Modelling strategies for the healing of burn wounds

Epidermal wound healing requires the coordinated involvement of complex cellular and biochemical processes. In the case of epidermal wounds associated with burns, the healing process may be less than optimal and may take a significant amount of time, possibly resulting in infection and scarring. An innovative method to assist in the repair of the epidermis (the outer layer of skin) is to use an aerosolised apparatus. This method involves taking skin cells from an area of the patient's undamaged skin, culturing the cells in a laboratory, encouraging them to rapidly proliferate, then harvesting and separating the cells from each other. The cells are then sprayed onto the wound surface. We investigate this novel treatment strategy for the healing of epidermal wounds, such as burns. In particular, we model the application of viable cell colonies to the exposed surface of the wound with the intent of identifying key factors that govern the healing process. Details of the evolution of the colony structure are explored in this two-dimensional model of the wound site, including the effect of varying the initial population cluster size and the initial distribution of cell types with different proliferative capacities. During injury, holoclones (which are thought to be stem cells) have a large proliferative capacity while paraclones (which are thought to be transient amplifying cells) have a more limited proliferative capacity. The model predicts the coverage over time for cells that are initially sprayed onto a wound. A detailed analysis of the underlying mathematical models yields novel mathematical results as well as insight into phenomena of healing processes under investigation. Two one-dimensional systems that are simplifications of the full model are investigated. These models are significant extensions of Fisher's equation and incorporate the mixed clonal population of quiescent and active cells. In the first model, an active cell type migrates and proliferates into the wound and undergoes a transition to a quiescent cell type that neither migrates nor proliferates. The analysis yields the identification of the key parameter constraints on the speed of the healing front of the cells on this model and hence the rate of healing of epidermal wounds. Approximations for the maximum cell densities are also obtained, including conditions for a less than optimal final state. The second model involves two active cell types with different proliferative capacity and a quiescent cell type. This model exhibits two distinct behaviours: either both cell types coexist or one of them dies out as the wound healing progresses leaving the other cell type to fill the wound space. Conditions for coexistence are explored

How Does An Online Version of A Class Compares To An In-Class Version?

Here, comparative data from the same course offered using two different methods: In-class (or traditional classroom offering) and online. This is for a sophomore-level class in engineering entitled "Energy, Environment and Society". The paper presents details how the two versions deviated or were similar in the different parts/aspects of this course. Some interesting observations can also be pulled from the online class and are noted here.Cockrell School of Engineerin

Research on the control of large space structures

The research effort on the control of large space structures at the University of Houston has concentrated on the mathematical theory of finite-element models; identification of the mass, damping, and stiffness matrix; assignment of damping to structures; and decoupling of structure dynamics. The objective of the work has been and will continue to be the development of efficient numerical algorithms for analysis, control, and identification of large space structures. The major consideration in the development of the algorithms has been the large number of equations that must be handled by the algorithm as well as sensitivity of the algorithms to numerical errors

Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition

In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning approaches in computer vision, we focus our attention towards learning salient spatial features via a convolutional neural network (CNN) and then map their temporal relationship with the aid of Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models. We also extensively evaluate their strengths and weaknesses. We find that by combining both the sets of features, the fully connected features effectively act as an attention mechanism to direct the LSTM to interesting parts of the convolutional feature sequence. The significance of our fusion method is its simplicity and effectiveness compared to other state-of-the-art methods. The evaluation results demonstrate that this hierarchical multi stream fusion method has higher performance compared to single stream mapping methods allowing it to achieve high accuracy outperforming current state-of-the-art methods in three widely used databases: UCF11, UCFSports, jHMDB.Comment: Published as a conference paper at WACV 201

Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments. In this paper, we propose a deep decision tree architecture for discriminative dictionary learning from adversarial multi-agent trajectories. We first build up a hierarchy for the tree structure by adding each layer and performing feature weight based clustering in the forward pass. We then fine tune the player role weights using back propagation. The hierarchical architecture ensures the interpretability and the integrity of the group representation. The resulting architecture is a decision tree, with leaf-nodes capturing a dictionary of multi-agent group interactions. Due to the ample volume of data available, we focus on soccer tracking data, although our approach can be used in any adversarial multi-agent domain. We present applications of proposed method for simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports (CVsports) at CVPR 201

A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection

State-of-the-art person re-identification systems that employ a triplet based deep network suffer from a poor generalization capability. In this paper, we propose a four stream Siamese deep convolutional neural network for person redetection that jointly optimises verification and identification losses over a four image input group. Specifically, the proposed method overcomes the weakness of the typical triplet formulation by using groups of four images featuring two matched (i.e. the same identity) and two mismatched images. This allows us to jointly increase the interclass variations and reduce the intra-class variations in the learned feature space. The proposed approach also optimises over both the identification and verification losses, further minimising intra-class variation and maximising inter-class variation, improving overall performance. Extensive experiments on four challenging datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed approach achieves state-of-the-art performance.Comment: Published in WACV 201

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer Vision (WACV), 201