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Academic Articles & Research Papers
Showing 20 of 506 academic articles
Amna Ikram, Azhar Imran • Informatics in Medicine Unlocked
(2026)
Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accura...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.imu.2026.101744
Fengsheng Zeng, Bahari Idrus, Mohammad Faidzul Nasrudin, Eddie Shahril Ismail • Sensors
(2026)
With the rapid advancement of quantum computing technology, IoT edge devices widely deployed in industrial settings are facing severe threats to their quantum-resistant security. This work focuses on the challenges of adapting Quantum Key D...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s26185859
Saravanan Paramasivam, J. Jenitha, S. Sanjana, Majid Haghparast • IEEE Access
(2023)
Quantum computing plays a vital role in the next generation computing platforms as researchers have achieved quantum supremacy by proving that quantum computers can outperform classical computers. These high performance computers will pose ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3289764
Jun Fujisaki, Kazunori Maruyama, Hirotaka Oshima, Shintaro Sato et al. • Physical Review Research
(2023)
Efficient decoding to estimate error locations from outcomes of syndrome measurements is the prerequisite for quantum error correction. Decoding in the presence of circuit-level noise including measurement errors should be considered in cas...
DOAJ
Open Access (DOAJ)
DOI: 10.1103/PhysRevResearch.5.043261
Meng‐Cheng Yen, Yung‐Chi Yao, Chia‐Jung Lee, Shi‐Hui Huang et al. • Advanced Science
(2025)
Abstract This study demonstrates that optical absorption of the CsPbBr3 quantum dots can be significantly enhanced through monolithic integration with the Tamm plasmon (TP) structure. This integration enables the resulting TP photodetector ...
DOAJ
Open Access (DOAJ)
DOI: 10.1002/advs.202503464
Mahfooz Ahmad, Eram Fatima, Ankit Shukla, Neeta Bhusal et al. • Journal of Information Security and Cybercrimes Research
(2024)
Quantum security is an evolving field that leverages principles of quantum physics to strengthen computing systems. Core concepts such as superposition and entanglement are foundation to this domain. However, current systems face significan...
DOAJ
Open Access (DOAJ)
DOI: 10.26735/FWSL8668
Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang et al. • Frontiers in Immunology
(2025)
BackgroundM2-like tumor-associated macrophages (TAMs) promote an immunosuppressive microenvironment and contribute to tumor progression and metastasis. However, their molecular characterization and prognostic value have not been fully explo...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fimmu.2025.1650726
Ruijie Meng, George Pîrlea, Abhik Roychoudhury, Ilya Sergey • ACM SIGSAC Conference on Computer and Communications Security (CCS)
(2023)
Grey-box fuzzing is the lightweight approach of choice for finding bugs in sequential programs. It provides a balance between efficiency and effectiveness by conducting a biased random search over the domain of program inputs using a feedba...
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DOI: https://doi.org/10.1145/3576915.3623097
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
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky • arXiv (Cornell University)
(2017)
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrice...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1706.08498
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra • arXiv (Cornell University)
(2015)
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1505.05424
Mikhail Yurochkin, Mayank Agarwal, Ghosh, Soumya, Kristjan H. Greenewald et al. • arXiv (Cornell University)
(2019)
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.12022
Tommy W. S. Chow, Siu‐Yeung Cho • Series in electrical and computer engineering
(2007)
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been co...
OPENALEX
DOI: https://doi.org/10.1142/p487
Hongsik Jeong, Luping Shi • Journal of Physics D Applied Physics
(2018)
Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...
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DOI: https://doi.org/10.1088/1361-6463/aae223
J. T. Gene Hwang, Aidong Adam Ding • Journal of the American Statistical Association
(1997)
The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data...
OPENALEX
DOI: https://doi.org/10.1080/01621459.1997.10474027
Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics
(1997)
The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a ight control architecture based on feedback linearization of the aircraft...
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DOI: https://doi.org/10.2514/2.4029
Stefano Nolfi, Domenico Parisi, Jeffrey L. Elman • Adaptive Behavior
(1994)
This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learn...
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DOI: https://doi.org/10.1177/105971239400300102
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov • arXiv (Cornell University)
(2015)
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required b...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1509.06569
M. V. Altaĭsky • arXiv (Cornell University)
(2001)
It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with d...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.quant-ph/0107012
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1609.07061