Results of phase one of land use information Delphi study

The Land Use Management Information System (LUMIS) is being developed for the city portion of the Santa Monica mountains. LUMIS incorporates data developed from maps and aerial photos as well as traditional land based data associated with routine city and county record keeping activities and traditional census data. To achieve the merging of natural resource data with governmental data LUMIS is being designed in accordance with restrictions associated with two other land use information systems currently being constructed by Los Angeles city staff. The two city systems are LUPAMS (Land Use Planning and Management System) which is based on data recorded by the County Assessor's office for each individual parcel of land in the city, and Geo-BEDS, a geographically based environmental data system

LUMIS Interactive graphics operating instructions and system specifications

The LUMIS program has designed an integrated geographic information system to assist program managers and planning groups in metropolitan regions. Described is the system designed to interactively interrogate a data base, display graphically a portion of the region enclosed in the data base, and perform cross tabulations of variables within each city block, block group, or census tract. The system is designed to interface with U. S. Census DIME file technology, but can accept alternative districting conventions. The system is described on three levels: (1) introduction to the systems's concept and potential applications; (2) the method of operating the system on an interactive terminal; and (3) a detailed system specification for computer facility personnel

The availability of local aerial photography in southern California

Some of the major photography and photogrammetric suppliers and users located in Southern California are listed. Recent trends in aerial photographic coverage of the Los Angeles basin area are also noted, as well as the uses of that imagery

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Temperature and Emission-Measure Profiles Along Long-Lived Solar Coronal Loops Observed with TRACE

We report an initial study of temperature and emission measure distributions along four steady loops observed with the Transition Region and Coronal Explorer (TRACE) at the limb of the Sun. The temperature diagnostic is the filter ratio of the extreme-ultraviolet 171-angstrom and 195-angstrom passbands. The emission measure diagnostic is the count rate in the 171-angstrom passband. We find essentially no temperature variation along the loops. We compare the observed loop structure with theoretical isothermal and nonisothermal static loop structure.Comment: 10 pages, 3 postscript figures (LaTeX, uses aaspp4.sty). Accepted by ApJ Letter

Assessing Deep Generative Models in Chemical Composition Space

The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed

Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts

BACKGROUND: This paper describes a quantitative analysis of the cyst lining architecture in radicular cysts (of inflammatory aetiology) and odontogenic keratocysts (thought to be developmental or neoplastic) including its 2 counterparts: solitary and associated with the Basal Cell Naevus Syndrome (BCNS). METHODS: Epithelial linings from 150 images (from 9 radicular cysts, 13 solitary keratocysts and 8 BCNS keratocysts) were segmented into theoretical cells using a semi-automated partition based on the intensity of the haematoxylin stain which defined exclusive areas relative to each detected nucleus. Various morphometrical parameters were extracted from these "cells" and epithelial layer membership was computed using a systematic clustering routine. RESULTS: Statistically significant differences were observed across the 3 cyst types both at the morphological and architectural levels of the lining. Case-wise discrimination between radicular cysts and keratocyst was highly accurate (with an error of just 3.3%). However, the odontogenic keratocyst subtypes could not be reliably separated into the original classes, achieving discrimination rates slightly above random allocations (60%). CONCLUSION: The methodology presented is able to provide new measures of epithelial architecture and may help to characterise and compare tissue spatial organisation as well as provide useful procedures for automating certain aspects of histopathological diagnosis

Time-Dependent Ionization in Radiatively Cooling Gas

We present new computations of the equilibrium and non-equilibrium cooling efficiencies and ionization states for low-density radiatively cooling gas containing cosmic abundances of the elements H, He, C, N, O, Ne, Mg, Si, S, and Fe. We present results for gas temperatures between 1e4 and 1e8 K, assuming dust-free and optically thin conditions, and no external radiation. For non-equilibrium cooling we solve the coupled time-dependent ionization and energy loss equations for a radiating gas cooling from an initially hot, >5e6K equilibrium state, down to 1e4K. We present results for heavy element compositions ranging from 1e-3 to 2 times the elemental abundances in the Sun. We consider gas cooling at constant density (isochoric) and at constant pressure (isobaric). We calculate the critical column densities and temperatures at which radiatively cooling clouds make the dynamical transition from isobaric to isochoric evolution. We construct ion ratio diagnostics for the temperature and metallicity in radiatively cooling gas. We provide numerical estimates for the maximal cloud column densities for which the gas remains optically thin to the cooling radiation. We present our computational results in convenient on-line figures and tables (see http://wise-obs.tau.ac.il/~orlyg/cooling/).Comment: 20 pages, 12 figures. ApJS in press. Electronic data available at http://wise-obs.tau.ac.il/~orlyg/cooling

Improving plastic management by means of people awareness

In past decades the usage of plastic has seen a tremendous increment. This raise is mainly caused by industrial development and by the spread of this material in every aspect of people life, from food package to aerospace application. For sure plastic has a key role in society and it is not possible to erase, nevertheless its overuse has a serious impact on the environment as well know. In particular, just a few percentage of the total amount of plastic is recycled, the rest has to be landfilled or burnt causing serious pollution side effect. This poor circularity in plastic value chain is mainly caused by difficulties in sorting processes and expensiveness of recycling. By the way a great part of plastic applications could be avoided without implying a reduction in life quality for the people. In addition, a better education in plastic objects shopping and plastic waste management could decrease the difficulties in sorting and recycling. One of the crucial reason why these applications and incorrect behaviour are still present is that the information on alternatives are not present or very hard to be found. In the present paper a novel platform to enhance a more plastic-free life is presented. First a detailed description of the problem is stated, then the process to achieve the proposed solution is described. Finally the platform prototype is analysed in details among its functionalities

Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients

da inserireBipolar disorders are characterized by a mood swing, ranging from mania to depression. A system that could monitor and eventually predict these changes would be useful to improve therapy and avoid dangerous events. Speech might convey relevant information about subjects' mood and there is a growing interest to study its changes in presence of mood disorders. In this work we present an automatic method to characterize fundamental frequency (F0) dynamics in voiced part of syllables. The method performs a segmentation of voiced sounds from running speech samples and estimates two categories of features. The first category is borrowed from Taylor's Tilt intonational model. However, the meaning of the proposed features is different from the meaning of Taylor's ones since the former are estimated from all voiced segments without performing any analysis of intonation. A second category of features takes into account the speed of change of F0. In this work, the proposed features are first estimated from an emotional speech database. Then, an analysis on speech samples acquired from eleven psychiatric patients experiencing different mood states, and eighteen healthy control subjects is introduced. Subjects had to perform a text reading task and a picture commenting task. The results of the analysis on the emotional speech database indicate that the proposed features can discriminate between high and low arousal emotions. This was verified both at single subject and group level. An intra-subject analysis was performed on bipolar patients and it highlighted significant changes of the features with different mood states, although this was not observed for all the subjects. The directions of the changes estimated for different patients experiencing the same mood swing, were not coherent and were task-dependent. Interestingly, a single-subject analysis performed on healthy controls and on bipolar patients recorded twice with the same mood label, resulted in a very small number of significant differences. In particular a very good specificity was highlighted for the Taylor-inspired features and for a subset of the second category of features, thus strengthening the significance of the results obtained with patients. Even if the number of enrolled patients is small, this work suggests that the proposed features might give a relevant contribution to the demanding research field of speech-based mood classifiers. Moreover, the results here presented indicate that a model of speech changes in bipolar patients might be subject-specific and that a richer characterization of subject status could be necessary to explain the observed variability