Poster earthquake 2

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Антон Корзунов «Как разместить тысячи объектов на карте без увеличения нагрузки на браузер» Конференция Dump, Екатеринбург, 25 мая 2012 1. Методы передачи данных на клиент. 2. Особенности хранения и выборки геоданных. 3. Механизмы визуализации данных в браузере. Возможности и ограничения. Новые инструменты в API Яндекс.Карт 2.0.

Антон Корзунов «Как разместить тысячи объектов на карте без увеличения нагруз...

Yandex

‘Abbiamo visto come sia improbabile aspettarsi che amministratori pubblici creino ambienti favorevoli al dialogo e alla deliberazione… Perché questo avvenga, hanno bisogno di un ‘campo di pratica’ (Senge 1990) che funzioni come un laboratorio di apprendimento’ (Hartz-Karp, J., How and Why Deliberative Democracy Enables Co-Intelligence and Brings Wisdom to Governance, in Journal of Public Deliberation, vol. 3, 1, 2007, article 6).

REGIONE TOSCANA - Rapporto partecipazione 2009

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LA SOCIETÀ DELL’INFORMAZIONE E DELLA CONOSCENZA IN TOSCANA - Rapporto 2009 Dicembre 2009 È stata la scommessa di questa legislatura, sarà l’impegno anche della prossima: costruire in Toscana una piena società dell’informazione, capace di sviluppare al massimo le opportunità delle nuove tecnologie. Tutto questo con la consapevolezza che è su questo terreno che si gioca una partita decisiva per la nostra economia ma anche per i diritti di ognuno di noi, che è sulla Rete e con la Rete che la nostra Regione può diventare più competitiva e costruire una più matura idea di cittadinanza. È un cammino che non può riguardare solo alcune realtà all’avanguardia. Mai come in questo caso ricerca e innovazione devono porsi al servizio della comunità toscana e portare idee, proposte, soluzioni nelle imprese e nelle case.

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midiasvidal

A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction. Decision trees provide an intuitive visual form that can capture complex interactions between multiple variables. Effective methods exist for inferring decision trees automatically but it has been shown that these techniques can be improved upon via the manual interventions of experts. Here, we introduce Branch, a new Web-based tool for the interactive construction of decision trees from genomic datasets. Branch offers the ability to: (1) upload and share datasets intended for classification tasks (in progress), (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision trees, (4) create feature functions that aggregate content from multiple independent features into single decision nodes (e.g. pathways) and (5) evaluate decision tree classifiers in terms of precision and recall. The tool is optimized for genomic use cases through the inclusion of gene and pathway-based search functions. Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers. A prototype of Branch is available at http://biobranch.org/

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