Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Clustering of risk behaviors with cigarette consumption: a population-based survey.

OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.

EFNS guidelines on the clinical management of amyotrophic lateral sclerosis (MALS)--revised report of an EFNS task force.

BACKGROUND: The evidence base for the diagnosis and management of amyotrophic lateral sclerosis (ALS) is weak. OBJECTIVES: To provide evidence-based or expert recommendations for the diagnosis and management of ALS based on a literature search and the consensus of an expert panel. METHODS: All available medical reference systems were searched, and original papers, meta-analyses, review papers, book chapters and guidelines recommendations were reviewed. The final literature search was performed in February 2011. Recommendations were reached by consensus. RECOMMENDATIONS: Patients with symptoms suggestive of ALS should be assessed as soon as possible by an experienced neurologist. Early diagnosis should be pursued, and investigations, including neurophysiology, performed with a high priority. The patient should be informed of the diagnosis by a consultant with a good knowledge of the patient and the disease. Following diagnosis, the patient and relatives/carers should receive regular support from a multidisciplinary care team. Medication with riluzole should be initiated as early as possible. Control of symptoms such as sialorrhoea, thick mucus, emotional lability, cramps, spasticity and pain should be attempted. Percutaneous endoscopic gastrostomy feeding improves nutrition and quality of life, and gastrostomy tubes should be placed before respiratory insufficiency develops. Non-invasive positive-pressure ventilation also improves survival and quality of life. Maintaining the patient's ability to communicate is essential. During the entire course of the disease, every effort should be made to maintain patient autonomy. Advance directives for palliative end-of-life care should be discussed early with the patient and carers, respecting the patient's social and cultural background.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

The alcohol purchase task in young men from the general population.

BACKGROUND: The alcohol purchase task (APT), which presents a scenario and asks participants how many drinks they would purchase and consume at different prices, has been used among students and small clinical samples to obtain measures of alcohol demand but not in large, general population samples. METHODS: We administered the APT to a large sample of young men from the general population (Cohort Study on Substance Use Risk Factors). Participants who reported drinking in the past year (n=4790), reported on past 12 months alcohol use, on DSM-5 alcohol use disorder (AUD) criteria and on alcohol related consequences were included. RESULTS: Among the APT's demand parameters, intensity was 8.7 (SD=6.5) indicating that, when drinks are free, participants report a planned consumption of almost 9 drinks. The maximum alcohol expenditure (Omax) was over 35CHF (1CHF=1.1USD) and the demand became elastic (Pmax) at 8.4CHF (SD=5.6). The mean price at which the consumption was suppressed was 15.6CHF (SD=5.4). Exponential equation provided a satisfactory fit to individual responses (mean R(2): 0.8, median: 0.8). Demand intensity was correlated with alcohol use, number of AUD criteria and number of consequences (all r≥0.3, p<0.0001). Omax was correlated with alcohol use (p<0.0001). The elasticity parameter was weakly correlated with alcohol use in the expected direction. CONCLUSION: The APT measures are useful in characterizing demand for alcohol in young men in the general population. Demand may provide a clinically useful index of strength of motivation for alcohol use in general population samples.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Optimizing patient-ventilator synchrony during invasive ventilator assist in children and infants remains a difficult task.

OBJECTIVES: To document and compare the prevalence of asynchrony events during invasive-assisted mechanical ventilation in pressure support mode and in neurally adjusted ventilatory assist in children. DESIGN: Prospective, randomized, and crossover study. SETTING: Pediatric and Neonatal Intensive Care Unit, University Hospital of Geneva, Switzerland. PATIENTS: Intubated and mechanically ventilated children, between 4 weeks and 5 years old. INTERVENTIONS: Two consecutive ventilation periods (pressure support and neurally adjusted ventilatory assist) were applied in random order. During pressure support, three levels of expiratory trigger setting were compared: expiratory trigger setting as set by the clinician in charge (PSinit), followed by a 10% (in absolute values) increase and decrease of the clinician's expiratory trigger setting. The pressure support session with the least number of asynchrony events was defined as PSbest. Therefore, three periods were compared: PSinit, PSbest, and neurally adjusted ventilatory assist. Asynchrony events, trigger delay, and inspiratory time in excess were quantified for each of them. MEASUREMENTS AND MAIN RESULTS: Data from 19 children were analyzed. Main asynchrony events during PSinit were autotriggering (3.6 events/min [0.7-8.2]), ineffective efforts (1.2/min [0.6-5]), and premature cycling (3.5/min [1.3-4.9]). Their number was significantly reduced with PSbest: autotriggering 1.6/min (0.2-4.9), ineffective efforts 0.7/min (0-2.6), and premature cycling 2/min (0.1-3.1), p < 0.005 for each comparison. The median asynchrony index (total number of asynchronies/triggered and not triggered breaths ×100) was significantly different between PSinit and PSbest: 37.3% [19-47%] and 29% [24-43%], respectively, p < 0.005). With neurally adjusted ventilatory assist, all types of asynchrony events except double-triggering and inspiratory time in excess were significantly reduced resulting in an asynchrony index of 3.8% (2.4-15%) (p < 0.005 compared to PSbest). CONCLUSIONS: Asynchrony events are frequent during pressure support in children despite adjusting the cycling off criteria. Neurally adjusted ventilatory assist allowed for an almost ten-fold reduction in asynchrony events. Further studies should determine the clinical impact of these findings.

Intelligent task-level grasp mapping for robot control

In the future, robots will enter our everyday lives to help us with various tasks.For a complete integration and cooperation with humans, these robots needto be able to acquire new skills. Sensor capabilities for navigation in real humanenvironments and intelligent interaction with humans are some of the keychallenges.Learning by demonstration systems focus on the problem of human robotinteraction, and let the human teach the robot by demonstrating the task usinghis own hands. In this thesis, we present a solution to a subproblem within thelearning by demonstration field, namely human-robot grasp mapping. Robotgrasping of objects in a home or office environment is challenging problem.Programming by demonstration systems, can give important skills for aidingthe robot in the grasping task.The thesis presents two techniques for human-robot grasp mapping, directrobot imitation from human demonstrator and intelligent grasp imitation. Inintelligent grasp mapping, the robot takes the size and shape of the object intoconsideration, while for direct mapping, only the pose of the human hand isavailable.These are evaluated in a simulated environment on several robot platforms.The results show that knowing the object shape and size for a grasping taskimproves the robot precision and performance

Iowa Gender-Specific Services Task Force

An Iowa Commission on the Status of Women Gender Task Force.

Discourse Type Clustering using POS n-gram Profiles and High-Dimensional Embeddings

Abstract: To cluster textual sequence types (discourse types/modes) in French texts, K-means algorithm with high-dimensional embeddings and fuzzy clustering algorithm were applied on clauses whose POS (part-ofspeech) n-gram profiles were previously extracted. Uni-, bi- and trigrams were used on four 19th century French short stories by Maupassant. For high-dimensional embeddings, power transformations on the chi-squared distances between clauses were explored. Preliminary results show that highdimensional embeddings improve the quality of clustering, contrasting the use of bi and trigrams whose performance is disappointing, possibly because of feature space sparsity.