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Xiong S, Yang X. Optical color routing enabled by deep learning. NANOSCALE 2024. [PMID: 38592716 DOI: 10.1039/d4nr00105b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.
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Affiliation(s)
- Shijie Xiong
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China.
| | - Xianguang Yang
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 511443, China.
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2
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Chen X, Xu S, Shabani S, Zhao Y, Fu M, Millis AJ, Fogler MM, Pasupathy AN, Liu M, Basov DN. Machine Learning for Optical Scanning Probe Nanoscopy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2109171. [PMID: 36333118 DOI: 10.1002/adma.202109171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 07/09/2022] [Indexed: 06/16/2023]
Abstract
The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial-intelligence (AI) and machine-learning (ML) algorithms. Augmented with AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
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Affiliation(s)
- Xinzhong Chen
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Suheng Xu
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Sara Shabani
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Yueqi Zhao
- Department of Physics, University of California at San Diego, La Jolla, CA, 92093-0319, USA
| | - Matthew Fu
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Andrew J Millis
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Michael M Fogler
- Department of Physics, University of California at San Diego, La Jolla, CA, 92093-0319, USA
| | - Abhay N Pasupathy
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, USA
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - D N Basov
- Department of Physics, Columbia University, New York, NY, 10027, USA
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Zhao E, Mak TH, He C, Ren Z, Pak KK, Liu YJ, Jo GB. Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms. OPTICS EXPRESS 2022; 30:37786-37794. [PMID: 36258360 DOI: 10.1364/oe.473770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning algorithms enable physicists to analyze experimental data with unprecedented high sensitivities, and identify quantum phases even in the presence of unavoidable noises. Here, we report a successful identification of topological phase transitions using a deep convolutional neural network trained with low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions. We apply the trained network to unseen data to map out a whole phase diagram, which predicts the positions of the two topological phase transitions that are consistent with the results obtained by using the conventional method on higher SNR data. By visualizing the filters and post-convolutional results of the convolutional layer, we further find that the CNN uses the same information to make the classification in the system as the conventional analysis, namely spin imbalance, but with an advantage concerning SNR. Our work highlights the potential of machine learning techniques to be used in various quantum systems.
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Wang Y, Wang Y, Chen C, Jiang R, Huang W. Development of variational quantum deep neural networks for image recognition. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Analysis of Ice and Snow Path Planning System Based on MNN Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1586006. [PMID: 35295272 PMCID: PMC8920659 DOI: 10.1155/2022/1586006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/20/2022] [Indexed: 11/17/2022]
Abstract
Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow sports lovers are eager to solve this problem. This paper designs a path planning method based on three-dimensional ice and snow model. The path planning method of moving snow and ice based on MNN (Multiclass Neural Networks) algorithm is studied from many aspects. MNN algorithm is used for comprehensive analysis and evaluation. The mobile phone provides data information on key nodes, air resistance, momentum change, ice and snow movement track, and so on. The results show that the ice and snow path planning system based on MNN algorithm designed in this paper has the advantages of high feasibility, high data accuracy, and good prediction effect and can effectively improve the efficiency of ice and snow path planning.
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Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1148954. [PMID: 34899886 PMCID: PMC8664500 DOI: 10.1155/2021/1148954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/29/2021] [Accepted: 11/05/2021] [Indexed: 11/17/2022]
Abstract
Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.
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Roig PJ, Alcaraz S, Gilly K, Bernad C, Juiz C. Modeling of a Generic Edge Computing Application Design. SENSORS 2021; 21:s21217276. [PMID: 34770582 PMCID: PMC8587040 DOI: 10.3390/s21217276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 12/29/2022]
Abstract
Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.
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Affiliation(s)
- Pedro Juan Roig
- Computer Engineering Department, Miguel Hernández University, 03202 Elche, Spain; (S.A.); (C.B.)
- Correspondence: (P.J.R.); (K.G.); Tel.: +34-966658388 (P.J.R.); +34-966658565 (K.G.)
| | - Salvador Alcaraz
- Computer Engineering Department, Miguel Hernández University, 03202 Elche, Spain; (S.A.); (C.B.)
| | - Katja Gilly
- Computer Engineering Department, Miguel Hernández University, 03202 Elche, Spain; (S.A.); (C.B.)
- Correspondence: (P.J.R.); (K.G.); Tel.: +34-966658388 (P.J.R.); +34-966658565 (K.G.)
| | - Cristina Bernad
- Computer Engineering Department, Miguel Hernández University, 03202 Elche, Spain; (S.A.); (C.B.)
| | - Carlos Juiz
- Mathematics and Computer Science Department, University of the Balearic Islands, 07022 Palma de Mallorca, Spain;
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Bohrdt A, Kim S, Lukin A, Rispoli M, Schittko R, Knap M, Greiner M, Léonard J. Analyzing Nonequilibrium Quantum States through Snapshots with Artificial Neural Networks. PHYSICAL REVIEW LETTERS 2021; 127:150504. [PMID: 34678012 DOI: 10.1103/physrevlett.127.150504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 08/11/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Current quantum simulation experiments are starting to explore nonequilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and timescales. Therefore, the question emerges as to which observables are best suited to study the dynamics in such quantum many-body systems. Using machine learning techniques, we investigate the dynamics and, in particular, the thermalization behavior of an interacting quantum system that undergoes a nonequilibrium phase transition from an ergodic to a many-body localized phase. We employ supervised and unsupervised training methods to distinguish nonequilibrium from equilibrium data, using the network performance as a probe for the thermalization behavior of the system. We test our methods with experimental snapshots of ultracold atoms taken with a quantum gas microscope. Our results provide a path to analyze highly entangled large-scale quantum states for system sizes where numerical calculations of conventional observables become challenging.
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Affiliation(s)
- A Bohrdt
- Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), Schellingstrasse 4, D-80799 München, Germany
- ITAMP, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, USA
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - S Kim
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - A Lukin
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - M Rispoli
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - R Schittko
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - M Knap
- Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), Schellingstrasse 4, D-80799 München, Germany
| | - M Greiner
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - J Léonard
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
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