Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs

We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes

Novel processes for large area gallium nitride single crystal and nanowire growth.

III-nitrides (InN, GaN, AlN) are some of the most promising materials for making blue light emitting diodes (LED), blue laser diodes (LD) and high power, high temperature field effect transistors (FET). Current techniques produce GaN films with defect densities on the order of 107/cm2 or higher. The performance and life-time of the devices critically depend upon the defect densities and high power, high frequency devices require the defect densities to be lower than 104/cm2. So, the need for new processes to produce large size GaN crystals with defect densities less than 107/cm2 is immediate. In addition to large area single crystals (or wafers), the nanowires also present as an alternating platform for making devices. So, the processes for controlled synthesis, modifying and integrating sub 100 nm nanowires into electronic devices are of great interest. This thesis presents a new concept of ‘self-oriented growth\u27 of GaN platelets shaped crystals on molten gallium to produce near single crystal quality GaN films over large areas (\u3e 1 cm²). The process involves direct nitridation of Ga films using nitrogen plasma at low pressures (few mTorr). GaN flakes with areas over 25 mm² have been successfully obtained. Raman spectra of the resulting GaN crystals show no stress and low native donor concentration on the order of 1017/cm³. XRD texture analysis showed an overall c-axis tilt of 2.2o between GaN domains within the flake. The cross-sectional TEM micrographs showed that the resulting GaN films are free from dislocation crops inside the grains but showed diffraction contrast due to small mis-orientation between the grains. The twist and tilt angles between adjacent columnar grains were determined using convergent beam electron diffraction technique to be less than 8o and 1o, respectively. HRTEM micrographs of the grain boundaries showed sharp interfaces resulting with both twisted and perfect attachments. This thesis also presents direct synthesis approach for GaN nanowires with control on growth directions using modified nitridation conditions. The nitridation in the presence of hydrogen or ammonia resulted in oxide sheath free GaN nanowires as thin as 20 nm and long as 100 Ìm in \u3c0001\u3e direction. The nitridation using low Ga flux in a vapor transport set-up resulted in sub 100 nm GaN nanowires with \u3c10-10\u3e growth direction. The difference in the nucleation and growth mechanism allowed control on the nanowire directions. Homo-epitaxial experiments onto pre-synthesized GaN nanowires with the above two growth directions using the vapor transport of Ga and dissociated ammonia exhibited different morphologies, e.g. micro hexagonal columns for \u3c0001\u3e nanowires and micro belts for nanowires with \u3c10-10\u3e growth direction. The results further illustrate a new phenomenon of enhanced surface diffusion on nanowires in general but more pronounced for wires with \u3c0001\u3e growth direction. The results from homo-epitaxy experiments suggest that the \u3c10-10\u3e direction wires could be used as seeds for growing large area GaN crystals in vapor phase homo-epitaxy schemes

The molecular mechanism of solvent cryoprotection

The cryopreservation of animal and human cells, tissues and organs as well as germplasm of endangered plant species is a key area in contemporary biotechnology. In this thesis, certain classes of chemicals known as cryoprotective agents (CPA) are investigated in detail. The structural, dynamic and vitrification properties of representative CPAs are studied using the state-of-the art molecular dynamics simulation techniques. The simulations provide a rationale at the molecular level of the cryoprotective properties of aqueous solutions of compounds such as DMSO, methanol and ethanol.This is a brief synopsis of the thesis:CHAPTER 1. This chapter provides a general introduction of various aspects of cryoprotection. A brief overview of the challenges that are encountered in the cryopreservation protocol is given, followed by a list of known cryoprotectants. Next various mechanisms to account for cryopreservation have been explained in detail. This section is followed by one of the important theme of this thesis, vitrification, and finally water and its properties.CHAPTER 2. This chapter discusses the theoretical background of molecular dynamics simulations and various analyses, and a brief overview of water forcefields.CHAPTER 3. Molecular dynamics simulations of DMSO in water elucidating its structural, dynamic and hydrogen bonding properties are described in this chapter. Comparison with its chemical analogue, Acetone, is made in an attempt to rationalise DMSO‟s exceptional properties in a wide range of temperatures.CHAPTER 4. Structural and hydrogen bonding properties of aqueous ethanol and methanol solutions have been described in this chapter.CHAPTER 5. Estimation of glass transition temperatures of aqueous mixtures of DMSO, acetone, ethanol, methanol and water using simulated annealing molecular dynamics techniques has been described in this chapter.CHAPTER 6. Important conclusions arising from this study and scope of the work have been summarised in this chapter

Biogeochemistry of a Saline, Alkaline, Terminal Lake Ecosystem in Transition; Walker Lake, Nevada

Walker Lake is a saline, alkaline, terminal lake ecosystem located in west-central Nevada. For over one hundred years, anthropogenic streamflow diversions within the Walker River Basin have ultimately led to little or no water reaching Walker Lake, the basin’s terminus for water flow. These diversions have resulted in a \u3e46 meter decrease in the lake surface altitude and increases in salinity and dissolved salt constituents that have caused the elimination of native fish species. This study examines how the lack of freshwater inflow has altered the physical, chemical, and microbiological structure of Walker Lake during the lake’s ongoing desiccation. Between 2007 and 2015, water and sediment samples were collected from a central lake location of Walker Lake, coinciding with the historical timing of late summer thermal stratification. Physical parameters and chemical constituent measurements show Walker Lake to have shifted from a monomictic to a polymictic system sometime after 2008, with salinity increasing conservatively to values over 21 g L-1 in 2015. Illumina sequencing of the V4 region of the 16S rRNA gene was completed on all environmental filter and sediment samples to observe trends and changes in the microbial populations of the water column and sediment as a result of the changing lake dynamics. Over time, distinct differences in overall community composition and diversity were observed between sampling dates. The sediment communities were found to be highly dissimilar from the overlying pelagic microbial communities and showed more similarity to microbial communities from anoxic hypolimnion water samples from the 2008 sampling event when lake stratification was observed. The anthropogenic and climatic factors that Walker Lake has faced over the past century have dramatically altered the ecosystem. This study aims to contribute to the overall understanding of the Walker River Basin and to other terminal lake basins throughout the world. By examining the microbial communities of Walker Lake and documenting the limnological shift of this transitioning ecosystem, we gain insights into the physiological aspects of Walker Lake and possible ways to manage and restore this unique environment to the thriving ecosystem it once was

Feature Papers in Electronic Materials Section

This book entitled "Feature Papers in Electronic Materials Section" is a collection of selected papers recently published on the journal Materials, focusing on the latest advances in electronic materials and devices in different fields (e.g., power- and high-frequency electronics, optoelectronic devices, detectors, etc.). In the first part of the book, many articles are dedicated to wide band gap semiconductors (e.g., SiC, GaN, Ga2O3, diamond), focusing on the current relevant materials and devices technology issues. The second part of the book is a miscellaneous of other electronics materials for various applications, including two-dimensional materials for optoelectronic and high-frequency devices. Finally, some recent advances in materials and flexible sensors for bioelectronics and medical applications are presented at the end of the book

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

ENZYMES: Catalysis, Kinetics and Mechanisms

Onemarvelsattheintricate designoflivingsystems,andwecannotbutwonderhow life originated on this planet. Whether ?rst biological structures emerged as the selfreproducing genetic templates (genetics-?rst origin of life) or the metabolic universality preceded the genome and eventually integrated it (metabolism-?rst origin of life) is still a matter of hot scienti?c debate. There is growing acceptance that the RNA world came ?rst – as RNA molecules can perform both the functions of information storage and catalysis. Regardless of which view eventually gains acceptance, emergence of catalytic phenomena is at the core of biology. The last century has seen an explosive growth in our understanding of biological systems. The progression has involved successive emphasis on taxonomy ! physiology ! biochemistry ! molecular biology ! genetic engineering and ?nally the large-scale study of genomes. The ?eld of molecular biology became largely synonymous with the study of DNA – the genetic material. Molecular biology however had its beginnings in the understanding of biomolecular structure and function. Appreciationofproteins,catalyticphenomena,andthefunctionofenzymeshadalargeroleto play in the progress of modern biology