Non-photorealistic rendering: a critical examination and proposed system.

In the first part of the program the emergent field of Non-Photorealistic Rendering is explored from a cultural perspective. This is to establish a clear understanding of what Non-Photorealistic Rendering (NPR) ought to be in its mature form in order to provide goals and an overall infrastructure for future development. This thesis claims that unless we understand and clarify NPR's relationship with other media (photography, photorealistic computer graphics and traditional media) we will continue to manufacture "new solutions" to computer based imaging which are confused and naive in their goals. Such solutions will be rejected by the art and design community, generally condemned as novelties of little cultural worth ( i.e. they will not sell). This is achieved by critically reviewing published systems that are naively described as Non-photorealistic or "painterly" systems. Current practices and techniques are criticised in terms of their low ability to articulate meaning in images; solutions to this problem are given. A further argument claims that NPR, while being similar to traditional "natural media" techniques in certain aspects, is fundamentally different in other ways. This similarity has lead NPR to be sometimes proposed as "painting simulation" — something it can never be. Methods for avoiding this position are proposed. The similarities and differences to painting and drawing are presented and NPR's relationship to its other counterpart, Photorealistic Rendering (PR), is then delineated. It is shown that NPR is paradigmatically different to other forms of representation — i.e. it is not an "effect", but rather something basically different. The benefits of NPR in its mature form are discussed in the context of Architectural Representation and Design in general. This is done in conjunction with consultations with designers and architects. From this consultation a "wish-list" of capabilities is compiled by way of a requirements capture for a proposed system. A series of computer-based experiments resulting in the systems "Expressive Marks" and 'Magic Painter" are carried out; these practical experiments add further understanding to the problems of NPR. The exploration concludes with a prototype system "Piranesi" which is submitted as a good overall solution to the problem of NPR. In support of this written thesis are : - • The Expressive Marks system • Magic Painter system • The Piranesi system (which includes the EPixel and Sketcher systems) • A large portfolio of images generated throughout the exploration

Towards PACE-CAD Systems

Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored. Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed. Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied. Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Optimising associations of arbuscular mycorrhizal fungi with wheat

PhD ThesisArbuscular mycorrhizal fungi (AMF) are ubiquitous symbionts of most vascular plants and essential contributors to soil health for which reason their application in agriculture has been investigated extensively. In wheat as one of the staple foods where large amounts of fertiliser and pesticides are used, the integration of mycorrhizal benefits such as increased nutrient uptake and plant health is desirable, but mutualistic outcomes of the symbiosis are determined by variety, agronomic management practices as well as nitrogen (N) and phosphorus (P) content of the soil. The present study investigated the impact of different fertiliser sources (biogas digestate, farmyard manure and mineral N) on AMF at five key development stages of two wheat varieties (Aszita and Skyfall) +/- crop protection over two cropping seasons 2017-18 and 2018-19 in a P-depleted soil. Additionally, the effect of a commercial AMF inoculum (INOQ Advantage) on plant performance, yield and grain quality was assessed. AMF-root colonisation (AMF-RC) was consistently higher in the modern variety Skyfall which also showed lower abundances of native AMF in response to AMF inoculation. Biogas digestate and farmyard manure application decreased AMF-RC in both years, whereas mineral N only reduced AMFRC when soil N was high in the first season following grass-clover, but not in the second season following wheat (i.e. 2nd wheat crop). Amplicon sequencing of the ITS1-region revealed that mycorrhizal communities in roots were dominated by Glomus spp. and were not affected by agronomic management or variety. Differential abundance analyses based on sequences of the small subunit (SSU) however indicated increased diversity of fine root endopyhtes (FRE) in response to mineral N. Although the AMF inoculum was not detected in roots using strainspecific primers in digital droplet PCR, inoculation with AMF increased biomass production of wheat without fertiliser and decreased biomass production with mineral N treatment, but these changes did not affect grain yields. A pot experiment that tested a cellulose-based seed coating with the INOQ Advantage root powder showed negative effects on plant growth, but without root colonisation. The results of this study imply a key role of N that impacts AMF-RC, FRE and the effect of biostimulants. The use of such in wheat production requires further optimisation to guarantee economic benefit for farmers while excluding side-effects of exogenous strains on native AMF.Marie Skłodowska-Curie research programme of the European Union

The Vezo communities and fisheries of the coral reef ecosystem in the Bay of Ranobe, Madagascar

Madagascar, a country whose extraordinary levels of endemism and biodiversity are celebrated globally by scientists and laymen alike, yet historically has received surprisingly little research attention, is the setting of the present dissertation. Here, I contribute to the need for applied research by: 1) focusing on the most intensely fished section of the Toliara Barrier Reef, the Bay of Ranobe; 2) characterizing the marine environment, the human population, and the fisheries; and 3) collecting the longest known time-series of data on fisheries of Madagascar, thereby providing a useful baseline for future analyses. In Chapter 1, the bathymetry of the Bay was characterized following a unique application of the boosted regression tree classifier to the RGB bands of IKONOS imagery. Derivation of water depths, based on DOS-corrected images, following a generic, log-transformed multiple linear regression approach produced a predictive accuracy of 1.28 m, whereas model fitting performed using the boosted regression tree classifier, allowing for interaction effects (tree complexity= 2), provided increased accuracy (RMSE= 1.01 m). Estimates of human population abundance, distribution, and dynamics were obtained following a dwelling-unit enumeration approach, using IKONOS Panchromatic and Google Earth images. Results indicated, in 2016, 31,850 people lived within 1 km of the shore, and 28,046 people lived within the 12 coastal villages of the Bay. Localized population growth rates within the villages, where birth rates and migration are combined, ranged from 2.96% - 6.83%, greatly exceeding official estimates of 2.78%. Annual pirogue counts demonstrated a shift in fishing effort from south to the north. Gear and boat (pirogue) profiles were developed, and the theoretical maximum number of fishermen predicted (n= 4,820), in 2013, from a regression model based on pirogue lengths (R2= 0.49). Spatial fishing effort distribution was mapped following a satellite-based enumeration of fishers-at-sea, resulting in a bay-wide estimate of intensity equaling 33.3 pirogue-meters km-2. Landings and CPUE were characterized, with respect to finfish, by family, species, gear, and village. Expansion of landings to bay-wide fisheries yields indicated 1,885.8 mt year-1 of mixed fisheries productivity, with an estimated wholesale value of 1.64 million USD per annum

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

Commissioning and First Science Results of the Desert Fireball Network: a Global-Scale Automated Survey for Large Meteoroid Impacts

This thesis explores the first results from the Desert Fireball Network, a distributed global observatory designed to characterise fireballs caused by meteoroid impacts. To deal with the >50 terabytes of data influx per week, innovative data reduction techniques have been developed. The science topics investigated in this work include airbursts caused by large meteoroids impacting the Earth's atmosphere, the recovery of a meteorite and its orbital history, and the structure of a meteor shower