“…Since this way the most dominant entries are retained, the overall performance of the network is not significantly impaired. On the contrary, max pooling introduces a certain degree of translational invariance and improves the computational efficiency of the network 61 , 62 . Figure 9 Schematic visualisation of a residual unit, consisting of two residual blocks followed by max pooling.…”
A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.
“…Since this way the most dominant entries are retained, the overall performance of the network is not significantly impaired. On the contrary, max pooling introduces a certain degree of translational invariance and improves the computational efficiency of the network 61 , 62 . Figure 9 Schematic visualisation of a residual unit, consisting of two residual blocks followed by max pooling.…”
A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.
“…For each input feature map, these layers output smaller summarized feature maps. In a max-pooling layer, for example, a 100 • 100 input feature map would be divided into 5 • 5 areas and the largest value in each would be returned 53 . The end result is a 20 • 20 output that records the presence of important visual features while reducing the input from areas that lack features (e.g., the radiograph's black background).…”
ä In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning.ä Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities that are commonly used in orthopaedics, including radiographs, computed tomographic scans, and magnetic resonance imaging scans.ä There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography, as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs.ä Deep learning is currently in the very early stages of entering the clinical setting, involving validation and proof-ofconcept studies for automated medical image interpretation.ä The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before (not after) the technology ends up in front of us and our patients. That is the purpose of this article.Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/H59).
“…We need a pooling layer after the convolution layer to reduce the tensor dimensions. Moreover, we use max pooling [36], which only takes the maximum value from one kernel. Several convolution and pooling layers can be utilized to achieve better performance.…”
We propose a machine learning method to investigate the propagation of cosmic
rays based on the precisely measured spectra of the primary and secondary cosmic
ray nuclei of Li, Be, B, C, and O from AMS-02, ACE, and Voyager-1. We train two
convolutional neural networks. One network learns how to infer propagation and
source parameters from the energy spectra of cosmic rays, and the other network,
which is similar to the former, has the flexibility to learn from the data with
added artificial fluctuations. Together with the simulated data generated by
GALPROP, we find that both networks can properly invert the propagation
process and infer the propagation and source parameters reasonably well.
This approach can be much more efficient than the traditional Markov chain
Monte Carlo fitting method for deriving the propagation parameters if users
choose to update confidence intervals with new experimental data.
Both of the trained networks are available at (https://github.com/alan200276/CR_ML).
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