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Academic Articles & Research Papers
Showing 20 of 227 academic articles
Shuai-Peng Wang, Alberto Mercurio, Alessandro Ridolfo, Yuqing Wang et al. • Nature Communications
(2025)
Abstract The realization of strong nonlinear coupling between single photons has been a long-standing goal in quantum optics and quantum information science, promising wide impact applications, such as all-optical deterministic quantum logi...
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Open Access (DOAJ)
DOI: 10.1038/s41467-025-63783-7
Hiroomi Chono, Hayato Goto • APL Quantum
(2025)
Kerr parametric oscillators (KPOs), two-photon driven Kerr-nonlinear resonators, can stably hold coherent states with opposite-sign amplitudes and are promising devices for quantum computing. Recently, we have theoretically proposed a two-q...
DOAJ
Open Access (DOAJ)
DOI: 10.1063/5.0241996
Jr-Shin Li, Justin Ruths, Steffen J. Glaser • Nature Communications
(2017)
Coherent control of two-level systems is crucial for achieving fidelity in spectroscopy and quantum computing, but inherent nonlinearities and parameter variation have, to date, required an approximate, numerical approach. Here, Li et al. s...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41467-017-00441-7
Sanjib Ghosh, T. C. H. Liew • npj Quantum Information
(2020)
Abstract Exciton-polariton condensates have attractive features for quantum computation, e.g., room temperature operation, high dynamical speed, ease of probe, and existing fabrication techniques. Here, we present a complete theoretical sch...
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cc-by
DOI: https://doi.org/10.1038/s41534-020-0244-x
Pedro Pablo Cárdenas Alzate, Jaime Alberto Echeverri Franco, Jacobo Echeverri Hoyos • Epidemiologia
(2026)
Background/Objectives: Cholera remains a major public health problem, particularly in regions with limited access to safe drinking water and sanitation. Traditional compartmental models may underrepresent adaptive human behavioral responses...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/epidemiologia7050142
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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cc-by-nc-nd
DOI: https://doi.org/10.1162/coli_r_00312
Kumpati S. Narendra, Snehasis Mukhopadhyay • IEEE Conference on Decision and Control (CDC)
(2005)
Some of the problems that arise in the control of nonlinear systems in the presence of uncertainty are considered. Multilayer neural networks and radial basis function networks are used in the design of identifiers and controllers, and grad...
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DOI: https://doi.org/10.1109/cdc.1992.371803
George N. Bebis, Michael Georgiopoulos • IEEE Potentials
(1994)
One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the numbe...
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Open Access
DOI: https://doi.org/10.1109/45.329294
R. Glen Donaldson, Mark J. Kamstra • Journal of Forecasting
(1996)
This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces fo...
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DOI: https://doi.org/10.1002/(sici)1099-131x(199601)15:1<49::aid-for604>3.0.co;2-2
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
W.D. Penny, David Frost • Medical Decision Making
(1996)
Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical de...
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DOI: https://doi.org/10.1177/0272989x9601600409
Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks
(2001)
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...
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DOI: https://doi.org/10.1002/047084535x
T. Sorsa, Heikki N. Koivo, Hannu J. Koivisto • IEEE Transactions on Systems Man and Cybernetics
(1991)
Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural netw...
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DOI: https://doi.org/10.1109/21.108299
Richard J. Mammone (1994)
Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...
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M.J. Willis, Christine Di Massimo, Gary A. Montague, Ming T. Tham et al. • IEE Proceedings D Control Theory and Applications
(1991)
Artificial neural networks are made up of highly interconnected layers of simple ‘neuron-like’ nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that, given t...
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DOI: https://doi.org/10.1049/ip-d.1991.0036
Donald F. Specht • IEEE Transactions on Neural Networks
(1991)
A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with...
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DOI: https://doi.org/10.1109/72.97934
Peter A. Getting • Annual Review of Neuroscience
(1989)
ical, and computational techniques have provided the tools to begin uncovering concepts underlying neural network function. This review is a summary of insights into the organization and operation of neural circuits acquired through applica...
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DOI: https://doi.org/10.1146/annurev.ne.12.030189.001153
J. Lee, Ronald C. Weger, S.K. Sengupta, Ronald M. Welch • IEEE Transactions on Geoscience and Remote Sensing
(1990)
It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...
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DOI: https://doi.org/10.1109/36.58972
Stephan Rasp, Sebastian Lerch • Monthly Weather Review
(2018)
Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...
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other-oa
DOI: https://doi.org/10.1175/mwr-d-18-0187.1
Hervé Abdi • Journal of Biological Systems
(1994)
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...
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DOI: https://doi.org/10.1142/s0218339094000179