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1. Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling [Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling]

   https://doi.org/10.5220/0009156201850195  

   Olmschenk, Greg ; Tang, Hao ; Zhu, Zhigang ( January 2020 , 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications)

   Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures. 

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2. PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

   https://doi.org/10.1175/JHM-D-19-0110.1  

   Sadeghi, Mojtaba ; Asanjan, Ata Akbari ; Faridzad, Mohammad ; Nguyen, Phu ; Hsu, Kuolin ; Sorooshian, Soroosh ; Braithwaite, Dan ( November 2019 , Journal of Hydrometeorology)

   Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.

    

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3. Multichannel Many-Class Real-Time Neural Spike Sorting With Convolutional Neural Networks

   https://doi.org/10.1109/OJCAS.2022.3184302  

   Yi, Jinho ; Xu, Jiachen ; Chen, Ethan ; Chamanzar, Maysamreza ; Chen, Vanessa ( January 2022 , IEEE Open Journal of Circuits and Systems)
4. On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and Beyond

   https://doi.org/10.1109/ICCD56317.2022.00050  

   Hu, Zhirui ; Li, Jinyang ; Pan, Zhenyu ; Zhou, Shanglin ; Yang, Lei ; Ding, Caiwen ; Khan, Omer ; Geng, Tong ; Jiang, Weiwen ( October 2022 , 2022 IEEE 40th International Conference on Computer Design (ICCD))
5. Detecting Quantum Critical Points of Correlated Systems by Quantum Convolutional Neural Network Using Data from Variational Quantum Eigensolver

   https://doi.org/10.3390/quantum4040042  

   Wrobel, Nathaniel ; Baul, Anshumitra ; Tam, Ka-Ming ; Moreno, Juana ( December 2022 , Quantum Reports)

   Machine learning has been applied to a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems, for classifying phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions corresponding to the paramagnetic and ferromagnetic phases of the TFIM with reasonable accuracy. The QCNN can be trained to predict the corresponding ‘phase’ of wavefunctions around the putative quantum critical point even though it is trained by wavefunctions far away. The paper provides a basis for exploiting the QCNN to identify the quantum critical point. 

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