The rapid advancement of Machine Learning (ML) and Deep Learning (DL) has revolutionized various domains, necessitating efficient and cost-effective ML inference capabilities. Function-as-a-Service (FaaS) has emerged as a promising approach for hosting ML inference services, providing a serverless computing environment that streamlines development cycles and offers scalability and simplified infrastructure management. However, existing autoscaling strategies employed by popular FaaS platforms often overlook critical factors such as response time and tail latency. Additionally, Python's Global Interpreter Lock (GIL) poses challenges for parallel computing in high-request traffic scenarios. This thesis addresses the need for efficient and cost-effective Machine Learning (ML) inference capabilities by exploring batching and autoscaling strategies for Serverless Inference instances. The study proposes a prototype FaaS framework that provides adaptive request batching, reactive autoscaling policies, and SLO monitoring, thus allowing Serverless Inference workloads to meet their SLO targets even during peak traffic. The proposed approach aims to optimize resource utilization, mitigate tail latency, and improve overall system performance. / Master of Science / Machine Learning (ML) and Deep Learning (DL) are advanced techniques that allow computers to learn from data and make predictions or decisions without being explicitly programmed. This has led to significant advancements in various fields. Inference refers to the process of applying a trained ML model to new data to make predictions or extract insights. In the context of ML, there is a growing need for efficient and cost-effective inference capabilities. A new approach called Function-as-a-Service (FaaS) has emerged that can address this need. FaaS is a way of abstracting the server infrastructure away from the developers. This means developers can focus on writing the ML code without worrying about managing the underlying infrastructure. FaaS offers benefits such as scalability, simplified infrastructure management, and faster development cycles. However, existing FaaS platforms face challenges in ensuring fast response times and handling high levels of incoming requests. This thesis aims to address these challenges by proposing a prototype FaaS framework. The framework incorporates adaptive request batching, reactive autoscaling policies, and Service-Level Objectives (SLOs) monitoring. Request batching allows the framework to process multiple requests together, improving efficiency. Autoscaling policies ensure the system dynamically adjusts its resources based on the incoming workload. Monitoring SLOs helps track and meet performance targets, even during peak traffic. By optimizing resource utilization, reducing delays in processing requests, and improving overall system performance, the proposed approach seeks to provide efficient and cost-effective ML inference capabilities in a serverless environment.

Esta investigación, que pretende continuar y actualizar la realizada en mi TFM de COMINCREA (Master oficial de comunicación e industrias creativas de la Universidad de Alicante) sobre la viabilidad de la sección de cine de las tiendas FNAC con la digitalización de contenidos, analiza el modelo de negocio de Netflix en España y sus estrategias empresariales para la consolidación de su producto digital. El inesperado éxito del mismo, superando con creces las pesimistas expectativas de los estudios académicos previos a su llegada, ha intensificado las profundas transformaciones en la producción, distribución y consumo de los productos audiovisuales de entretenimiento, poniendo en serio peligro el futuro del formato físico y las tiendas dedicadas a su venta. Dichas estrategias emergen, en realidad, como una optimización de las utilizadas en el pasado por las propias tiendas, por lo que se indaga en la viabilidad de que dichas tiendas físicas y mixtas (las que ofertan también venta online de dicho producto físico) puedan, a su vez, implementar (de manera mejorada y adaptada) dichas estrategias con el objetivo de seguir siendo un negocio viable.

Social media and user engagement are bigger than ever. Users are presented with various types of content curated by algorithms, which partially dictate what is shown to them. These algorithms lack transparency and clarity for the user. In this thesis we have developed a toolset to tail fit data of user engagement to show what behaviours this data actually shows. We want to see the differences between categories of content and show how user engagement in social media behaves. From our study we have found that there are differences between how users engage with different leanings within political content andcontents of differing credibility. We have also found that more narrow metrics in choosingdata can present different results and behaviours. From this we can determine that choice of data is crucial when working with tails. Future work is imperative to keep creating understanding for these social media platforms and how users engage with different types of content. To keep up with the constantly changing environment of social media new tools and methods will needed to create understanding for our most used platforms for public interaction.

Enhancers regulate gene expression through epigenetic mechanisms influenced by internal and external stimuli. In mouse liver, growth hormone and environmental chemical exposures activate enhancers regulating gene expression in part through changes in chromatin accessibility. Identifying functional enhancers in the complex environment of mouse liver is challenging. This thesis presents HDI-STARR-seq, a massively parallel STARR-seq reporter assay that combines hydrodynamic injection (HDI)-driven plasmid delivery to mouse liver cells in vivo with expression from the liver-specific Albumin promoter to assay functional changes in enhancer-driven transcription induced by sex-dependent hormonal stimulation and xenobiotic exposure. HDI-STARR-seq employs a minimal albumin promoter, shows minimal innate immune response and apparently facilitates plasmid chromatinization recapitulating endogenous liver chromatin states. The assay is robustly reproducible in a small reporter library (~100 regions) and can be scalable for genome-wide analysis using 25,000-50,000 synthetic DNA fragments or enzyme-released mouse liver genomic fragments. Single nucleus-based 10x Genomics Multiome analysis identified accessible mouse liver chromatin regions genome-wide, their linked target genes in hepatocytes, and their differential accessibility between sexes and following exposure to TCPOBOP, a CAR (Nr1i3) agonist ligand. Using HDI-STARR-seq, functional enhancer activity was determined for 1,789 accessible chromatin regions across biological conditions, identifying many sex-biased, GH-regulated enhancers undergoing chromatin changes. The regulated enhancers were associated with enrichment for H3K4me1 and H3K27ac histone marks, showed regulated activities that matched activating histone marks and contained Hnf4a and other enriched motifs in male-biased, GH-repressed enhancer sets. Further, the impact of TCPOBOP exposure for 1 day or 2 weeks on HDI-STARR-seq activity was evaluated in mice of both sexes at 1,834 accessible chromatin regions. Induced H3K27ac and static H3K4me1 were enriched at TCPOBOP-responsive enhancers, as were DR4 motifs bound by CAR/RXR heterodimers. Motifs bound by Tcf, involved in cell proliferation, were enriched in HDI-STARR-seq active enhancers only after 2-week TCPOBOP exposure, indicating activation of a late responding signaling pathway. This research gives novel insights into the relationship between functional enhancer activity in mouse liver and hormonal and xenobiotic regulated epigenetic landscapes, and establishes the utility of HDI-STARR-seq for discovery of biologically relevant enhancer sequences in vivo. / 2026-09-18T00:00:00Z

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Asset allocation is the most influential factor driving investment performance. While researchers have made substantial progress in the field of asset allocation since the introduction of mean-variance framework by Markowitz, there is little agreement about appropriate portfolio choice for multi-period long horizon investors. Nowhere this is more evident than trustees of retirement plans choosing different asset allocation strategies as default investment options for their members. This doctoral dissertation consists of four essays each of which explores either a novel or an unresolved issue in the area of asset allocation for individual retirement plan participants. The goal of the thesis is to provide greater insight into the subject of portfolio choice in retirement plans and advance scholarship in this field. The first study evaluates different constant mix or fixed weight asset allocation strategies and comments on their relative appeal as default investment options. In contrast to past research which deals mostly with theoretical or hypothetical models of asset allocation, we investigate asset allocation strategies that are actually used as default investment options by superannuation funds in Australia. We find that strategies with moderate allocation to stocks are consistently outperformed in terms of upside potential of exceeding the participant’s wealth accumulation target as well as downside risk of falling below that target by very aggressive strategies whose allocation to stocks approach 100%. The risk of extremely adverse wealth outcomes for plan participants does not appear to be very sensitive to asset allocation. Drawing on the evidence of the previous study, the second essay explores possible solutions to the well known problem of gender inequality in retirement investment outcomes. Using non-parametric stochastic simulation, we simulate iv and compare the retirement wealth outcomes for a hypothetical female and male worker under different assumptions about breaks in employment, superannuation contribution rates, and asset allocation strategies. We argue that modest changes in contribution and asset allocation strategy for the female plan participant are necessary to ensure an equitable wealth outcome in retirement. The findings provide strong evidence against gender-neutral default contribution and asset allocation policy currently institutionalized in Australia and other countries. In the third study we examine the efficacy of lifecycle asset allocation models which allocate aggressively to risky asset classes when the employee participants are young and gradually switch to more conservative asset classes as they approach retirement. We show that the conventional lifecycle strategies make a costly mistake by ignoring the change in portfolio size over time as a critical input in the asset allocation decision. Due to this portfolio size effect, which has hitherto remained unexplored in literature, the terminal value of accumulation in retirement account is critically dependent on the asset allocation strategy adopted by the participant in later years relative to early years. The final essay extends the findings of the previous chapter by proposing an alternative approach to lifecycle asset allocation which incorporates performance feedback. We demonstrate that strategies that dynamically alter allocation between growth and conservative asset classes at different points on the investment horizon based on cumulative portfolio performance relative to a set target generally result in superior wealth outcomes compared to those of conventional lifecycle strategies. The dynamic allocation strategy exhibits clear second-degree stochastic dominance over conventional strategies which switch assets in a deterministic manner as well as balanced diversified strategies.

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Flutter is one of the primary aeroelastic phenomena that must be considered in aircraft design. Flutter is a self-sustaining structural vibration in which energy is extracted from the air flow and transferred to the structure. The amplitude of the vibration grows exponentially until structural failure occurs. Flutter stability requirements often influence the design of an aircraft, making accurate flutter prediction capabilities an essential part of the design process. Advances in computational fluid dynamics and computational power make it possible to solve the fluid flow and structural dynamics simultaneously, providing highly accurate solutions especially in the transonic flow regime. This procedure is, however, too time-consuming to be used in the design optimisation process. As a result panel codes, e.g., the doublet lattice method, and modal-based structural analysis methods are still being used extensively and continually improved. One application that is lagging in terms of accuracy and simplicity (from the user’s perspective) is the flutter analysis of T-tails. The flutter analysis of a T-tail usually involves the calculation of additional aerodynamic loads, apart from the loads calculated by the standard unsteady aerodynamic codes for conventional empennages. The popular implementations of the doublet lattice method do not calculate loads due to the in-plane motion (i.e., lateral or longitudinal motion) of the horizontal stabiliser or the in-plane loads on the stabiliser. In addition, these loads are dependent on the steady-state load distribution on the stabiliser, which is ignored in the doublet lattice method. The objective of the study was to extend the doublet lattice method to calculate the additional aerodynamic loads that are crucial for T-tail flutter analysis along with the customary unsteady air loads for conventional configurations. This was achieved by employing the Kutta-Joukowski theorem in the calculation of unsteady air loads on lifting surface panels. Calculating the additional unsteady air loads for T-tails within the doublet lattice method significantly reduces the human effort required for T-tail flutter analysis as well as the opportunities for introducing errors into the analysis. During the course of the study it became apparent that it was necessary to consider the quadratic mode shape components in addition to the linear mode shape components. Otherwise the unsteady loads due to the rotation (“tilting”) of the steady-state load on the stabiliser, one of the additional aerodynamic loads that are crucial for T-tail flutter analysis, would give rise to spurious generalised forces. In order to reduce the additional burden of determining the quadratic mode shape components, methods for calculating quadratic mode shape components using linear finite element analysis or estimating them from the linear mode shape components were developed. Wind tunnel tests were performed to validate the proposed computational method. A T-tail flutter model which incorporated a mechanism for changing the incidence angle of the horizontal stabiliser, and consequently the steady-state load distribution on the horizontal stabiliser, was used. The flutter speed of this model as a function of the horizontal stabiliser incidence was determined experimentally and compared to predictions. Satisfactory correlation was found between predicted and experimentally determined flutter speeds. / Thesis (M.Ing. (Chemical Engineering))--North-West University, Potchefstroom Campus, 2012

Serotonin receptors play a variety of functional roles in the body. Some indications and treatment claims for one of the classes of serotonin receptors, the 5-HT3 receptor family, include: anxiety, depression, chemotherapy- and radiation-induced emesis, constipation, irritable bowel syndrome, pain, drug addiction, and satiety control. A 5-HT3 receptor partial agonist, MD-354, served as a lead compound in the development of new 5-HT3 receptor ligands. Using halogenated analogs the study investigated their effect on binding to the 5-HT3 receptor. Conformationally-constrained analogs (quinazolines) were shown to be a novel class of 5-HT3 receptor antagonists. The log P values were determined for several analogs, and indicated that these ligands should be able to penetrate the blood-brain barrier. A homology model of the 5-HT3 receptor was built and the docking modes were assessed for these two series. Quinazolines were investigated for antidepressant properties using the mouse tail suspension test, and were shown to possess antidepressant-like activity.