Relativistic structure formation models and gravitoelectromagnetism

In the framework of Lagrangian perturbation theory in general relativity we discuss the possibility to split the Einstein equations, written in terms of spatial Cartan coframes within a 3+1 foliation of spacetime, into gravitoelectric and gravitomagnetic parts. While the former reproduces the full hierarchy of the Newtonian perturbation solutions, the latter contains non-Newtonian aspects like gravitational waves. This split can be understood and made unique through the Hodge decomposition of Cartan coframe fields.Comment: 6 pages; contribution to the proceedings of MG14, Parallel Session DE

DEMON: a Local-First Discovery Method for Overlapping Communities

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.Comment: 9 pages; Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 201

Lagrangian theory of structure formation in relativistic cosmology III: gravitoelectric perturbation and solution schemes at any order

The relativistic generalization of the Newtonian Lagrangian perturbation theory is investigated. In previous works, the first-order trace solutions that are generated by the spatially projected gravitoelectric part of the Weyl tensor were given together with extensions and applications for accessing the nonperturbative regime. We furnish here construction rules to obtain from Newtonian solutions the gravitoelectric class of relativistic solutions, for which we give the complete perturbation and solution schemes at any order of the perturbations. By construction, these schemes generalize the complete hierarchy of solutions of the Newtonian Lagrangian perturbation theory.Comment: 17 pages, a few minor extensions to match the published version in PR

Releasing Down and Swinging Up: Exploring External Expression through Internal Fall

The goal of my thesis was to investigate how to externalize onstage my inner experience with authenticity and integrity. Using the challenge of playing the role of Irina in Anton Chekhovs Three Sisters, a version of the play where she is Deaf, I investigated how to externally communicate an internal experience through the use of the fall. This paper begins with in-studio research of my personal artistic challenge of releasing in order to externalize. It documents my summer work on physicality and voice while researching and training in American Sign Language. My scholarly research explores the phenomena of the fall as distinct from collapse and release, the life of the playwright, the context of the play, and finally, character analysis of Irina. This paper concludes with my findings from the rehearsal process

Major public transport hubs in the light rail design and operation

Light Rail Transport systems (shortly LRT in the text) are experiencing a second youth in Europe in general and more specifically in Northern European countries in recent years. New lines are built and existing LRT networks are extended, both in countries where they previously existed (like in Denmark) or where they see the light for the first time. We see an important goal in the planning process of a LRT in conceiving and designing user friendly projects, which will attract the maximal number of customers. Only by obtaining this goal a LRT will be a successful alternative to road/private traffic and hence be the driving factor for an environmentally conscious urban development. Thus a key factor in the realization of the design and the operational concept of a high performance LRT is to guarantee rapid and trustworthy connections combined with optimized and reliable interchanges with other public transport modes. This will give the customers the feeling of a competitive transport service they will choose because of its attractiveness. The paper will analyze the role of major public transport hubs in LRT projects during the realization of the design and the operational concept: which are the main issues integrating a new LRT hub in the existing public transport network? what operational, infrastructural or systems solutions make the hub attractive? what operational challenges have to be overcome

Local Rule-Based Explanations of Black Box Decision Systems

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. %Therefore, we need explanations that reveals the reasons why a predictor takes a certain decision. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box

An analytical framework to nowcast well-being using mobile phone data

An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory

Algal Biotechnology: Properties of Bioactive Derivatives and Pharmaceutical Applications

Continuous development of new pathologies and mutations together with the increment of drug resistance make the research of new treatments and therapies more urgent and essential. Among the renewable resources, algae and related bioactive compounds are strongly considered. Algae are eukaryotic organisms characterised by high therapeutic potential. Indeed, because of biotic and abiotic factors, algae produce a wide variety of metabolites, which are useful for treating dysfunctions and diseases. The most produced metabolites are proteins, carbohydrates, lipids, vitamins, polyphenols, and pigments, which find several applications in daily life, as indicated in Fig. 1. The different classes of metabolites are relevant to the species they belong to; they are also divided into groups according to their medical properties. Over the years, advantages and performances of algae derivatives have been demonstrated by a growing number of analyses and researches, especially in recent years. Among the various properties of algae metabolites, anti-inflammatory, antiviral, antibacterial, antioxidant and antidiabetic are the most promising. Pigments (e.g. fucoxanthin) and polyphenols are the main compounds with anti-inflammatory activity; the latter also show antiviral, antidiabetic and antibacterial effects. Other compounds with antidiabetic activity are some xanthophylls and some polysaccharides (e.g. fucoidan and alginate). Among the antioxidant metabolites of algae, the most useful are flavonoids (i.e. polyphenols), carotenoids, pigments, vitamins, minerals and enzymes. Fatty acids show antibacterial ability, while carrageenans and other polysaccharides show both antibacterial and antiviral effects. Supporting algal research is a valid strategy to improve ongoing trials, expand or confirm obtained results, discover and include new molecules in biotechnology applications with the aim to introduce novel medical and pharmacological uses in modern medicine. A typical example is related to diabetes mellitus, which is a disease in constant growth. Nowadays, numerous trials are ongoing to develop innovative and more efficient treatments and several algae are analysed with respect to this pathology. Indeed, some algal bioactive compounds, in particular polyphenol derivatives, polysaccharides and pigments, have antidiabetic properties; these metabolites inhibit the enzymes α-glucosidase, α-amylase and aldose reductase, reduce reactive oxygen species, decrease lipid peroxidation and interfere on metabolic pathways. The results are decrement of blood glucose levels and increment of insulin values, which are critical in diabetic patients.