Diamonds are forever, wars are not. Is conflict bad for private firms?

This paper studies the relationship between civil war and the value of firms in a poor, resource abundant country using microeconomic data for Angola. We focus on diamond mining firms and conduct an event study on the sudden end of the conflict, marked by the death of the rebel movement leader in 2002. We find that the stock market perceived this event as “bad news” rather than “good news” for companies holding concessions in Angola, as their abnormal returns declined by 4 percentage points. The event had no effect on a control portfolio of otherwise similar diamond mining companies. This finding is corroborated by other events and by the adoption of alternative methodologies. We interpret our findings in the light of conflict-generated entry barriers, government bargaining power and transparency in the licensing process.Microeconomics ; Mineral industries

Probabilistic temporal multimedia datamining

Ph.DDOCTOR OF PHILOSOPH

Encoding & Characterization of process models for Deep Predictive Process Monitoring.

La sempre crescente digitalizzazione di molti aspetti della vita, sta modificando l'esecuzione operativa di molte attività umane, producendo anche una grande quantità di informazione sotto forma di log di dati. Questi possono essere sfruttati per migliorare la qualità di queste esecuzioni. Un modo per sfruttare queste informazioni è usarle per predire come l'esecuzione di un'attività umana possa evolvere fino al suo completamento, così da supportare i manager nel determinare, per esempio, se intervenire per prevenire delle situazioni indesiderate o per meglio allocare le risorse a disposizione. Nella presente tesi, si propone un approccio che usa l'informazione relativa al parallelismo presente tra le attività per eseguire i task tipici del Predictive Process Monitoring. Questo viene fatto rappresentando le esecuzioni di processo con il corrispondente Instance Graph e processandole utilizzando delle graph convolutional neural networks. Inoltre, per definire gli ambiti in cui tale approccio funziona al meglio nel presente elaborato si illustra una nuova metrica ideata per misurare il parallelismo all'interno dei processi di business. Infine, è presentato un insieme di metriche che descrivono il contesto di esecuzione di una attività all'interno di un processo per rappresentare l'attività stessa. Questo è utilizzato sia per definire un meccanismo di "querying" per le attività all'interno dei processi sia per introdurre la nozione di "location" come un ulteriore obiettivo di predizione per le tecniche di Predictive Process Monitoring. Gli approcci proposti sono stati valutati utilizzando vari dataset reali e i risultati ottenuti sono promettenti.Ever-increasing digitalization of all aspects of life modifies the operative executions of most human tasks and produces a huge wealth of information, in the form of data logs, that could be leveraged to further improve the general quality of such executions. One way of leveraging such information is to predict how the execution of such tasks will unfold until their completion so as to be capable of supporting the managers in determining, for example, whether to intervene to prevent undesired process outcomes or how to best allocate resources. In the present thesis, it is proposed an approach that uses the information about the parallelism among activities for the Predictive Process Monitoring tasks, by representing process executions with their corresponding Instance Graph and processing them using deep graph convolutional neural networks. Also, to define the scope to best apply such an approach is devised a novel metric that manages to effectively measure the parallelism in a business process model. Lastly, the definition of a set of metrics that describe the execution context of an activity inside a process to represent the activity itself is presented. This is used both to define a querying mechanism for activities in processes and to introduce the notion of "location" as a further relevant prediction target for Predictive Process Monitoring techniques. The proposed techniques have been experimentally evaluated using several real-world datasets and the results are promising

A Time Series Analysis: Exploring the Link between Human Activity and Blood Glucose Fluctuation

In this thesis, time series models are developed to explore the correlates of blood glucose (BG) fluctuation of diabetic patients. In particular, it is investigated whether certain human activities and lifestyle events (e.g. food and medication consumption, physical activity, travel and social interaction) influence BG, and if so, how. A unique dataset is utilized consisting of 40 diabetic patients who participated in a 3-day study involving continuous monitoring of blood glucose (BG) at five minute intervals, combined with measures for sugar; carbohydrate; calorie and insulin intake; physical activity; distance from home; time spent traveling via public transit and private automobile; and time spent with other people, dining and shopping. Using a dynamic regression model fitted with autoregressive integrated moving average (ARIMA) components, the influence of independent predictive variables on BG levels is quantified, while at the same time the impact of unknown factors is defined by an error term. Models were developed for individuals with overall findings demonstrating the potential for continuous monitoring of diabetic (DM) patients who are trying to control their BG. Model results produced significant BG predicting variables that include food consumption, exogenous insulin administration and physical activity