Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

A survey on deep learning in medical image analysis

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individuallyq We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

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Robust principal component analysis

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

In an embodiment, a flexible bag is provided and includes opposing flexible films composed of a polymeric material. The flexible films define a common peripheral edge. The flexible bag includes a microcapillary strip located between the opposing flexible films and extending along a portion of the common peripheral edge. A peripheral seal extends along at least a portion of the common peripheral edge. The peripheral seal seals the microcapillary strip between the opposing flexible films. The peripheral seal forms a closed compartment. The flexible bag includes an amount of a flowable solid particulate material (FSPM) in the storage compartment.

Flexible bag with microcapillary strip

This paper presents a new medical image segmentation method by using wavelet frame energy distribution, which is the sum of squares of the wavelet frame coefficients at each pixel. This work shows that the wavelet frame energy distribution contains the fine texture information extracted from images with low intensity contrast and complex structures using wavelet frame transform. Thus it is employed to enhance the segmentation quality under some challenge conditions such as low intensity contrast, weak/ambiguous boundaries, intensity inhomogeneity and heavy noise. Furthermore, this paper adopts convex relaxation approach to solve the corresponding optimization problem instead of classical level-set method, so the leading numerical computation is efficient and robust to initialization values. Experimental results also illustrate the efficiency of the proposed segmentation method for biomedical images under these extreme imaging conditions.

A Wavelet Frame Energy-based Segmentation Method for Biomedical Images

The pumped storage power plants (PSPPs) are a special power source with flexible operation modes and various functions, which more and more have been put into operation in recent years. Compared with the traditional plants, the main wiring structure is also different in the PSPPs. Several high-voltage cables of almost one kilometer in length are usually used to connect the ground switching station and the underground main transformers. There are some abnormal or damaged accidents of transformers and high voltage cables. In this paper, we found that the long high-voltage cable and the no-load transformer cause the oscillation in the process of de-energizing. A simulation model is built by ATP-EMTP software. The field measurement of the de-energizing waveform was used to verify the correctness of the ATP-EMTP model. The results show that the number of oscillations increased to more than 15 times with a 2000 m high-voltage cable.

Study of Oscillation Phenomenon Caused by High-voltage Cable and No-load Transformer in the Process of De-energizing

In recent years, cyber attacks against the power industry have occurred frequently, causing huge losses to national infrastructure construction. As an important carrier of network attacks, malware has a great threat to the power system. This paper proposes an E-Gemini model based on the analysis of binary executable files and traditional malware detection techniques. This model was developed by Gemini [1] and has a stronger analysis ability for malware in power systems. This paper also alleviates the problem of the small size of the current power system malware dataset by pretraining and retraining. Experiments have verified the effectiveness of the E-Gemini model. The results show that the proposed model can achieve a maximum accuracy of 89%, which is significantly improved compared to the baseline method.

Weimin Huang

Papers

Flexible bag with microcapillary strip

A Wavelet Frame Energy-based Segmentation Method for Biomedical Images

Study of Oscillation Phenomenon Caused by High-voltage Cable and No-load Transformer in the Process of De-energizing

An Approach of Malware Detection in Power System