6,146 research outputs found

    Making friends with failure in STS

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    Urban Space, Genre and Subjectivity in African and Latin American Cinema

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    This project studies twelve African and Latin American films from a range of eras and countries, with an emphasis on their treatment of urban space, their manipulation of genre elements, and their approaches to character subjectivity. The analysis draws on major works of urban theory by Henri Lefebvre, Manuel Castells, David Harvey, Jane Jacobs, and others in order to investigate the relationship between cinema and the urban experience. As the films in the study are mostly set in cities that are not discussed by the theorists, the analysis entails testing their theories against the realities of these other settings, as depicted in the films. Furthermore, as these films depict places and people not usually featured in commercial cinema, this project will emphasize ways in which the films challenge dominant patterns of cinematic representation with regard to African and Latin American people, places and culture. Finally, this project will analyze important structural and stylistic nuances of each film in order to contribute to existing discussions of African and Latin American film and global film in general

    Operatic Pasticcios in 18th-Century Europe

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    In Early Modern times, techniques of assembling, compiling and arranging pre-existing material were part of the established working methods in many arts. In the world of 18th-century opera, such practices ensured that operas could become a commercial success because the substitution or compilation of arias fitting the singer's abilities proved the best recipe for fulfilling the expectations of audiences. Known as »pasticcios« since the 18th-century, these operas have long been considered inferior patchwork. The volume collects essays that reconsider the pasticcio, contextualize it, define its preconditions, look at its material aspects and uncover its aesthetical principles

    Representations of Materials for Machine Learning

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    High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research 5

    Scallop: A Language for Neurosymbolic Programming

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    We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Advanced 1,2,3-triazolate-based coordination compounds: from carbonic anhydrase mimics, molecular building blocks, and catalyst supports to electrically conducting spin-crossover MOFs

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    Kuratowski complexes and related metal-organic frameworks (MOF), especially of the MFU-4-type, built from 1,2,3-triazolate-based ligands gained increasing interest in the last years due to their variable side ligands and metal sites. Such materials and their post-synthetic modifications have shown an outstanding potential for applications such as adsorption, capture, separation and kinetic trapping of gases, drug delivery, atmospheric water harvesting, sensing, H2/D2 quantum sieving, investigation of fundamental magnetic phenomena, and in particular catalysis. In this respect, MFU-4-type MOF catalysts were shown to outperform other heterogeneous catalysts for the dimerization and polymerization of olefins with some applications already advancing toward commercial applicability. This thesis mainly aims to extend the functionality of 1,2,3-triazolate-based coordination materials via advanced linker designs, novel framework assembly strategies, and post-synthetic modifications, as well as through a better understanding of the underlying material properties. During this project, several new organic and complex building blocks, as well as advanced framework structures were prepared and characterized. Furthermore, additional emphasis was directed to the investigation and interpretation of resulting physical phenomena like phase transitions, magnetism, and electrical conductivity. The Zn-MFU-4l ([Zn5IICl4(BTDD)3]; H2-BTDD = bis(1H-1,2,3-triazolo[4,5-b][4′,5′-i])dibenzo[1,4]dioxin) and Co-MFU-4l ([Zn1.3IICo3.7IICl4(BTDD)3]) metal-organic frameworks were prepared according to the literature procedures and modified by a post-synthetic side ligand exchange of the chloride anions, which led to MFU-4-type structures featuring organometallic metal-carbon bonds. Overall, five new Zn-MFU-4l structures of the general formula [Zn5IILxCl4–x(BTDD)3] (4 ≥ x > 3; L = methanido, ethanido, n-butanido, tert-butanido, 3,3-dimethyl-1-butyn-1-ido; Zn-MFU-4l-Me, -Et, -n-Bu, -t-Bu, -Butyne) and two new Co-MFU-4l structures, Co-MFU-4l-Me ([Zn1.5IICo3.5IIMe3.1Cl0.9(BTDD)3]) and Co-MFU-4l-OH ([Zn1.4IICo3.6II (OH)3.1Cl0.9(BTDD)3]), were obtained. Such side ligands were not characterized for MFU-4-type MOFs before, although they are presumed responsible for the metal site activation during olefin catalysis reactions, which require organometallic co-catalysts. For this purpose, a combination of simulated and measured IR spectra was developed as well-suited characterization technique for such insoluble materials, which preclude analytical methods like liquid state NMR and mass spectroscopy. A high stability of the organometallic Zn-MFU-4l derivatives was observed, whereas the Co-MFU-4l-Me was of a pyrophoric nature and reacted upon water contact to Co-MFU-4l-OH, which exhibited a CO2 binding mechanism comparable to that of carbonic anhydrase. Synthesis of Kuratowski complexes built from 1H-benzotriazole-5,6-diamine (H-btda) ligands and post-synthetic exchange of the chloride side ligands with Tp/Tp* (Tp= hydrotris(pyrazolyl)borate; Tp* = hydrotris(3,5-dimethyl-1-pyrazolyl)borate) provided us with a variety of six-fold diamine-functionalized molecular building blocks intended for the development of novel MOF construction pathways. Crystallization of those compounds have already led to the assembly of porous metal hydrogen-bonded frameworks (M-HOF), some of which have even exhibited permanent porosity. This is a rare property of this material class, which is still in its infancy with only a few structures reported so far. Overall, five new metal hydrogen-bonded framework assemblies (CFA-20-X ((2,6-lutidinium)+[Zn5X4(btda)6X]−· n(DMF); X= Cl−, Br−), CFA-20-Tp, CFA-20-Tp*, CFA-20-Tp*-DMSO ([Zn5Y4(btda)6]; Y = Tp, Tp*) could be characterized, thus representing a significant contribution to this field of study. Although no MOFs could be crystallized from reactions of these complexes with metal salts, preliminary results have shown that direct incorporation of metal sites is a suitable pathway to convert M-HOFs into more stable MOFs. Taking the functionality of MFU-4-type frameworks to the next level, the novel 1,1',5,5'-tetrahydro-6,6'-biimidazo[4,5-f]benzotriazole (H4-bibt) ligand was developed to potentiate the post-synthetic modification possibilities compared to other MFU-4-type frameworks via introduction of additional and easily accessible biimidazole coordination sites at the linker backbone. This gave rise to the five most sophisticated MFU-4-type structures prepared so far. Post-synthetic Tp ligand exchange in the resulting MFU-4-type CFA-19 ([Co5IICl4(H2-bibt)3]) provided the stable CFA-19-Tp ([Co5IICl0.4Tp3.6(H2-bibt)3]) framework, in which the additional coordination sites were saturated in a third modification step with MIBr(CO)3 (M= Re, Mn) moieties or deprotonated via introduction of ZnEt moieties. The resulting materials exhibit high metal site density single-crystal X-ray structures with over 1700 atoms per unit cell for the ReBr(CO)3@CFA-19-Tp ([Co5IICl0.4Tp3.6(H2-bibt)3·(ReIBr(CO)3)2.8]) and a thermally induced release of all CO ligands for the MnBr(CO)3@CFA-19-Tp ([Co5IICl0.4Tp3.6(H2-bibt)3(MnIBr(CO)3)3]·3.1(MnIBr(CO)X)). Preliminary results also indicate a facile incorporation of other coordination moieties such as MIICl2 (M= PdII, PtII). These proof-of-principle incorporations of coordination moieties and open metal sites render such CFA-19-type scaffolds promising supports for an even larger variety of active species intended for the binding and activation of small molecules in future investigations. Coincidental synthesis of the novel CFA-23 ((((propan-2-yl)oxidanium)+[Mn6IICl5(ta)8]−; H-ta= 1H-1,2,3-triazole) coordination framework provided the opportunity to investigate changes of the resulting magnetic properties in comparison to a similar structure built from 1H-1,2,3-benzotriazole, as well as the ultra-narrow character of the pore channels in CFA-23. High purity samples of the literature-known Fe(ta)2 (H-ta= 1H-1,2,3-triazole) framework were prepared and investigated in detail to unveil its record hysteresis spin-crossover phase transition. Aiming at the use of Fe(ta)2 in surface acoustic wave-based sensor applications, experimental and theoretical insights into the material’s electrical conductivity changes upon adsorption of inert gases were assisted with the measurement of adsorption isotherms and the determination of the resulting isosteric enthalpies of adsorption

    DKWS: A Distributed System for Keyword Search on Massive Graphs (Complete Version)

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    Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks, and RDF graphs, keyword search for querying such graphs has been proposed. As graphs have become voluminous, large-scale distributed processing has attracted much interest from the database research community. While there have been several distributed systems, distributed querying techniques for keyword search are still limited. This paper proposes a novel distributed keyword search system called \DKWS. First, we \revise{present} a {\em monotonic} property with keyword search algorithms that guarantees correct parallelization. Second, we present a keyword search algorithm as monotonic backward and forward search phases. Moreover, we propose new tight bounds for pruning nodes being searched. Third, we propose a {\em notify-push} paradigm and \PINE {\em programming model} of \DKWS. The notify-push paradigm allows {\em asynchronously} exchanging the upper bounds of matches across the workers and the coordinator in \DKWS. The \PINE programming model naturally fits keyword search algorithms, as they have distinguished phases, to allow {\em preemptive} searches to mitigate staleness in a distributed system. Finally, we investigate the performance and effectiveness of \DKWS through experiments using real-world datasets. We find that \DKWS is up to two orders of magnitude faster than related techniques, and its communication costs are 7.67.6 times smaller than those of other techniques

    Detecting Novel Subtypes of Cancer Using Bayesian Unsupervised Clustering

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    Although there have been many advances in screening programs and treatments in recent years that have reduced the mortality rate of cancer, it remains the second leading cause of death worldwide, accounting for almost 10 million deaths worldwide in 2020. Identifying and characterising subtypes based on molecular classifications can help identify the aggressiveness of the disease so that the best treatment pathway can be identified, and new treatment options developed. This has been exemplified in breast cancer. Latent Process Decomposition (LPD) is a soft clustering technique that has been successfully applied to expression data to discover subtypes, including a poor prognosis subtype called DESNT. The benefit of LPD is that it better models the heterogenous structure of tumours. The aim of this thesis is to apply LPD on transcriptome data from The Cancer Genome Atlas to detect and characterise subtypes of numerous cancer types and create a resource of the results. This was achieved through the development of Automata, an R package used to automate this methodology. In total I have identified 168 cancer subtypes spanning across 28 cancer types. Moreover, I have characterised the features of each subtype, generating a unique encyclopaedic compendium of molecular subtypes of cancer that provides an in-depth source of information for the research community. I have successfully validated my findings by comparing them with known subtypes from breast carcinoma, prostate adenocarcinoma, colorectal adenocarcinoma and lung cancer. Additionally, I have discovered common features that characterise subtypes across cancer types. Finally, I have identified 26 subtypes which have a significant association with outcome including some that were not picked up by traditional clustering methods. The results presented in this thesis are the foundation for the long-term impact of a more personalised approach to cancer patient care

    Machine learning methods for genomic high-content screen data analysis applied to deduce organization of endocytic network

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    High-content screens are widely used to get insight on mechanistic organization of biological systems. Chemical and/or genomic interferences are used to modulate molecular machinery, then light microscopy and quantitative image analysis yield a large number of parameters describing phenotype. However, extracting functional information from such high-content datasets (e.g. links between cellular processes or functions of unknown genes) remains challenging. This work is devoted to the analysis of a multi-parametric image-based genomic screen of endocytosis, the process whereby cells uptake cargoes (signals and nutrients) and distribute them into different subcellular compartments. The complexity of the quantitative endocytic data was approached using different Machine Learning techniques, namely, Clustering methods, Bayesian networks, Principal and Independent component analysis, Artificial neural networks. The main goal of such an analysis is to predict possible modes of action of screened genes and also to find candidate genes that can be involved in a process of interest. The degree of freedom for the multidimensional phenotypic space was identified using the data distributions, and then the high-content data were deconvolved into separate signals from different cellular modules. Some of those basic signals (phenotypic traits) were straightforward to interpret in terms of known molecular processes; the other components gave insight into interesting directions for further research. The phenotypic profile of perturbation of individual genes are sparse in coordinates of the basic signals, and, therefore, intrinsically suggest their functional roles in cellular processes. Being a very fundamental process, endocytosis is specifically modulated by a variety of different pathways in the cell; therefore, endocytic phenotyping can be used for analysis of non-endocytic modules in the cell. Proposed approach can be also generalized for analysis of other high-content screens.:Contents Objectives Chapter 1 Introduction 1.1 High-content biological data 1.1.1 Different perturbation types for HCS 1.1.2 Types of observations in HTS 1.1.3 Goals and outcomes of MP HTS 1.1.4 An overview of the classical methods of analysis of biological HT- and HCS data 1.2 Machine learning for systems biology 1.2.1 Feature selection 1.2.2 Unsupervised learning 1.2.3 Supervised learning 1.2.4 Artificial neural networks 1.3 Endocytosis as a system process 1.3.1 Endocytic compartments and main players 1.3.2 Relation to other cellular processes Chapter 2 Experimental and analytical techniques 2.1 Experimental methods 2.1.1 RNA interference 2.1.2 Quantitative multiparametric image analysis 2.2 Detailed description of the endocytic HCS dataset 2.2.1 Basic properties of the endocytic dataset 2.2.2 Control subset of genes 2.3 Machine learning methods 2.3.1 Latent variables models 2.3.2 Clustering 2.3.3 Bayesian networks 2.3.4 Neural networks Chapter 3 Results 3.1 Selection of labeled data for training and validation based on KEGG information about genes pathways 3.2 Clustering of genes 3.2.1 Comparison of clustering techniques on control dataset 3.2.2 Clustering results 3.3 Independent components as basic phenotypes 3.3.1 Algorithm for identification of the best number of independent components 3.3.2 Application of ICA on the full dataset and on separate assays of the screen 3.3.3 Gene annotation based on revealed phenotypes 3.3.4 Searching for genes with target function 3.4 Bayesian network on endocytic parameters 3.4.1 Prediction of pathway based on parameters values using Naïve Bayesian Classifier 3.4.2 General Bayesian Networks 3.5 Neural networks 3.5.1 Autoencoders as nonlinear ICA 3.5.2 siRNA sequence motives discovery with deep NN 3.6 Biological results 3.6.1 Rab11 ZNF-specific phenotype found by ICA 3.6.2 Structure of BN revealed dependency between endocytosis and cell adhesion Chapter 4 Discussion 4.1 Machine learning approaches for discovery of phenotypic patterns 4.1.1 Functional annotation of unknown genes based on phenotypic profiles 4.1.2 Candidate genes search 4.2 Adaptation to other HCS data and generalization Chapter 5 Outlook and future perspectives 5.1 Handling sequence-dependent off-target effects with neural networks 5.2 Transition between machine learning and systems biology models Acknowledgements References Appendix A.1 Full list of cellular and endocytic parameters A.2 Description of independent components of the full dataset A.3 Description of independent components extracted from separate assays of the HC
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