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Academic Articles & Research Papers
Showing 20 of 817 academic articles
Zhihui Song, Xin Zhou, Jinchen Xu, Xiaodong Ding et al. • Scientific Reports
(2024)
Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and languag...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-024-63021-y
Anup Basnet Chetry, Keisuke Ohto • Journal of Cheminformatics
(2025)
Abstract Chemoinformatics is a rapidly advancing field that integrates chemistry, computer science, and data analysis to enhance the study and application of chemical systems. This interdisciplinary approach leverages computational tools an...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s13321-025-00978-6
Hocheol Lim, Hyeon-Nae Jeon, June-Koo Rhee, Byungdu Oh et al. • Scientific Reports
(2022)
Abstract Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-022-11537-6
Thomas Spriggs, Arash Ahmadi, Bokai Chen, Eliska Greplova • Machine Learning: Science and Technology
(2025)
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a basis for representing quantum states via neural networks. ...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/2632-2153/adaca2
Eric C. Rowell, Zhenghan Wang • Bulletin of the American Mathematical Society
(2018)
In topological quantum computing, information is encoded in “knotted” quantum states of topological phases of matter, thus being locked into topology to prevent decay. Topological precision has been confirmed in quantum Hall liquids by expe...
OPENALEX
Open Access
DOI: https://doi.org/10.1090/bull/1605
John P T Stenger, C Stephen Hellberg, Daniel Gunlycke • New Journal of Physics
(2026)
We propose a hybrid variational quantum algorithm that has variational parameters used by both the quantum circuit and the subsequent classical optimization. Similar to the variational quantum eigensolver, this algorithm applies a parameter...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/1367-2630/aea2ba
Runze Li, Yibo Dong, Fengsong Qian, Yiyang Xie et al. • PhotoniX
(2023)
Abstract The rapid development of neuromorphic computing has stimulated extensive research interest in artificial synapses. Optoelectronic artificial synapses using laser beams as stimulus signals have the advantages of broadband, fast resp...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s43074-023-00082-8
Young‐Seok Kim, Andrew Eddins, Sajant Anand, Ken Xuan Wei et al. • Nature
(2023)
Abstract Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely ...
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DOI: https://doi.org/10.1038/s41586-023-06096-3
PMC: 10266970
G.R. Nudd, Darren J. Kerbyson, E. Papaefstathiou, S. C. Perry et al. • The International Journal of High Performance Computing Applications
(2000)
This paper describes a methodology that provides detailed predictive performance information throughout the software design and implementation cycles. It is structured around a hierarchy of performance models that describe the computing sys...
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DOI: https://doi.org/10.1177/109434200001400306
Tushar Deepak Chandra, Sam Toueg • Journal of the ACM
(1996)
We introduce the concept of unreliable failure detectors and study how they can be used to solve Consensus in asynchronous systems with crash failures. We characterise unreliable failure detectors in terms of two properties—completeness and...
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Open Access
DOI: https://doi.org/10.1145/226643.226647
Doug Terry • IEEE Transactions on Software Engineering
(1987)
Caching reduces the average cost of retrieving data by amortizing the lookup cost over several references to the data. Problems with maintaining strong cache consistency in a distributed system can be avoided by treating cached information ...
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DOI: https://doi.org/10.1109/tse.1987.232834
Mohammadhossein Homaei, Óscar Mogollón Gutiérrez, José Carlos Sancho Núñez, Mar Ávila et al. • Artificial Intelligence Review
(2024)
Abstract The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjuncti...
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DOI: https://doi.org/10.1007/s10462-024-10805-3
Ben D. Sawyer, Peter A. Hancock • Human Factors The Journal of the Human Factors and Ergonomics Society
(2018)
OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...
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Open Access
DOI: https://doi.org/10.1177/0018720818780472
Stéphane M. Meystre, Peter John Haug • BMC Medical Informatics and Decision Making
(2005)
BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However,...
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DOI: https://doi.org/10.1186/1472-6947-5-30
PMC: 1208893
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02830
• IGI Global eBooks
(2004)
Neural Network is defined as the ability of a group to solve more problems than its individual members.The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individu...
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DOI: https://doi.org/10.4018/978-1-59140-176-6
Binh‐Son Hua, Minh-Khoi Tran, Sai-Kit Yeung • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2018)
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....
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Open Access
DOI: https://doi.org/10.1109/cvpr.2018.00109
Vivek Prakash Nigam, Daniel Graupe • Neurological Research
(2004)
Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced ne...
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DOI: https://doi.org/10.1179/016164104773026534
Tim Hill, Marcus J. O’Connor, William Remus • Management Science
(1996)
Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series ...
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DOI: https://doi.org/10.1287/mnsc.42.7.1082
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler et al. • arXiv (Cornell University)
(2018)
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1806.08734