A fine grained heuristic to capture web navigation patterns

In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information obtained from web logs is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm’s running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures the distance between the current rule-set and its corresponding maximal set obtained by the BFS algorithm. When the stopping parameter takes the value zero the heuristic corresponds to the BFS algorithm and as the parameter takes values closer to one the number of rules obtained decreases accordingly. Experiments were conducted with both real and synthetic data and the results show that for a given cut-point the number of rules induced increases smoothly with the decrease of the stopping criterion. Therefore, by setting the value of the stopping criterion the analyst can determine the number and quality of rules to be induced; the quality of a rule is measured by both its length and probability

Wearable device to assist independent living.

Older people increasingly want to remain living independently in their own homes. The aim of the ENABLE project is to develop a wearable device that can be used both within and outside of the home to support older people in their daily lives and which can monitor their health status, detect potential problems, provide activity reminders and offer communication and alarm services. In order to determine the specifications and functionality required for development of the device user surveys and focus groups were undertaken and use case analysis and scenario modeling carried out. The project has resulted in the development of a wrist worn device and mobile phone combination that can support and assist older and vulnerable wearers with a range of activities and services both inside and outside of their homes. The device is currently undergoing pilot trials in five European countries. The aim of this paper is to describe the ENABLE device, its features and services, and the infrastructure within which it operates

Monitoring COVID-19 on social media: development of an end-to-end natural language processing pipeline using a novel triage and diagnosis approach

Background: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients’ posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems

Bordering of symmetric matrices and an application to the minimum number of distinct eigenvalues for the join of graphs

An important facet of the inverse eigenvalue problem for graphs is to determine the minimum number of distinct eigenvalues of a particular graph. We resolve this question for the join of a connected graph with a path. We then focus on bordering a matrix and attempt to control the change in the number of distinct eigenvalues induced by this operation. By applying bordering techniques to the join of graphs, we obtain numerous results on the nature of the minimum number of distinct eigenvalues as vertices are joined to a fixed graph.</p

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Safety and Efficacy of Erythrocyte Encapsulated Thymidine Phosphorylase in Mitochondrial Neurogastrointestinal Encephalomyopathy.

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is an ultra-rare autosomal recessive disorder of nucleoside metabolism that is caused by mutations in the nuclear thymidine phosphorylase gene (TYMP) gene, encoding for the enzyme thymidine phosphorylase. There are currently no approved treatments for MNGIE. The aim of this study was to investigate the safety, tolerability, and efficacy of an enzyme replacement therapy for the treatment of MNGIE. In this single centre study, three adult patients with MNGIE received intravenous escalating doses of erythrocyte encapsulated thymidine phosphorylase (EE-TP; dose range: 4 to 108 U/kg/4 weeks). EE-TP was well tolerated and reductions in the disease-associated plasma metabolites, thymidine, and deoxyuridine were observed in all three patients. Clinical improvements, including weight gain and improved disease scores, were observed in two patients, suggesting that EE-TP is able to reverse some aspects of the disease pathology. Transient, non-serious adverse events were observed in two of the three patients; these did not lead to therapy discontinuation and they were managed with pre-medication prior to infusion of EE-TP. To conclude, enzyme replacement therapy with EE-TP demonstrated biochemical and clinical therapeutic efficacy with an acceptable clinical safety profile

Migrations and habitat use of the smooth hammerhead shark (Sphyrna zygaena) in the Atlantic Ocean

The smooth hammerhead shark, Sphyrna zygaena, is a cosmopolitan semipelagic shark captured as bycatch in pelagic oceanic fisheries, especially pelagic longlines targeting swordfish and/or tunas. From 2012 to 2016, eight smooth hammerheads were tagged with Pop-up Satellite Archival Tags in the inter-tropical region of the Northeast Atlantic Ocean, with successful transmissions received from seven tags (total of 319 tracking days). Results confirmed the smooth hammerhead is a highly mobile species, as the longest migration ever documented for this species (> 6600 km) was recorded. An absence of a diel vertical movement behavior was noted, with the sharks spending most of their time at surface waters (0-50 m) above 23 degrees C. The operating depth of the pelagic long-line gear was measured with Minilog Temperature and Depth Recorders, and the overlap with the species vertical distribution was calculated. The overlap is taking place mainly during the night and is higher for juveniles (similar to 40% of overlap time). The novel information presented can now be used to contribute to the provision of sustainable management tools and serve as input for Ecological Risk Assessments for smooth hammerheads caught in Atlantic pelagic longline fisheries.Oceanario de Lisboa through Project "SHARK-TAG: Migrations and habitat use of the smooth hammerhead shark in the Atlantic Ocean"; Investigador-FCT from the Portuguese Foundation for Science and Technology (FCT, Fundacao para a Ciencia e Tecnologia) [Ref: IF/00253/2014]; EU European Social Fund; Programa Operacional Potencial Human

Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting

The coefficient of determination, known as R2, is commonly used as a goodness-of-fit criterion for fitting linear models. R2 is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fitting a parametric distribution to a data set using, say, the maximum likelihood estimation method. A general approach to measure the goodness-of-fit of the fitted parameters, which is advocated herein, is to use a non- parametric measure for comparison between the empirical distribution, comprising the raw data, and the fitted model. In particular, for this purpose we put forward the Survi- val Jensen-Shannon divergence (SJS) and its empirical counterpart (ESJS) as a metric which is bounded, and is a natural generalisation of the Jensen-Shannon divergence. We demonstrate, via a straightforward procedure making use of the ESJS, that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution. Furthermore, we show the validity of the proposed method with simulated data, and three empirical data sets