July 2023 arXiv papers — page 17

Alejandra Duque-Torres, Claus Klammer, Dietmar Pfahl, Stefan Fischer

Regression testing is crucial in ensuring that pure code refactoring does not adversely affect existing software functionality, but it can be expensive, accounting for half the cost of software maintenance. Automated test case generation reduces effort but may generate weak test suites. Test amplification is a promising solution that enhances tests by genera

Fanming Cai, Zhaomiao Liu, Nan Zheng, Yan Pang

The field trap effect on the microcavity surface under the action of an electric field is not conducive to boiling heat transfer. This numerical study found that using conducting-insulating microcavity surfaces in an electric field removes the field trap effect, increasing the critical heat flux by more than 200%. Bubble behavior and heat transfer mechanisms

Benjamin Wong

The purpose of this study is to test the effectiveness of current straggler mitigation techniques over different important iterative convergent machine learning(ML) algorithm including Matrix Factorization (MF), Multinomial Logistic Regression (MLR), and Latent Dirichlet Allocation (LDA) . The experiment was conducted to implemented using the FlexPS system,

Na Chen, Xianming Kuang, Feiyu Liu, Kehao Wang

Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on designing increasingly complex deep backbones. Unfortunately, the task remains very challenging due to the difficulty of transferring the knowledge learned in training classes

Zahra Tajik, Nastaran Farhang, Hossein Safari, Michael S. Wheatland

Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star's atmospheric conditions. Hence, detecting and identifying these features (also known as magnetic elements) are among the essential topics in the community. Here, we apply the complex network approach to recogni

Alejandra Duque-Torres, Dietmar Pfahl, Claus Klammer, Stefan Fischer

Identifying and selecting high-quality Metamorphic Relations (MRs) is a challenge in Metamorphic Testing (MT). While some techniques for automatically selecting MRs have been proposed, they are either domain-specific or rely on strict assumptions about the applicability of a pre-defined MRs. This paper presents a preliminary evaluation of MetaTrimmer, a meth

Eimantas Ledinauskas, Egidijus Anisimovas

The representation of a quantum wave function as a neural network quantum state (NQS) provides a powerful variational ansatz for finding the ground states of many-body quantum systems. Nevertheless, due to the complex variational landscape, traditional methods often employ the computation of quantum geometric tensor, consequently complicating optimization te

Lorenzo Mella

In [1] the authors studied the closed tour problem on the $8\times 8$ chessboard of a chess piece, called $k$-prince, leaving open the existence of such a tour when $k=7$. In this note we find a solution to this open case.

Marin Tharrault, Eva Desgué, Dominique Carisetti, Bernard Plaçais

Raman spectroscopy is widely used to assess the quality of 2D materials thin films. This report focuses on $\rm{PtSe_2}$, a noble transition metal dichalcogenide which has the remarkable property to transit from a semi-conductor to a semi-metal with increasing layer number. While polycrystalline $\rm{PtSe_2}$ can be grown with various crystalline qualities,

Jade Master, Martha Lewis

The Fifth International Conference on Applied Category Theory took place at the University of Strathclyde in Glasgow, Scotland on 18-22 July 2022. This conference follows the previous meetings at Leiden (2018), Oxford (2019), MIT (2020, fully online), and Cambridge (2021). The conference comprised 59 contributed talks, a poster session, an industry showcase

Carlos Hugo López-Caraballo, Beatriz Ruiz-Granados, Ricardo Genova Santos, Mateo Fernández-Torreiro

We use the new QUIJOTE-MFI wide survey (11, 13, 17 and 19 GHz) to produce spectral energy distributions (SEDs), on an angular scale of 1 deg, of the supernova remnants (SNRs) CTB 80, Cygnus Loop, HB 21, CTA 1, Tycho and HB 9. We provide new measurements of the polarized synchrotron radiation in the microwave range. For each SNR, the intensity and polarizatio

Jianyi Cheng, Cheng Zhang, Zhewen Yu, Christos-Savvas Bouganis

Model quantization represents both parameters (weights) and intermediate values (activations) in a more compact format, thereby directly reducing both computational and memory cost in hardware. The quantization of recent large language models (LLMs) faces challenges to achieve competitive memory density compared to other models such as convolutional neural n

Enrique Dehaerne, Bappaditya Dey, Hossein Esfandiar, Lander Verstraete

Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed self-assembly (DSA) for which no traditional, automatic defect inspection software exists. Machine Learning-based SEM image analysis has become an increasingly popular research topic for de

Kangming Chen, Ellen H. Fukuda, Hiroyuki Sato

In this paper, we propose nonlinear conjugate gradient methods for vector optimization on Riemannian manifolds. The concepts of Wolfe and Zoutendjik conditions are extended for Riemannian manifolds. Specifically, we establish the existence of intervals of step sizes that satisfy the Wolfe conditions. The convergence analysis covers the vector extensions of t

Jaime Corsetti, Davide Boscaini, Fabio Poiesi

Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algorithms or by directly regressing the pose with end-to-end optimisations. We argue that learning point-level discriminative features is overlooked in the literature. To this end, we

Hyun Gyu Lee, Deok-Sun Lee

The prevalence of wealth inequality propels us to characterize its origin and progression, via empirical and theoretical studies. The Yard-Sale(YS) model, in which a portion of the smaller wealth is transferred between two individuals, culminates in the concentration of almost all wealth to a single individual, while distributing rest of the wealth with a po

Joshua Erde, Mihyun Kang, Florian Lehner, Bojan Mohar

The game of \emph{Cops and Robber} is usually played on a graph, where a group of cops attempt to catch a robber moving along the edges of the graph. The \emph{cop number} of a graph is the minimum number of cops required to win the game. An important conjecture in this area, due to Meyniel, states that the cop number of an $n$-vertex connected graph is $O(\

Vladimir Mikhailets, Aleksandr Murach, Oksana Tsyhanok

The article studies the convergence of trigonometric Fourier series via a new Tauberian theorem for Ces\`{a}ro summable series in abstract normed spaces. This theorem generalizes some known results of Hardy and Littlewood for number series. We find sufficient conditions for the convergence of trigonometric Fourier series in homogeneous Banach spaces over the

Xueming Liu, Kunda Liu, Tianjiang Hu, Qingrui Zhang

In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance and self-displacement measurements. A relative localization technique is developed based on the recursive least square estimation (RLSE) technique with a forgetting factor to estima

Tao Feng, Letian Gan, Shiheng Zhao, Zheng Chang

Acoustic metamaterials, particularly the topological insulators, exhibit exceptional wave characteristics that have sparked considerable research interest. The study of imperfect interfaces affect is of significant importance for the modeling of wave propagation behavior in topological insulators. This paper models a soft Rayleigh beam system with imperfect

Brielen Madureira, Patrick Kahardipraja, David Schlangen

Incremental dialogue model components produce a sequence of output prefixes based on incoming input. Mistakes can occur due to local ambiguities or to wrong hypotheses, making the ability to revise past outputs a desirable property that can be governed by a policy. In this work, we formalise and characterise edits and revisions in incremental sequence labell

Jolien Despeghel, Jeroen Tant, Johan Driesen

In the literature, when optimizing the sizing and operation of a residential PV system in combination with a battery energy storage system, the efficiency of the battery and the converter is generally assumed constant, which corresponds to a linear loss model that can be readily integrated in an optimization model. However, this assumption does not always re

Annika Ries, Tina Dorosti, Johannes Thalhammer, Daniel Sasse

Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff between number of views, IQ, and diagnostic confidence. Methods: CT images from 41 subjects aged 62.8 $\pm$ 10.6 years (mean $\pm$ standard deviation), 23 men, 34 with lung metastasi

Abouzar Ghavami, Ali Abedi

Wireless localization is essential for tracking objects in indoor environments. Internet of Things (IoT) enables localization through its diverse wireless communication protocols. In this paper, a hybrid section-based indoor localization method using a developed Radio Frequency Identification (RFID) tracking device and multiple IoT wireless technologies is p

Ben Ajzner, Alexandros Alexakis

Reproducing complex phenomena with simple models marks our understanding of the phenomena themselves and this is what Jack Herring's work demonstrated multiple times. In that spirit, this work studies a turbulence shell model consisting of a hierarchy of structures of different scales $\ell_n$ such that each structure transfers its energy to two substructure

Shihao Liang, Runchu Tian, Kunlun Zhu, Yujia Qin

Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of instructions in the training data can consistently enhance generalization performance, which facilitates a recent endeavor to collect various instructions and integrate existing ins

Joshua Stock, Oliver Hauke, Julius Weißmann, Hannes Federrath

This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods.F L is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data. By

Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang

We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion priors to improve the denoising process. Our approach improve

Mojtaba Mohareri, Behrooz Mashayekhy

Karol Borsuk in 1968 asked: Is it true that every finite polyhedron dominates only finitely many different shapes? Danuta Kolodziejczyk showed that generally an answer to the Borsuk question is negative and also presented a positive answer by proving that every polyhedron with finite fundamental group dominates only finitely many different homotopy types (he

Caterina Boscolo Meneguolo, Elisa Bernardini, Sarah Mancina

The IceCube Neutrino Observatory has the invaluable capability of continuously monitoring the whole sky. This has affirmed the role of IceCube as a sentinel, providing real-time alerts to the astrophysical community on the detection of high-energy neutrinos and neutrino flares from a variety of astrophysical sources. As a response to the IceCube alerts, diff

Pu Zhang, Xiaomeng Zhu

Let $M$ be the Hardy-Littlewood maximal function. Denote by $M_b$ and $[b,M]$ the maximal and the nonlinear commutators of $M$ with a function $b$. The boundedness of $M_b$ and $[b,M]$ on weighted Lebesgue spaces are characterized when the symbols $b$ belong to weighted Lipschitz (weighted Morrey-Campanato) spaces. Some new characterizations for weighted Lip

Rik W. S. Westdorp, Hermen Jan Hupkes

This paper studies the behavior of solitons in the Korteweg-de Vries equation under the influence of multiplicative noise. We introduce stochastic processes that track the amplitude and position of solitons based on a rescaled frame formulation and stability properties of the soliton family. We furthermore construct tractable approximations to the stochastic

Patrick Dorey, Davide Polvara

Recently we showed how, in two-dimensional scalar theories, one-loop threshold diagrams can be cut into the product of one or more tree-level diagrams arXiv:2206.09368. Using this method on the ADE series of Toda models, we computed the double- and single-pole coefficients of the Laurent expansion of the S-matrix around a pole of arbitrary even order, findin

Victor S. Monakhov, Irina L. Sokhor

A subgroup $H$ of a finite group $G$ is submodular in $G$ if there is a subgroup chain $H=H_0\leq\ldots\leq H_i\leq H_{i+1}\leq \ldots \leq H_n=G$ such that $H_i$ is a modular subgroup of $H_{i+1}$ for every $i$. We investigate finite factorised groups with submodular primary (cyclic primary) subgroups in factors. We indicate a general approach to the descri

Amir Ramezani Dooraki, Alexandros Iosifidis

Curiosity is one of the main motives in many of the natural creatures with measurable levels of intelligence for exploration and, as a result, more efficient learning. It makes it possible for humans and many animals to explore efficiently by searching for being in states that make them surprised with the goal of learning more about what they do not know. As

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality. Recent attempts rely on a combination of Monte Carlo methods and variational formulations, using neural networks for function approximation. Extending previou

Arpan Bhattacharyya, S. Shajidul Haque, Ghadir Jafari, Jeff Murugan

We study the spectral properties of two classes of random matrix models: non-Gaussian RMT with quartic and sextic potentials, and RMT with Gaussian noise. We compute and analyze the quantum Krylov complexity and the spectral form factor for both of these models. We find that both models show suppression of the spectral form factor at short times due to decoh

Kevin Denamganaï, Daniel Hernandez, Ozan Vardal, Sondess Missaoui

Natural language instruction following is paramount to enable collaboration between artificial agents and human beings. Natural language-conditioned reinforcement learning (RL) agents have shown how natural languages' properties, such as compositionality, can provide a strong inductive bias to learn complex policies. Previous architectures like HIGhER combin

Andreas Liesenfeld, Alianda Lopez, Mark Dingemanse

Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for

Peng Zhang, Mingyong Jing, Zheng Wang, Yan Peng

Measurement sensitivity is one of the critical indicators for Rydberg atomic radio receivers. This work quantitatively studies the relationship between the atomic superheterodyne receiver's sensitivity and the number of atoms involved in the measurement. The atom number is changed by adjusting the length of the interaction area. The results show that for the

Angèle Niclas, Josselin Garnier

This paper proposes a fully automated method for recovering the location of a source and medium parameters in shallow waters. The scenario involves an unknown source emitting low-frequency sound waves in a shallow water environment, and a single hydrophone recording the signal. Firstly, theoretical tools are introduced to understand the robustness of the war

Minghui Liwang, Bingshuo Guo, Zhanxi Ma, Yuhan Su

To effectively process high volume of data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that can provide reliable, cost-effective, and timely computing services. This article explores computation-intensive task scheduling over mobile vehicular clouds (MVCs). We use undirected weighted graphs

Fabian Parzer, Clemens Kirisits, Otmar Scherzer

We consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent work motivated by astronomical applications, we propose an approach that represents the uncertainty in the position and size of a blob by a region in a three-dimensional scale space. Motivated by classic tube metho

Beatriz Barbero-Lucas, Fernando Hernando, Helena Martín-Cruz, Gary McGuire

We construct new stabilizer quantum error-correcting codes from generalized monomial-Cartesian codes. Our construction uses an explicitly defined twist vector, and we present formulas for the minimum distance and dimension. Generalized monomial-Cartesian codes arise from polynomials in $m$ variables. When $m=1$ our codes are MDS, and when $m=2$ and our lower

Payman Eskandari

Grothendieck's theory of blended extensions (extensions panach\'ees) gives a natural framework to study 3-step filtrations in abelian categories. We give a generalization of this theory that is suitable for filtrations with an arbitrary finite number of steps. We use this generalization to study two natural classification problems for objects with a fixed as

Sandeep K. Rout, Mariano Mendez, Federico Garcia

The study of quasi periodic oscillations (QPOs) plays a vital role in understanding the nature and geometry of the Comptonizing medium around black-hole X-ray binaries. The spectral-state dependence of various types of QPOs (namely A, B, & C) suggests that they could have different origins. The simultaneous presence of different types of QPOs would therefore

Alessandro Burigana, Paolo Felli, Marco Montali, Nicolas Troquard

The use of Dynamic Epistemic Logic (DEL) in multi-agent planning has led to a widely adopted action formalism that can handle nondeterminism, partial observability and arbitrary knowledge nesting. As such expressive power comes at the cost of undecidability, several decidable fragments have been isolated, mainly based on syntactic restrictions of the action

Shahd Bakhet, Rasa Mardosaitė, Mohamed Ahmed Baba, Asta Tamulevičienė

High-efficiency antiviral surfaces can be effective means to fight against viral diseases such as the recent Covid-19 pandemic. Copper and copper oxides, as well as their nanoparticles (Cu NPs) and coatings, are among the effective antiviral materials having internal and external biocidal effects on viruses. In this work, stable Cu NP colloids were produced

Chunyu Qiang, Hao Li, Hao Ni, He Qu

Recently, there has been a growing interest in text-to-speech (TTS) methods that can be trained with minimal supervision by combining two types of discrete speech representations and using two sequence-to-sequence tasks to decouple TTS. However, existing methods suffer from three problems: the high dimensionality and waveform distortion of discrete speech re

Max Franke, Steffen Koch

Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and tedious to explore. Automated analysis methods exist, but can be too costly for smooth, interactive analysis

Jitao Liu, Yunxiao Zhao

It is well known that the tropical climate model is an important model to describe the interaction of large scale flow fields and precipitation in the tropical atmosphere. In this paper, we address the issue of global well-posedness for 2D temperature-dependent tropical climate model in a smooth bounded domain. Through classical energy estimates and De Giorg

Oleg Gutik, Inna Pozdniakova

We study injective endomorphisms of the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}}$ with the two-elements family $\mathscr{F}$ of inductive nonempty subsets of $\omega$. We describe the elements of the semigroup $\boldsymbol{End}^1_*(\boldsymbol{B}_{\omega}^{\mathscr{F}})$ of all injective monoid endomorphisms of the monoid $\boldsymbol{B}_{\omega}^{\m

Christina A. Garcia, Patricia Angela R. Abu, Rosula SJ. Reyes

This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for non-invasive detection of Diabetes Mellitus (DM) using facial block texture features. Participants within age bracket 20 to 79 years old were chosen for the dataset. 12mp and 7mp mobile cameras, and a laptop camera were

Varazdat Stepanyan, Andreas F. Tzortzakakis, David Petrosyan, Armen E. Allahverdyan

We revisit the one-dimensional ferromagnetic Ising spin-chain with a finite number of spins and periodic boundaries and derive analytically and verify numerically its various stationary and dynamical properties at different temperatures. In particular, we determine the probability distributions of magnetization, the number of domain walls, and the correspond

Matthias Brucker, Andrei Cramariuc, Cornelius von Einem, Roland Siegwart

Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do not have enough classes to account for all possible scenarios, and datasets featuring objects on railways are challenging to obtain. We propose utilizing a shallow network to lear

Zhongkai Mi, Quanshui Wu, Milen Yakimov

Discriminant ideals of noncommutative algebras $A$, which are module finite over a central sublagebra $C$, are key invariants that carry important information about $A$, such as the sum of the squares of the dimensions of its irreducible modules with a given central character. There has been substantial research on the computation of discriminants, but very

Floriane Arrouas, Lucas Gabardos, Nicolas Ombredane, Etienne Dionis

Optimal control is a valuable tool for quantum simulation, allowing for the optimized preparation, manipulation, and measurement of quantum states. Through the optimization of a time-dependent control parameter, target states can be prepared to initialize or engineer specific quantum dynamics. In this work, we focus on the tailoring of a unitary evolution le

Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins

Even though machine learning (ML) pipelines affect an increasing array of stakeholders, there is little work on how input from stakeholders is recorded and incorporated. We propose FeedbackLogs, addenda to existing documentation of ML pipelines, to track the input of multiple stakeholders. Each log records important details about the feedback collection proc

Renyu Zhu, Haoyu Liu, Runze Wu, Minmin Lin

In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto

Oleg Kovalenko

In the article we propose a general scheme for solutions of some approximation problems under a rather general setting. We illustrate the application of the proposed scheme by a series of examples, in particular we show that many results in the area of Ostrowski type inequalities can be obtained by standard arguments.

Wei Zhao

Momentum-scalar coupling turbulence, a phenomenon observed in both natural and engineering contexts, involves the intricate interaction between multicomponent scalars and multiscale forces (i.e. multiple coupling mechanisms), resulting in a wide array of manifestations. Despite its importance, limited research has been conducted to comprehend the influence o

David Bayani

Over the eight months since its release, ChatGPT and its underlying model, GPT3.5, have garnered massive attention, due to their potent mix of capability and accessibility. While a niche-industry of papers have emerged examining the scope of capabilities these models possess, the information fed to and extracted from these networks has been either natural la

Jorge Cayao, Masatoshi Sato

We study non-Hermitian Josephson junctions formed by superconductors with a finite phase difference under non-Hermiticity, which naturally appears due to coupling to normal reservoirs. Depending on the structure of non-Hermiticity, captured in terms of retarded self-energies, the low-energy spectrum hosts topologically stable exceptional points either at zer

Kasit Chatsirisupachai, João Pedro de Magalhães

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic

Christopher D. Woodgate, Christopher E. Patrick, Laura H. Lewis, Julie B. Staunton

The magnetocrystalline anisotropy energy of atomically ordered $\mathrm{L}1_0$ FeNi (the meteoritic mineral tetrataenite) is studied within a first-principles electronic structure framework. Two compositions are examined: equiatomic Fe$_{0.5}$Ni$_{0.5}$ and an Fe-rich composition, Fe$_{0.56}$Ni$_{0.44}$. It is confirmed that, for the single crystals modelled

Sheikh Salman Hassan, Yu Min Park, Yan Kyaw Tun, Walid Saad

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable

Felix Günther

We study compact polyhedral surfaces as Riemann surfaces and their discrete counterparts obtained through quadrilateral cellular decompositions and a linear discretization of the Cauchy-Riemann equation. By ensuring uniformly bounded interior and intersection angles of diagonals, we establish the convergence of discrete Dirichlet energies of discrete harmoni

Ecem Acar, Sevilay Kirci Serenbay, Saleem Yaseen Majeed Al Khalidy

In this paper, we firstly introduce nonlinear truncated Baskakov operators on compact intervals and obtain some direct theorems. Also, we give the approximation of fuzzy numbers by truncated nonlinear Baskakov operators.

Juan E. Galeote-Cazorla, Alejandro Ramírez-Arroyo, F. Javier Lopez-Martinez, Juan F. Valenzuela-Valdés

As new wireless standards are developed, the use of higher operation frequencies comes in hand with new use cases and propagation effects that differ from the well-established state of the art. Numerous stochastic fading models have recently emerged under the umbrella of generalized fading conditions to provide a fine-grain characterization of propagation ch

Andres Algaba, Carmen Mazijn, Carina Prunkl, Jan Danckaert

Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output. However, this approach suffers from several shortcomings, such as philosophical disagreement, mutual incompatibility, and lack of interpretability. These shortcomings have spurred the research on complementary bias detection methods that offer add

Jorge Martinez-Gil

Data catalogs play a crucial role in modern data-driven organizations by facilitating the discovery, understanding, and utilization of diverse data assets. However, ensuring their quality and reliability is complex, especially in open and large-scale data environments. This paper proposes a framework to automatically determine the quality of open data catalo

Andrés Mateo-Gabín, Kenza Tlales, Eusebio Valero, Esteban Ferrer

We present a novel unsupervised machine-learning sock sensor based on Gaussian Mixture Models (GMMs). The proposed GMM sensor demonstrates remarkable accuracy in detecting shocks and is robust across diverse test cases with significantly less parameter tuning than other options. We compare the GMM-based sensor with state-of-the-art alternatives. All methods

Fuga Kawamata, Hiroshi Unno, Taro Sekiyama, Tachio Terauchi

Algebraic effects and handlers are a mechanism to structure programs with computational effects in a modular way. They are recently gaining popularity and being adopted in practical languages, such as OCaml. Meanwhile, there has been substantial progress in program verification via refinement type systems. However, thus far there has not been a satisfactory

Albert Le Floch, Guy Ropars

During reading, dyslexic readers exhibit more and longer fixations than normal readers. However, there is no significant difference when dyslexic and control readers perform only visual tasks on a string of letters, showing the importance of cognitive processes in reading. This linguistic and cognitive processing demand in reading is often perturbed for dysl

Ioana Mazilu, Shunxin Wang, Sven Dummer, Raymond Veldhuis

Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of diseases. We propose a method that can deblur images as well as synthesize defocus blur. We train autoencoders wi

Yi Zhang, Ce Zhang, Yushun Tang, Zhihai He

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, establish the correlation between texts and images, achieving remarkable success on various downstream tasks with fine-tuning. In existing fine-tuning methods, the class-specific text description is matched against the whole image. We recognize that this whole image matching is not effectiv

Martijn H. H. Schoot Uiterkamp

Motivated by the problem of scheduling electric vehicle (EV) charging with a minimum charging threshold in smart distribution grids, we introduce the resource allocation problem (RAP) with a symmetric separable convex objective function and disjoint interval bound constraints. In this RAP, the aim is to allocate an amount of resource over a set of $n$ activi

Stefan Mijin, Dominic Power, Ryan Holden, William Hornsby

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treat

Karol Makuch, Konrad Giżyński, Robert Hołyst, Anna Maciołek

Equilibrium thermodynamics describes the energy exchange of a body with its environment. Here, we describe the global energy exchange of an ideal gas in the Coutte flow in a thermodynamic-like manner. We derive a fundamental relation between internal energy as a function of parameters of state. We analyze a non-equilibrium transition in the system and postul

Jacek Cyranka, Kevin E M Church, Jean-Philippe Lessard

We raise concerns about controllers' robustness in simple reinforcement learning benchmark problems. We focus on neural network controllers and their low neuron and symbolic abstractions. A typical controller reaching high mean return values still generates an abundance of persistent low-return solutions, which is a highly undesirable property, easily exploi

Kaushal Kumar Maurya, Maunendra Sankar Desarkar, Manish Gupta, Puneet Agrawal

Query auto-completion (QAC) aims to suggest plausible completions for a given query prefix. Traditionally, QAC systems have leveraged tries curated from historical query logs to suggest most popular completions. In this context, there are two specific scenarios that are difficult to handle for any QAC system: short prefixes (which are inherently ambiguous) a

20k Collaboration, P. Agnes, I. Ahmad, S. Albergo

The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils (NR) produced in a target material from the WIMP elastic scattering. A promising experimental strategy for direct dark matter search employs argon dual-phase time projection chambers (TPC). One of the advantages of the TPC is

Giovanni Sileno, Jean-Louis Dessalles

The paper presents the main characteristics and a preliminary implementation of a novel computational framework named CompLog. Inspired by probabilistic programming systems like ProbLog, CompLog builds upon the inferential mechanisms proposed by Simplicity Theory, relying on the computation of two Kolmogorov complexities (here implemented as min-path searche

Vishakha Agrawal, Serhiy Kandul, Markus Kneer, Markus Christen

AI is getting more involved in tasks formerly exclusively assigned to humans. Most of research on perceptions and social acceptability of AI in these areas is mainly restricted to the Western world. In this study, we compare trust, perceived responsibility, and reliance of AI and human experts across OECD and Indian sample. We find that OECD participants con

Alessandro Burigana, Paolo Felli, Marco Montali

Dynamic Epistemic Logic (DEL) provides a framework for epistemic planning that is capable of representing non-deterministic actions, partial observability, higher-order knowledge and both factual and epistemic change. The high expressivity of DEL challenges existing epistemic planners, which typically can handle only restricted fragments of the whole framewo

Suravi Pal, Jaydeb Chakrabarti, Srabani Chakrabarty nee Sarkar

We explore phase behaviour of a binary colloidal system under external spatially periodic modulation. We perform Monte Carlo simulation on a binary mixture of big and small repulsive Lennard-Jones particles with diameter ratio 1:2. We characterise structure by isotropic and anisotropic pair correlation function, cluster size distribution, bond angle distribu

Mike Thelwall, Stephen Pinfield

Journal field classifications in Scopus are used for citation-based indicators and by authors choosing appropriate journals to submit to. Whilst prior research has found that Scopus categories are occasionally misleading, it is not known how this varies for different journal types. In response, we assessed whether specialist, cross-field and general academic

Ianto Cannon, Giovanni Soligo, Marco E. Rosti

We perform direct numerical simulations of surfactant-laden droplets in homogeneous isotropic turbulence with Taylor Reynolds number of 180. The droplets are modelled using the volume of fluid method, and the soluble surfactant is transported using an advection-diffusion equation. Effects of surfactant on the droplet and local flow statistics are well approx

Allen Lobo, Vinod Kumar Sayal

This article studies the vortical nature and structure of phase-space holes -- nonlinear B.G.K. trapping modes found in the phase-space collision-free plasmas. A fluid-like outlook of the particles' phase-space is explored, which makes it convenient to analytically identify electron and ion holes as vortices -- similar to that of ordinary two-dimensional flu

Qi'an Guan, Zheng Yuan

In this article, we consider a generalization of the conjugate Hardy $H^2$ spaces, and give some properties of the minimal norm of the generalization and some relations between the norm of the generalization and the minimal $L^2$ integrals. As applications, we give some monotonicity results for the conjugate Hardy $H^2$ kernels and the Bergman kernels on pla

Per Grafström

The ATLAS and TOTEM collaborations have measured the differential elastic cross section at centre-of mass energy $\sqrt{s}$=13 TeV and at small four-moment squared $|t|$. The data at very small $|t|$ i.e. $|t|<0.01GeV^{2}$ have been analysed in terms of so called AKM (Auberson, Kinoshita and Martin) oscillations. An indication of a possible oscillation of th

Mojgan Forootan, Mohsen Rajabnia, Ahmad R Mafi, Hamed Azhdari Tehrani

In the recent years, artificial intelligence (AI) and its leading subtypes, machine learning (ML) and deep learning (DL) and their applications are spreading very fast in various aspects such as medicine. Today the most important challenge of developing accurate algorithms for medical prediction, detection, diagnosis, treatment and prognosis is data. ERCPMP

Kang Fu, Xiaohong Liu, Jun Jia, Zicheng Zhang

Invisible image watermarking is essential for image copyright protection. Compared to RGB images, RAW format images use a higher dynamic range to capture the radiometric characteristics of the camera sensor, providing greater flexibility in post-processing and retouching. Similar to the master recording in the music industry, RAW images are considered the or

Raphael Korbmacher, Huu-Tu Dang, Antoine Tordeux

Predicting human trajectories is a challenging task due to the complexity of pedestrian behavior, which is influenced by external factors such as the scene's topology and interactions with other pedestrians. A special challenge arises from the dependence of the behaviour on the density of the scene. In the literature, deep learning algorithms show the best p

Yuna G. Kwon, Stefano Bagnulo, Alberto Cellino

This study presents the first optical spectropolarimetric study of large C-complex asteroids. A total of 64 C-complex asteroids of different subclasses are analyzed using archival polarimetric and reflectance data to refine the link between polarimetric parameters and surface properties of the asteroids. We find a consistent difference in the polarization sp

Holger Klein, Noémie Jaquier, Andre Meixner, Tamim Asfour

The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a challenge. In this context, Riemannian geometry offers promising solutions by identifying robot motions with geodesics on the so-called configuration space manifold. While this manifo

Oliver Fernandez-Gil, Fabio Patrizi, Giuseppe Perelli, Anni-Yasmin Turhan

Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally

Alejandro D. Mousist

In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of electronic components makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challen

Akiyoshi Tomonaga, Roberto Stassi, Hiroto Mukai, Franco Nori

We experimentally investigate a superconducting circuit composed of two flux qubits ultrastrongly coupled to a common LC resonator. Owing to the large anharmonicity of the flux qubits, the system can be described well by a generalized Dicke Hamiltonian containing spin spin interaction terms. In the experimentally measured spectrum, we observed two key phenom

Marcel Sarraseca, Sergi Alcaide, Francisco Fuentes, Juan Carlos Rodriguez

Safety-critical systems such as those in automotive, avionics and space, require appropriate safety measures to avoid silent data corruption upon random hardware errors such as those caused by radiation and other types of electromagnetic interference. Those safety measures must be able to prevent faults from causing the so-called common cause failures (CCFs)