Hasil untuk "Electronic computers. Computer science"

A. Kandala, Antonio Mezzacapo, K. Temme et al.

Quantum computers can be used to address electronic-structure problems and problems in materials science and condensed matter physics that can be formulated as interacting fermionic problems, problems which stretch the limits of existing high-performance computers. Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and Monte Carlo methods are unsuitable owing to the fermionic sign problem. These limitations of classical computational methods have made solving even few-atom electronic-structure problems interesting for implementation using medium-sized quantum computers. Yet experimental implementations have so far been restricted to molecules involving only hydrogen and helium. Here we demonstrate the experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms, determining the ground-state energy for molecules of increasing size, up to BeH2. We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepared trial states that are tailored specifically to the interactions that are available in our quantum processor, combined with a compact encoding of fermionic Hamiltonians and a robust stochastic optimization routine. We demonstrate the flexibility of our approach by applying it to a problem of quantum magnetism, an antiferromagnetic Heisenberg model in an external magnetic field. In all cases, we find agreement between our experiments and numerical simulations using a model of the device with noise. Our results help to elucidate the requirements for scaling the method to larger systems and for bridging the gap between key problems in high-performance computing and their implementation on quantum hardware.

A. Marowka

B. Efron, R. Tibshirani

F. Roggio, Bruno Trovato, Martina Sortino et al.

The accurate measurement and analysis of human movement are essential in fields ranging from rehabilitation and neuroscience to sports science and ergonomics. Traditional methods, though precise, are often constrained by cost, accessibility, and controlled environments. The advent of machine learning (ML) pose estimation models (PEMs) offers an alternative solution, enabling detailed motion analysis using low-cost imaging systems in various settings. The aim of this review is to evaluate ML PEMs and their impact on human movement sciences, focusing on recent advancements in machine learning and computer vision for accurate, non-invasive motion analysis using low-cost imaging systems. A narrative review was conducted by searching electronic databases, including PubMed and Google Scholar, using key terms such as "machine learning," "pose estimation models," and "human movement sciences." Thematic analysis identified key advancements, applications, and challenges in ML PEMs across clinical, sports, and ergonomic contexts. The review highlights the development, capabilities, and applications of models such as OpenPose, PoseNet, AlphaPose, DeepLabCut, HRNet, MediaPipe Pose, BlazePose, EfficientPose, and MoveNet, emphasizing their potential for non-invasive, cost-effective assessments. In clinical settings, these models enable objective gait and posture analysis, aiding in diagnosing musculoskeletal disorders and tracking rehabilitation progress. In sports, ML PEMs enhance performance analysis and injury prevention by providing real-time feedback and detailed biomechanical data. In ergonomics, they offer proactive solutions for workplace injury prevention through real-time posture and movement analysis. While promising, the implementation of ML PEMs faces challenges in accuracy, data quality, and integration into existing practices. Establishing standardized protocols and frameworks is crucial for ensuring reliable, interdisciplinary applications. This review can be useful for coaches, healthcare professionals, and researchers in evaluating and implementing ML PEMs, ultimately advancing the field of human movement sciences.

Abdullah A. A. Ahmed, N. Alegret, Bethany Almeida et al.

Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain–machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain–machine interfaces and look forward in discussing perspectives and limitations based on the authors’ expertise across a range of complementary disciplines—from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.

Yi Jiang, Ye Bi, Yinng Li et al.

Near-eye display (NED) technology constitutes a fundamental component of head-mounted display (HMD) systems. The compact form factor required by HMDs imposes stringent constraints on optical design, often resulting in pronounced wavefront aberrations that significantly degrade visual fidelity. In addition, natural eye movements dynamically induce varying blur that further compromises image quality. To mitigate these challenges, a gaze-contingent neural network framework has been developed to compensate for aberrations within the foveal region. The network is trained in an end-to-end manner to minimize the discrepancy between the optically degraded system output and the corresponding ground truth image. A forward imaging model is employed, in which the network output is convolved with a spatially varying point spread function (PSF) to accurately simulate the degradation introduced by the optical system. To accommodate dynamic changes in gaze direction, a foveated attention-guided module is incorporated to adaptively modulate the pre-correction process, enabling localized compensation centered on the fovea. Additionally, an end-to-end trainable architecture has been designed to integrate gaze-informed blur priors. Both simulation and experimental validations confirm that the proposed method substantially reduces gaze-dependent aberrations and enhances retinal image clarity within the foveal region, while maintaining high computational efficiency. The presented framework offers a practical and scalable solution for improving visual performance in aberration-sensitive NED systems.

Mathieu Calvat, Chris Bean, Dhruv Anjaria et al.

Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together, these steps offer a method to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.

Hassan-Roland Nasser, Marianne Cockburn, Marie Schneider

Studying animals’ rhythmicity provides insights into their physiological and psychological states. The degree of functional coupling (DFC) is one of the algorithms available to assess rhythmicity in activity-related time series data, such as accelerometer or GPS data. However, DFC computation is complex, as it includes frequency spectrum analysis and statistical significance testing. This paper introduces digiRhythm, an R package that makes the DFC-based rhythmicity analysis easily accessible. Beyond the DFC, the package includes an additional set of tools, which are crucial for rhythmicity investigations, such as actogram generation, daily activity visualization, and diurnality index computation.

S J Watts, L Crow

In recent years generative artificial intelligence has been used to create data to support scientific analysis. For example, generative adversarial networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a significantly reduced computing time. $N$ training events for a GAN can result in $NG$ generated events with the gain factor $G$ being greater than one. This appears to violate the principle that one cannot get information for free. This is not the only way to amplify data so this process will be referred to as data amplification which is studied using information theoretic concepts. It is shown that a gain greater than one is possible whilst keeping the information content of the data unchanged. This leads to a mathematical bound, $2\log (\text{Generated}\ \text{Events}) \unicode{x2A7E} {\text{3log(Training Events)}}$ , which only depends on the number of generated and training events. This study determined the conditions for both the underlying and reconstructed probability distributions to ensure this bound. In particular, the resolution of variables in amplified data is not improved by the process but the increase in sample size can still improve statistical significance. The bound was confirmed using computer simulation and analysis of GAN generated data from the literature.

Guido Macas-Acosta, Jesús Estupiñán Ricardo, Arnaldo Vergara-Romero et al.

This article uses the Delphi and neutrosophic analytic hierarchy process (NAHP) and neutrosophic social choice theory (NSC). NAHP+NSC methodology is used to investigate the potential direct effects of a rise in the Value Added Tax (VAT) on company sales. The primary question is how a change in VAT may affect corporate activity; this is a simple enough question despite its weighty ramifications. Despite the large number of economic research, it seems that the literature has not yet gone into great length on how these particular techniques might provide an in-depth understanding of possible company responses to tax increases. It's interesting to note that the study not only closes a significant research gap, but also uses advanced approaches to examine the impact. Findings that would not have been reached by more conventional methods are achieved by combining the Delphi technique for expert viewpoints with NAHP+NSC for a more in-depth study. The results imply that, depending on a number of variables, including industry type and company size, a rise in VAT might have varying impacts on business sales. This research provides helpful tools for firms and politicians looking to adjust to possible changes in the tax environment, in addition to offering a fresh viewpoint on the topic of tax policy. In the end, the study broadens our theoretical knowledge and offers helpful advice for navigating the intricate realm of tax laws and their implications on the economy.

Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar et al.

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system for predicting blackout events in smart grids is presented. The system is based on deep learning models: convolutional neural networks (CNN) and deep self-organizing maps (DSOM), and is designed to analyse data from various sources, such as power demand, generation, transmission, distribution and weather forecasts. The system performance is evaluated using a dataset of time windows and labels, where the labels indicate whether a blackout event occurred within a given time window. It is found that the system is able to achieve an accuracy of 98.71% and a precision of 98.65% in predicting blackout events. The results suggest that the early warning system presented in this paper is a promising tool for improving the resilience and reliability of electrical grids and for mitigating the impacts of blackout events on communities and businesses.

Conrad H. Stansbury, Alessandra Lanzara

Scientific data acquisition is a problem domain that has been underserved by its computational tools despite the need to efficiently use hardware, to guarantee validity of the recorded data, and to rapidly test ideas by configuring experiments quickly and inexpensively. High-dimensional physical spectroscopies, such as angle-resolved photoemission spectroscopy, make these issues especially apparent because, while they use expensive instruments to record large data volumes, they require very little acquisition planning. The burden of writing data acquisition software falls to scientists, who are not typically trained to write maintainable software. In this paper, we introduce AutodiDAQt to address these shortfalls in the scientific ecosystem. To ground the discussion, we demonstrate its merits for angle-resolved photoemission spectroscopy and high bandwidth spectroscopies. AutodiDAQt addresses the essential needs for scientific data acquisition by providing simple concurrency, reproducibility, retrospection of the acquisition sequence, and automated user interface generation. Finally, we discuss how AutodiDAQt enables a future of highly efficient machine-learning-in-the-loop experiments and analysis-driven experiments without requiring data acquisition domain expertise by using analysis code for external data acquisition planning.

Lei Zhang, Zhi Pei

In the present paper, the online valet driving problem (OVDP) is studied. In this problem, customers request a valet driving service through the platform, then the valets arrive on e-bikes at the designated pickup location and drive the vehicle to the destination. The key task is to assign the valets effectively for driving orders to minimize the overall cost. To serve that purpose, we first propose a new online scheduling strategy that divides the planning horizon into several rounds with fixed length of time, and each round consists of pooling time and scheduling time. By including the features of online scheduling and the power level of e-bikes, this OVDP becomes more practical but nevertheless challenging. To solve the OVDP, we formulate it into a set partitioning model and design a branch-and-price (B&P) algorithm. To improve the computation efficiency, a label setting algorithm is incorporated to address the pricing subproblem, which is accelerated via a heuristic pricing method. As an essential part of the algorithm design, an artificial column technique and a greedy-based constructive heuristic are implemented to obtain the initial solution. Based on the numerical analysis of various scaled instances, it is verified that the proposed B&P algorithm is not only effective in optimum seeking, but also shows a high level of efficiency in comparison with the off-the-shelf commercial solvers. Furthermore, we also explore the impact of pooling and scheduling time on the OVDP and discover a bowl-shaped trend of the objective value with respect to the two time lengths.

Maximilian Hoffmann, Ralph Bergmann

In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.

ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang

Different from artistic style transfer,the challenge of photorealistic style transfer is to maintain the authenticity of the output while transferring the color style of the style input.Now,most photorealistic style transfer methods perform pre-proces-sing or post-processing based on artistic style transfer methods,to maintain the authenticity of the output image.However,artistic style transfer methods usually cannot make full use of global color information to achieve a more coordinated overall impression,and pre-processing and post-processing operations are often tedious and time-consuming.To solve the above problems,this paper establishes a photorealistic style transfer network guided by global information,and proposes a color-partition-mean loss(<i>L_cpm</i>) to measure the similarity of the global color distribution between output and the style input.Adaptive instance normalization(AdaIN) is improved,and partition adaptive instance normalization(AdaIN-P) is proposed to better adapt to the color style transfer of real images.In addition,this paper also introduces a cross-channel partition attention module to make better use of global context information and improve the overall coordination of output images.Through the above methods,the decoder of network is guided to make full use of global information.Experimental results show that,compared with other state-of-the-art me-thods,the proposed model can achieve a better photorealistic style transfer effect while maintaining image details.

James Philip Howard, Frances A. Wood, Darrel P. Francis

Citra Widya Herawati

<p>Diploma verification is still done manually. During this time, the diploma is verified by displaying the original diploma. Because the campus does not match the diploma with the existing archives, diploma falsification is possible. IAIN Bukittinggi uses manual methods to verify the authenticity of diplomas. The goal of this research is to create a diploma verification system based on QR CODE to verify the authenticity of diplomas. Research and Development is the type of research employed (R&amp;D). The system development model employs a waterfall approach and the System Development Life Cycle (SDLC). The validity test results are valid, with an average of 0.90. The average practicality test result is 92, indicating that the product is very practical. And the effectiveness test results with an average of 0.90, which is very high.</p>

Haile Derbew, Ansha Nega, Worku Tefera et al.

Background Computer vision syndrome (CVS) is an amalgam of visual symptoms caused by continued use of computers. Worldwide, up to 70 million workers are at risk for computer vision syndrome resulting in reduced productivity at work and reduced quality of life. Bank employees are among the risky workers with unknown magnitude of the syndrome. Therefore, the main aim of this study was to determine the prevalence of CVS and its associated personal factors among employees of Commercial Bank of Ethiopia. Methods A total of three hundred and fifty-nine bank workers participated in the study between February and March 2018. A self-administered structured questionnaire was used to collect sociodemographic data, CVS symptoms, and its personal factors. Snellen chart tool was used to measure blurred vision. Data entry and analysis were performed via Epi Info™ 7 and Statistical Package for the Social Sciences (SPSS) version 21. Binary logistic regression and multivariable logistic regression were performed to assess the association and control the potential confounders. Result The prevalence of computer vision syndrome in the last 12 months among the total study subjects, 359 (98% response rate), was 262 (74.6%) (95% confidence interval [CI] = 70.1, 79.5). Risk factors that could not be intervened with were sex (AOR: 1.8; 95% CI (1–3)) and age group (AOR: 3.11; 95% CI (1.2–8)). Causal factors that could be intervened with were use of electronic materials outside work (AOR: 3.11; 95% CI (1.15–8.36). Protective factor that could be intervened with was habit of taking a break (AOR: 0.44; 95% CI (0.3–0.8)). Conclusion and Recommendation. Three-fours of the employees were at risk. Sex, age, habit of taking a break, and use of electronic materials outside work environment were significantly associated with the presence of CVS. Remedial actions need to be considered at individual level.

Alaa Ali S. Almohanna, K. Win, Shahla Meedya

Background Supporting women to initiate and continue breastfeeding is a global challenge. A range of breastfeeding interventions employing electronic technologies (e-technologies) are being developed, which offer different delivery modes and features over the internet; however, the impact of internet-based e-technologies on breastfeeding outcomes remains unclear. Objective This study aimed to identify the characteristics of current internet-based breastfeeding interventions employing e-technologies and investigate the effects of internet-based e-technologies on breastfeeding outcomes. Methods A systematic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in the following databases: Scopus, Web of Science, the Cochrane Database of Systematic Reviews, ScienceDirect, Google Scholar, the Association for Computing Machinery, SpringerLink, and Institute of Electrical and Electronics Engineers Xplore. Results This systematic review included 16 studies published between 2007 and 2018, with 4018 women in 8 countries. The characteristics of the interventions were grouped based on (1) mode of delivery (web-based, mobile phone apps, and computer kiosk), (2) purpose of the interventions (education and support), and (3) key strategies (monitoring and breastfeeding tracking, personalization, online discussion forum, web-based consultation, and breastfeeding station locators). Combining educational activities with web-based personalized support through discussion forums appeared to be the most effective way to improve breastfeeding outcomes and long-term exclusive breastfeeding rates. Monitoring and breastfeeding trackers appeared to be the least effective ways. Conclusions This study demonstrated a variety of internet-based e-technologies that professionals can use to promote, educate, and support breastfeeding women. Future internet-based breastfeeding interventions employing e-technologies might consider improving interaction with mothers and personalizing the content of the proposed interventions.

P. Popvcevi'c, Y. Utsumi, I. Biało et al.

(Dated: Co 1 / 3 NbS 2 is the magnetic intercalate of 2H-NbS 2 where electronic itinerant and magnetic prop- erties strongly influence each other throughout the phase diagram. Here we report the angle-resolved photoelectron spectroscopy (ARPES) study in Co 1 / 3 NbS 2 . In agreement with previous reports, the observed electronic structure seemingly resembles the one of the parent material 2H-NbS 2 , with the shift in Fermi energy of 0.5 eV accounting for the charge transfer of approximately two electrons from each Co ion into the NbS 2 layers. However, in addition, and in contrast to previous reports, we observe significant departures that cannot be explained by the rigid band shift accompanied by minor deformation of bands: First, entirely unrelated to the 2H-NbS 2 electronic structure, a shallow electronic band is found crossing the Fermi level near the boundary of the first Brillouin zone of Co 1 / 3 NbS 2 . The evolution of the experimental spectra upon varying the incident photon energy suggests the Co origin of this band. Second, the Nb bonding band, found deeply submerged below the Fermi level at the Γ point, indicates that the interlayer-hybridization is significantly amplified by intercalation, with Co magnetic ions probably acting as strong covalent bridges between NbS 2 layers. The strong hybridization between orbitals that support the itinerant states and the orbitals hosting the local magnetic moments indicates the importance of strong electronic correlations, with the interlayer coupling playing an exquisite role.