Multi-Species Asymmetric Exclusion Process in Ordered Sequential Update

A multi-species generalization of the asymmetric simple exclusion process (ASEP) is studied in ordered sequential and sub-lattice parallel updating schemes. In this model particles hop with their own specific probabilities to their rightmost empty site and fast particles overtake slow ones with a definite probability. Using Matrix Product Ansatz (MPA), we obtain the relevant algebra, and study the uncorrelated stationary state of the model both for an open system and on a ring. A complete comparison between the physical results in these updates and those of random sequential introduced in [20,21] is made.Comment: Latex file 36 pages with 10 EPS figure

Optimization of Green-Times at an Isolated Urban Crossroads

We propose a model for the intersection of two urban streets. The traffic status of the crossroads is controlled by a set of traffic lights which periodically switch to red and green with a total period of T. Two different types of crossroads are discussed. The first one describes the intersection of two one-way streets, while the second type models the intersection of a two-way street with an one-way street. We assume that the vehicles approach the crossroads with constant rates in time which are taken as the model parameters. We optimize the traffic flow at the crossroads by minimizing the total waiting time of the vehicles per cycle of the traffic light. This leads to the determination of the optimum green-time allocated to each phase.Comment: 8 pages, 6 eps figures, more explanation added. To appear in EPJ

Decentering the prosecution-oriented approach: tackling both supply and demand in the struggle against human trafficking

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Multi-stream Longitudinal Data Analysis using Deep Learning

Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in healthcare, lost workplace productivity, and crime. Early identification and engagement of individuals at risk of developing a substance use disorder is a critical unmet need in healthcare which can be achieved by producing automatic artificial intelligence based tools trained using big healthcare data. In fact, healthcare data can be harnessed together with artificial intelligence and machine learning to advance our understanding of factors that increase the propensity for developing different diseases as well as those that aid in the treatment of these disorders. Here in, a disease prediction framework is first proposed based on recurrent neural networks. This framework includes three components: 1) data pre-processing, 2) disease prediction using long short term memory models, and 3) hypothesis exploration by varying the models and the inputs. This framework is assessed using two use cases: substance use disorder prediction and mild cognitive impairment prediction. Experimental results show that this proposed model can efficiently analyze patients\u27 data and creates efficient disease prediction tools. Second, the limitationsof current deep learning models including long short term memory models in claimsdata analysis are detected and addressed, and a novel model based on the transformer models is proposed. In fact, leveraging the real-world longitudinal claims data, a novel multi-stream transformer model is proposed for predicting opioid use disorder as an important case of substance use disorders. This model is designed to simultaneously analyze multiple types of data streams, such as medications, diagnoses, procedures and demographics, by attending to segments within and across these data streams. The proposed model tested on the IBM MarketScan data showed significantly better performance than the traditional models and recently developed deep learning models

Complementarity and cultural sensitivity: decision-making by the ICC prosecutor in relation to the situations in the Darfur region of the Sudan and the Democratic Republic of the Congo (DRC)

The complementarity regime created by the Rome Statute of the International Criminal Court (ICC) marked a radical departure for international criminal justice. It represented a significant break with the Westphalian state system of national sovereignty and a step towards a regime of global governance based on the rule of law. The ICC is rooted in a Kantian notion of cosmopolitan justice where there is a need for a response to state failures to eliminate gross human rights violations. However, it has also been seen as a post-colonial court representing the hegemony of western justice and western authority over local traditions, particularly in the Islamic world. The operation of the operation of the complementarity regime does not reflect all types of juridical traditions and is therefore viewed with suspicion by nations with different criminal justice ideologies and policies. This thesis examines the practical and moral legitimacy of the complementarity regime of the ICC from two possible perspectives, both of which in their different ways support the idea of universal jurisdiction. Kant’s moral philosophy represents the western justification for the regime, whereas the tradition of Islamic Shari’a epitomises the potential resistance from the developing world. Through an analysis of the exercise of prosecutorial discretion under the complementarity regime in relation to the Ituri region of the Democratic Republic of Congo (DRC) and the Darfur situation in Sudan, the thesis examines both the logistics of the decision-making in these cases, as well as the moral justifications for intervention. The fieldwork included a six month programme of participant observation and interviewing in the Office of the ICC Prosecutor in The Hague. The ICC is an independent court with a global jurisdiction which grants the Prosecutor a broad discretion to apply the complementarity regime to meet the expectations of the entire international community, regardless of the status, national origin or state citizenship of the accused. This thesis argues that a careful consideration of the moral case for the exercise of authority under the complementarity regime is important and depends upon an understanding of the inherent differences between the Rome Statute and national justice systems. The research highlights the fact that moral obligations do not end at national borders. It asserts that a credible complementarity mechanism requires the effective prosecution of international crimes in a manner which is legitimate in terms of local culture and traditions. Otherwise, as the research demonstrates, the Court will enjoy little support, particularly as enforcement has so far focused only on Islamic or less developed countries