Computing - Science topic

Deep materials informatics is a rapidly evolving field that employs deep learning techniques to develop predictive models for materials science. It involves the use of large datasets, advanced algorithms, and highperformance computing to extract key features from complex materials data. The aim of deep materials informatics is to speed up the proce...

Special Issue Information Dear Colleagues, The fields of Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) have undergone remarkable expansions over the past few decades. This surge in growth can be largely attributed to significant advancements in computing power and the unprecedented availability of vast amounts of data...

The Advanced Training in Laparoscopic Suturing (ATLAS) curriculum was developed to improve laparoscopic skills via a proficiency-based approach through six distinct tasks. Task 1 involves passing a needle through six holes positioned at various angles on a circular platform. Performance is assessed based on the task completion time and the number o...

Hypergraphs extend traditional graphs by allowing edges (known as hyperedges) to connect more than two vertices, rather than just pairs. This paper explores fundamental problems and algorithms in the context of SuperHypergraphs, an advanced extension of hypergraphs enabling modeling of hierarchical and complex relationships. Topics covered include...

Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. These devices are usually equipped...

Autonomous execution of tasks by unmanned aerial vehicles relies heavily on object detection. However, object detection in most images presents challenges such as complex backgrounds, small targets, and obstructions. Additionally, the limited computing speed and memory of the UAV processor affects the accuracy of conventional object detection algor...

Sequences of structured matrices of increasing size, such as generalized locally Toeplitz sequences, arise in many scientific applications and especially in the numerical discretization of linear differential problems. We assume that the eigenvalues of a matrix X_n , belonging to a sequence of such kind, are given by a regular expansion. Under this...

Correlating tree-ring parameters with daily resolved climate data is becoming increasingly common for understanding the complex relationships between tree growth and the surrounding environment. However, with an increased number of calculated correlations, there is an inherent risk of spurious significance. In this study, we present an analysis usi...

Compact and energy-efficient computing avenues such as in-memory computing and processing-in-memory (PIM) are being actively explored to address the limitations of the sparse von-Neumann computing systems. The recent advancements in the field of emerging non-volatile memories (e-NVMs), such as FeFETs, RRAMs, MRAMs, etc., have propelled the developm...

One of the traditional problems in survey sampling is to estimate the population parameter like mean vari ance etc. This article investigates the mathematical derivations and application of neutrosophic statistics to address the challenges posed by imprecise, indeterminacies or ambiguous data, such as daily stock prices, weather forecast, social me...

A long running data-intensive computational application acquires costly computing resources. With the emerging new architectures, like computing systems with multiple nodes of many-core CPUs and accelerators, while domain-specific tools and libraries employed in such an application leverage high parallelism on accelerators for intensive computation...

Non-local means (NLM) method is one of the most notable methods in the field of image processing. At first, it is proposed for image denoising. The denoised pixel is obtained by the weighted average of neighboring pixels. Usually, the weight is computed by using the mean square error (MSE). This weight can be called MSE-Weight. In recent years, str...

In this paper we use the double affine Hecke algebra to compute the Macdonald polynomial products \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E_\ell P_m$$\end{docum...

This article provides an in-depth exploration of 12 fundamental concurrent design patterns critical for developing efficient and robust applications in modern computing. Addressing the challenges posed by multi-core architectures and high-concurrency environments, the study categorizes patterns such as Active Object, Monitor Object, and Thread Pool...

Intelligent Reflecting Surfaces (IRS) enhance wireless communication by optimising signal reflection from the base station (BS) towards users. The passive nature of IRS components makes tuning phase shifters difficult and direct channel measurement problematic. This study presents a machine learning framework that directly maximises the beamformers...

The review of progress in quantum computing (QC) is very pertinent nowadays. There is a remarkable challenge in terms of the contributions that this field can provide at the level of improvements in computing time, but perhaps more importantly, in terms of how to rethink the way in which many of the current problems can be approached. Thus, the obj...

The many-body expansion, where one computes the total energy of a supersystem as the sum of the dimer, trimer, tetramer, etc., subsystems, provides a convenient approach to compute the lattice energies of molecular crystals. We investigate approximate methods for computing the non-additive three-body contributions to the crystal lattice energy of t...

Decades of technological development and innovation have led to an unprecedented digitalization of society. Graduates entering the modern workforce now need better computational competences. Higher education is thus forced to adapt and consider how to support these demands. To support educators in making decisions regarding how to integrate computi...

The main goal of this paper is to present some explicit formulas for computing the Łojasiewicz exponent in the Łojasiewicz inequality comparing the rate of growth of two real bivariate analytic function germs.

This paper delves into optimizing the Von Neumann architecture to improve modern computing efficiency. While John von Neumann's architecture has become a staple in computer design, its CPU operation is hindered by the merging of data and program memory, and the use of a single data bus, resulting in decreased capabilities. This challenge can be mit...

Markov chain transition probability matrices (TPMs) have traditionally been used to characterize land use and land cover (LULC) changes and species succession. However, previous studies relied solely on TPMs or transition area matrices to describe overall class area/proportion changes, overlooking important time correlation features. This study int...

This paper concerns the dynamical study of the q-deformed homographic map, namely, the q-homographic map, where q-deformation is introduced by Jagannathan and Sinha with the inspiration from Tsalli’s q-exponential function. We analyze the q-homographic map by computing its basic nonlinear dynamics, bifurcation analysis, and topological entropy. We...

Numerical simulations were carried out to optimize the design of an active semi-anechoic room. The active set-up includes control sources and microphones near the room ceiling and walls. The objective is to achieve global control, around an a priori unknown primary source, of the low-frequency wall reflections that are not adequately managed by abs...

Mechanical computing promises to integrate semiconductor‐based digital logic in several applications, but it needs straightforward programmable devices for changing computing rules in situ. A methodology based on strain‐governed, bistable soft shells that process digital information by interchanging their internal/external surfaces is proposed. Thi...

Graphics processing units (GPUs) provide massively parallel processing capabilities, enabling accelerated computation across diverse applications. However, their parallel architecture and shared resources also introduce significant security vulnerabilities, especially side-channel attacks that can compromise sensitive data. This article presents a...

Photonic computing, with potentials of high parallelism, low latency and high energy efficiency, have gained progressive interest at the forefront of neural network (NN) accelerators. However, most existing photonic computing accelerators concentrate on discriminative NNs. Large-scale generative photonic computing machines remain largely unexplored...

We present an evaluation of bucketed approximate top-$k$ algorithms. Computing top-$k$ exactly suffers from limited parallelism, because the $k$ largest values must be aggregated along the vector, thus is not well suited to computation on highly-parallel machine learning accelerators. By relaxing the requirement that the top-$k$ is exact, bucketed...

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six...

Our study investigates the moderating effects of perceived task interdependence (PTI) and ICT-Dependence (ICTD) on the relationship between various technostress creators and computer self-efficacy. We advance the technostress literature by introducing a framework that categorizes technostress creators into emotional (techno-insecurity, techno-uncer...

Traditional video transmission systems assisted by multiple Unmanned Aerial Vehicles (UAVs) are often limited by computing resources, making it challenging to meet the demands for efficient video processing. To solve this challenge, this paper presents a multi-UAV-assisted Device-to-Device (D2D) mobile edge computing system for the maximization of...

Workflows coordinate a series of computing tasks to create a sophisticated workflow logic. Ensuring the correctness of a workflow specification is essential for automating business processes. Errors in the specification should be identified and resolved as early as possible, during the design phase. In this paper, we propose a verification approach...

The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairn...

To comprehensively improve the thermal, static and dynamic characteristics and achieve lightweighting for CNC machine tools, this paper proposes a multi-objective joint optimization method based on coupled thermal–mechanical–vibration collaborative analysis. The dual-spindle component of a CNC machine tool is taken as the parameterized model. Accor...

Background Intraductal papillary mucinous neoplasms (IPMNs) are precursors to pancreatic cancer, but not all IPMNs progress to cancer. The objective of this study was to identify the germline genetic variants associated with IPMN clinical progression by conducting the first genome‐wide association study (GWAS) and computing a polygenic hazard score...

Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both ca...

The aim of this paper is to define Discrete Orlicz-Morrey spaces. Furthermore, we also present the sufficient and necessary conditions for the inclusion properties of among these spaces. Computing the norms of the characteristic sequences is one of the keys results. Similar results for Discrete weak Orlicz-Morrey spaces are also obtained.

Program mentoring merupakan salah satu program ITS yang bertujuan untuk mewujudkan kepribadian mahasiswa yang islami dan berintegritas. Demi mewujudkan tujuan dan kelancaran program tersebut ,tahun ini ITS pun meluncurkan aplikasi myITS Mentoring untuk mempermudah jalannya program ini. Namun karena aplikasi ini baru saja diluncurkan tentu ada beber...

As wildfire activity increases worldwide, developing effective methods for estimating how fast it can spread is critical. This study aimed to develop and validate a computer vision algorithm for fire spread estimation. Using visual flame data from laboratory experiments on excelsior and pine needle fuel beds, we explored fire spread predictions for...

The modeling of state transitions in computational machines holds great significance in diverse domains of science, technology, and engineering. Characterization of system behavior, its synthesis, and analysis demands insights into machine configuration, which can only be achieved through proper modeling of the state transitions. The current study...

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi...

Cities are heating up faster than their rural counterparts due to the urban heat island phenomenon, with severe heat accumulation in various areas – hotspots. This study proposes a new approach to analysing urban heat islands by detecting and hierarchising their associated hotspots based on their severity derived from the combination of intensity a...

Traditional video transmission systems assisted by multiple Unmanned Aerial Vehicles (UAVs) are often limited by computing resources, making it challenging to meet the demands for efficient video processing. To solve this challenge, this paper presents a multi-UAV-assisted Device-to-Device (D2D) mobile edge computing system for the maximization of...

Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning based methods have been widely applied to this task. However, classic deep learning approaches struggle to capture the complex geometry and specific topology of cerebrovascul...

In edge computing, emerging network slicing and computation offloading can support Edge Service Providers (ESPs) better handling diverse distributions of user requests, to improve Quality-of-Service (QoS) and resource efficiency. However, fluctuating traffic and heterogeneous resources seriously hinder their broader application in multi-edge system...

Some of the most impactful efforts to aggregate data around nonprofits, non-governmental organizations, and other participants of the Third Sector were undertaken by Anheier and Salamon through the Comparative Nonprofit Sector Project (CNP). In reflecting on the project’s shortcomings and opportunities for change and reinvigorated momentum, Anheier...

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges...

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing adoption in diverse services delivered through wireless networks. There is a growing trend toward longer prompts to better leverage LLMs' capabilities and address difficult tasks. However, longer prompts not only increase data transm...

This paper presents a sparsified Fourier neural operator for coupled time-dependent partial differential equations (ST-FNO) as an efficient machine learning surrogate for fluid and particle-based fusion codes such as NIMROD (Non-Ideal Magnetohydrodynamics with Rotation - Open Discussion) and GTC (Gyrokinetic Toroidal Code). ST-FNO leverages the str...

Time series analysis developed in the previous report [1] for UK mean summer temperature has been applied to further examples of weather trends, including temperature, rainfall and Sahara dust, and so at global, regional, national and local levels, in an attempt to assess whether wind power is having a deleterious effect on weather patterns. The an...

Neuromorphic computing, drawing inspiration from the brain, stands out for its high energy efficiency in executing complex tasks. Memristive device-based neuromorphic computing has demonstrated ultrahigh efficiency. While there are numerous review papers in this field, the majority concentrate on the device level, bypassing the connections among th...

The resolution of flow fields represents a significant factor influencing the accuracy of turbulent flow analysis. Nevertheless, the acquisition of high-resolution turbulence data remains a challenge due to the limitations imposed by computing resources. The interpolation method, while capable of achieving high-resolution turbulence at low cost, fa...

Compute-in-memory (CiM)-based binary neural network (CiM-BNN) accelerators marry the benefits of CiM and ultra-low precision quantization, making them highly suitable for edge computing. However, CiM-enabled crossbar (Xbar) arrays are plagued with hardware non-idealities like parasitic resistances and device non-linearities that impair inference ac...

While defects are undesirable for the reliability of electronic devices, particularly in scaled microelectronics, they have proven beneficial in numerous quantum and energy-harvesting applications. However, their potential for new computational paradigms, such as neuromorphic and brain-inspired computing, remains largely untapped. In this study, we...

This is a foundation for algebraic geometry, developed internal to the Zariski topos, building on the work of Kock and Blechschmidt (Kock (2006) [I.12], Blechschmidt (2017)). The Zariski topos consists of sheaves on the site opposite to the category of finitely presented algebras over a fixed ring, with the Zariski topology, that is, generating cov...

Recently Dong, Rath and Kudler-Flam proposed a modified cosmic brane prescription for computing the R\'{e}nyi entropy $S_\alpha$ of a holographic system in the presence of multiple extremal surfaces. This prescription was found by assuming a diagonal approximation, where the R\'{e}nyi entropy is computed after first measuring the areas of all extre...

We revisit certain one-parameter families of affine covers arising naturally from Euler's integral representation of hypergeometric functions. We introduce a partial compactification of this family. We show that the zeta function of the fibers in the family can be written as an explicit product of $L$-series attached to nondegenerate hypergeometric...

Deploying a Hierarchical Federated Learning (HFL) pipeline across the computing continuum (CC) requires careful organization of participants into a hierarchical structure with intermediate aggregation nodes between FL clients and the global FL server. This is challenging to achieve due to (i) cost constraints, (ii) varying data distributions, and (...

Edge computing is emerging as a key enabler of low-latency, high-efficiency processing for the Internet of Things (IoT) and other real-time applications. To support these demands, containerization has gained traction in edge computing due to its lightweight virtualization and efficient resource management. However, there is currently no established...

This paper demonstrates a new 22-T (transistor) hybrid adder for low-power applications using 22 nm shorted gate (SG) tri-gate (TG) FinFET technology. Large conducting channels and increased gate control make FinFET suitable for high-speed low-power computing circuits. The proposed hybrid adder is designed using pass transistor and CMOS logic. Also...

This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. Network slicing divides a physical network into virtual slices, each tailored to efficiently allocate resources and meet diverse service requirements. To maximize the completion rate of user-computing tas...

As demand for data continues to grow, traditional electronic networks are reaching their limits in speed, bandwidth, and energy efficiency. Integrating photonic and electronic components within optical networks offers a powerful solution, combining the fast, low-latency advantages of photonics with established electronic processing capabilities. Th...

The rapid evolution of SARS-CoV-2 during the pandemic, driven by a plethora of mutations, many of which enable the virus to evade host resistance, has likely altered its genome's compositional structure (i.e. the arrangement of compositional domains of varying lengths and nucleotide frequencies within the genome). To explore this hypothesis, we sum...

It's possible to explain Edge computing (EC) as a distributed system of IT that decentralized the power of processing in which the mobile Internet of effects (IoT) computing would be allowed. In EC, data reused by original tools, computers, or waiters, rather of being process and transmitted from the data center. still, with the wider capabilities...

This paper deals with the nonlinear inverse problem of computing the unknown underground conductivity distribution of a layered earth, from a set of measurements obtained by placing magnetic dipoles above the surface. A numerical strategy for the location of the initial guess of the minimization procedure is developed by rst considering a linear ap...

In this study, we investigate the integrability and linearizability problems of a family of cubic three-dimensional Lotka–Volterra systems with one zero eigenvalue, involving seventeen parameters. Necessary conditions on the parameters of the system for both integrability and linearizability are obtained by computing the resonant quantities using G...

Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an o...

Human skin provides crucial tactile feedback, allowing to skillfully perceive various objects by sensing and encoding complex deformations through multiple parameters in each tactile receptor. However, replicating this high‐dimensional tactile perception with conventional materials' electronic properties remains a daunting challenge. Here, a skin‐i...

The rapid growth of the Internet of Things (IoT) applications inflicts high requirements for computing resources and network bandwidth. A growing number of service providers are applying edge-cloud computing to improve the quality of their services. Deploying IoT applications to optimal computing nodes to minimize energy consumption and enhance sys...

Motivated by applications in Bayesian analysis, we introduce a multidimensional beta distribution in the ordered simplex. We study the properties of this distribution and connect them with the generalized incomplete beta function. This function is crucial in applications of multidimensional beta distribution. Thus, we present two efficient numerica...

Gate-teleportation circuits are arguably among the most basic examples of computations believed to provide a quantum computational advantage: In seminal work \cite{TerhalDiVincenzo04}, Terhal and DiVincenzo have shown that these circuits elude simulation by efficient classical algorithms under plausible complexity-theoretic assumptions. Here we con...

The integration of artificial intelligence (AI) in diverse industries has amplified the demand for scalable and efficient computing environments. Cloud-native technologies, particularly Kubernetes and Docker, have emerged as essential tools in orchestrating and managing AI workloads. Docker enables containerization, encapsulating applications and t...