ANN-MIND : dropout for neural network training with missing data

M.Sc. (Computer Science)Abstract: It is a well-known fact that the quality of the dataset plays a central role in the results and conclusions drawn from the analysis of such a dataset. As the saying goes, ”garbage in, garbage out”. In recent years, neural networks have displayed good performance in solving a diverse number of problems. Unfortunately, neural networks are not immune to this misfortune presented by missing values. Furthermore, in most real-world settings, it is often the case that, the only data available for training neural networks consists of missing values. In such cases, we are left with little choice but to use this data for the purposes of training neural networks, although doing so may result in a poorly trained neural network. Most systems currently in use- merely discard the missing observation from the training datasets, while others just proceed to use this data and ignore the problems presented by the missing values. Still other approaches choose to impute these missing values with fixed constants such as means and mode. Most neural network models work under the assumption that the supplied data contains no missing values. This dissertation explores a method for training neural networks in the event where the training dataset consists of missing values..

A Survey of z>5.8 Quasars in the Sloan Digital Sky Survey I: Discovery of Three New Quasars and the Spatial Density of Luminous Quasars at z~6

We present the results from a survey of i-dropout objects selected from ~1550 deg^2 of multicolor imaging data from the Sloan Digital Sky Survey, to search for luminous quasars at z>5.8. Objects with i*-z*>2.2 and z*<20.2 are selected, and follow-up J band photometry is used to separate L and T type cool dwarfs from high-redshift quasars. We describe the discovery of three new quasars, at z=5.82, 5.99 and 6.28, respectively. Their spectra show strong and broad Ly alpha+NV emission lines, and very strong Ly alpha absorption, with a mean continuum decrement D_A > 0.90. The ARC 3.5m spectrum of the z=6.28 quasar shows that over a range of 300 A immediately blueward of the Ly alpha emission, the average transmitted flux is only 0.003 +/-0.020 times that of the continuum level, consistent with zero flux, and suggesting a tentative detection of the complete Gunn-Peterson trough. The existence of strong metal lines suggests early chemical enrichment in the quasar enviornment. The three new objects, together with the previously published z=5.8 quasar form a complete color-selected flux-limited sample at z>5.8. We estimate that at $z=6$, the comoving density of luminous quasars at M_1450 < -26.89 (h=0.5, Omega=1)is 1.1x10^-9 Mpc^-3. This is a factor of ~2 lower than that at z~5, and is consistent with an extrapolation of the observed quasar evolution at low-z. We discuss the contribution of quasars to the ionizing background at z~6. The luminous quasars discussed in the paper have central black hole masses of several times 10^9 M_sun by the Eddington argument. Their observed space density provides a sensitive test of models of quasar and galaxy formation at high redshift. (Abridged)Comment: AJ in press (Dec 2001), 40 pages, 10 figures. Updated following referee report; minor change

Deep Learning for Semantic Video Understanding

The field of computer vision has long strived to extract understanding from images and videos sequences. The recent flood of video data along with massive increments in computing power have provided the perfect environment to generate advanced research to extract intelligence from video data. Video data is ubiquitous, occurring in numerous everyday activities such as surveillance, traffic, movies, sports, etc. This massive amount of video needs to be analyzed and processed efficiently to extract semantic features towards video understanding. Such capabilities could benefit surveillance, video analytics and visually challenged people. While watching a long video, humans have the uncanny ability to bypass unnecessary information and concentrate on the important events. These key events can be used as a higher-level description or summary of a long video. Inspired by the human visual cortex, this research affords such abilities in computers using neural networks. Useful or interesting events are first extracted from a video and then deep learning methodologies are used to extract natural language summaries for each video sequence. Previous approaches of video description either have been domain specific or use a template based approach to fill detected objects such as verbs or actions to constitute a grammatically correct sentence. This work involves exploiting temporal contextual information for sentence generation while working on wide domain datasets. Current state-of- the-art video description methodologies are well suited for small video clips whereas this research can also be applied to long sequences of video. This work proposes methods to generate visual summaries of long videos, and in addition proposes techniques to annotate and generate textual summaries of the videos using recurrent networks. End to end video summarization immensely depends on abstractive summarization of video descriptions. State-of- the-art neural language & attention joint models have been used to generate textual summaries. Interesting segments of long video are extracted based on image quality as well as cinematographic and consumer preference. This novel approach will be a stepping stone for a variety of innovative applications such as video retrieval, automatic summarization for visually impaired persons, automatic movie review generation, video question and answering systems

Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis

The development of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ has very high anatomical variability, and a full inspection is problematic due to the location of the pancreas behind the stomach. Therefore, accurate, automatic pancreas segmentation can consequently yield quantitative morphological measures such as volume and curvature, supporting biomedical research to establish the severity and progression of a condition, such as type 2 diabetes mellitus. Furthermore, it can also guide subject stratification after diagnosis or before clinical trials, and help shed additional light on detecting early signs of pancreatic cancer. This PhD thesis delivers a novel approach for automatic, accurate quantitative pancreas segmentation in mostly but not exclusively Magnetic Resonance Imaging (MRI), by harnessing the advantages of machine learning and classical image processing in computer vision. The proposed approach is evaluated on two MRI datasets containing 216 and 132 image volumes, achieving a mean Dice similarity coefficient (DSC) of 84:1 4:6% and 85:7 2:3% respectively. In order to demonstrate the universality of the approach, a dataset containing 82 Computer Tomography (CT) image volumes is also evaluated and achieves mean DSC of 83:1 5:3%. The proposed approach delivers a contribution to computer science (computer vision) in medical image analysis, reporting better quantitative pancreas segmentation results in comparison to other state-of-the-art techniques, and also captures detailed pancreas boundaries as verified by two independent experts in radiology and radiography. The contributions’ impact can support the usage of computational methods in biomedical research with a clinical translation; for example, the pancreas volume provides a prognostic biomarker about the severity of type 2 diabetes mellitus. Furthermore, a generalisation of the proposed segmentation approach successfully extends to other anatomical structures, including the kidneys, liver and iliopsoas muscles using different MRI sequences. Thus, the proposed approach can incorporate into the development of a computational tool to support radiological interpretations of MRI scans obtained using different sequences by providing a “second opinion”, help reduce possible misdiagnosis, and consequently, provide enhanced guidance towards targeted treatment planning