4,001 research outputs found

    Applications of Deep Learning Models in Financial Forecasting

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    In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Molekulargenetische Charakterisierung von Sarkomen zur Identifizierung prognostischer Risikogruppen und potentieller therapeutischer Angriffspunkte

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    Sarkome sind seltene Tumore, die sich durch eine erhebliche Heterogenität auf histologischer, molekularer und genetischer Ebene auszeichnen. Trotz aller Fortschritte in der modernen Krebsbehandlung haben Sarkom-Patienten im fortgeschrittenen Stadium weiterhin begrenzte therapeutische Möglichkeiten und eine ungünstige Prognose. Da die Untersuchung des genetischen Profils nicht nur die Identifizierung prognostischer, sondern auch therapierelevanter Veränderungen bei heterogenen Erkrankungen ermöglicht, sind genetische Analysen ein unverzichtbarer Bestandteil der modernen Krebsbehandlung geworden. In dieser Studie analysierten wir retrospektiv das genetische Profil einer real-life Kohorte von 53 Sarkom-Patienten anhand eines 720-Gen-Panels. In Anbetracht der Heterogenität von Sarkomen wurden mehrere histopathologische Subtypen analysiert, wobei das Leiomyosarkom (17 %) am häufigsten vorkam. Das Durchschnittsalter der Patienten zum Zeitpunkt der Analyse betrug 49 Jahre. Die durchschnittliche Zeitspanne von der Erstdiagnose bis zur genetischen Analyse betrug 46,8 Monate. Das Gesamtüberleben betrug im Durchschnitt 55,9 Monate. Jeder Patient erhielt eine Tumorgenomsequenzierung mit einem 720-Gene-Panel. Bei 76,9% der Patienten wurde ein niedriger TMB-Wert festgestellt. Keiner der Patienten wurde als mikrosatelliteninstabil identifiziert. 25% der Patienten wiesen einen Mangel an der Funktionalität der homologen Rekombination (HRD) auf. Bei 30,8% wurde ein Fusionsgen nachgewiesen, wobei EWSR1-FLI1 und EWSR1- WT1 am häufigsten waren. Insgesamt wurden 38 Kopienzahlveränderungen (CNAs) gefunden, was auf eine erhebliche genomische Instabilität hinweist. Bei 15 Patienten wurden Keimbahnmutationen gefunden, die alle behandlungsrelevant sind, wobei die Mutation im MUTYH-Gen die häufigste ist. Therapierelevante somatische Mutationen wurden bei 47 Patienten gefunden (3,2 Mutationen/ Patient). Die am häufigsten betroffenen Gene waren TP53, CDKN2A-C, CDK4, RB1 und ATRX. 93 Auf der Grundlage der NGS-Ergebnisse erhielten 39,6 % der Patienten eine personalisierte Antitumortherapie. Das mediane Gesamtüberleben (OS) der Patienten mit einer gemäß den Daten der NGS-Analyse ausgerichteten Behandlung betrug 43 gegenüber 33 Monaten bei Patienten ohne zielgerichtete Therapien. Unsere NGS-Daten aus einer heterogenen Kohorte von 53 Sarkom-Patienten deuten darauf hin, dass personalisierte Therapien, die auf den Ergebnissen einer 720 Gen-Panel-Sequenzierung basieren, zu verbesserten klinischen Ergebnissen bei Sarkom-Patienten führen könnten

    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions

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    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

    Development of variable and robust brain wiring patterns in the fly visual system

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    Precise generation of synapse-specific neuronal connections are crucial for establishing a robust and functional brain. Neuronal wiring patterns emerge from proper spatiotemporal regulation of axon branching and synapse formation during development. Several neuropsychiatric and neurodevelopmental disorders exhibit defects in neuronal wiring owing to synapse loss and/or dys-regulated axon branching. Despite decades of research, how the two inter-dependent cellular processes: axon branching and synaptogenesis are coupled locally in the presynaptic arborizations is still unclear. In my doctoral work, I investigated the possible role of EGF receptor (EGFR) activity in coregulating axon branching and synapse formation in a spatiotemporally restricted fashion, locally in the medulla innervating Dorsal Cluster Neuron (M- DCN)/LC14 axon terminals. In this work I have explored how genetically encoded EGFR randomly recycles in the axon branch terminals, thus creating an asymmetric, non-deterministic distribution pattern. Asymmetric EGFR activity in the branches acts as a permissive signal for axon branch pruning. I observed that the M-DCN branches which stochastically becomes EGFR ‘+’ during development are synaptogenic, which means they can recruit synaptic machineries like Syd1 and Bruchpilot (Brp). My work showed that EGFR activity has a dual role in establishing proper M-DCN wiring; first in regulating primary branch consolidation possibly via actin regulation prior to synaptogenesis. Later in maintaining/protecting the levels of late Active Zone (AZ) protein Brp in the presynaptic branches by suppressing basal autophagy level during synaptogenesis. When M-DCNs lack optimal EGFR activity, the basal autophagy level increases resulting in loss of Brp marked synapses which is causal to increased exploratory branches and post-synaptic target loss. Lack of EGFR activity affects the M-DCN wiring pattern that makes adult flies more active and behave like obsessive compulsive in object fixation assay. In the second part of my doctoral work, I have asked how non-genetic factors like developmental temperature affects adult brain wiring. To test that, I increased or decreased rearing temperature which is known to inversely affect pupal developmental rate. We asked if all the noisy cellular processes of neuronal assembly: filopodial dynamics, axon branching, synapse formation and postsynaptic connections scale up or down accordingly. I observed that indeed all the cellular processes slow down at lower developmental temperature and vice versa, which changes the DCN wiring pattern accordingly. Interestingly, behavior of flies adapts to their developmental temperature, performing best at the temperature they have been raised at. This shows that optimal brain function is an adaptation of robust brain wiring patterns which are specified by noisy developmental processes. In conclusion, my doctoral work helps us better understand the developmental regulation of axon branching and synapse formation for establishing precise brain wiring pattern. We need all the cell intrinsic developmental processes to be highly regulated in space and time. It is infact a combinatorial effect of such stochastic processes and external factors that contribute to the final outcome, a functional and robust adult brain

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum
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