Some Optimally Adaptive Parallel Graph Algorithms on EREW PRAM Model

The study of graph algorithms is an important area of research in computer science, since graphs offer useful tools to model many real-world situations. The commercial availability of parallel computers have led to the development of efficient parallel graph algorithms. Using an exclusive-read and exclusive-write (EREW) parallel random access machine (PRAM) as the computation model with a fixed number of processors, we design and analyze parallel algorithms for seven undirected graph problems, such as, connected components, spanning forest, fundamental cycle set, bridges, bipartiteness, assignment problems, and approximate vertex coloring. For all but the last two problems, the input data structure is an unordered list of edges, and divide-and-conquer is the paradigm for designing algorithms. One of the algorithms to solve the assignment problem makes use of an appropriate variant of dynamic programming strategy. An elegant data structure, called the adjacency list matrix, used in a vertex-coloring algorithm avoids the sequential nature of linked adjacency lists. Each of the proposed algorithms achieves optimal speedup, choosing an optimal granularity (thus exploiting maximum parallelism) which depends on the density or the number of vertices of the given graph. The processor-(time)2 product has been identified as a useful parameter to measure the cost-effectiveness of a parallel algorithm. We derive a lower bound on this measure for each of our algorithms

Graph-based analysis of brain structural MRI data in Multiple System Atrophy

Il lavoro che ho sviluppato presso l’unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull’analisi dei dati strutturali di risonanza magnetica mediante l’utilizzo della graph theory, con lo scopo di valutare eventuali differenze tra un campione di pazienti affetti da Atrofia Multi Sistemica (MSA) e uno di controlli sani (HC). L’MSA è una patologia neurodegenerativa sporadica e progressiva. Essa si divide in due sottotipi: MSA-P ed MSA-C. Circa un terzo delle persone affette da MSA sperimentano una particolare apnea respiratoria, chiamata Stridor. Nello studio sono stati confrontati tra loro tre coppie di gruppi: HC vs MSA, No-stridor vs Stridor, e MSA-C vs MSA-P. I grafi sono strutture matematiche definite da nodi e links, in campo neurologico, la graph theory è usata con lo scopo di comprendere il funzionamento del cervello visto come network. L'approccio qui utilizzato è bastato sulla correlazione volumetriche tra le diverse regioni del cervello. Per costruire un grafo per ogni gruppo il primo step è stato ottenere la parcellizzazione delle immagini cerebrali, in seguito sono stati valutati i volumi delle regioni cerebrali, e in fine le correlazioni tra esse. Una volta costruiti i grafi è stato possibile calcolare i parametri topologici che ne caratterizzano struttura ed organizzazione. Nei vari confronti fatti non sono state riscontrate differenze nelle proprietà globali del network. L’analisi regionale invece ha evidenziato un'alterazione tra MSA e HC relativa a regioni che appartengono al network centrale autonomico, particolarmente colpito dalla malattia. Sono state inoltre riscontrate alterazioni nella organizzazione modulare dei gruppi presi in esame. Questa analisi ha mostrato la possibilità di indagare la funzionalità dei network cerebrali e della loro architettura modulare con misure strutturali quali la covarianza dei volumi delle varie regioni cerebrali in gruppi di soggetti

A Pattern-based deadlock-freedom analysis strategy for concurrent systems

Local analysis has long been recognised as an effective tool to combat the state-space explosion problem. In this work, we propose a method that systematises the use of local analysis in the verification of deadlock freedom for concurrent and distributed systems. It combines a strategy for system decomposition with the verification of the decomposed subsystems via adherence to behavioural patterns. At the core of our work, we have a number of CSP refinement expressions that allows the user of our method to automatically verify all the behavioural restrictions that we impose. We also propose a prototype tool to support our method. Finally, we demonstrate the practical impact our method can have by analysing how it fares when applied to some examples

Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice

Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice Jacob R. Bumgarner Circadian rhythms are intrinsic biological processes that fluctuate in function with a period of approximately 24 hours. These rhythms are precisely synchronized to the 24- hour day of the Earth by external rhythmic signaling cues. Solar light-dark cycles are the most potent environmental signaling cue for terrestrial organisms to align internal rhythms with the external day. Proper alignment and synchrony of internal circadian rhythms with external environmental rhythms are essential for health and optimal biological function. The modern human environment on Earth is no longer conducive to properly aligned circadian rhythms. Following the industrial revolution, artificial lighting and an ever-growing 24-hour global economy have shifted humans away from natural environments suited for rhythmic behavior and physiology. Humans, and much of the natural environment, are routinely exposed to circadian rhythm disruptors. The most pervasive disruptor of circadian rhythms is artificial light at night (ALAN). A growing 80% of humans on Earth are exposed to ALAN beyond natural nighttime environmental lighting levels. ALAN exposure is associated with numerous negative consequences on behavior and physiology, including neuroinflammation, cardiovascular disease, and altered immune function. This dissertation examines two previously uninvestigated effects of ALAN exposure on physiology and behavior in mice. In Part 1, I investigated the effects of ALAN exposure on pain behavior in mice. I observed that ALAN exposure had detrimental effects on rodent pain behavior in contexts of both health and models of human disease. ALAN exposure heightened responsiveness to noxious cold stimuli and innocuous mechanical touch. Differences in these effects were noted based on sex and disease state. I conclude this section with a report on the mechanisms by which ALAN exposure altered pain behavior. In Part 2, I investigated the effects of ALAN exposure on cerebrovascular structure in mice. To conduct these investigations, I first developed VesselVio, an open-source application for the analysis and visualization of vasculature datasets. Using this application and additional analytical frameworks, I examined the effects of short-term ALAN exposure on hippocampal vasculature in mice. ALAN exposure reduced hippocampal vascular density in mice, with notable regional sex differences. I also observed that ALAN exposure altered hippocampal vascular network connectivity and structure, with persistent regional sex differences. The data in this dissertation contribute to the ever-growing field of circadian rhythm biology focused on studying circadian rhythm disruption. These data highlight the continuing need to mitigate the pervasiveness of ALAN in human and natural environments. Most importantly, the results presented in this dissertation emphasize the need to consider ALAN as a mitigating factor for the treatment of both cardiovascular disease and pain

Formation control of autonomous vehicles with emotion assessment

Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks

Topology matters: characteristics of functional brain networks in healthy subjects and patients with Epilepsy, Diabetes, or Amyotrophic Lateral Sclerosis during a resting-state paradigm.

The brain can be seen as a complex structural and functional network. Cognitive functioning strongly depends on the organization of functional brain networks. EEG/MEG resting-­‐state functional connectivity and functional brain networks studies attempt to characterize normal brain organization as well as deviation from it due to brain diseases. Despite the impact on the understanding of brain functioning that these tools provided, there are still methodological hurdles that might compromise the quality of the results. The main aim of this thesis was to gain an understanding of the role of functional connectivity and network topology on brain functioning by: (i) addressing the methodological issues intrinsic in the analysis that can bias the results; (ii) quantifying functional connectivity differences possibly induced by brain impairments; (iii) detecting and quantifying how network topology changes, due to brain impairments. In order to achieve these objectives, functional connectivity and functional brain networks obtained by empirical recordings were reconstructed. Recordings were acquired with different modalities (EEG or MEG) and under different pathologies: epilepsy, diabetes and amyotrophic lateral sclerosis. Specifically three research questions were addressed: • Do functional brain network architectures obtained from pharmaco-­‐resistant epileptic patients responding to vagal nerve stimulation (VNS) change compared to patients not responding to VNS? • Are functional connectivity alterations related to cognitive performance and clinical status in type I diabetes mellitus patients? • Is functional network topology related to disease duration in amyotrophic lateral sclerosis patients? In order to answer these questions, avoiding possible biases which may affect the results, two key choices were made: first, the selection of the phase lag index [1] as functional connectivity estimator because it is less sensible to common sources problem; second, the application of minimum spanning tree (MST) [2] approach to overcome the problem of network comparison and characterize network topology reliably. In summary, this thesis confirms that alterations of functional connectivity and functional brain networks in disease may be used as potential biomarkers for more objective diagnosis and the choice of effective treatment options. Specifically, in epileptic patients implanted with VNS the relation between network measures and clinical benefit suggest that these measures can be used as a marker in monitoring the efficacy of the treatment; in amyotrophic lateral sclerosis the relation between disease duration and whole brain network disruption suggests diagnostic relevance of network measures in evaluating and monitoring the disease; and finally in type 1 diabetic mellitus patients functional connectivity measures can be complementary to cognitive tests and may help to monitor the effect of T1DM on brain functions

Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe