Tropical sufficient statistics for persistent homology

We show that an embedding in Euclidean space based on tropical geometry generates stable sufficient statistics for barcodes. In topological data analysis, barcodes are multiscale summaries of algebraic topological characteristics that capture the “shape” of data; however, in practice, they have complex structures that make them difficult to use in statistical settings. The sufficiency result presented in this work allows for classical probability distributions to be assumed on the tropical geometric representation of barcodes. This makes a variety of parametric statistical inference methods accessible to barcodes, all while maintaining their initial interpretations. More specifically, we show that exponential family distributions may be assumed and that likelihood functions for persistent homology may be constructed. We conceptually demonstrate sufficiency and illustrate its utility in persistent homology dimensions 0 and 1 with concrete parametric applications to human immunodeficiency virus and avian influenza data

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Reflective-Physically Unclonable Function based System for Anti-Counterfeiting

Physically unclonable functions (PUF) are physical security mechanisms, which utilize inherent randomness in processes used to instantiate physical objects. In this dissertation, an extensive overview of the state of the art in implementations, accompanying definitions and their analysis is provided. The concept of the reflective-PUF is presented as a product security solution. The viability of the concept, its evaluation and the requirements of such a system is explored

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

Evaluation of blood-based microRNAs toward clinical use as biomarkers in common and rare diseases

According to the GLOBOCAN project of the International Agency for Research on Cancer, the top three common cancer diseases worldwide in the year 2020 were breast, lung and colorectal cancer. These are usually diagnosed via imaging methods (e.g. computer tomography) or invasive methods (e.g. biopsy). However, these techniques are potentially risky and expensive and thus not accessible to all patients, resulting in most cancers being detected in an advanced stage. Since the discovery of small non-coding RNAs and specifically microRNAs and their role as gene regulators, many researchers investigate their association with disease development. In particular, researchers examine body fluid based microRNAs which could present potential cost-effective and minimally- or non-invasive alternatives to the previously described established diagnosis methods. This dissertation focuses on microRNAs and investigates their suitability as minimally-invasive blood-borne biomarkers for potential diagnostic purposes. More specifically, the goals of this work are (1) to implement a new method to predict novel microRNAs, (2) to understand stability and characteristics of these small non-coding RNAs, possibly relevant for the last goal, (3) to discover potential diagnostic biomarkers in common and rare diseases. The first goal was addressed by developing miRMaster, a web service to predict new microRNAs. The tool uses machine learning and high-throughput sequencing data to find microRNA candidates that follow the known biogenesis pathways. The second goal was pursued in four publications. First, we performed a large scale evaluation of miRMaster by generating a high-resolution map of the human small non-coding RNA transcriptome for which we analyzed and validated potential microRNA candidates. Next, we examined the influence of seasonal effects on microRNA expression profiles and observed the largest difference between spring and the other seasons. Additionally, we evaluated the evolutionary conservation of small non-coding RNAs in zoo animals and showed that the distribution of sncRNA classes varies across species, while common microRNA families are present in more diverse organisms than assumed so far. Furthermore, we analyzed if microRNAs are technically stable, and whether biological variation is preserved when using capillary dried blood spots as an alternative sample collection device to venous blood specimens. Finally, we investigated the suitability of microRNAs as biomarkers for two diseases: lung cancer and Marfan disease. We identified blood-borne biomarker candidates for lung cancer detection in a large-scale multi-center study via machine learning. For the rare Marfan disease we analyzed the paired messenger RNA and microRNA expression levels in whole-blood samples. This highlighted several significantly deregulated microRNAs and messenger RNAs, which we subsequently validated in an independent cohort. In summary, this thesis provides valuable results toward potential clinical use of microRNAs, and the herein described projects represent comprehensive analyses of them from different perspectives: starting with microRNA discovery, addressing various technical and biological questions and ending with the potential use as biomarkers.Nach Angaben des GLOBOCAN-Projekts der International Agency for Research on Cancer sind die drei häufigsten Krebserkrankungen weltweit im Jahr 2020 Brust-, Lungen- und Darmkrebs. Diese werden in der Regel durch bildgebende Verfahren (z.B. Computertomographie) oder invasive Methoden (z.B. Biopsie) diagnostiziert. Diese Verfahren sind jedoch potenziell risikoreich und teuer und daher nicht für alle Patienten zugänglich. Dies führt dazu, dass die meisten Krebsarten erst in einem fortgeschrittenen Stadium entdeckt werden. Seit der Entdeckung der kurzen nichtkodierenden RNAs und insbesondere der microRNAs und ihrer Rolle als Genregulatoren untersuchen viele Forscher ihren Zusammenhang mit der Krankheitsentwicklung. Insbesondere untersuchen die Forscher die in Körperflüssigkeiten vorkommenden microRNAs, die potenziell kosteneffiziente und minimal- oder nicht-invasive Alternativen zu den bisher beschriebenen etablierten Diagnosemethoden darstellen könnten. Diese Dissertation konzentriert sich auf microRNAs und untersucht deren Eignung als minimal-invasive blutbasierte Biomarker für potenzielle diagnostische Zwecke. Genauer gesagt sind die Ziele dieser Arbeit (1) die Implementierung einer neuen Methode zur Vorhersage neuartiger microRNAs, (2) das Verständnis über die Stabilität und Charakteristika dieser kurzen nicht-kodierenden RNAs, die möglicherweise für das nächste Ziel relevant sind, (3) die Entdeckung potenzieller diagnostischer Biomarker für verschiedene Anwendungen. Das erste Ziel wurde durch die Entwicklung von miRMaster verfolgt, einem Webdienst zur Vorhersage neuer microRNAs. Das Tool nutzt maschinelles Lernen und Hochdurchsatz-Sequenzierungsdaten, um microRNA-Kandidaten zu finden, die den bekannten Wege der Biogenese folgen. Das zweite Ziel wurde in vier Veröffentlichungen verfolgt. Zunächst führten wir eine groß angelegte Evaluierung von miRMaster durch, indem wir eine High-Resolution Map des menschlichen Transkriptoms kurzer nichtkodierender RNAs erstellten, für die wir potenzielle microRNA-Kandidaten analysierten und validierten. Anschließend untersuchten wir den Einfluss saisonaler Effekte auf die microRNA-Expressionsprofile und beobachteten den größten Unterschied zwischen dem Frühling und den anderen Jahreszeiten. Darüber hinaus untersuchten wir die evolutionäre Erhaltung kurzer nichtkodierender RNAs in Zoo-Tieren und zeigten, dass die Verteilung der kurzer nichtkodierenden RNA-Klassen zwischen den Arten variiert, während gemeinsame microRNA-Familien in verschiedeneren Organismen vorkommen als bisher angenommen. Darüber hinaus analysierten wir, ob microRNAs technisch stabil sind und ob die biologische Variation erhalten bleibt, wenn kapillares Trockenblut als alternatives Probenentnahmeverfahren zu venösen Blutproben verwendet werden. Schließlich untersuchten wir die Eignung von microRNAs als Biomarker für zwei Krankheiten: Lungenkrebs und Marfan-Krankheit. In einer groß angelegten multizentrischen Studie identifizierten wir mit Hilfe von maschinellem Lernen Biomarker-Kandidaten aus dem Blut für die Erkennung von Lungenkrebs. Für die seltene Marfan-Krankheit analysierten wir die gepaarten Expressionsniveaus von messengerRNA und microRNA in Vollblutproben. Dabei wurden mehrere signifikant deregulierte microRNAs und messengerRNAs festgestellt, die wir anschließend in einer unabhängigen Kohorte validierten. Zusammenfassend lässt sich sagen, dass diese Arbeit wertvolle Ergebnisse im Hinblick auf die potenzielle klinische Verwendung von microRNAs liefert. Die hier beschriebenen Projekte stellen umfassende Analysen aus verschiedenen Blickwinkeln dar: angefangen bei der Entdeckung von microRNAs, über verschiedene technische und biologische Fragen bis hin zur potenziellen Verwendung als Biomarker

An affective computing and image retrieval approach to support diversified and emotion-aware reminiscence therapy sessions

A demência é uma das principais causas de dependência e incapacidade entre as pessoas idosas em todo o mundo. A terapia de reminiscência é uma terapia não farmacológica comummente utilizada nos cuidados com demência devido ao seu valor terapêutico para as pessoas com demência. Esta terapia é útil para criar uma comunicação envolvente entre pessoas com demência e o resto do mundo, utilizando as capacidades preservadas da memória a longo prazo, em vez de enfatizar as limitações existentes por forma a aliviar a experiência de fracasso e isolamento social. As soluções tecnológicas de assistência existentes melhoram a terapia de reminiscência ao proporcionar uma experiência mais envolvente para todos os participantes (pessoas com demência, familiares e clínicos), mas não estão livres de lacunas: a) os dados multimédia utilizados permanecem inalterados ao longo das sessões, e há uma falta de personalização para cada pessoa com demência; b) não têm em conta as emoções transmitidas pelos dados multimédia utilizados nem as reacções emocionais da pessoa com demência aos dados multimédia apresentados; c) a perspectiva dos cuidadores ainda não foi totalmente tida em consideração. Para superar estes desafios, seguimos uma abordagem de concepção centrada no utilizador através de inquéritos mundiais, entrevistas de seguimento, e grupos de discussão com cuidadores formais e informais para informar a concepção de soluções tecnológicas no âmbito dos cuidados de demência. Para cumprir com os requisitos identificados, propomos novos métodos que facilitam a inclusão de emoções no loop durante a terapia de reminiscência para personalizar e diversificar o conteúdo das sessões ao longo do tempo. As contribuições desta tese incluem: a) um conjunto de requisitos funcionais validados recolhidos com os cuidadores formais e informais, os resultados esperados com o cumprimento de cada requisito, e um modelo de arquitectura para o desenvolvimento de soluções tecnológicas de assistência para cuidados de demência; b) uma abordagem end-to-end para identificar automaticamente múltiplas informações emocionais transmitidas por imagens; c) uma abordagem para reduzir a quantidade de imagens que precisam ser anotadas pelas pessoas sem comprometer o desempenho dos modelos de reconhecimento; d) uma técnica de fusão tardia interpretável que combina dinamicamente múltiplos sistemas de recuperação de imagens com base em conteúdo para procurar eficazmente por imagens semelhantes para diversificar e personalizar o conjunto de imagens disponíveis para serem utilizadas nas sessões.Dementia is one of the major causes of dependency and disability among elderly subjects worldwide. Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used within dementia care due to its therapeutic value for people with dementia. This therapy is useful to create engaging communication between people with dementia and the rest of the world by using the preserved abilities of long-term memory rather than emphasizing the existing impairments to alleviate the experience of failure and social isolation. Current assistive technological solutions improve reminiscence therapy by providing a more lively and engaging experience to all participants (people with dementia, family members, and clinicians), but they are not free of drawbacks: a) the multimedia data used remains unchanged throughout sessions, and there is a lack of customization for each person with dementia; b) they do not take into account the emotions conveyed by the multimedia data used nor the person with dementia’s emotional reactions to the multimedia presented; c) the caregivers’ perspective have not been fully taken into account yet. To overcome these challenges, we followed a usercentered design approach through worldwide surveys, follow-up interviews, and focus groups with formal and informal caregivers to inform the design of technological solutions within dementia care. To fulfil the requirements identified, we propose novel methods that facilitate the inclusion of emotions in the loop during reminiscence therapy to personalize and diversify the content of the sessions over time. Contributions from this thesis include: a) a set of validated functional requirements gathered from formal and informal caregivers, the expected outcomes with the fulfillment of each requirement, and an architecture’s template for the development of assistive technology solutions for dementia care; b) an end-to-end approach to automatically identify multiple emotional information conveyed by images; c) an approach to reduce the amount of images that need to be annotated by humans without compromising the recognition models’ performance; d) an interpretable late-fusion technique that dynamically combines multiple content-based image retrieval systems to effectively search for similar images to diversify and personalize the pool of images available to be used in sessions

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282