10,578 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Measuring socioeconomic position in studies of health inequalities
There is a consistent finding that the higher the socioeconomic position (SEP), the better the health. The choice of SEP indicator is crucial in explaining these socioeconomic inequalities. However, a poorly motivated use of SEP indicators prevails in the literature on social health inequalities, hampering the transparency and comparability across studies. Its primary aim is to explore different ways of measuring SEP to identify social inequalities in health. The thesis focuses on the most common, objective SEP indicators (education, occupation, and income); subjective SEP; and childhood circumstances.
This thesis consists of three papers. Papers I and III apply data from the Tromsø Study, and Paper II is based on an online survey investigating people's views on SEP, conducted in Norway and Australia. Paper I investigates the potential to combine education and income into a composite score for SEP and how it predicts inequalities in health-related quality of life (HRQoL). Paper II assesses the relative importance of objective SEP indicators and childhood circumstances in estimating subjective SEP. Paper III explores the role of circumstances and lifestyle factors in estimating inequalities in HRQoL and self-rated health.
While we found that the combination of education and income demonstrated a non-linear relationship with overall SEP, the composite SEP score was not superior as a predictor of HRQoL compared to including education and income separately. Furthermore, we found that childhood circumstances demonstrated a lasting, independent impact on subjective SEP. Paper III revealed that there were inequalities arising from circumstances, with substantial contributions from financial circumstances in childhood and education.
This thesis demonstrates the need to motivate the choice of SEP indicator in studies of health inequalities. It also stresses the importance of early-life factors as determinants of adult health, advocating for policies targeting childhood circumstances in equalising early life chances.Et svært vanlig funn på tvers av land, studiepopulasjoner og helseutfall er at desto høyere sosioøkonomisk posisjon (SEP), desto bedre helse. Valg av SEP-indikator som skal reflektere de sosioøkonomiske dimensjonene i helse er avgjørende for å forklare disse helseulikhetene. Likevel er det slik at bruken av SEP-indikatorer i studier om sosial ulikhet i helse ofte preges av svak eller ingen begrunnelse med utgangspunkt i teori og hypoteser, noe som begrenser muligheten til sammenligning mellom studier. Denne avhandlingen bruker ulike tilnærminger for å måle SEP i studier av helseulikhet. Et overordnet formål er å utforske ulike måter å måle sosial posisjon for å identifisere sosiale ulikhet i helse, og hvordan livsstilsfaktorer i tillegg påvirker dette forholdet. Fokuset vil være på de tre vanligste objektive SEP-indikatorene (utdanning, yrke og inntekt); subjektiv SEP; og indikatorer for barndomsforhold.
Avhandlingen består av tre artikler. Artikkel I og III er basert på data fra Tromsøundersøkelsen, mens Artikkel II benytter data fra på en nettbasert spørreundersøkelse om folks betraktninger omkring SEP, som har blitt gjennomført i Norge og Australia. Alle de tre artiklene utforsker bruken av ulike SEP-indikatorer i en helseulikhetssammenheng. Artikkel I undersøker potensialet for å kombinere utdanning og inntekt til en samleindikator for SEP, samt hvordan denne samleindikatoren predikerer helse-relatert livskvalitet (HRQoL). Artikkel II måler objektive SEP-indikatorer (utdanning, yrke og inntekt) og barndomsforholds relative betydning i å estimere subjektiv SEP. Artikkel III utforsker hvordan variabler om barndomsforhold på den ene siden og livsstilsfaktorer på den andre estimerer HRQoL og selvrapportert helse, både på et bestemt tidspunkt og over tid.
Vi fant at kombinasjonen av utdanning og inntekt viste en sterk ikke-lineær sammenheng med total SEP, mens samleindikatoren for SEP viste seg å ikke være bedre i å predikere HRQoL sammenlignet med å inkludere utdanning og inntekt separat. Videre fant vi at barndomsforhold så ut til å ha en vedvarende påvirkning på subjektiv SEP, som var uavhengig av objektiv SEP. Artikkel III viste at det var ulikheter i helse med røtter i barndomsforhold, med særlig påvirkning fra økonomiske forhold i barndommen og egen utdanning.
Denne avhandlingen viser behovet for å gjøre et faglig motivert valg av SEP-indikator i studier av helseulikhet. Den understreker også viktigheten av barndomsforhold som bestemmende faktorer for helseutfall senere i livet, og etterlyser dermed politikk rettet mot tidlige barndomsforhold for å utjevne ulikheter og sikre gode livssjanser
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
In this work we present a non-parametric online market regime detection
method for multidimensional data structures using a path-wise two-sample test
derived from a maximum mean discrepancy-based similarity metric on path space
that uses rough path signatures as a feature map. The latter similarity metric
has been developed and applied as a discriminator in recent generative models
for small data environments, and has been optimised here to the setting where
the size of new incoming data is particularly small, for faster reactivity.
On the same principles, we also present a path-wise method for regime
clustering which extends our previous work. The presented regime clustering
techniques were designed as ex-ante market analysis tools that can identify
periods of approximatively similar market activity, but the new results also
apply to path-wise, high dimensional-, and to non-Markovian settings as well as
to data structures that exhibit autocorrelation.
We demonstrate our clustering tools on easily verifiable synthetic datasets
of increasing complexity, and also show how the outlined regime detection
techniques can be used as fast on-line automatic regime change detectors or as
outlier detection tools, including a fully automated pipeline. Finally, we
apply the fine-tuned algorithms to real-world historical data including
high-dimensional baskets of equities and the recent price evolution of crypto
assets, and we show that our methodology swiftly and accurately indicated
historical periods of market turmoil.Comment: 65 pages, 52 figure
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Knowledge-based recommender system for stocks using clustering and nearest neighbors
Recommendation systems and algorithms are part of many services we use today. Online marketplaces, social media sites, streaming services, and many others lean on the algorithms to provide content for a user that match one’s likings. A practical example of such system is Netflix which may recommend movies to a user based on one’s viewing history. “Since you watched X, you might also be interested in Y”. Even though these algorithms are used in multiple services, there are still applications where the power of recommendation systems hasn’t been fully utilized for a public consumer. One of these are publicly traded stocks. Investing into publicly listed stocks is a common way to generate wealth. There are thousands of companies listed in NYSE and NASDAQ stock markets in the USA only. For an investor this is a lot to choose from. Some may prefer growth stocks and others blue-chip stocks with high dividend yield. One can search higher risk-reward returns from stocks that are dropping heavily and other seek steady growth in their preferred stocks. This thesis aims to implement a knowledge-based recommendation system that considers not only stock’s financial data but also historical price development to give meaningful stock recommendations based on an input of a single stock in a casebased manner. The implementation considers two different approaches when combining these distinctly different data types. The experimental development relies on clustering techniques to categorize similar stocks into different recommendation lists and finally sorting the lists using nearest neighbors. The evaluation of the approaches is conducted using machine learning evaluation methods combined with evaluation metrics used in recommender systems. The final best performing implementation is built on top of K-means clustering technique and t-SNE dimensionality reduction method. Trendlines and financial data of the stocks are combined using separately computed distance matrices. Similarity between the trendlines is computed using customized cosine-distance function. Finally the thesis presents a Stock Recommender using Similarity-based Methods (StockRSM)
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Latent Spaces for Antimicrobial Peptide Design
Current antibacterial treatments cannot overcome the growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have emerged as a powerful method for generating novel therapeutic candidates from existing datasets; however, there has been less research focused on evaluating the search spaces associated with these generators. In this research I employ five deep learning model architectures for de novo generation of antimicrobial peptide sequences and assess the properties of their associated latent spaces. I train a RNN, RNN with attention, WAE, AAE and Transformer model and compare their abilities to construct desirable latent spaces in 32, 64, and 128 dimensions. I assess reconstruction accuracy, generative capability, and model interpretability and demonstrate that while most models are able to create a partitioning in their latent spaces into regions of low and high AMP sampling probability, they do so in different manners and by appealing to different underlying physicochemical properties. In this way I demonstrate several benchmarks that must be considered for such models and suggest that for optimization of search space properties, an ensemble methodology is most appropriate for design of new AMPs. I design an AMP discovery pipeline and present candidate sequences and properties from three models that achieved high benchmark scores. Overall, by tuning models and their accompanying latent spaces, targeted sampling of anti-microbial peptides with ideal characteristics is achievable
Computational creativity: an interdisciplinary approach to sequential learning and creative generations
Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities.
We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative.
In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity.
Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts
IMAGINING, GUIDING, PLAYING INTIMACY: - A Theory of Character Intimacy Games -
Within the landscape of Japanese media production, and video game production in particular, there is a niche comprising video games centered around establishing, developing, and fulfilling imagined intimate relationships with anime-manga characters. Such niche, although very significant in production volume and lifespan, is left unexplored or underexplored. When it is not, it is subsumed within the scope of wider anime-manga media. This obscures the nature of such video games, alternatively identified with descriptors including but not limited to ‘visual novel’, ‘dating simulator’ and ‘adult computer game’.
As games centered around developing intimacy with characters, they present specific ensembles of narrative content, aesthetics and software mechanics. These ensembles are aimed at eliciting in users what are, by all intents and purposes, parasocial phenomena towards the game’s characters. In other words, these software products encourage players to develop affective and bodily responses towards characters. They are set in a way that is coherent with shared, circulating scripts for sexual and intimate interaction to guide player imaginative action. This study defines games such as the above as ‘character intimacy games’, video game software where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters. To do so, however, player must recognize themselves as playing that type of game, and to be looking to develop that kind of response towards the game’s characters. Character Intimacy Games are contingent upon player developing affective and bodily responses, and thus presume that players are, at the very least, non-hostile towards their development. This study approaches Japanese character intimacy games as its corpus, and operates at the intersection of studies of communication, AMO studies and games studies.
The study articulates a research approach based on the double need of approaching single works of significance amidst a general scarcity of scholarly background on the subject. It juxtaposes data-driven approaches derived from fan-curated databases – The Visual Novel Database and Erogescape -Erogē Hyōron Kūkan – with a purpose-created ludo-hermeneutic process. By deploying an observation of character intimacy games through fan-curated data and building ludo-hermeneutics on the resulting ontology, this study argues that character intimacy games are video games where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters and recognizing themselves as doing so. To produce such conditions, the assemblage of software mechanics and narrative content in such games facilitates intimacy between player and characters. This is, ultimately, conductive to the emergence of parasocial phenomena. Parasocial phenomena, in turn, are deployed as an integral assumption regarding player activity within the game’s wider assemblage of narrative content and software mechanics
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