Resources in the British Isles of interest to language arts and dramatic teachers

Thesis (Ed.M.)--Boston Universit

MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected

Mechanical Systems: Symmetry and Reduction

Reduction theory is concerned with mechanical systems with symmetries. It constructs a lower dimensional reduced space in which associated conservation laws are taken out and symmetries are \factored out" and studies the relation between the dynamics of the given system with the dynamics on the reduced space. This subject is important in many areas, such as stability of relative equilibria, geometric phases and integrable systems

Building vicarious bridges through colour workshops for pupils with visual impairment

he inclusion of pupils with visual impairment, within Italian mainstream schools, is an area of interest for the field of special education that is involved in identifying the most effective teaching strategies to promote the teaching-learning process. The perceptive difficulties that the pupils with visual impairment encounter in the first step of development can be a significant obstacle to learning and to the development of representative thinking. For this reason, it is fundamental that the teaching style adopted is oriented to promote learning through strategies that exploit the natural vicarious activity of the brain. With this aim, the present paper describes the potential of a hands-on activity with high inclusive value, which is based on a theoretical framework, that brings together contributions from different scientific domains and which, from an interdisciplinary perspective, explores the concept of "vicariance" as proposed by the physiologist of perception Alain Bertho

A competitive cell-permeable peptide impairs Nme-1 (NDPK-A) and Prune-1 interaction: therapeutic applications in cancer.

The understanding of protein–protein interactions is crucial in order to generate a second level of functional genomic analysis in human disease. Within a cellular microenvironment, protein–protein interactions generate new functions that can be defined by single or multiple modes of protein interactions. We outline here the clinical importance of targeting of the Nme-1 (NDPK-A)–Prune-1 protein complex in cancer, where an imbalance in the formation of this protein–protein complex can result in inhibition of tumor progression. We discuss here recent functional data using a small synthetic competitive cell-permeable peptide (CPP) that has shown therapeutic efficacy for impairing formation of the Nme-1–Prune-1 protein complex in mouse preclinical xenograft tumor models (e.g., breast, prostate, colon, and neuroblastoma). We thus believe that further discoveries in the near future related to the identification of new protein–protein interactions will have great impact on the development of new therapeutic strategies against various cancers

A rock physics and seismic tomography study to characterize the structure of the Campi Flegrei caldera

The Campi Flegrei (CF) caldera experiences dramatic ground deformations unsurpassed anywhere in the world. The source responsible for this phenomenon is still debated. With the aim of exploring the structure of the caldera as well as the role of hydrothermal fluids on velocity changes, a multidisciplinary approach dealing with 3-D delay-time tomography and rock physics characterization has been followed. Selected seismic data were modeled by using a tomographic method based on an accurate finite-difference travel-time computation which simultaneously inverts P-wave and S-wave first-arrival times for both velocity model parameters and hypocenter locations. The retrieved P-wave and S-wave velocity images as well as the deduced Vp/Vs images were interpreted by using experimental measurements of rock physical properties on CF samples, to take into account steam/water phase transition mechanisms affecting P-wave and S-wave velocities. Also, modelling of petrophysical properties for site-relevant rocks constrains the role of overpressured fluids on velocity. A flat and low Vp/Vs anomaly lies at 4 km depth under the city of Pozzuoli. Earthquakes are located at the top of this anomaly. This anomaly implies the presence of fractured over-pressured gas-bearing formations and excludes the presence of melted rocks. At shallow depth, a high Vp/Vs anomaly located at 1 km suggests the presence of rocks containing fluids in the liquid phase. Finally, maps of the Vp*Vs product show a high Vp*Vs horse-shoe shaped anomaly located at 2 km depth. It is consistent with gravity data and well data and might constitute the on-land remainder of the caldera rim, detected below sea level by tomography using active source seismic data. For a more exhaustive description of the utilized methodologies, of synthetic tests for spatial resolution and uncertainty assessment and, the interpretation of results, the reader may refer to the paper Vanorio et al. (2005)

Variability and Trends in Streamflow in Northeast United States

Abstract There is general consensus that climate is undergoing change but whether climate change is occurring or not is still being debated in certain scientific, political, and religious quarters. Hydrologic variability influences the design of civil works and assessment of long-term climate change would help improve design criteria. To this end, long-term variability of streamflow was estimated using Shannon entropy. Three statistical tests were applied to determine trends in annual and seasonal daily streamflow with 5% two-sided confidence limit. Daily streamflow data spanning 70 years (from 1943 to 2012) from 669 stream gauge stations located in 23 states in the northeastern part of United States of America, covering six different water regions were employed. The time variability of annual and seasonal daily streamflow was assessed using the Mean Decadal Apportionment Disorder Index ( MDADI ). Analysis showed that in all cases minimum and maximum streamflows had higher variability than average and median streamflows. A significant number of stations exhibited trends. Considering annual minimum, average and median daily streamflows, approximately 50% of the stations followed trends and for almost all these stations trends were increasing. Only for annual maximum daily streamflow, 15% of the stations showed increasing trend and 10% decreasing trend. In terms of geographical distribution, the stations with increasing trend were essentially located along the Atlantic coast and near Great Lakes and in the Upper Mississippi Water Region. Similar considerations apply for seasonal time series as well

Distribution-Free Statistical Dispersion Control for Societal Applications

Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range. However, for many high-stakes applications, it is crucial to understand and control the dispersion of a loss distribution, or the extent to which different members of a population experience unequal effects of algorithmic decisions. We initiate the study of distribution-free control of statistical dispersion measures with societal implications and propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work. Our methods are verified through experiments in toxic comment detection, medical imaging, and film recommendation.Comment: Accepted by NeurIPS as spotlight (top 3% among submissions

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.Comment: 24 pages, 4 figures. Code is available at https://github.com/jakesnell/quantile-risk-contro