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.

/pdf/robust-principal-component-analysis-59kf1xh72d.pdf

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

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool [1]. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.

Combining EPI and motion correction for fMRI human brain images with big motion

The Heavy Ion Research Facility on Lanzhou (HIRFL) and the High-Intensity Heavy-ion Accelerator Facility (HIAF) are the leading heavy-ion physics centers. The increased scale of physics experiments at HIRFL and HIAF has put forward urgent requirements on high-speed serial data transmission links. Also, the intense radiation environment is a significant challenge to commercial application specific integrated circuit (ASIC). This article presents the HiGBt, a general-purpose 5 Gb/s Serializer/Deserializer (SerDes) ASIC designed in a <inline-formula> <tex-math notation="LaTeX">$0.13 \mu \text{m}$ </tex-math></inline-formula> CMOS process for data transmission at HIRFL and HIAF. The HiGBt mainly consists of a Serializer, a Deserializer, and a high-speed clock system. The Serializer includes three stages to realize high-speed serialization. The Deserializer adopts a high linearity phase-interpolator-based structure to avoid coupling multi-voltage-controlled oscillators (VCOs). Finally, the high-speed clock system has a single event upsets (SEUs)-protected divider. The simulation results indicate that the power consumption of the SerDes is 146.3 mW, while the layout area is <inline-formula> <tex-math notation="LaTeX">$310\times 310\,\,\mu \text{m}$ </tex-math></inline-formula>.

Design and Simulation of HiGBt, a 5 Gb/s SerDes for Heavy-Ion Physics Experiments

Characterisation of IC packaging interfaces and loading effects

The efficacy and safety of laparoscopic common bile duct exploration and cholecystectomy for the treatment of difficult common bile duct stones combined with gallstones: a multicenter retrospective study

During alveolar repair, alveolar type 2 (AT2) epithelial cell progenitors rapidly proliferate and differentiate into flat type 1 alveolar epithelial cells. Failure of normal alveolar repair mechanisms can lead to loss of alveolar structure (emphysema) or development of fibrosis, depending on the type and severity of injury. To test if β1-containing integrins are required during repair following acute injury, we administered E. coli lipopolysaccharide (LPS) by intratracheal injection to mice with a post-developmental deletion of β1 integrin in AT2 cells. While control mice recovered from LPS injury without structural abnormalities, β1-deficient mice had more severe inflammation and developed emphysema. In addition, recovering alveoli were repopulated with an abundance of rounded epithelial cells co-expressing type 2, type 1, and mixed intermediate cell state markers, with few mature type 1 cells. β1-deficient AT2 cells showed persistently increased proliferation after injury, which was blocked by inhibiting NF-κB activation in these cells. Lineage tracing experiments revealed that β1-deficient AT2 cells failed to differentiate into mature type 1 alveolar epithelial cells. Together, these findings demonstrate that functional alveolar repair after injury with terminal alveolar epithelial differentiation requires β1-containing integrins.

Weimin Huang

Papers

Combining EPI and motion correction for fMRI human brain images with big motion

Design and Simulation of HiGBt, a 5 Gb/s SerDes for Heavy-Ion Physics Experiments

Characterisation of IC packaging interfaces and loading effects

The efficacy and safety of laparoscopic common bile duct exploration and cholecystectomy for the treatment of difficult common bile duct stones combined with gallstones: a multicenter retrospective study

Alveolar Repair Following Lipopolysaccharide-induced Injury Requires Cell-Extracellular Matrix Interactions