Disentangling different types of El Ni\~no episodes by evolving climate network analysis

Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In this work, we apply the recently proposed climate network approach for characterizing the evolving correlation structure of the Earth's climate system based on reanalysis data of surface air temperatures. We provide a detailed study on the temporal variability of several global climate network characteristics. Based on a simple conceptual view on red climate networks (i.e., networks with a comparably low number of edges), we give a thorough interpretation of our evolving climate network characteristics, which allows a functional discrimination between recently recognized different types of El Ni\~no episodes. Our analysis provides deep insights into the Earth's climate system, particularly its global response to strong volcanic eruptions and large-scale impacts of different phases of the El Ni\~no Southern Oscillation (ENSO).Comment: 20 pages, 12 figure

Discovery of distant RR Lyrae stars in the Milky Way using DECam

We report the discovery of distant RR Lyrae stars, including the most distant known in the Milky Way, using data taken in the $g-$band with the Dark Energy Camera as part of the High cadence Transient Survey (HiTS; 2014 campaign). We detect a total of 173 RR Lyrae stars over a ~120 deg^2 area, including both known RR Lyrae and new detections. The heliocentric distances d_H of the full sample range from 9 to >200 kpc, with 18 of them beyond 90 kpc. We identify three sub-groups of RR Lyrae as members of known systems: the Sextans dwarf spheroidal galaxy, for which we report 46 new discoveries, and the ultra-faint dwarf galaxies Leo IV and Leo V. Following an MCMC methodology, we fit spherical and ellipsoidal profiles of the form rho(R) ~ R^n to the radial density distribution of RR Lyrae in the Galactic halo. The best fit corresponds to the spherical case, for which we obtain a simple power-law index of n=-4.17^{+0.18}_{-0.20}, consistent with recent studies made with samples covering shorter distances. The pulsational properties of the outermost RR Lyrae in the sample (d_H>90 kpc) differ from the ones in the halo population at closer distances. The distribution of the stars in a Period-Amplitude diagram suggest they belong to Oosterhoff-intermediate or Oosterhoff II groups, similar to what is found in the ultra-faint dwarf satellites around the Milky Way. The new distant stars discovered represent an important addition to the few existing tracers of the Milky Way potential in the outer halo.Comment: Accepted for publication in The Astrophysical Journa

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions

Context-Aware Anomaly Detection and Analysis Using Spatial-Temporal Data

With the thriving of sensing and internet-of-things technologies, an increasing number of research communities and industries are stepping into the Era of Big Data. Following this technology trend, the amount and complexity of data generated by each domain are growing exponentially. The demand for automated monitoring, detecting and analyzing unusual events from those data are also increasing. These predictive analyses seek to identify and capture meaningful patterns in massive, highly heterogeneous data from various domains such as environmental sensing and cyber-physical systems. However, performing analysis such as anomaly detection faces a variety of challenges. For instance, the lack of prior knowledge regarding what is normal and what is abnormal, and the power consumption limitation for low-profile computing devices. These challenges constrain the flexibility of analysis methods. All these pose real problems to existing anomaly detection methods. Most existing techniques for anomaly detection only consider the content of the data source, i.e., the data itself directly gathered from sensing devices, not taking the context of the data into consideration. Therefore, anomalies under complicated settings are difficult to be identified. Hence, it is essential to design anomaly detection methods, especially the feature space design under a specific anomaly context. The context can be semantic, spatial, or temporal. This thesis studies the context-aware data analysis approaches using spatial-temporal data. A general principle to design a context-aware data analysis framework for spatial-temporal data is proposed and investigated in three different problems: contextual anomaly detection in remotely sensed imagery, hierarchical context-aware fault diagnosis in photovoltaic systems and energy-efficient wearable computing empowered by context-aware predictive analysis. Results include: (1) an automated contextual anomaly detection approach is proposed and implemented. The method constructs and utilizes spatial-temporal neighborhood context. Average precision and recall of 98.1\% and 95.7\% for contextual outlier detection are achieved. Also, meaningful and validated unusual events are detected from remotely sensed imagery. (2) A new hierarchical context-aware anomaly detection algorithm is proposed. With this algorithm, the fault detection accuracy of large-scale photovoltaic systems improves by 20\% (from 63\% to 83\%) for top-100 detected anomalies, compared with existing solutions. (3) By identifying and predicting the intra-signal context, the proposed sparse adaptive sensing algorithm achieves 97.7\% accuracy with 76.9\% to 99\% reduced energy consumption (83.6\% average reduction under real-world testing). These three studies demonstrate the utility of combining the spatial-temporal context in any future big data anomaly detection

Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.Environmental SciencesD. Phil. (Environmental Science

Cultural Dynamics in a Globalized World

The book contains essays on current issues in arts and humanities in which peoples and cultures compete as well as collaborate in globalizing the world while maintaining their uniqueness as viewed from cross- and inter-disciplinary perspectives. The book covers areas such as literature, cultural studies, archaeology, philosophy, history, language studies, information and literacy studies, and area studies. Asia and the Pacific are the particular regions that the conference focuses on as they have become new centers of knowledge production in arts and humanities and, in the future, seem to be able to grow significantly as a major contributor of culture, science and arts to the globalized world. The book will help shed light on what arts and humanities scholars in Asia and the Pacific have done in terms of research and knowledge development, as well as the new frontiers of research that have been explored and opening up, which can connect the two regions with the rest of the globe

Systems&design:beyond processes and thinking

El entorno social,el territorio, los productos y las empresas, son ámbitos comunes,en los que se pretende realizar una optimización en la gestión del conocimiento,y desde la que se nos debe permitir observar el mayor número de factores con incidencia en la decisión proyectual necesaria para el diseño de nuevos productos y o servicios.Los retos que plantea la complejidad inherente a estos nuevos tiempos, exige la observación y estudio desde diferentes abordajes e investigaciones, que deberán ser capaces de interpretar las múltiples relaciones complejas, considerando su comportamiento y afectación en el proceso de diseño desde el ámbito complejo de lo multidisciplinar.Hernandis Ortuño, B. (2016). Systems&design:beyond processes and thinking. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/73710EDITORIA