason, Michele Fabrizio

Green's function zeros, which can emerge only if correlation is strong, have been for long overlooked and believed to be devoid of any physical meaning, unlike Green's function poles. Here, we prove that Green's function zeros instead contribute on the same footing as poles to determine the topological character of an insulator. The key to the proof, worked

Vladimir Hinich

We present a proof of the formula (given in Lurie's Higher Algebra) for the operad governing diagrams of operad algebras. We believe that our proof corrects a flaw in the original argument. 2nd version: a corrected proof given.

Ram Dershan, Amir M. Soufi Enayati, Zengjie Zhang, Dean Richert

Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally, RL agents are sensitive to the discrepancies between the simulation and the real world, known as the sim-to-real gap. The application of domain randomization, a technique used to

Ganesh Prasad, Håkan Johansson

The beam-oriented digital predistortion (BO-DPD) is not sufficient to linearize the output from a subarray of power amplifiers (PAs) in different directions except the desired direction. Therefore, subsequent to the BO-DPD operation, we perform a post-weighting (PW) processing to minimize the nonlinear radiations in the wide range of directions under crossta

Luka Klinčić, Vinko Zlatić, Guido Caldarelli, Hrvoje Štefančić

The study of systemic risk is often presented through the analysis of several measures referring to quantities used by practitioners and policy makers. Almost invariably, those measures evaluate the size of the impact that exogenous events can exhibit on a financial system without analysing the nature of initial shock. Here we present a symmetric approach an

B. A. Tay

The Liouvillian exceptional points for a quantum Markovian master equation of an oscillator in a generic environment are obtained. They occur at the points when the modified frequency of the oscillator vanishes, whereby the eigenvalues of the Liouvillian become real. In a generic system there are two parameters that modify the oscillator's natural frequency.

Ming-Liang Hu, Heng Fan

Entanglement and uncertainty relation are two focuses of quantum theory. We relate entanglement sharing to the entropic uncertainty relation in a $(d\times d)$-dimensional system via weak measurements with different pointers. We consider both the scenarios of one-sided sequential measurements in which the entangled pair is distributed to multiple Alices and

Adrian Riekert

In this article we identify a general class of high-dimensional continuous functions that can be approximated by deep neural networks (DNNs) with the rectified linear unit (ReLU) activation without the curse of dimensionality. In other words, the number of DNN parameters grows at most polynomially in the input dimension and the approximation error. The funct

Panchi Li, Shuting Gu, Jin Lan, Jingrun Chen

Magnetic skyrmions widely exist in a diverse range of magnetic systems, including chiral magnets with a non-centrosymmetric structure characterized by Dzyaloshinkii-Moriya interaction~(DMI). In this study, we propose a generalized semi-implicit backward differentiation formula projection method, enabling the simulations of the Landau-Lifshitz~(LL) equation i

Svetlin Georgiev, Sergey Kryzhevich

We consider small nonlinear perturbations of linear systems on a time scale with the phase space being finite or infinite-dimensional. For $\Delta$-differential operators, corresponding to linear dynamic systems we consider their solvability in various functional spaces. Based on these techniques, we prove several results on the existence of a bounded soluti

Kilian Hersent, Jean-Christophe Wallet

We study one-loop perturbative properties of scalar field theories on the $\rho$-Minkowski space. The corresponding star-product, together with the involution are characterized from a combination of Weyl quantization and defining properties of the convolution algebra of the Euclidean group linked to the coordinate algebra of the $\rho$-Minkowski space. The n

Alexander Söderberg Rousu

We study the $\phi^6 - \hat{\phi}^4$ model with $O(N)$-symmetry near three dimensions. This model has a sextic bulk-interaction and a quartic boundary-interaction. The bulk two-point correlator is found upto two-loops by solving the equation of motion and applying the boundary conditions. Finally we apply the Coleman-Weinberg mechanism to this model, which a

Zifan Wang, Akshay Joshi, Angkur Jyoti Dipanka Shaikeea, Vikram Susdhir Deshpande

X-ray computed tomography (XCT) has become a reliable metrology tool for measuring internal flaws and other microstructural features in engineering materials. However, tracking of material points to measure three-dimensional (3D) deformations has hitherto relied on either artificially adding tracer particles (speckles) or exploiting inherent microstructural

Shohini Bhattacharya, Duxin Zheng, Jian Zhou

We demonstrate that the longitudinal single target-spin asymmetry in exclusive $\pi^0$ production in $ep$ collisions can give access to the imaginary part of the gluon generalized transverse momentum distribution (GTMD) $F_{1,4}$. Such a longitudinal spin asymmetry that results from the Coulomb-nuclear interference effect, leads to a characteristic azimuthal

Sandra Martinková, Karolina Stańczak, Isabelle Augenstein

Pre-trained language models have been known to perpetuate biases from the underlying datasets to downstream tasks. However, these findings are predominantly based on monolingual language models for English, whereas there are few investigative studies of biases encoded in language models for languages beyond English. In this paper, we fill this gap by analysi

Sourav Pal, Nitin Tomar

For $0<r<1$, let us consider the following annulus: \[ \mathbb A_r= \{ z\in \mathbb C\, : \, r<|z|<1 \}. \] A Hilbert space operator $T$ for which $\overline{\mathbb A}_r$ is a spectral set is called an $\mathbb A_r$-\textit{contraction}. Also, a normal operator $U$ whose spectrum lies on the boundary $\partial \mathbb A_r$ of $\mathbb A_r$ is called an $\ma

Hubert Lacoin

Given $d\ge 1$, we provide a construction of the random measure - the critical Gaussian Multiplicative Chaos - formally defined $e^{\sqrt{2d}X}\mathrm{d} \mu$ where $X$ is a $\log$-correlated Gaussian field and $\mu$ is a locally finite measure on $\mathbb R^d$. Our construction generalizes the one performed in the case where $\mu$ is the Lebesgue measure. I

S K H Auluck

Space propulsion is unique among many proposed applications of the Dense Plasma Focus in being critically dependent on the availability of a scaling theory that is well-grounded in physics, in conformity with existing experimental knowledge and applicable to experimentally untested configurations. This paper derives such a first-principles-based scaling theo

Chunyang Zhao, Youjun Lu, Qingbo Chu, Wen Zhao

Kilonovae produced by mergers of binary neutron stars (BNSs) are important transient events to be detected by time domain surveys with the alerts from the ground-based gravitational wave detectors. The observational properties of these kilonovae depend on the physical processes involved in the merging processes and the equation of state (EOS) of neutron star

Silia Maksuti, Ani Bicaku, Mario Zsilak, Igor Ivkić

The Internet of Things (IoT) is rapidly changing the number of connected devices and the way they interact with each other. This increases the need for an automated and secure onboarding procedure for IoT devices, systems and services. Device manufacturers are entering the market with internet connected devices, ranging from small sensors to production devic

S. E. van Terwisga, A. Hacar

External FUV irradiation of protoplanetary disks has an important impact on their evolution and ability to form planets. However, nearby (<300 pc) star-forming regions lack sufficiently massive young stars, while the Trapezium Cluster and NGC 2024 have complicated star-formation histories and their O-type stars' intense radiation fields ($>10^4\,G_0$) destro

Amir M. Soufi Enayati, Zengjie Zhang, Kashish Gupta, Homayoun Najjaran

Reinforcement learning (RL) suffers from low sample efficiency, particularly in high-dimensional continuous state-action spaces of complex robotic manipulation tasks. RL performance can improve by leveraging prior knowledge, even when demonstrations are limited and collected from simplified environments. To address this, we define General Abstract Symmetry (

Igor Ivkić, Dominik Thiede, Nicholas Race, Matthew Broadbent

The importance of cloud computing has grown over the last years, which resulted in a significant increase of Data Center (DC) network requirements. Virtualisation is one of the key drivers of that transformation and enables a massive deployment of computing resources, which exhausts server capacity limits. Furthermore, the increased network endpoints need to

Maja Lindström, Rohit Sahasrabuddhe, Anton Holmgren, Christopher Blöcker

Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, reliably identifying such overlapping flow-based communities requires higher-order relational data, which are often unavailable. To address this challenge, we

Michalis Mountantonakis, Yannis Tzitzikas

There is a recent trend for using the novel Artificial Intelligence ChatGPT chatbox, which provides detailed responses and articulate answers across many domains of knowledge. However, in many cases it returns plausible-sounding but incorrect or inaccurate responses, whereas it does not provide evidence. Therefore, any user has to further search for checking

A. S. Bonomo, X. Dumusque, A. Massa, A. Mortier

The exoplanet population characterized by relatively short orbital periods ($P<100$ d) around solar-type stars is dominated by super-Earths and sub-Neptunes. However, these planets are missing in our Solar System and the reason behind this absence is still unknown. Two theoretical scenarios invoke the role of Jupiter as the possible culprit: Jupiter may have

Nicolas Chahine, Ana-Stefania Calarasanu, Davide Garcia-Civiero, Theo Cayla

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). Due t

Rangel Baldasso, Marcelo R. Hilario, Daniel Kious, Augusto Teixeira

In this article, we prove a lower bound for the fluctuations of symmetric random walks on dynamic random environments in dimension $1 + 1$ in the perturbative regime where the walker is weakly influenced by the environment. We suppose that the random environment is invariant with respect to translations and reflections, satisfies the FKG inequality and a mil

Maico Hendrikus Wilhelmus Engelaar, Licio Romao, Yulong Gao, Mircea Lazar

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stoch

Sanghoon Lee, Sergej Flach, Alexei Andreanov

One-dimensional all-bands-flat lattices are networks with all bands being flat and highly degenerate. They can always be diagonalized by a finite sequence of local unitary transformations parameterized by a set of angles \(\theta_{i}\). In our previous work, Ref.~\onlinecite{lee2023critical}, we demonstrated that quasiperiodic perturbations of the one-dimens

Jan Olucak, Walter Fichter, Torbjørn Cunis

Model predictive control allows solving complex control tasks with control and state constraints. However, an optimal control problem must be solved in real-time to predict the future system behavior, which is hardly possible on embedded hardware. To solve this problem, this paper proposes to compute a sequence of one-step optimizations aided by pre-computed

Andrea Bianchi, Giordano d'Aloisio, Francesca Marzi, Antinisca Di Marco

Reproducibility is a crucial aspect of scientific research that involves the ability to independently replicate experimental results by analysing the same data or repeating the same experiment. Over the years, many works have been proposed to make the results of the experiments actually reproducible. However, very few address the importance of data reproduci

Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude, Kenichi Matsumoto

Due to the increasing number of attacks targeting open source library ecosystems, assisting maintainers has become a top priority. This is especially important since maintainers are usually overworked. Although the motivation of Open Source developers has been widely studied, the extent to which maintainers assist libraries that they depend on is unknown. Su

Georgy Kostin, Alexander Gavrikov

An optimal control problem for longitudinal motions of a thin elastic rod is considered. We suppose that a normal force, which changes piecewise constantly along the rod's length, is applied to the cross-section so that the positions of force jumps are equidistantly placed along the length. Additionally, external loads act at the rod ends. These distributed

Samia Touileb, Lilja Øvrelid, Erik Velldal

We investigate in this paper how distributions of occupations with respect to gender is reflected in pre-trained language models. Such distributions are not always aligned to normative ideals, nor do they necessarily reflect a descriptive assessment of reality. In this paper, we introduce an approach for measuring to what degree pre-trained language models a

Sofia B. S. D. Castro

We provide a novel approach to achieving a desired outcome in a coordination game: the original 2x2 game is embedded in a 2x3 game where one of the players may use a third action. For a large set of payoff values only one of the Nash equilibria of the original 2x2 game is stable under replicator dynamics. We show that this Nash equilibrium is the {\omega}-li

Apara Tripathi, Neelam Panwar, Saurabh Sharma, Brijesh Kumar

We present photometric and kinematic analysis of an intermediate age open cluster NGC 1027 using $UBV(RI)_c$ and Gaia Early Data Release 3 (EDR3) data. Structural and fundamental parameters such as cluster center, cluster extent, reddening, age and distance are estimated in this study. Cluster center is found about 2 arcmin away from the center reported earl

Colin M. Gray, Shruthi Sai Chivukula, Janna Johns, Matthew Will

Ethics as embodied by technology practitioners resists simple definition, particularly as it relates to the interplay of identity, organizational, and professional complexity. In this paper we use the linguistic notion of languaging as an analytic lens to describe how technology and design practitioners negotiate their conception of ethics as they reflect up

Hao-Ran Liu, Ming-Xia Li, Wei-Xing Zhou

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the

Anand Katailiha, Paul C. Lou, Ravindra G. Bhardwaj, Ward P. Beyermann

The superposition of atomic vibrations and flexoelectronic effect gives rise to a cross correlation between free charge carriers and temporal magnetic moment of phonons in conducting heterostructures under an applied strain gradient. The resulting dynamical coupling is expected to give rise to quasiparticle excitations called as magnetoelectronic electromagn

Muhammad Rameez Ur Rahman, Luca Scofano, Edoardo De Matteis, Alessandro Flaborea

The task of collaborative human pose forecasting stands for predicting the future poses of multiple interacting people, given those in previous frames. Predicting two people in interaction, instead of each separately, promises better performance, due to their body-body motion correlations. But the task has remained so far primarily unexplored. In this paper,

Xin Yi, Qiao Chen, Kexin Wang, Yuanyang Yu

Interfacial coupling between graphene and other 2D materials can give rise to intriguing physical phenomena. In particular, several theoretical studies predict that the interplay between graphene and an antiferromagnetic insulator could lead to the emergence of quantum anomalous Hall phases. However, such phases have not been observed experimentally yet, and

Aleksa Krsmanovic

DNA methylation is a significant driver of cell-type heterogeneity and has been implicated in various regulatory processes ranging from cell differentiation to imprinting. As the methyl group is embedded in the DNA molecule, assessing DNA methylation is particularly promising in liquid-biopsy-based approaches, as cell-free DNA retains information related to

Dan Ruta, Gemma Canet Tarres, Alexander Black, Andrew Gilbert

Representation learning aims to discover individual salient features of a domain in a compact and descriptive form that strongly identifies the unique characteristics of a given sample respective to its domain. Existing works in visual style representation literature have tried to disentangle style from content during training explicitly. A complete separati

Bing Han, Zhengyang Chen, Yanmin Qian

Automatic speaker verification task has made great achievements using deep learning approaches with the large-scale manually annotated dataset. However, it's very difficult and expensive to collect a large amount of well-labeled data for system building. In this paper, we propose a novel and advanced self-supervised learning framework which can construct a h

Li Zhu, Lekai Liu, Changshi Yu

Landslide susceptibility prediction has always been an important and challenging content. However, there are some uncertain problems to be solved in susceptibility modeling, such as the error of landslide samples and the complex nonlinear relationship between environmental factors. A self-screening graph convolutional network and long short-term memory netwo

Dong Wang, Jia Guo, Qiqi Shao, Haochi He

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which o

Trung-Nghia Le, Tam V. Nguyen, Minh-Quan Le, Trong-Thuan Nguyen

3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations,

Daniel G. Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin

Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. However, to date, its localization analysis

Kathlén Kohn, Guido Montúfar, Vahid Shahverdi, Matthew Trager

We study the geometry of linear networks with one-dimensional convolutional layers. The function spaces of these networks can be identified with semi-algebraic families of polynomials admitting sparse factorizations. We analyze the impact of the network's architecture on the function space's dimension, boundary, and singular points. We also describe the crit

Meng-Ting Wang, Hong-Yue Jiang, Yu-Xiao Liu

The connection between quantum information and quantum gravity has captured the imagination of physicists. Recently, a broad new class of gravitational observables have been proposed to provide new possibilities for holographic complexity \cite{Belin:2021bga} , which is an extension of volume in the Complexity=Volume proposal. In this paper, we investigate g

Wei Ji, Jingjing Li, Qi Bi, Tingwei Liu

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into

Yuxing Tian, Mingjie Zhu, Jiachi Luo, Song Li

This study focuses on long-term forecasting (LTF) on continuous-time dynamic graph networks (CTDGNs), which is important for real-world modeling. Existing CTDGNs are effective for modeling temporal graph data due to their ability to capture complex temporal dependencies but perform poorly on LTF due to the substantial requirement for historical data, which i

E. Slootman, W. Cherifi, L. Eek, R. Arouca

Symmetry is one of the cornerstones of modern physics and has profound implications in different areas. In symmetry-protected topological systems, symmetries are responsible for protecting surface states, which are at the heart of the fascinating properties exhibited by these materials. When the symmetry protecting the edge mode is broken, the topological ph

Aiwei Guan, Chuanfu Yang, Natalia P. Bondarenko

In this paper, we consider Barcilon's inverse problem, which consists of the recovery of the fourth-order differential operator from three spectra. We obtain the relationship of Barcilon's three spectra with the Weyl-Yurko matrix. Moreover, we prove the uniqueness theorem for the inverse problem solution by developing the ideas of the method of spectral mapp

Pablo Valgañón, Andrés F. Useche, David Soriano-Paños, Gourab Ghoshal

In the absence of vaccines, the most widespread reaction to curb COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their outcome in terms of mobility reduction and the sub

Valiollah Khalili

In this paper we study of the structure of non-commutative Poisson algebras with an arbitrary set $\ss.$ We show that any of such an algebra $\pp$ decomposes as $$\pp=\uu\oplus\sum_{[\lambda]\in(\Lambda_\ss\setminus\{0\})/\sim}\pp_{[\lambda]},$$ where $\uu$ is a linear subspace complement of $\span_{\bbbf}\{ [\pp_{\mu}, \pp_{\eta}]+\pp_{\mu}\pp_{\eta} : \mu,

Haiyong Wang

Laguerre spectral approximations play an important role in the development of efficient algorithms for problems in unbounded domains. In this paper, we present a comprehensive convergence rate analysis of Laguerre spectral approximations for analytic functions. By exploiting contour integral techniques from complex analysis, we prove that Laguerre projection

Anja Dakić, Benjamin Rainer, Markus Hofer, Thomas Zemen

Wireless vehicular communication will increase the safety of road users. The reliability of vehicular communication links is of high importance as links with low reliability may diminish the advantage of having situational traffic information. The goal of our investigation is to obtain a reliable coverage area for non-stationary vehicular scenarios. Therefor

Çisem Güneş Aktaş

We give a complete equisingular deformation classification of simple spatial quartic surfaces which are in fact $K3$-surfaces.

Beatriz Paula, Plinio Moreno

The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view's periphery. However, the majority of existing object localization methods rely on images acquired by image sensors with space-invariant resolution, i

Leonardo Cella, Ryan Martin

Statisticians are largely focused on developing methods that perform well in a frequentist sense -- even the Bayesians. But the widely-publicized replication crisis suggests that these performance guarantees alone are not enough to instill confidence in scientific discoveries. In addition to reliably detecting hypotheses that are (in)compatible with data, in