pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-Temporal Forecasting With Gridded Remote Sensing Data Using Feed-Backward Decoding
Authors: Mário Cardoso, Jacinto  Estima, Bruno  Martins
Date: 2020-11-01 00:00:00
Tasks: Spatio-Temporal Forecasting

We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform the original STConvS2S and other baseline models on tasks related to predicting future time-steps. In tests related to predicting a missing time-step, some of the proposed extensions also lead to improvements over the original STConvS2S architecture, although simpler models seem to be beneficial in this scenario.
ImageNet Pretrained CNNs for JPEG Steganalysis
Authors: Yassine Yousfi, Jan Butora, Eugene Khvedchenya, Jessica Fridrich
Date: 2020-11-24 00:00:00
Tasks: Image Steganography, Steganalysis

In this paper, we investigate pre-trained computervision deep architectures, such as the EfficientNet, MixNet, and
ResNet for steganalysis. These models pre-trained on ImageNet
can be rather quickly refined for JPEG steganalysis while offering
significantly better performance than CNNs designed purposely
for steganalysis, such as the SRNet, trained from scratch. We
show how different architectures compare on the ALASKA
II dataset. We demonstrate that avoiding pooling/stride in the
first layers enables better performance, as noticed by other
top competitors, which aligns with the design choices of many
CNNs designed for steganalysis. We also show how pre-trained
computer-vision deep architectures perform on the ALASKA I
dataset
Multi-channel optical neuromorphic processor for frequency-multiplexed signals
Authors: Mariia Sorokina
Date: 2020-12-15 00:00:00

Here we develop the first optical neuromorphic processor for frequency-multiplexed and multi-channel signals and demonstrate its application for orthogonal frequency-division multiplexing and wavelength-division multiplexing. The presented architecture supports multichannel operation through incorporation of the optical Fourier transform and dispersion-managed fiber echo state network analogue enabling processing of dual-quadrature high bandwidth signals. We demonstrate applicability of the design for prediction and equalization tasks. The proposed technology paves the way to multi-channel neuromorphic signal processing.
MR-based camera-less eye tracking using deep neural networks
Authors: Markus Frey, Matthias Nau, Christian F. Doeller
Date: 2020-12-01 00:00:00
Tasks: Position

Viewing behavior provides a window into many central aspects of human cognition and health,
and it is an important variable of interest or confound in many fMRI studies. To make eye tracking
freely and widely available for MRI research, we developed DeepMReye: a convolutional neural
network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less
eye tracking at sub-imaging temporal resolution in held-out participants with little training data
and across a broad range of scanning protocols. Critically, it works even in existing datasets and
when the eyes are closed. Decoded eye movements explain network-wide brain activity also in
regions not associated with oculomotor function. This work emphasizes the importance of eye
tracking for the interpretation of fMRI results and provides an open-source software solution that
is widely applicable in research and clinical settings.
Separating and denoising seismic signals with dual-path recurrent neural network architecture
Authors: Artemii Novoselov, Peter Balazs, Götz Bokelmann
Date: 2020-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Denoising, Transfer Learning

Separation of overlapping signals is an important task in signal processing, with application in music, speech, and seismic signal processing. We show that separation is possible also for seismic recordings, using techniques from machine learning (and even those recorded with a single sensor).<br />This may have an impact on seismic applications such as <br />ambient noise tomography, induced seismicity, earthquake analysis, aftershock analysis, nuclear verification, and seismoacoustics/infrasound.<br />The machine learning technique that we use for seismic signal separation is based on a dual-path recurrent neural network which is applied directly to the time domain data. <br />We train the network on seismic data produced by trains, and recorded with a Raspberry Shake sensor at the University of Vienna. We demonstrate that the network predicts the signals from a synthetic mixture very well.<br />We then use a transfer learning approach to fine-tune this pre-trained network for earthquake signals and denoise them. We also perform a task outside of its initial training domain - a P- and S- wave arrival picking, demonstrating the wide potential for applications of such a network. Furthermore, we argue that a network built this way can serve as a Bidirectional Encoder Representation (BERT) pre-training step in waveform Machine Learning applications, thus reducing necessary training time for potential applications. This work proves the concept and steers the direction for further research of earthquake-induced source separation. We have therefore aimed to describe the technicalities in detail. We provide a reproducible research repository with the algorithms and datasets.
尖椒部落-电报频道（女性，女权，女工，女童，性少数LGBT） @jianjiaobuluo
Authors: 尖椒部落
Date: 2019-01-02 00:00:00

尖椒部落，放大妳的声音！中国女工专属资讯平台，欢迎您的关注！@jianjiaobuluo
关键词：女性权益，性别平等，性教育，反防性骚扰性侵家庭暴力，女性主义，女权，女生，女工，女童，同性恋，性少数LGBT，赋权，平等，自由，人权，联合国公约。

尖椒部落，放大妳的声音！中国女工专属资讯平台，欢迎您的关注！@jianjiaobuluo
关键词：女性权益，性别平等，性教育，反防性骚扰性侵家庭暴力，女性主义，女权，女生，女工，女童，同性恋，性少数LGBT，赋权，平等，自由，人权，联合国公约。
ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies
Authors: Philipp Sodmann, Marcus Vollmer
Date: 2020-04-23 00:00:00
Tasks: Medical Diagnosis, Medical Image Segmentation

Electrocardiography allows fast and noninvasive diagnosis and screening of a wide range of cardiac diseases.
The interpretation of ECGs is difficult, and depends on the
levels of training of the physician.
In consequence, pathologies can remain undiagnosed or
norm-variations are interpreted as pathological.
The PhysioNet/Computing in Cardiology Challenge
2020 aims to classify various cardiac pathologies in 12-
lead ECGs [1, 2], data was collected across a variety of
different clinics and countries to pave the way for a common evaluation of ECGs.
Our Team Heartly-AI proposes a two step algorithm using a UNet and XGBoost for the 2020 PhysioNet Computing in Cardiology Challenge ”Classification of 12 lead
ECGs”. We scored 0.159 on the official testset and ranked
about 199th out of the 200 teams that participated in this
year’s Challenge.
3D-CNN for Facial Emotion Recognition in Videos
Authors: Jad Haddad, Olivier Lezoray, Philippe Hamel
Date: 2020-12-07 00:00:00
Tasks: 3D Facial Expression Recognition, Emotion Recognition, Facial Emotion Recognition

In this paper, we present a video-based emotion recognition neural network operating on three dimensions. We show that 3D convolutional neural networks (3D-CNN) can be very good for predicting facial emotions that are expressed over a sequence of frames. We optimize the 3D-CNN architecture through hyper-parameters search, and prove that this has a very strong influence on the results, even if architecture tuning of 3D CNNs has not been much addressed in the literature. Our proposed resulting architecture improves over the results of the state-of-the-art techniques when tested on the CK+ and Oulu-CASIA datasets. We compare the results with cross-validation methods. The designed 3D-CNN yields a 97.56% using Leave-One-Subject-Out cross-validation, and 100% using 10-fold cross-validation on the CK+ dataset, and 84.17% using 10-fold cross-validation on the Oulu-CASIA dataset.
Assessment of Subjective Creativity Skill Using EEG Induced Capsule Network
Authors: Sayantani Ghosh, Lidia Ghosh, Amit Konar, Atulya K. Nagar
Date: 2021-01-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Problem solving tests associated with convergent thinking are often used to analyze the creative ability of an individual. Such tests consist of problems whose solution can be reached by an analytical or a creative insight based approach. Detection of creative individuals amongst others undertaking such tests is thus quite challenging since a correct response to the presented problem can be reached using two different cognitive styles. The paper attempts to address a solution to this issue using EEG analysis system. The proposed technique intends to classify the two cognitive styles involved in solving convergent problems with the main motivation of selecting creative individuals amongst other examinees. Moreover, the proposed strategy also attempts to highlight the creative ability of every selected creative individual under three levels: low, medium or high. In other words, the present scheme aims to distinguish creative subjects along with their levels of creative ability from the analytical ones. The procurement of EEG signals from the subjects engaged in the problem solving test are fed to a novel attention induced capsule network to categorize the desired class labels after undergoing the pre-processing and feature extraction phases. Performance analysis undertaken confirms the superlative behavior of the proposed classifier over the state-of-the-art techniques. Thus, the proposed scheme can find its applications in the recruitment of creative individuals in various strata of organizations linked with creation and innovation such as advertising, business, engineering and the like.
State-of-the-Art Review of Machine Learning Applications in Constitutive Modeling of Soils
Authors: Pin Zhang, Zhen-Yu Yin, Yin-Fu Jin
Date: 2021-01-05 00:00:00
Tasks: BIG-bench Machine Learning, Sand

Machine learning (ML) may provide a new methodology to directly learn from raw data to develop constitutive models for soils by using pure mathematic skills. It has presented success and versatility in cases of simple stress paths due to its strong non-linear mapping capacity without limitations of constitutive formulations. However, current studies on the ML-based constitutive modeling of soils is still very limited. This study comprehensively reviews the application of ML algorithms in the development of constitutive models of soils and compares the performance of different ML algorithms. First, the basic principles of typical ML algorithms used in describing soil behaviors are briefly elaborated. The main characteristics and the limitations of such ML algorithms are summarized and compared. Then, the methodology of developing a ML-based soil model is reviewed from six aspects, such as adopted ML algorithms, data source, framework of the ML-based model, training strategy, generalization ability and application scope. Finally, five new ML-based models are developed using five typical ML algorithms (i.e. BPNN, RBF, LSTM, GRU and BiLSTM that can predict multi outputs together) based on same set of experimental results of sand, and compare each other in terms of the predictive accuracy and generalization ability. Results show the long short-term memory (LSTM) neural network and its variants are most suitable for developing constitutive models. Moreover, some useful suggestions for developing the ML-based soil model are also provided for the community.
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning
Authors: Marina Adriana Mercioni, Stefan Holban
Date: 2021-01-01 00:00:00
Tasks: Image Classification, Transfer Learning

In order to improve the performance of a deep neural network, the activation function is an important aspect that we must research continuously, that is why we have expanded the research in this direction. We introduced a novel P-Swish activation function (Parametric Swish), which is able to bring performance improvements on object classification tasks using datasets such as CIFAR-10, CIFAR-100, but we will see that we also used datasets for Natural Language Processing (NLP). To test it, we used several types of architectures, including LeNet-5, Network in Network (NiN), and ResNet34 compared to popular activation functions such as sigmoid, ReLU, Swish, and our proposals. In particular, the P-Swish function facilitates fast network training, which makes it suitable for the Transfer Learning technique.
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning
Authors: Satwik Ramchandre, Bhargav Patil, Shardul Pharande, Karan Javali, Himangi Pande
Date: 2021-01-01 00:00:00
Tasks: Diabetic Retinopathy Detection, Medical Diagnosis, Transfer Learning

Diabetic Retinopathy is a primary complication of diabetes which more often than not, affects both eyes and anyone with type-1 or type-2 diabetes can develop it. A Diabetic patient should undergo eye tests periodically as the pace of development of this condition is slow. A Dataset of Fundus Photographs of retina is considered. Thus, there is a notable value in automatically categorizing the Fundus Photographs. Therefore, to get a consolidated and objective medical diagnosis, this paper proposes a transfer learning based approach for Diabetic Retinopathy categorization. The resizing of the dataset is performed, which converts the varied images into 224x224 format. The images are augmented using AUGMIX and pooled using GeM. Then, we have used pretrained models, namely SEResNeXt32x4d and EfficientNetb3. The pretraining of the aforementioned neural networks has been done on the ImageNet dataset. Then, the Diabetic Retinopathy images are migrated to these models. Based on the dataset already available, the output is ultimately split up into 5 levels according to the seriousness of the degree of DR. The experimental results show that the training accuracy of this method can reach as high as 0.91. Hence, the retina images of the Diabetic and Healthy patients can be easily classified using our proposed methodology, consequently reducing the number of reviews by medical professionals.
Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases
Authors: ChauChung Song, ChihLun Wang, Yifeng Yang
Date: 2020-12-29 00:00:00
Tasks: Image Classification, object-detection, Object Detection

In recent years, the development of precision agriculture is a new technology. The main reason for the automation of agricultural processes is to save the time and energy required to perform repeated farming tasks and to increase production by treating each crop separately and applying smart agricultural concepts. In this paper, an automatic detection and image recognition of citrus diseases is presented that can help farmer find the disease and identify it from the captured images. This method use YOLO(You Only Look Once) algorithm which is an object detection model to detect and recognize the diseases from citrus leaf images. YOLO can realtime detect the disease and circle around it on the image and video. The dataset includes images of citrus leaf with two kinds of diseases: Citrus Canker, Citrus Greening.
SOC Estimation of Li-ion Batteries with Learning Rate-Optimized Deep Fully Convolutional Network
Authors: M. A. Hannan, D. N. T. How, M. S. Hossain Lipu, Pin Jern Ker, Z. Y. Dong, M. Mansur, Frede Blaabjerg
Date: 2020-12-02 00:00:00

In this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 °C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (–20–25 °C).
Class Attention Network for Semantic Segmentation of Remote Sensing Images
Authors: Zhibo Rao, Mingyi He, Yuchao Dai
Date: 2020-12-31 00:00:00
Tasks: Earth Observation, Scene Parsing, Segmentation, Semantic Segmentation

Semantic segmentation in remote sensing images is beneficial to detect objects and understand the scene in earth observation. However, classical networks always failed to obtain an accuracy segmentation map in remote sensing images due to the imbalanced labels. In this paper, we proposed a novel class attention module and decomposition-fusion strategy to cope with imbalanced labels. Based on this motivation, we investigate related architecture and strategy by follows. (1) we build a class attention module to generate multi-class attention maps, which forces the network to keep attention to small sample categories instead of being flooded by large sample data. (2) we introduce salient detection, which decomposes semantic segmentation into multi-class salient detection and then fuses them to produce a segmentation map. Extensive experiments on popular benchmarks (e.g., US3D dataset) show that our approach can serve as an efficient plug-and-play module or strategy in the previous scene parsing networks to help them cope with the problem of imbalance labels in remote sensing images.
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge
Authors: Zhibo Rao, Mingyi He, Bo Li, Renjie He
Date: 2020-11-01 00:00:00
Tasks: Stereo Matching

Our goal in this ECCV'20 Robust Vision Challenge (RVC) is to develop a novel stereo matching system that is robust over multiple datasets instead of one specific dataset. Our developed NLCA-Net v2 used in this RVC is an extension of our previous NLCA-Net [1]. NLCA-Net v2 applied NLCA-Net as backbone and used an improved cost volume construction and the refinement modules, inspired by GwcNet and CSPN. Moreover, we updated our model normalization and activate function to further improve the performance on the multiple cross datasets. To make the results close to individual training on each dataset, multiple data augmentations are used to train the model, which include random cropping, translation transformations, and datasets balance. The contest's results have shown: 1), our model can reach top performance in the robust vision challenge; 2), our training strategy can alleviate the detrimental effect of mixed training, leading to the state-of-the-art result. 1. Our Method Given this goal, we employ our previous work NLCA-Net [1] as the backbone, which has demonstrated very good performance on individual datasets. The network architecture used in this RVC, called as NLCA-Net v2, is consists of four parts: feature extraction, cost volume construction, feature matching, and refinement, as shown in Fig. 1. As NLCA-Net can be found from our recent publication [1], in this report, only the three modifications essential to RVC will be presented briefly, including the cost volume construction, the refinement module, and the model normalization. Fig. 1: Non-local context attention network v2 (NLCA-Net v2). (1) Cost Volume Construction: Inspired by GwcNet [2], we adopt this idea to improve our previous structure in the cost volume construction. However, we use our variance volume [1] to replace group-wise correlation volume [2]. Therefore, we apply concatenation volume (concat volume) and variance volume to construct combined volume in the cost volume construction. Then, this combined volume is feed to our non-local attention matching module. (2) Refinement Module: Inspired by the convolutional spatial propagation network (CSPN) [3], we design a new refinement process with a simple structure (2D U-Net) to generate an affinity matrix as guidance and choose the probability parameter τ lager than 0.65 to construct a confident map as a sparse disparity sample. Finally, a geometry refinement module in NLCA-Net is used at the end of the network. (3) Model Normalization and Activation Function: As normalization and activation functions serve critical components and performance evaluation of the neural networks [4-6], normalization strategies and activation functions have been investigated and compared experimentally. The group normalization (GN) [7] and mish activation function (Mish) [8] are finally selected for relieving the small-batch issue and improving performance in our stereo matching on ECCV'20 RVC.
A State Representation Dueling Network for Deep Reinforcement Learning
Authors: Haomin Qiu, Feng Liu
Date: 2020-12-24 00:00:00
Tasks: Deep Reinforcement Learning, General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years there have been many successes in boosting the performance of Deep Q-Networks (DQN). Dueling DQN uses simple dueling architecture but significantly improves the performance of DQN [1]. However, Dueling DQN is only concerned about dueling in estimating Q-values. In this paper, we introduce a state representation dueling network, which provides an auxiliary task designed to be combined with other reinforcement learning algorithms to improve the performance of Deep RL. The state representation dueling network is designed to be beneficial for solving reinforcement learning tasks with high dimensional observation, such as camera input. The experiment shows that adding the state representation dueling network to Dueling DQN improves both the training speed and performance of Dueling DQN in CartPole environment.
Recent Advances of Generic Object Detection with Deep Learning: A Review
Authors: Xin Li, YingYing Li, Shushu Li
Date: 2020-12-19 00:00:00
Tasks: Action Recognition, Data Augmentation, Deep Learning, Image Segmentation, Object, object-detection, Object Detection, Object Tracking, Semantic Segmentation

Object detection is an important and challenging problem in computer vision. It has been widely applied in many vision tasks, such as object tracking, image segmentation, action recognition, etc. With the rapid development of deep learning, more state-of-the-art object detection methods based on deep learning with some modifications have effectively improved the detection performance. This paper comprehensively reviews object detection methods in the recent five years based on deep learning from object detection framework, including significant advances of the backbone network, multi-scale learning, data augmentation. Finally, we investigate the performance of typical object detection algorithms on popular datasets MS-COCO, PASCAL-VOC, and point out the existing problem for further research.
A review of flywheel energy storage systems: state of the art and opportunities
Authors: Xiaojun Li, Alan Palazzolo
Date: 2021-03-09 00:00:00

Thanks to the unique advantages such as long life cycles, high power density, minimal environmental impact, and high power quality such as fast response and voltage stability, the flywheel/kinetic energy storage system (FESS) is gaining attention recently. There is noticeable progress made in FESS, especially in utility, large-scale deployment for the electrical grid, and renewable energy applications. This paper gives a review of the recent developments in FESS technologies. Due to the highly interdisciplinary nature of FESSs, we survey different design approaches, choices of subsystems, and the effects on performance, cost, and applications. This review focuses on the state of the art of FESS technologies, especially for those who have been commissioned or prototyped. We also highlighted the opportunities and potential directions for the future development of FESS technologies.