706 research outputs found
Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors
Whenever researchers wish to study the behaviour of individuals choosing among a set of alternatives, they usually rely on models based on the random utility theory, which postulates that the single individuals modify their behaviour so that they can maximise of their utility. These models, often identified as discrete choice models (DCMs), usually require the definition of the utilities for each alternative, by first identifying the variables influencing the decisions. Traditionally, DCMs focused on observable variables and treated users as optimizing tools with predetermined needs. However, such an approach is in contrast with the results from studies in social sciences which show that choice behaviour can be influenced by psychological factors such as attitudes and preferences. Recently there have been formulations of DCMs which include latent constructs for capturing the impact of subjective factors. These are called hybrid choice models or integrated choice and latent variable models (ICLV). However, DCMs are not exempt from issues, like, the fact that researchers have to choose the variables to include and their relations to define the utilities. This is probably one of the reasons which has recently lead to an influx of numerous studies using machine learning (ML) methods to study mode choice, in which researchers tried to find alternative methods to analyse travellersâ choice behaviour. A ML algorithm is any generic method that uses the data itself to understand and build a model, improving its performance the more it is allowed to learn. This means they do not require any a priori input or hypotheses on the structure and nature of the relationships between the several variables used as its inputs. ML models are usually considered black-box methods, but whenever researchers felt the need for interpretability of ML results, they tried to find alternative ways to use ML methods, like building them by using some a priori knowledge to induce specific constrains. Some researchers also transformed the outputs of ML algorithms so that they could be interpreted from an economic point of view, or built hybrid ML-DCM models. The object of this thesis is that of investigating the benefits and the disadvantages deriving from adopting either DCMs or ML methods to study the phenomenon of mode choice in transportation. The strongest feature of DCMs is the fact that they produce very precise and descriptive results, allowing for a thorough interpretation of their outputs. On the other hand, ML models offer a substantial benefit by being truly data-driven methods and thus learning most relations from the data itself. As a first contribution, we tested an alternative method for calculating the value of travel time (VTT) through the results of ML algorithms. VTT is a very informative parameter to consider, since the time consumed by individuals whenever they need to travel normally represents an undesirable factor, thus they are usually willing to exchange their money to reduce travel times. The method proposed is independent from the mode-choice functions, so it can be applied to econometric models and ML methods equally, if they allow the estimation of individual level probabilities. Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of the individuals, but also psycho-attitudinal indicators, to better describe the influence of psychological factors on choice behaviour. The results were estimated by using two different datasets. Since NN results are dependent on the values of their hyper-parameters and on their initialization, several NNs were estimated by using different hyper-parameters to find the optimal values, which were used to verify the stability of the results with different initializations
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between âdrug likeâ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalâs new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Decisions, decisions, decisions: the development and plasticity of reinforcement learning, social and temporal decision making in children
Human decision-making is the flexible way people respond to their environment, take actions, and plan toward long-term goals. It is commonly thought that humans rely on distinct decision-making systems, which are either more habitual and reflexive or deliberate and calculated. How we make decisions can provide insight into our social functioning, mental health and underlying psychopathology, and ability to consider the consequences of our actions. Notably, the ability to make appropriate, habitual or deliberate decisions depending on the context, here referred to as metacontrol, remains underexplored in developmental samples. This thesis aims to investigate the development of different decision-making mechanisms in middle childhood (ages 5-13) and to illuminate the potential neurocognitive mechanisms underlying value-based decision-making. Using a novel sequential decision-making task, the first experimental chapter presents robust markers of model-based decision-making in childhood (N = 85), which reflects the ability to plan through a sequential task structure, contrary to previous developmental studies. Using the same paradigm, in a new sample via both behavioral (N = 69) and MRI-based measures (N = 44), the second experimental chapter explores the neurocognitive mechanisms that may underlie model-based decision-making and its metacontrol in childhood and links individual differences in inhibition and cortical thickness to metacontrol. The third experimental chapter explores the potential plasticity of social and intertemporal decision-making in a longitudinal executive function training paradigm (N = 205) and initial relationships with executive functions. Finally, I critically discuss the results presented in this thesis and their implications and outline directions for future research in the neurocognitive underpinnings of decision-making during development
Connecting to make a difference : social learning and radical collective change in prefigurative online communities
In view of the current global social and ecological predicament, what might constitute relevant forms of radical collective change? What role can processes of social learning play in facilitating such change? And to what extent are online networks able to support the unfolding of such processes? This thesis addresses these questions. I first present the results of two participatory action research projects, taking place in two different prefigurative online communities attempting to bring about very different forms of collective change. The first focuses on building a transnational, decentralised grassroots economic system as an alternative to global capitalism, but struggles to shake free from the toxic influence of global financial markets, and from unhelpful ways of relating and organising. The second aims to foster self-organisation and new forms of relationality between humans and with the rest of the living world, but struggles to address the heritage of historical violence and injustice, or to bring about visible political change. With the help of the Wenger-Trayner social learning theory and evaluation framework, I consider what processes of social learning have been taking place (or not) in these networks, and their outcomes; and what other social change efforts may learn from these experiments and their limitations. Finally, I present a reflexive account of my own process of learning and unlearning through my involvement with these projects and others, with regards to the question of what may constitute radical collective change. This critical assessment of my own thinking and aspirations leads me to argue in favour of decolonial approaches to social change as potentially relevant responses to the global predicament. This thesis contributes to the understanding of social learning processes within prefigurative online communities, and to the practice of social change efforts in such contexts
Parallel and Flow-Based High Quality Hypergraph Partitioning
Balanced hypergraph partitioning is a classic NP-hard optimization problem that is a fundamental tool in such diverse disciplines as VLSI circuit design, route planning, sharding distributed databases, optimizing communication volume in parallel computing, and accelerating the simulation of quantum circuits.
Given a hypergraph and an integer , the task is to divide the vertices into disjoint blocks with bounded size, while minimizing an objective function on the hyperedges that span multiple blocks.
In this dissertation we consider the most commonly used objective, the connectivity metric, where we aim to minimize the number of different blocks connected by each hyperedge.
The most successful heuristic for balanced partitioning is the multilevel approach, which consists of three phases.
In the coarsening phase, vertex clusters are contracted to obtain a sequence of structurally similar but successively smaller hypergraphs.
Once sufficiently small, an initial partition is computed.
Lastly, the contractions are successively undone in reverse order, and an iterative improvement algorithm is employed to refine the projected partition on each level.
An important aspect in designing practical heuristics for optimization problems is the trade-off between solution quality and running time.
The appropriate trade-off depends on the specific application, the size of the data sets, and the computational resources available to solve the problem.
Existing algorithms are either slow, sequential and offer high solution quality, or are simple, fast, easy to parallelize, and offer low quality.
While this trade-off cannot be avoided entirely, our goal is to close the gaps as much as possible.
We achieve this by improving the state of the art in all non-trivial areas of the trade-off landscape with only a few techniques, but employed in two different ways.
Furthermore, most research on parallelization has focused on distributed memory, which neglects the greater flexibility of shared-memory algorithms and the wide availability of commodity multi-core machines.
In this thesis, we therefore design and revisit fundamental techniques for each phase of the multilevel approach, and develop highly efficient shared-memory parallel implementations thereof.
We consider two iterative improvement algorithms, one based on the Fiduccia-Mattheyses (FM) heuristic, and one based on label propagation.
For these, we propose a variety of techniques to improve the accuracy of gains when moving vertices in parallel, as well as low-level algorithmic improvements.
For coarsening, we present a parallel variant of greedy agglomerative clustering with a novel method to resolve cluster join conflicts on-the-fly.
Combined with a preprocessing phase for coarsening based on community detection, a portfolio of from-scratch partitioning algorithms, as well as recursive partitioning with work-stealing, we obtain our first parallel multilevel framework.
It is the fastest partitioner known, and achieves medium-high quality, beating all parallel partitioners, and is close to the highest quality sequential partitioner.
Our second contribution is a parallelization of an n-level approach, where only one vertex is contracted and uncontracted on each level.
This extreme approach aims at high solution quality via very fine-grained, localized refinement, but seems inherently sequential.
We devise an asynchronous n-level coarsening scheme based on a hierarchical decomposition of the contractions, as well as a batch-synchronous uncoarsening, and later fully asynchronous uncoarsening.
In addition, we adapt our refinement algorithms, and also use the preprocessing and portfolio.
This scheme is highly scalable, and achieves the same quality as the highest quality sequential partitioner (which is based on the same components), but is of course slower than our first framework due to fine-grained uncoarsening.
The last ingredient for high quality is an iterative improvement algorithm based on maximum flows.
In the sequential setting, we first improve an existing idea by solving incremental maximum flow problems, which leads to smaller cuts and is faster due to engineering efforts.
Subsequently, we parallelize the maximum flow algorithm and schedule refinements in parallel.
Beyond the strive for highest quality, we present a deterministically parallel partitioning framework.
We develop deterministic versions of the preprocessing, coarsening, and label propagation refinement.
Experimentally, we demonstrate that the penalties for determinism in terms of partition quality and running time are very small.
All of our claims are validated through extensive experiments, comparing our algorithms with state-of-the-art solvers on large and diverse benchmark sets.
To foster further research, we make our contributions available in our open-source framework Mt-KaHyPar.
While it seems inevitable, that with ever increasing problem sizes, we must transition to distributed memory algorithms, the study of shared-memory techniques is not in vain.
With the multilevel approach, even the inherently slow techniques have a role to play in fast systems, as they can be employed to boost quality on coarse levels at little expense.
Similarly, techniques for shared-memory parallelism are important, both as soon as a coarse graph fits into memory, and as local building blocks in the distributed algorithm
ACARORUM CATALOGUS IX. Acariformes, Acaridida, Schizoglyphoidea (Schizoglyphidae), Histiostomatoidea (Histiostomatidae, Guanolichidae), Canestrinioidea (Canestriniidae, Chetochelacaridae, Lophonotacaridae, Heterocoptidae), Hemisarcoptoidea (Chaetodactylidae, Hyadesiidae, Algophagidae, Hemisarcoptidae, Carpoglyphidae, Winterschmidtiidae)
The 9th volume of the series Acarorum Catalogus contains lists of mites of 13 families, 225 genera and 1268 species of the superfamilies Schizoglyphoidea, Histiostomatoidea, Canestrinioidea and Hemisarcoptoidea. Most of these mites live on insects or other animals (as parasites, phoretic or commensals), some inhabit rotten plant material, dung or fungi. Mites of the families Chetochelacaridae and Lophonotacaridae are specialised to live with Myriapods (Diplopoda). The peculiar aquatic or intertidal mites of the families Hyadesidae and Algophagidae are also included.Publishe
Development and implementation of in silico molecule fragmentation algorithms for the cheminformatics analysis of natural product spaces
Computational methodologies extracting specific substructures like functional groups or molecular scaffolds from input molecules can be grouped under the term âin silico molecule fragmentationâ. They can be used to investigate what specifically characterises a heterogeneous compound class, like pharmaceuticals or Natural Products (NP) and in which aspects they are similar or dissimilar. The aim is to determine what specifically characterises NP structures to transfer patterns favourable for bioactivity to drug development. As part of this thesis, the first algorithmic approach to in silico deglycosylation, the removal of glycosidic moieties for the study of aglycones, was developed with the Sugar Removal Utility (SRU) (Publication A). The SRU has also proven useful for investigating NP glycoside space. It was applied to one of the largest open NP databases, COCONUT (COlleCtion of Open Natural prodUcTs), for this purpose (Publication B). A contribution was made to the Chemistry Development Kit (CDK) by developing the open Scaffold Generator Java library (Publication C). Scaffold Generator can extract different scaffold types and dissect them into smaller parent scaffolds following the scaffold tree or scaffold network approach. Publication D describes the OngLai algorithm, the first automated method to identify homologous series in input datasets, group the member structures of each group, and extract their common core. To support the development of new fragmentation algorithms, the open Java rich client graphical user interface application MORTAR (MOlecule fRagmenTAtion fRamework) was developed as part of this thesis (Publication E). MORTAR allows users to quickly execute the steps of importing a structural dataset, applying a fragmentation algorithm, and visually inspecting the results in different ways. All software developed as part of this thesis is freely and openly available (see https://github.com/JonasSchaub)
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