Unveiling the Secrets of Gamma Ray Bursts

Gamma Ray Bursts (GRBs) are unpredictable and brief flashes of gamma rays that occur about once a day in random locations in the sky. Since gamma rays do not penetrate the Earth's atmosphere, they are detected by satellites, which automatically trigger ground-based telescopes for follow-up observations at longer wavelengths. In this introduction to Gamma Ray Bursts we review how building a multi-wavelength picture of these events has revealed that they are the most energetic explosions since the Big Bang and are connected with stellar deaths in other galaxies. However, in spite of exceptional observational and theoretical progress in the last 15 years, recent observations raise many questions which challenge our understanding of these elusive phenomena. Gamma Ray Bursts therefore remain one of the hottest topics in modern astrophysics.Comment: 20 pages, 11 figures, review article, draft version, final version will appear in Contemporary Physic

An optimization approach to study the phase changing behavior of multi-component mixtures

The appropriate design, construction, and operation of carbon capture and storage (CCS) and enhanced oil recovery (EOR) processes require a deep understanding of the resulting phases behavior in hydrocarbons-CO_2 multi-component mixtures under reservoir conditions. To model this behavior a nonlinear system consists of the equation of states and some mixing rules (for each component) needed to be solved simultaneously. The mixing usually requires to model the binary interaction between the components of the mixture. This work employs optimization techniques to enhance the predictions of such model by optimizing the binary interaction parameters. The results show that the optimized parameters, although obtained mathematically, are in physical ranges and can reproduce successfully the experimental observations, specially for the multi-component hydrocarbons systems containing Carbon dioxide at reservoir temperatures and pressuresComment: To be published in Cadernos do IME - S\'erie Matem\'atica da UER

Illustrative Informationsvisualisierung

Mit wachsender Größe von Daten wird es zunehmend schwerer, die in der Informationsvisualisierung erzeugte visuelle Repräsentation zu interpretieren und die relevanten Informationen adäquat darzustellen. Die Illustration beschäftigt sich seit längerem mit der Kommunikation wichtiger Bildinformationen. Das Ziel dieser Dissertation ist es deshalb, illustrative Verfahren in Techniken der Informationsvisualisierung zu integrieren - sowohl konzeptuell als auch in praktischer Anwendung. Als Ergebnis unterstützen die neu entwickelten Lösungsansätze die Kommunikation dargestellter Informationen

Comparison of syntax tree visualization: Toward Malay Language (BM) syntax tree

This study will analyze natural language syntax tree visualizations to compare visualization methods in order to choose the optimum solution for visualizing a BM syntax tree.Currently no syntax tree visualization for BM has been introduced, and no visualization is yet available in the form of computer software or a prototype.Methods that can be dealt with in creating a BM syntax tree include: tokenizing, a performing search and comparison, matching with the associated rules, and composing.Ten systems were analyzed, and the Link Grammar system was found to be the most viable.The Link Grammar system does not have a hierarchical structure that reflects the language syntax as compared to the SSTC (Structured String-Tree Correspondence) application which does.However, the SSTC shows the tree structure in a hierarchical manner, but it does not have a suitable method to follow in visualizing the BM sentence syntax tree

Image Description using Deep Neural Networks

Current research in computer vision and machine learning has demonstrated some great abilities at detecting and recognizing objects in natural images. Current state-of-the-art results in object detection, classification and localization in ImageNet Challenges have the validation accuracy for top 5 predictions for classification to be at 3.08% while similar classification experiments run by trained humans report an accuracy of 5.1%. While some people might argue that human accuracy is a function of training time it can be said with great confidence that automated classification models are at least as good as trained humans in classification problems. The ability of these models to analyze and describe complex images, however, is still an active area of research. Image description is a good starting point for imparting artificial intelligence to machines by allowing them to analyze and describe complex visual scenes. This thesis work introduces a generic end-to-end trainable Fusion-based Recurrent Multi-Modal (FRMM) architecture to address multi-modal applications. FRMM allows each input modality to be independent in terms of architecture, parameters and length of input sequences. FRMM image description models seamlessly blend convolutional neural network feature descriptors with sequential language data in a recurrent framework. In addition to introducing FRMMs, this work also analyzes the impact of varying activation functions and vocabulary size. For training and testing Flickr8k, Flickr30K and MSCOCO datasets have been used, demonstrating state-of-the-art description results

Properties of the galactic-scale gas circulation generated by stellar feedback

Questo lavoro di tesi si propone di esaminare la simulazione di una galassia isolata simile alla Via Lattea e, in particolare, di analizzare i processi di feedback dovuto ad esplosioni di supernove, il meccanismo responsabile della generazione di outflow gassosi dal disco. Scopo della tesi è quello di testare la validità dei risultati ottenuti dalla simulazione (generata dal recente modello di feedback SMUGGLE presente nel codice a griglia mobile AREPO) dal punto di vista della formazione stellare, del tasso di esplosioni di supernove e dei fenomeni di outflow di gas su scala galattica. Sono stati indagati vari andamenti evolutivi delle proprietà cinematiche del gas, della formazione stellare e dell'efficienza di rilascio di energia/impulso da parte delle supernove al fine di ricercare relazioni che mettano in luce lo stretto legame tra questi processi astrofisici. La simulazione è stata in grado di generare fenomeni di esplusione di gas in maniera auto-consistente e, in combinazione con l'esaurirsi del gas a causa della formazione stellare, ha condotto la galassia a regolare il suo tasso di formazione stellare e quello relativo alle esplosioni di supernove a dei livelli in accordo con le osservazioni. Inoltre la simulazione è stata in grado di riprodurre la relazione osservativa di Kennicutt-Schmidt. Infine questo lavoro di tesi ha evidenziato una possibile connessione tra la variazione del tasso di densita' superficiale di formazione stellare (SFRD) e l'ammontare di massa di gas esplusa dal disco. Ciò è stato fatto al fine di valutare l'esistenza di un possibile valore critico per la SFRD tale per cui, al di sopra di questa soglia, si possano chiaramente osservare fenomeni di espulsione di gas su scala galattica. I risultati ottenuti però non mostrano evidenze dell'esistenza di un tale valore e che ulteriori approfondimenti e simulazioni di confronto sono necessari per avvalorare questi risultati preliminari

Cartoon Contracts and the Proactive Visualization of Law

Contracts have always relied on text first, foremost, and usually exclusively. Yet, this approach leaves many users of contracts in the dark as to the actual meaning of the transactional documents and instruments they enter into. The average contract routinely uses language that only lawyers, law-trained readers, and highly literate persons can truly understand. There is a movement in the law in the United States and many other nations called the visualization of law movement that attempts to bridge these gaps in contractual communication by using highly visual instruments. In appropriate circumstances, even cartoons and comic book forms of sequential narrative have been used to communicate contract terms to all parties, but particularly to contractors who are illiterate or less-than-fully literate in the language of the instrument. The goal of this Article is to apply the lens of visual legal rhetoric and visual literacy to the current visualization movement in Proactive Law and Legal Design in their efforts to promote visual, non-verbal communication in contracts through cartoon, comic book, and highly pictorial legal instruments. The lens will be applied to evaluate and critique five aspects of proactive visual legal instruments: • Immediate Visual Context • Immediate Verbal Context • Visual Cultural Context • Mise en Scène and Arrangement • Visual Rhetoric, Ethics and Professionalism This Article analyzes whether highly visual contracts and legal instruments fulfill the potential for greater access to and understanding of contract terms particularly with audiences whose language skills and cultural experience might make the comprehension and acceptance of purely verbal contracts more difficult. When visuals can overcome barriers in communication that words alone cannot, contracts and other legal instruments can be made more universal in their application, interpretation, performance, and enforcement

Determinants of households´ consumption in Portugal - a machine learning approach

Machine Learning has been widely adopted by researchers in several academic fields.Although at a slow pace, the field of economics has also started to acknowledge the pos-sibilities of these algorithm based methods for complementing or even replace traditionalEconometric approaches. This research aims to apply Machine Learning data-driven variable selection models for accessing the determinants of Portuguese households’ consumption using the Household Finance and Consumption Survey. I found that LASSO Regression and Elastic Net have the best performance in this setting and that wealth related variables have the highest impact on households’ consumption levels, followed by income, household’s characteristics and debt and consumption credit