Hasil untuk "Computer Science"

Yongjun Xu, Qi Wang, Zhulin An et al.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Jing Pei, Lei Deng, Sen Song et al.

Iyad Rahwan, Manuel Cebrian, Nick Obradovich et al.

Federico Monti, Davide Boscaini, Jonathan Masci et al.

Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches.

Xin-She Yang

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

P. Merolla, J. Arthur, Rodrigo Alvarez-Icaza et al.

I. Ahmadianfar, Ali Asghar Heidari, Saeed Noshadian et al.

a Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran Email: im.ahmadian@gmai.com, i.ahmadianfar@bkatu.ac.ir (Iman Ahmadianfar). Saeed.noshadian@gmail.com (Saeed Noshadian). b School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran Email: as_heidari@ut.ac.ir, aliasghar68@gmail.com c Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore Email: aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu d College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China Email: chenhuiling.jlu@gmail.com e Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia Email: gandomi@uts.edu.au

M. Newman

F. Scarselli, M. Gori, A. Tsoi et al.

Ming Li, P. Vitányi

J. Kempe

This article aims to provide an introductory survey on quantum random walks. Starting from a physical effect to illustrate the main ideas we will introduce quantum random walks, review some of their properties and outline their striking differences to classical walks. We will touch upon both physical effects and computer science applications, introducing some of the main concepts and language of present day quantum information science in this context. We will mention recent developments in this new area and outline some open questions.

Christos H. Papadimitriou, Kenneth Steiglitz

D. Hopkins, Gary King

Greg Newman, A. Wiggins, Alycia Crall et al.

Citizen science creates a nexus between science and education that, when coupled with emerging technologies, expands the frontiers of ecological research and public engagement. Using representative technologies and other examples, we examine the future of citizen science in terms of its research processes, program and participant cultures, and scientific communities. Future citizen-science projects will likely be influenced by sociocultural issues related to new technologies and will continue to face practical programmatic challenges. We foresee networked, open science and the use of online computer/video gaming as important tools to engage non-traditional audiences, and offer recommendations to help prepare project managers for impending challenges. A more formalized citizen-science enterprise, complete with networked organizations, associations, journals, and cyberinfrastructure, will advance scientific research, including ecology, and further public education.

Ahmed Métwalli, Fares Fathy, Esraa Khatab et al.

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>γ</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling.

Marvin Steinke

The surge for computing resource demand is increasing global electricity consumption in data centers which is expected to exceed 1000 TWh by 2026, mainly attributable to adoption of new AI technologies. Carbon-aware computing strategies can mitigate their environmental impact by aligning power consumption with the production of low-carbon renewable energy, but they face challenges due to the scarcity of development environments. Existing solutions either rely on costly and complex physical system architectures that are difficult to integrate and maintain or on full simulations that, while more economical, often lack realism by ignoring system overheads, and real-time node power consumption and resource fluctuations. This thesis remediates these issues by proposing a self-calibrating energy-aware software testbed that uses the Software-in-the-Loop co-simulation framework Vessim to integrate renewable energy production simulators, while including real computing nodes. The application-level power consumption of these are first approximated by the Kepler framework and then calibrated within Vessim's microgrid simulation using an external socket power meter as a definitive measurement source on the system-level. The evaluation of the testbed with GPU and CPU intensive workloads reveal fairly accurate power approximation of the whole computing node by the Kepler framework, with an average regression coefficient of 1.01 and R^2 values of 0.95, though certain machine learning workloads showed higher deviation. The average static y-intercept of the regression line of ~5.23 W indicate inaccuracies in the idle power approximation. Calibration of dynamic per-process power consumption improved accuracy for GPU workloads by ~50%, while CPU workloads saw a modest improvement of ~3.5%.

Zhen Li, Ji Liu, Yoshimichi Ohki et al.

Abstract Surface flashover is a gas–solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic, electrical, and spacecraft applications. Despite the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance, the influence mechanisms of the present technologies have yet to be systematically discussed and summarised. This review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among ‘microscopic structure‐mesoscopic charge transport‐macroscopic insulation failure’. Moreover, it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover performance. The review categorises and presents structure optimisation technologies that govern electric field distribution. All identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover performance. Finally, this review proposes recommendations encompassing mathematical, chemical, evaluation, and manufacturing technologies. This systematic summary of current technologies, their influence mechanisms, and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

Zdenek Krejcik, David Kundrat, Jiri Klema et al.

Abstract Background Myelodysplastic neoplasms (MDS) are heterogeneous hematopoietic disorders characterized by ineffective hematopoiesis and genome instability. Mobilization of transposable elements (TEs) is an important source of genome instability leading to oncogenesis, whereas small PIWI-interacting RNAs (piRNAs) act as cellular suppressors of TEs. However, the roles of TEs and piRNAs in MDS remain unclear. Methods In this study, we examined TE and piRNA expression through parallel RNA and small RNA sequencing of CD34+ hematopoietic stem cells from MDS patients. Results Comparative analysis of TE and piRNA expression between MDS and control samples revealed several significantly dysregulated molecules. However, significant differences were observed between lower-risk MDS (LR-MDS) and higher-risk MDS (HR-MDS) samples. In HR-MDS, we found an inverse correlation between decreased TE levels and increased piRNA expression and these TE and piRNA levels were significantly associated with patient outcomes. Importantly, the upregulation of PIWIL2, which encodes a key factor in the piRNA pathway, independently predicted poor prognosis in MDS patients, underscoring its potential as a valuable disease marker. Furthermore, pathway analysis of RNA sequencing data revealed that dysregulation of the TE‒piRNA axis is linked to the suppression of processes related to energy metabolism, the cell cycle, and the immune response, suggesting that these disruptions significantly affect cellular activity. Conclusions Our findings demonstrate the parallel dysregulation of TEs and piRNAs in HR-MDS patients, highlighting their potential role in MDS progression and indicating that the PIWIL2 level is a promising molecular marker for prognosis. Graphical Abstract

Ann-Sophie L. Schenk, Stefan Schiffer, Heqiu Song

In this paper we report on first insights from interviews with teachers and students on using social robots in computer science class in sixth grade. Our focus is on learning about requirements and potential applications. We are particularly interested in getting both perspectives, the teachers' and the learners' view on how robots could be used and what features they should or should not have. Results show that teachers as well as students are very open to robots in the classroom. However, requirements are partially quite heterogeneous among the groups. This leads to complex design challenges which we discuss at the end of this paper.

Johannes Buchner

The aim of this paper is to discuss the potential of using methods from Reinforcement Learning for Life Cycle Assessment in a circular economy, and to present some new ideas in this direction. To give some context, we explain how Reinforcement Learning was successfully applied in computer chess (and beyond). As computer chess was historically called the "drosophila of AI", we start by describing a method for the board representation called 'rotated bitboards' that can potentially also be applied in the context of sustainability. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete implementation of the move-generator in FUSc# (a chess engine developed at FU Berlin in C# some years ago) is described. In addition, rotated binary neural networks are discussed briefly. The second part deals with reinforcement learning in computer chess (and beyond). We exemplify the progress that has been made in this field in the last 15-20 years by comparing the "state of the art" from 2002-2008, when FUSc# was developed, with the ground-breaking innovations connected to "AlphaZero". We review some application of the ideas developed in AlphaZero in other domains, e.g. the "other Alphas" like AlphaFold, AlphaTensor, AlphaGeometry and AlphaProof. In the final part of the paper, we discuss the computer-science related challenges that changing the economic paradigm towards (absolute) sustainability poses and in how far what we call 'progressive computer science' needs to contribute. Concrete challenges include the closing of material loops in a circular economy with Life Cycle Assessment in order to optimize for (absolute) sustainability, and we present some new ideas in this direction.