Multi-stream CNN based Video Semantic Segmentation for Automated Driving

Majority of semantic segmentation algorithms operate on a single frame even in the case of videos. In this work, the goal is to exploit temporal information within the algorithm model for leveraging motion cues and temporal consistency. We propose two simple high-level architectures based on Recurrent FCN (RFCN) and Multi-Stream FCN (MSFCN) networks. In case of RFCN, a recurrent network namely LSTM is inserted between the encoder and decoder. MSFCN combines the encoders of different frames into a fused encoder via 1x1 channel-wise convolution. We use a ResNet50 network as the baseline encoder and construct three networks namely MSFCN of order 2 & 3 and RFCN of order 2. MSFCN-3 produces the best results with an accuracy improvement of 9% and 15% for Highway and New York-like city scenarios in the SYNTHIA-CVPR'16 dataset using mean IoU metric. MSFCN-3 also produced 11% and 6% for SegTrack V2 and DAVIS datasets over the baseline FCN network. We also designed an efficient version of MSFCN-2 and RFCN-2 using weight sharing among the two encoders. The efficient MSFCN-2 provided an improvement of 11% and 5% for KITTI and SYNTHIA with negligible increase in computational complexity compared to the baseline version.Comment: Accepted for Oral Presentation at VISAPP 201

Micro BCA Analysis of Proteins

The main objective of this project was to develop and validate methods for detection and analysis of proteins using a newly introduced microdrop plate reader equipped with a 16 well microdrop reader (2 uL sample volume). We used commercially available BSA, egg albumin, trypsinogen, Pepsin, β-lactoblobulin and lysozyme as out test moieties. Protein drop size, time, BCA reagent quantity, was assessed at various levels using a microdrop micro-BCA protocol (Pierce Chemical Company) with the appropriate controls (PBS blank) the method validated using the appropriate statistical and method parameters (i.e %RSD, and regression analysis of the standard curves). The micro method was used to determine the concentration of 5 known proteins and the method compared to the standard 96 well BCA plate method. The overarching strategy in our group is to develop, simple robust assays that use minimal sample amounts and reagents given the high cost and limited availability of many proteins and reagents (not to mention limiting waste disposal). Moreover, the technique will aid researches in quickly identifying and analyzing proteins during expression, analysis, and isolation

The state of commercial augmentative biological control: plenty of natural enemies, but a frustrating lack of uptake

Augmentative biological control concerns the periodical release of natural enemies. In com- mercial augmentative biological control, natural enemies are mass-reared in biofactories for release in large numbers to obtain an immediate control of pests. The history of commercial mass production of natural enemies spans a period of roughly 120 years. It has been a successful, environmentally and eco- nomically sound alternative for chemical pest control in crops like fruit orchards, maize, cotton, sugar cane, soybean, vineyards and greenhouses. Currently, aug- mentative biological control is in a critical phase, even though during the past decades it has moved from a cottage industry to professional production. Many efficient species of natural enemies have been discovered and 230 are commercially available today. The industry developed quality control guidelines, mass production, shipment and release methods as well as adequate guidance for farmers. However, augmentative biological control is applied on a frustratingly small acreage. Trends in research and application are reviewed, causes explaining the limited uptake are discussed and ways to increase application of augmentative biological control are explored

Intermixing of InGaAs/GaAs quantum wells and quantum dots using sputter-deposited silicon oxynitride capping layers

Various approaches can be used to selectively control the amount of intermixing in III-Vquantum well and quantum dotstructures. Impurity-free vacancy disordering is one technique that is favored for its simplicity, however this mechanism is sensitive to many experimental parameters. In this study, a series of silicon oxynitride capping layers have been used in the intermixing of InGaAs/GaAs quantum well and quantum dotstructures. These thin films were deposited by sputter deposition in order to minimize the incorporation of hydrogen, which has been reported to influence impurity-free vacancy disordering. The degree of intermixing was probed by photoluminescence spectroscopy and this is discussed with respect to the properties of the SiOxNyfilms. This work was also designed to monitor any additional intermixing that might be attributed to the sputtering process. In addition, the high-temperature stress is known to affect the group-III vacancy concentration, which is central to the intermixing process. This stress was directly measured and the experimental values are compared with an elastic-deformation model.This work has been made possible with access to the ACT Node of the Australian National Fabrication Facility and through the financial support of the Australian Research Council

Hierarchical Decomposition of Large Deep Networks

Teaching computers how to recognize people and objects from visual cues in images and videos is an interesting challenge. The computer vision and pattern recognition communities have already demonstrated the ability of intelligent algorithms to detect and classify objects in difficult conditions such as pose, occlusions and image fidelity. Recent deep learning approaches in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) are built using very large and deep convolution neural network architectures. In 2015, such architectures outperformed human performance (94.9% human vs 95.06% machine) for top-5 validation accuracies on the ImageNet dataset, and earlier this year deep learning approaches demonstrated a remarkable 96.43% accuracy. These successes have been made possible by deep architectures such as VGG, GoogLeNet, and most recently by deep residual models with as many as 152 weight layers. Training of these deep models is a difficult task due to compute intensive learning of millions of parameters. Due to the inevitability of these parameters, very small filters of size 3x3 are used in convolutional layers to reduce the parameters in very deep networks. On the other hand, deep networks generalize well on other datasets and outperform complex datasets with less features or Images. This thesis proposes a robust approach for large scale visual recognition by introducing a framework that automatically analyses the similarity between different classes among the dataset and configures a family of smaller networks that replace a single larger network. Classes that are similar are grouped together and are learnt by a smaller network. This allows one to divide and conquer the large classification problem by identifying the class category from its coarse label to its fine label, deploying two or more stages of networks. In this way the proposed framework learns the natural hierarchy and effectively uses it for the classification problem. A comprehensive analysis of the proposed methods show that hierarchical models outperform traditional models in terms of accuracy, reduced computations and attribute to expanding the ability to learn large scale visual information effectively

Lasers and photodetectors for mid-infrared 2–3 μm applications

This paper presents an overview of the recent developments in III–V semiconductor lasers and detectors operating in the 2–3 μm wavelength range, which are highly desirable for various important applications, such as military, communications, molecular spectroscopy, biomedical surgery, and environmental protection. The lasers and detectors with different structure designs are discussed and compared. Advantages and disadvantages of each design are also discussed. Promising materials and structures to obtain high performance lasers and detectors operating in the 2–3 μm region are also suggested.Thanks are due to Australian Research Council for the financial support

Feeling Exhausted? Tuning Irf4 Energizes Dysfunctional T Cells.

The regulatory mechanisms governing T cell exhaustion remain incompletely understood. Man et al. (2017) and Wu et al. (2017) report that the T cell receptor responsive transcription factor Irf4 promotes T cell exhaustion in chronic viral infection but dampens exhaustion in response to tissue allografts