pwc-archive/papers-with-abstracts huggingface dataset:
Connecting Optical Morphology, Environment, and H I Mass Fraction for Low-Redshift Galaxies Using Deep Learning
Authors: John Wu
Date: 2019-12-31 00:00:00

A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to learn a galaxy's optical morphological information in order to estimate its neutral atomic hydrogen (HI) content directly from SDSS gri image cutouts. We are able to accurately predict a galaxy's logarithmic HI mass fraction, ≡log(MHI/M⋆), by training a CNN on galaxies in the ALFALFA 40% sample. Using pattern recognition (PR), we remove galaxies with unreliable  estimates. We test CNN predictions on the ALFALFA 100%, xGASS, and NIBLES catalogs, and find that the CNN consistently outperforms previous estimators. The HI-morphology connection learned by the CNN appears to be constant in low- to intermediate-density galaxy environments, but it breaks down in the highest-density environments. We also use a visualization algorithm, Gradient-weighted Class Activation Maps (Grad-CAM), to determine which morphological features are associated with low or high gas content. These results demonstrate that CNNs are powerful tools for understanding the connections between optical morphology and other properties, as well as for probing other variables, in a quantitative and interpretable manner.
Library Learning for Neurally-Guided Bayesian Program Induction
Authors: Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Joshua B. Tenenbaum
Date: 2018-12-01 00:00:00
Tasks: Program induction, regression, Symbolic Regression

Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm called EC2that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.
RMNv2: Reduced Mobilenet V2 for CIFAR10
Authors: Maneesh Ayi, Mohamed El-Sharkawy
Date: 2020-01-08 00:00:00
Tasks: Autonomous Vehicles, Data Augmentation, Object Recognition

In this paper, we developed a new architecture called Reduced Mobilenet V2 (RMNv2) for CIFAR10 dataset. The baseline architecture of our network is Mobilenet V2. RMNv2 is architecturally modified version of Mobilenet V2. The proposed model has a total number of parameters of 1.06M which is 52.2% lesser than the baseline model. The overall accuracy of RMNv2 for CIFAR10 dataset is 92.4% which is 1.9% lesser than the baseline model. The architectural modifications involve heterogeneous kernel-based convolutions, mish activation, etc. Also, we include a data augmentation technique called AutoAugment that contributes to increasing accuracy of our model. This architectural modification makes the model suitable for resource-constrained devices like embedded devices, mobile devices deployment for real-time applications like autonomous vehicles, object recognition, etc.
MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis
Authors: Ming Li, Chengjia Wang, Heye Zhang, Guang Yang
Date: 2020-05-01 00:00:00
Tasks: Anatomy, Decision Making, Prognosis, Segmentation

Multiview based learning has generally returned dividends in performance because additional information can be extracted for the representation of the diversity of different views. The advantage of multiview based learning fits the purpose of segmenting cardiac anatomy from multiview echocardiography, which is a non-invasive, low-cost and low-risk imaging modality. Nevertheless, it is still challenging because of limited training data, a poor signal-to-noise ratio of the echocardiographic data, and large variances across views for a joint learning. In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle. To tackle these challenges, a multiview recurrent aggregation network (MV-RAN) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis. Experiments have been carried out on multicentre and multi-scanner clinical studies consisting of spatio-temporal (2D + t) datasets. Compared to other state-of-the-art deep learning based methods, the MV-RAN method has achieved significantly superior results (0.92 ± 0.04 Dice scores) for the segmentation of the left ventricle on the independent testing datasets. For the estimation of clinical indices, our MV-RAN method has also demonstrated great promise and will undoubtedly propel forward the understanding of pathophysiological processes, computer-aided diagnosis and personalised prognosis using echocardiography.
Optimized Light-Weight Convolutional Neural Networks for Histopathologic Cancer Detection
Authors: Yimeng Sun; Fairoza Amira Binti Hamzah; Badr Mochizuki
Date: 2020-03-12 00:00:00
Tasks: Medical Diagnosis

Cancer is one of the leading causes of death across the world, and early detection of cancer will greatly increase the chances of successful treatment. Histopathology diagnosis of cancer is crucial because most diagnoses of malignancy are made by pathological confirmation. In Japan, lack of pathologist has become a serious problem. The lack of pathologists to diagnose the huge amounts of pathological images has resulted the late of medical diagnosis thus delaying the necessary treatment for the patients. Recently, there has been extensive research work on how to incorporate artificial intelligence in the diagnosis process. For example, using convolutional neural networks (CNN) for histopathologic cancer detection can not only speed up diagnosis but also can increase the diagnosis accuracy. In this study, we consider EfficientNet-B6 CNN model for histopathologic cancer detection and apply different activation functions as well as gradient descent optimization algorithms, to analyze its effects on diagnosis accuracy. EfficientNet-B6 is a light-weighted model proposed by Google, an easy-to-deploy-model and works efficiently in classification and detection tasks. Patch-Camelyon benchmark dataset that consists of non-cancerous and cancerous histopathologic images is used in this study. It was found that the top accuracy of this method is 97.94% when it is trained by EfficientNetB6 with Rectified Adam as the optimizer and Mish as the activation function.
Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis
Authors: N. Russkikh, D. Antonets, D. Shtokalo, A. Makarov, Y. Vyatkin, A. Zakharov, E. Terentyev
Date: 2020-04-28 00:00:00
Tasks: Prediction, Style Transfer

The transcriptomic data is being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment etc.) can be used as style components.
The proposed style transfer solution is based on Conditional Variational Autoencoders, Y-Autoencoders and adversarial feature decomposition. In order to quantitatively measure the quality of the style transfer, neural network classifiers which predict the style and semantics after training on real expression were used. Comparison with several existing style-transfer based approaches shows that proposed model has the highest style prediction accuracy on all considered datasets while having comparable or the best semantics prediction accuracy.
A Review of Activation Function for Artificial Neural Network
Authors: Andrinandrasana David Rasamoelina, Fouzia Adjailia, Peter Sinčák
Date: 2020-06-05 00:00:00

The activation function plays an important role in the training and the performance of an Artificial Neural Network. They provide the necessary non-linear properties to any Artificial Neural Network. In this work, we provide a review of the most common and recent activation functions used in the hidden layer of an artificial Neural Network.
An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries
Authors: Weiju Chen, WanChen Wu, Hao-Wei Chang, Wei-Liang Lin, Changhua Yang, Yang-wei Liu
Date: 2020-06-09 00:00:00
Tasks: Deep Learning, Model Compression, Object, object-detection, Object Detection

Deep learning-based object detection algorithms have been developed through extensive research and achieved impressive performance. However, due to the heavy computation of deep convolutional neural networks, it is not suitable to deploy all object detection algorithms on resource-constrained embedded systems, such as NVIDIA Jetson TX2. In this paper, a size-efficient YOLO v3-based model is adopted with two key points, a better backbone convolutional neural network and a fusing structure for the multiscale feature map. We have deployed the model on NVIDIA Jetson TX2 to test the performance of accuracy, model size, computational complexity and detecting time. Furthermore, this methodology has been awarded as the best bicycle detector in the 2020 embedded deep learning object detection model compression competition for traffic in Asian countries.
PATS: A New Neural Network Activation Function with Parameter
Authors: Baoyou Zheng, Zhiping Wang
Date: 2020-06-16 00:00:00

Activation function is crucial to the recent successes of deep neural networks. In this paper, we propose a new activation function with parameters, named PATS. Specifically, PATS is a non-monotonic function which combines arctangent function and sigmoid function. In the process of network model training, the parameter of PATS is a random number from the uniform distribution, which improves the flexibility of network model and reduces the risk of over fitting. In addition, PATS can be widely used in existing deep network models. We use several classic deep network models to test the performance of PATS. The experimental results on CIFAR-10 and CIFAR-100 datasets show that compared with other activation functions, PATS has better performance in improving the learning ability and robustness of deep network model.
Sound Event Detection in Domestic Environments using Dense Recurrent Neural Network
Authors: Tianchu Yao, Chuang Shi, Huiyong Li
Date: 2020-11-04 00:00:00
Tasks: Data Augmentation, Event Detection, Sound Event Detection

In this paper, we introduce our sound events detection system using a mean-teacher model with convolutional recurrent neural network (CRNN) for DCASE 2020 Task4, which include residual
convolutional block and dense recurrent neural network (DRNN)
block. To improve the performance of system, we propose to use
various methods such as multi-scale input layer, data augmentation, median window filters and model fusion. By combining those
method, our system achieves 15% improvement on macro-averaged F-score on the development set, as compared to the baseline.
Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations:
Authors: Dachuan Shi, Yunguang Ye, Marco Gillwald, Markus Hecht
Date: 2020-07-24 00:00:00
Tasks: Bayesian Optimization, Fault Diagnosis

Many freight waggons in Europe have been recently equipped with embedded systems (ESs) for vehicle tracking. This provides opportunities to implement the real-time fault diagnosis algorithm on ESs without additional investment. In this paper, we design a 1D lightweight Convolutional Neural Network (CNN) architecture, i.e. LightWFNet, guided by Bayesian Optimization for wheel flat (WF) detection. We tackle two main challenges. (1) Carbody acceleration has to be used for WF detection, where signal-to-noise ratio is much lower than at axle box level and thus the WF detection is much more difficult. (2) ESs have very limited computation power and energy supply. To verify the proposed LightWFNet, the field data measured on a tank waggon under operational condition are used. In comparison to the state-of-the-art lightweight CNNs, LightWFNet is validated for WF detection by using carbody accelerations with much lower computational costs.
Deep Embeddings of Contextual Assessment Data for Improving Performance Prediction
Authors: Benjamin Clavié, K. Gal
Date: 2020-07-13 00:00:00

We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students’ online behaviour and meta-data about students and educational content. The model uses these representations to predict student performance. We evaluate DPE on standard datasets from the literature, showing superior performance to the state-of-the-art systems in predicting whether or not students will answer a given question correctly. In particular, DPE is unaffected by the cold-start problem which arises when new students come to the system with little to no data available. We also show strong performance of the model when removing students’ histories altogether, relying in part on contextual information about the questions. This strong performance without any information about the learners’ histories demonstrates the high potential of using deep embedded representations of contextual information in educational data mining.
Gait Recognition using FMCW Radar and Temporal Convolutional Deep Neural Networks
Authors: Pia Addabbo, Mario Luca Bernardi, Filippo Biondi, Marta Cimitile, Carmine Clemente, Danilo Orlando
Date: 2020-08-06 00:00:00
Tasks: Anomaly Detection In Surveillance Videos, Deep Learning, Gait Recognition

The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical survaillance systems are based on videocameras, requiring high computational/storing resources, which are very sensitive to light and weather conditions. In this paper, an efficient classifier based on deep learning is used for the purpose of identifying individuals features by resorting to the micro-Doppler data extracted from low-power frequency-modulated continuous-wave radar measurements. Results obtained through the application of a deep temporal convolutional neural networks confirms the applicability of deep learning to the problem at hand. Best obtained identification accuracy is 0.949 with an F-measure of 0.88 using a temporal window of four seconds.
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping
Authors: Tanzeel Rehman, Dongdong Ma, Liangju Wang, Libo Zhang, Jian Jin
Date: 2020-10-01 00:00:00
Tasks: Hyperspectral Image Classification, Plant Phenotyping

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Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition
Authors: Pavan Samtani, Francisco Leiva, Javier Ruiz-del-Solar
Date: 2021-03-09 00:00:00
Tasks: Deep Reinforcement Learning

This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. We propose a basic multi-agent extension of TD3 for learning the policy of each player, in a decentralized manner. To ease learning, the task of 2v2 soccer is divided in three stages: 1v0, 1v1 and 2v2. The process of learning in multi-agent stages (1v1 and 2v2) uses agents trained on a previous stage as fixed opponents. In addition, we propose using experience sharing, a method that shares experience from a fixed opponent, trained in a previous stage, for training the agent currently learning, and a form of frame-skipping, to raise performance significantly. Our results show that high quality soccer play can be obtained with our approach in just under 40M interactions. A summarized video of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.
A deep learning-based approach for the automated surface inspection of copper clad laminate images
Authors: Xiaoqing Zheng, Jie Chen, Hongcheng Wang, Song Zheng, Yaguang Kong
Date: 2020-09-19 00:00:00
Tasks: Anomaly Detection, Defect Detection

Surface quality inspection and control are extremely important for electronic manufacturing. The use of machine vision technology to automatically detect the defects of products has become an indispensable means for better quality control. A machine vision-based surface quality inspection system is usually composed of two processes: image acquisition and automatic defect detection. In this paper, we propose a deep learning-based approach for the defect detection of Copper Clad Laminate (CCL) images acquired from an industrial CCL production line. In the proposed approach, a new convolutional neural network (CNN) that realizes fast defect detection while maintaining high accuracy is designed. Our proposed approach makes four contributions. First, we introduce the depthwise separable convolution to reduce the calculation time. Second, we improve the squeeze-and-excitation block to improve network performance. Third, we introduce the squeeze-and-expand mechanism to reduce the computation cost. Fourth, we employ a smoother activation function (Mish) to allow improved information flow. The proposed network is compared with the benchmark CNNs (including Inception, ResNet and MobileNet). The experimental results show that compared with the benchmark networks, our proposed network has achieved the best results regarding the accuracy and suboptimal results in terms of the speed compared with the benchmark networks. Therefore, our proposed method has been integrated into an industrial CCL production line as a guideline for online defective product rejection.
QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information
Authors: Masato Tamura, Hiroki Ohashi, Tomoaki Yoshinaga
Date: 2021-03-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Human-Object Interaction Concept Discovery, Human-Object Interaction Detection

We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based methods face the following three major drawbacks; they cannot leverage image-wide features due to CNN's locality, they rely on a manually defined location-of-interest for the feature aggregation, which sometimes does not cover contextually important regions, and they cannot help but mix up the features for multiple HOI instances if they are located closely. To overcome these drawbacks, we propose a transformer-based feature extractor, in which an attention mechanism and query-based detection play key roles. The attention mechanism is effective in aggregating contextually important information image-wide, while the queries, which we design in such a way that each query captures at most one human-object pair, can avoid mixing up the features from multiple instances. This transformer-based feature extractor produces so effective embeddings that the subsequent detection heads may be fairly simple and intuitive. The extensive analysis reveals that the proposed method successfully extracts contextually important features, and thus outperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.7 mAP on V-COCO). The source codes are available at $\href{https://github.com/hitachi-rd-cv/qpic}{\text{this https URL}}$.
MACD R-CNN: An Abnormal Cell Nucleus Detection Method
Authors: Baoyan Ma, Jian Zhang, Feng Cao, Yongjun He
Date: 2020-07-28 00:00:00
Tasks: Cell Detection, Classification, General Classification, Medical Diagnosis, Medical Object Detection, Semantic Segmentation

The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system. Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performance in abnormal cell detection is still unsatisfactory. To solve this problem, we design a new deep neural network for abnormal cell detection based on the Mask R-CNN, named mask abnormal cell detection R-CNN (MACD R-CNN). First, in the classification branch of Mask R-CNN, it generates the same size of feature maps from different size of RoIs as the input. The nuclei in this part of the feature maps will be deformed to varying degrees. We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification. Then we use the attention mechanism to merge the original RoI and Fixed RoI features. Finally, we increase the depth of the convolution layer to further improve the accuracy of cell classification. Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection.
Triple-GAN with Variable Fractional Order Gradient Descent Method and Mish Activation Function
Authors: Cancan Yang, Zan Yang, Shan Liao, Zheming Hong, Wei Nai
Date: 2020-09-23 00:00:00
Tasks: Image Generation

As is known to all, Generative Antagonism Network (GAN) plays an important role in the field of image generation and classification. However, there are some shortcomings in it, one of which is that the generator and discriminator cannot converge to the real data at the same time. Therefore, Triple-GAN is proposed. Compared with GAN, it is composed of three parts: classifier, generator and discriminator, which guarantees that the data distribution of generator and classifier can converge to the real distribution. Yet, there are still some traditional limitations in the field of activation function and gradient descent such as gradient disappearance and gradient explosion. Thus, this paper adopts the new activation function Mish, the gradient ascending method and the gradient descending method instead of the original activation function and the gradient ascending algorithm. When a certain sparsity prevents the gradient from exploding and disappearing, the smooth curve of negative input value near the origin makes up for the defect of inactivation of neurons flowing into large gradient,and thus achieving better activation efficiency and greatly improving the efficiency of deep learning.
Early Detection of Parkinson Disease using Deep Neural Networks on Gait Dynamics
Authors: Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Riccardo Pecori
Date: 2020-09-28 00:00:00
Tasks: Medical Diagnosis

Parkinson's disease is a degenerative movement disorder causing considerable disability. However, the early detection of this syndrome and of its progression rates may be decisive for the identification of appropriate therapies. For this reason, the adoption of Neural Networks to detect this disease on the base of walking information is gaining more and more interest. In this paper, we defined a Deep Neural Network based approach allowing one to exploit the information coming from various sensors located under the feet of a person. The proposed approach allows one to discriminate people affected by the Parkinson syndrome and detect the progression rates of the disease itself. To evaluate the proposed architecture we used a known dataset with the aim to compare its performance with other similar approaches. Moreover, we performed an in-depth hyper-parameter optimization to find out the best neural network configuration for the specific task. The comparison shows that the proposed classifier, trained with the best parameters, outperforms the results proviously obtained in other studies on the same dataset.