11,680 research outputs found
Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.
The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design
Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions
Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units.
The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures.
The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system.
Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter.
The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation
Explorations into Appendicular Ontogeny using a Cross-Sectional, Contemporary U.S. Sample
Investigations into the subadult skeleton have been restricted by sample availability in biological anthropology. Alternatively, the same source of longitudinal data has been repeatedly used, which does not reflect the variability of growth and development (i.e., ontogeny) or the United States (U.S.) population. Small and/or homogenous samples have often resulted in limited or inappropriate modeling choices to investigate the growth and development and variation of the subadult skeleton. Recent technological advancements have made virtual anthropology possible. The use of computed tomography (CT) scans has opened the doors to increasing sample sizes of minority groups and in turn increasing the variation of skeletal information. One repository, the Subadult Virtual Anthropology Database (SVAD), has focused on increasing and diversifying subadult skeletal data to increase the possibilities of subadult research in biological anthropology. The articles in this (non)dissertation collection use the SVAD (M=610, F=416) and the Forensic Anthropology Data Bank (FDB; M=285, F=161) to evaluate three different perspectives of appendicular (i.e., long bone) ontogeny: absolute, relative, and index. First, relative long bone lengths and nonlinear modeling are used as the first-ever evaluation of long bone growth through adult stabilization. Second, the brachial and crural indices are used to explore the chronological ontogenetic trajectories of each index and their ecogeographic patterns. Third, absolute long bone breadth and length measurements are used to create linear and nonlinear equations for estimating subadult stature for forensic application. In doing so, this is the first comprehensive collection of studies that explore three distinct perspectives of long bone ontogeny and variation from the same source of subadult skeletal data, demonstrating the need for additional contemporary subadult samples and novel modeling approaches
Detecting and Mitigating Adversarial Attack
Automating arrhythmia detection from ECG requires a robust and trusted system that retains high accuracy under electrical disturbances. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Many approaches have reached human-level performance in classifying arrhythmia from ECGs. Even convolutional neural networks are susceptible to adversarial examples as well that can also misclassify ECG signals. Moreover, they do not generalize well on the out-of-distribution dataset. Adversarial attacks are small crafted perturbations injected in the original data which manifest the out-of-distribution shifts in signal to misclassify the correct class. However, these architectures are vulnerable to adversarial attacks as well. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. In this work, we propose two novel conditional generative adversarial networks (GAN), ECG-Adv-GAN and ECG-ATK-GAN, to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. The model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Moreover, the ECG-ATK-GAN is robust against adversarial attacked ECG signals and retains high accuracy when exposed to various types of adversarial attacks while classifying arrhythmia. We benchmark our architecture on six different white and black-box attacks and compare them with other recently proposed arrhythmia classification models. When considering the defense strategy, the variation of the adversarial attacks, both targeted and non-targeted, can determine the perturbation by calculating the gradient. Novel defenses are being introduced to improve upon existing techniques to fend off each new attack. This back-and-forth game between attack and defense is persistently recurring, and it became significant to understand the pattern and behavior of the attacker to create a robust defense. One widespread tactic is applying a mathematically based model like Game theory. To analyze this circumstance, we propose a computational framework of game theory to analyze the CNN Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values in CAPTCHA dataset. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies from the Game theoretical perspective
The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes
The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved.
The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity.
This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted.
The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer
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Methane Production by Methanogens In Simulated Subsurface Martian Environments
Methane has a typical atmospheric photochemical lifetime of ∼300 years on Mars, making contemporary reported detections (and non-detections) of methane a fiercely debated topic, due to the potential need for a present-day source. On Earth, most methane is produced by methanogenic microbes present in, e.g., ruminants, wetlands, lakes and permafrost. Of the four metabolic pathways on Earth, the hydrogenotrophic pathway is the most common, utilising CO2 and H2 as substrates. Both gases are present on Mars, plus liquid water and essential elements (CHNOPS) that are requirements for life, and organics. Surface conditions on Mars are sterilising, however, the temperature and pressure of the subsurface are potentially favourable to life and provide a shield to sterilising surface conditions, and are thus a possible habitat for methanogens. A meta-analysis was
conducted, motivated by these subsurface parameters, that redefined the statistical representation for several growth parameters for all type-strains of methanogens and analysed multiple parameters
simultaneously across multiple categories (e.g. metabolism), showing that the optimal average conditions in which to grow methanogens would be a meso-temperate (20 to 39â—¦C), hypersaline and slightly acidic environment. Two martian subsurface environments were simulated to determine whether environmental or chemical factors are inhibitory to methanogenesis. (1) Methanoculleus marisnigri was grown in a custom-built, high-pressure manifold at 60 bar and 25â—¦C to simulate the subsurface of Mars, although no methane was produced, due to a technical issue resulting in oxygenated medium. However, some cells survived five weeks of oxygenation. (2) Methanothermococcus okinawensis was grown in a simulated chemical environment at 1 bar and 60â—¦C, that included a regolith simulant, a proposed martian brine and a Mars-relevant organic source (carbonaceous chondrite). The simulated parameters of the chemical environment of subsurface Mars were not inhibitory to hydrogenotrophic methanogenesis, suggesting it is feasible (from a metabolic perspective) that subsurface methanogens could be producing contemporary methane on Mars
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
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Mitigating Data Scarcity for Neural Language Models
In recent years, pretrained neural language models (PNLMs) have taken the field of natural language processing by storm, achieving new benchmarks and state-of-theart performances. These models often rely heavily on annotated data, which may not always be available. Data scarcity are commonly found in specialized domains, such as medical, or in low-resource languages that are underexplored by AI research. In this dissertation, we focus on mitigating data scarcity using data augmentation and neural ensemble learning techniques for neural language models. In both research directions, we implement neural network algorithms and evaluate their impact on assisting neural language models in downstream NLP tasks. Specifically, for data augmentation, we explore two techniques: 1) creating positive training data by moving an answer span around its original context and 2) using text simplification techniques to introduce a variety of writing styles to the original training data. Our results indicate that these simple and effective solutions improve the performance of neural language models considerably in low-resource NLP domains and tasks. For neural ensemble learning, we use a multi-label neural classifier to select the best prediction outcome from a variety of individual pretrained neural language models trained for a low-resource medical text simplification task
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe.
However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing.
In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software.
Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
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