Mapping a guided image filter on the HARP reconfigurable architecture using OpenCL

Intel recently introduced the Heterogeneous Architecture Research Platform, HARP. In this platform, the Central Processing Unit and a Field-Programmable Gate Array are connected through a high-bandwidth, low-latency interconnect and both share DRAM memory. For this platform, Open Computing Language (OpenCL), a High-Level Synthesis (HLS) language, is made available. By making use of HLS, a faster design cycle can be achieved compared to programming in a traditional hardware description language. This, however, comes at the cost of having less control over the hardware implementation. We will investigate how OpenCL can be applied to implement a real-time guided image filter on the HARP platform. In the first phase, the performance-critical parameters of the OpenCL programming model are defined using several specialized benchmarks. In a second phase, the guided image filter algorithm is implemented using the insights gained in the first phase. Both a floating-point and a fixed-point implementation were developed for this algorithm, based on a sliding window implementation. This resulted in a maximum floating-point performance of 135 GFLOPS, a maximum fixed-point performance of 430 GOPS and a throughput of HD color images at 74 frames per second

Deep learning for compilers

Constructing compilers is hard. Optimising compilers are multi-million dollar projects spanning years of development, yet remain unable to fully exploit the available performance, and are prone to bugs. The rapid transition to heterogeneous parallelism and diverse architectures has raised demand for aggressively-optimising compilers to an all time high, leaving compiler developers struggling to keep up. What is needed are better tools to simplify compiler construction. This thesis presents new techniques that dramatically lower the cost of compiler construction, while improving robustness and performance. The enabling insight for this research is the leveraging of deep learning to model the correlations between source code and program behaviour, enabling tasks which previously required significant engineering effort to be automated. This is demonstrated in three domains: First, a generative model for compiler benchmarks is developed. The model requires no prior knowledge of programming languages, yet produces output of such quality that professional software developers cannot distinguish generated from hand-written programs. The efficacy of the generator is demonstrated by supplementing the training data of predictive models for compiler optimisations. The generator yields an automatic improvement in heuristic performance, and exposes weaknesses in state-of-the- art approaches which, when corrected, yield further performance improvements. Second, a compiler fuzzer is developed which is far simpler than prior techniques. By learning a generative model rather than engineering a generator from scratch, it is implemented in 100 fewer lines of code than the state-of-the-art, yet is capable of exposing bugs which prior techniques cannot. An extensive testing campaign reveals 67 new bugs in OpenCL compilers, many of which have now been fixed. Finally, this thesis addresses the challenge of feature design. A methodology for learning compiler heuristics is presented that, in contrast to prior approaches, learns directly over the raw textual representation of programs. The approach outperforms state-of-the-art models with hand-engineered features in two challenging optimisation domains, without requiring any expert guidance. Additionally, the methodology enables models trained in one task to be adapted to perform another, permitting the novel transfer of information between optimisation problem domains. The techniques developed in these three contrasting domains demonstrate the exciting potential of deep learning to simplify and improve compiler construction. The outcomes of this thesis enable new lines of research to equip compiler developers to keep up with the rapidly evolving landscape of heterogeneous architectures

Control of morphogenesis in the budding Alphaproteobacterium Hyphomonas neptunium

The size and shape of bacteria are manifold just as their modes of propagation. The cell wall, composed of peptidoglycan (PG), is the major cell shape determinant in most bacteria. So far research on spatiotemporal coordination of morphology and cell division has mainly focused on rod-shaped bacteria like Escherichia coli and Bacillus subtilis. In this study, we investigate the dimorphic Alphaproteobacteria Hyphomonas neptunium as a new model organism for the study of asymmetric morphology and reproduction by budding at the distal end of a stalk. Our goal was to comprehensively analyse the growth mode and budding mechanism of H. neptunium. Detailed electron cryo-tomography images revealed that, unlike previously suggested, the stalk and the bud form a continuum with the mother cell up until cell division. We show that during budding the daughter cell incorporates part of the stalk belonging to the mother cell to complete its own growth. Furthermore, we demonstrate that H. neptunium can accomplish more replicative cycles than previously proposed. By monitoring the incorporation of nascent PG with HADA, we identified four different growth phases in H. neptunium that can be divided into dispersed (swarmer cell growth and bud formation) and zonal growth (stalk biogenesis and cell division). PG composition analysis revealed a very high PG turnover rate as well as an unusually high incorporation of glycine instead of alanine at the 5th position of the stem peptide. A comprehensive analysis of the PG biosynthesis machinery in H. neptunium shows that the conserved actin homologue MreB, the PG synthases PBP2 and PBP3, and the PG hydrolase LmdC play a vital role in cell growth in H. neptunium. Polar PG biogenesis seems to be modulated by an array of mostly redundant synthases and hydrolases, in which LD-transpeptidases do not partake. We postulate that the morphological asymmetry of H. neptunium underlies a much more complex intracellular asymmetry determined by distinct, multiple sites of dispersed and zonal growth. To maintain its correct cell shape, H. neptunium requires MreB as well as a coiled-coil rich protein termed CCRP and the non-canonical bactofilins, which are a new class of nucleotide-independent polymer-forming cytoskeletal elements. Upon inhibition of MreB with A22 or MP265, cells become increasingly spherical and eventually cease growing. The deletion of ccrp causes elongated stalks accompanied by slight cell chaining and irregular cell shape. In the absence of both bactofilin para-logues BacA and BacB, H. neptunium cells adopt a severely distorted cell morphology with multiple and branched stalks. In addition to bud formation at the distal end of the stalk, these mutants can generate buds directly from the cell body of the mother cell. Both bactofilins localize dynamically at the future stalked pole throughout the cell cycle and within the stalk just adjacent to the tip and later at the future division site. Time-lapse microscopy of the double deletion mutant revealed that the first step which leads to loss of cell morphology is the relinquishment of the stalk as a reproductive organelle, which is unimpededly incorporated by the emerging bud. Thus the stalk is lost, which leads to deregulation of cell wall biogenesis within the complete cell, generating amorphous cell bodies. However, further experiments indicate that bactofilins are not essential for stalk biogenesis, they merely seem to pay a role in confining cell growth to the terminal region of the stalk. In short, bactofilins play a vital role in the maintenance of PG incorporation at the stalked pole and consequently ensure proper cell morphology

On the popularization of digital close-range photogrammetry: a handbook for new users.

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Synthesis, Properties, and Assemblies of Uniform Polymer-ligated Nanocrystals

Colloidal synthesis represents one of the most widely used strategies to produce inorganic nanocrystals for a variety of applications in optoelectronics, catalysis, biotechnology, among other areas. Despite significant advances achieved in the past two decades, the capability of developing functional inorganic nanocrystals (NCs) with versatile morphology, uniform dimension, outstanding stabilities, tunable compositions, desirable physical properties, and tailorable surface chemistry remains challenging. Therefore, in this dissertation, we aim to capitalize on an array of nonlinear block copolymers with tunable and precisely controlled dimensions, compositions and architectures as nanoreactors to craft inorganic NCs of distinctive compositions with uniform morphology, enhanced stabilities, and desirable surface chemistry, which is unattainable by conventional colloidal synthesis. As a consequence, the intriguing size-, shape-, and composition-dependent physical properties of these inorganic NCs as well as the relationship between surface chemistry and physical properties can be scrutinized. Furthermore, the applications of polymer-ligated inorganic NCs are exemplified in optoelectronics, photocatalysis, and drug delivery. Specifically, the key findings of this dissertation are reflected as follows: First, a set of uniform metal chalcogenide semiconducting nanoparticles (NPs) were in-situ crafted via a diversity of star-like block copolymer nanoreactors, yielding uniform morphology, variable surface chemistry, size-dependent NIR optical properties, and remarkable air tolerance. The outer blocks of star-like block copolymers were covalently connected to the inner core and thus permanently situated on the surface of as-prepared NPs, suggesting the formation of a stable protective polymer shell over each individual NP and thus markedly improved air stability of NPs. Most importantly, the effects of surface chemistry on the air stability of these lead chalcogenide NPs were unraveled by judiciously alternating the compositions and chain lengths of polymers that perpetually tethered outside the NPs, providing essential fundamental understandings for the synthesis and design of stable semiconducting NPs. Second, we designed and synthesized a series of star-like PAA-b-P3HT block copolymers for crafting uniform, highly stable P3HT-ligated metal halide perovskite NPs. For the first time, we realized the ligation of semiconducting conjugated polymers on the surface of perovskite NPs during in-situ colloidal synthesis. The formation of type-II electronic band alignment between P3HT and perovskite NPs yielded efficient interfacial charge carriers separation, which are then found to be pronouncedly affected by the molecular weight (MW) of tethered P3HT and size of perovskite NPs. As a result, the P3HT-ligated perovskite NPs exhibited promising potential as robust photocatalysts. Third, extended from star-like block copolymer, we demonstrated a universal strategy to produce a variety of 1D perovskite nanorods (NRs) of distinctive compositions with stable, uniform and tailored morphology via capitalizing on amphiphilic bottlebrush-like block copolymers (BBCPs) as nanoreactors. The synthesis route affords an unprecedented and ideal platform for scrutinizing their dimension-dependent physical properties, which has yet to be explored, while imparts remarkable structural and optical stabilities as well upon ambient storage and against heat, UV irradiation, and polar solvents. Fourth, self-assembly of polymer-ligated inorganic NCs were performed via three routes. In the first route, the PS-ligated perovskite NRs were assembled into periodic patterns through controlled evaporative self-assembly in confined geometry. In the second route, the chain ends of star-like block copolymers were functionalized with complementary hydrogen bonding moieties prior to acting as nanoreactors to direct the growth of perovskite NPs. The self-assembly of perovskite NPs of tailorable morphology can then be realized via regulating the mixing temperature to manipulate hydrogen bonding, resulting in varied inter-particle interaction and exciton coupling in perovskite NPs. In the third route, light-responsive polymers (i.e., PMAMC) were incorporated in the synthesis of cellulose-g-(PAA-b-PMAMC) BBCPs which were then used as cylindrical nanoreactors to yield light-responsive PMAMC-ligated Au NRs. The light responsiveness of PMAMC blocks induce reversible and stable self-assembly/disassembly of Au NRs under UV light irradiation of different wavelengths, which pronouncedly depends on the chain length of PMAMC blocks, indicating potential application in drug delivery. In summary, advances in colloidal synthesis and self-assembly of inorganic NCs were achieved by utilizing rationally designed nonlinear block copolymers, yielding NCs with variable architectures, uniform and tunable dimensions, controlled compositions, as well as versatile, stable and well-defined surface chemistry. Therefore, the structure-composition-property correlations of a myriad of inorganic NCs can be revealed, suggesting promising opportunity to apply them for a variety of practical applications.Ph.D

Shapes of Edges: Exploring the notion of boundary, drawing on experiments in force and form

Shapes of edges explores the boundaries we perceive - the surfaces of objects we touch, constellations in the night sky, the fenced borders of home and nation, lines between animate and inanimate, and the outer edges of the ideals which we hold to our hearts. Through elaborate physical experiments in force and form, and applied philosophical thought, the underlying structures of knowledge which we use to define the world are examined. Three basic experiments, documented in detail, form the foundation of the study. The physical experiments are: colliding of hollow metal boxes in air; studies of smoke vortex formation; and spark discharge imaging of solid forms. Machines are purpose-built for the experiments, and documentation carried out in traditional and alternative media. Many of the methods of recording require bespoke apparatus, created with a single process in mind, and the processes inevitably become entangled with expectation, perception, and habitual patterns of thought. The experiments and the machines, the documentation and their processes, the exhibited work, the philosophical research, and these written words, all merge. Such a mesh of information creates rich ground for new knowledge, arising from an intimate relation of disparate parts, all brought to bear upon one notion, that of boundary

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources

Effect of space conditions on neuronal plasticity and connectivity

Looking for opportunities to explore new frontiers and developing new technologies have always been in the nature of mankind. In 1957, the first rocket in space opened a new era for space traveling towards other planets. Concomitantly, a wide range of concerns related to human health risks that could occur during spaceflight was raised. Up to now, a large number of experiments has been performed to determine the biological effects of space conditions on human health, in order to develop appropriate countermeasures. However, extensive investigations still need to be performed before considering long-term spaceflight towards other planets such as Mars. Since the first human space flight, it has been observed that in weightlessness conditions, equilibrium sense organs can send misleading inputs to the central nervous system which is forced to develop new strategies and adapt to adequately translate these messages. Furthermore, cosmic radiations are known to induce oxidative stress as well as genomic damages. In this thesis, we studied concomitant microgravity and radiation exposures as models for space conditions and developed various methods to analyse their specific and combined effects on in vitro neuronal network models. In vitro primary neuronal network cultures were established and exposed to simulated space conditions to investigate neuronal network remodelling (plasticity and connectivity) as well as genomic damage/repair dynamics. This work was performed to address questions on neuronal network disorders occurring during spaceflights and, in the future, to develop strategies against these effects

A History of Materials and Technologies Development

The purpose of the book is to provide the students with the text that presents an introductory knowledge about the development of materials and technologies and includes the most commonly available information on human development. The idea of the publication has been generated referring to the materials taken from the organic and non-organic evolution of nature. The suggested texts might be found a purposeful tool for the University students proceeding with studying engineering due to the fact that all subjects in this particular field more or less have to cover the history and development of the studied object. It is expected that studying different materials and technologies will help the students with a better understanding of driving forces, positive and negative consequences of technological development, etc