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08:30: Registration<\/p>\n
09.00-12.00: Parallel working sessions with coffee break at 10.30am<\/p>\n
12.00-02.00: Lunch Break in SDH-250<\/p>\n
02.00-05.00: Parallel working sessions continue with coffee break at 03.30pm<\/p>\n
Meeting room 240, CITRIS-SDH Building<\/p>\n
Bertrand Thirion, Loic Esteve, Inria; Jean-Baptiste Poline, UC Berkeley<\/p>\n
Associate Teams:\u00a0 CRISP<\/span><\/a>, GEOMSTATS, METAMRI<\/span><\/p>\n The development and quality of neuroscience research depends on the quality of analysis tools and thus on the quality of data analysis software packages. In a neuroimaging laboratory, 80% of a PhD student or post-doc work is spent analyzing data using various software packages. Improving tools is a major challenge in this field. With the size of available data increasing, the future of this field depends more and more on the quality of software packages and their ability to scale.<\/p>\n Neuroimaging is one of the main way of accessing the organisation of the human brain. This field comprises diverse functional imaging modalities, e.g. functional MRI (fMRI), electroencephalography (EEG) or magnetoencephalography (MEG), as well as anatomic ones, e.g. T1 and diffusion weighted imaging. In the past few years, there has been some convergence around the Python language in the neuroimaging community and Python packages have been developed by many different laboratories. More generally, the international Python scientific community is growing fast. The goal of this workshop is to gather developers working at Inria, Stanford or Berkeley on various neuroimaging python projects (Nipy, nipype, nilearn, dipy, PySurfer, etc) , in order to foster and coordinate the development of these projects as well as discuss best practices.<\/p>\n See the dedicated wiki page at: https:\/\/github.com\/nipy\/nipy\/wiki\/BIS-2015-workshop<\/a>.<\/p>\n Back to top<\/span><\/a><\/span><\/p>\n Meeting room 242, CITRIS-SDH Building<\/p>\n Laura Grigori, Inria; Eric Darve, Stanford University<\/p>\n Associate Teams:\u00a0 AQUARIUS<\/span><\/a>, BIGDATANET<\/span><\/a>, COALA<\/span><\/a>, DALHIS<\/span><\/a>, FASTLA<\/span><\/a>, GOAL <\/span><\/p>\n This session will gather Inria and Bay Area researchers working on different aspects of data science and high performance computing. Speakers will present novel results covering aspects from numerical and randomized linear algebra, parallel and heterogeneous computing, big data analysis, and uncertainty quantification..<\/p>\n 9:00 am – 12:00 pm, with cofee break: 10:30 am – 11:00 am<\/strong><\/p>\n Lunch break : 12:00 pm – 1:30 pm <\/strong><\/p>\n 1:30 pm – 3 pm<\/strong><\/p>\n Coffee break: 3:00 pm – 3:30 pm<\/strong><\/p>\n 3:30 pm – 5:00 pm<\/strong><\/p>\n Back to top<\/span><\/a><\/span><\/p>\n Meeting room 254, CITRIS-SDH Building<\/p>\n Valerie Issarny, Sara Hachem, Inria@SiliconValley; Alexey Pozdnukhov, UC Berkeley<\/p>\n Associate Teams: COMFORT<\/span><\/a>, ORESTE<\/span><\/a>, OAKSAD<\/span><\/a>, REALMS, <\/span>RIPPES, <\/a>SNOWFLAKE<\/a><\/p>\n CityLab@Inria Teams<\/span><\/a><\/span><\/p>\n Other Inria teams: RITS<\/span><\/a><\/span><\/p>\n The smart city vision raises the prospect that cities will become more sustainable environments, ultimately enhancing the citizens\u2019 well being. There is the additional promise of enabling radically new ways of living in, regulating, operating and managing cities, through the increasing active involvement of citizens by ways of crowdsourcing\/sensing and social networking.<\/p>\n From the more technical perspective, smart cities are fascinating, yet challenging systems of systems for the digital science and technologies due to the key characteristics of connected cities and especially their scale. Moreover, the vision of what smart cities should be about is evolving at a fast pace in close concert with the latest technology trends and especially mobile social networking, the IoT and open data.<\/p>\n Inria and its partners of the Inria@SiliconValley program have engaged into research collaboration on smart cities and related issue of urban mobility. This session is structured around panels and is the occasion to exchange about ongoing as well as potential future collaborations on the theme.<\/p>\n 09:00: Welcome<\/p>\n 09:30: Panel – “Sensing & Networking”<\/span><\/p>\n 10:30: Break<\/p>\n 11:00: Keynote – From smart urban systems to smart environments: Wireless in the Woods, Steven Glaser<\/span><\/a>, University of California, Berkeley & REALMS Associate Team <\/span><\/p>\n Wireless sensor networks are becoming an integral component of real-time SWE monitoring infrastructure, but formalized methodologies for their design and optimization have not been sufficiently developed. Representative sampling locations and network topologies are optimized heuristically in the field which is resource intensive, lacks repeatability, and lowers the likelihood of deploying resilient networks. These methods do not account for non-uniform correlations of physiographic variables underlying SWE distributions, lack metrics for determining the optimal number of sensor stations at each site, and do not provide performance guarantees for wireless network structures in complex terrain. We show how pattern recognition and optimization algorithms can be combined with remotely sensed data to optimize sensor networks prior to deployment.<\/p>\n A high resolution dataset of independent physiographic variables is extracted from LiDAR and aerial photographs, and combined into a multivariate feature space normalized and scaled by each variable\u2019s correlation with snow depth. Representative sampling regions are determined using expectation maximization of a Gaussian Mixture Model. An information theoretic metric is used to determine the optimal number of sensor stations. Resilient network structures are found by optimizing a non-convex, multi-objective function using a combination of stochastic gradient descent and graph searching algorithms.<\/p>\n This enables prior knowledge of network behavior to inform optimal deployments for new networks. We compare the results of the algorithm to existing sites at the Southern Sierra Critical Zone Observatory (SSCZO) and in the American River basin. Placements determined by the GMM are at least as representative as existing expert-placed networks. To investigate the effect of site properties on the optimal number of sensor stations needed to representatively measure the site, we apply the algorithm at two other CZOs: Jemez River and Boulder Creek. We show that the distribution and relative correlation of independent variables can have a considerable effect on the optimal number of sensor stations, which ranges from twelve to seventeen in this study.<\/p>\n 12:00: Lunch Break (SDH-250)<\/p>\n 02:00: Panel – “Urban Distributed Systems”<\/span><\/p>\n 03:30: Coffee break<\/p>\n 04:00: Panel – “Urban Mobility”<\/span><\/p>\nOverview<\/span><\/strong><\/h3>\n
Programme<\/span><\/strong><\/h3>\n
<\/a>Working session on Scientific and Large Scale Computing <\/span><\/strong><\/span><\/h1>\n
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See the dedicated web page at: https:\/\/who.rocq.inria.fr\/Laura.Grigori\/Events\/ScientificDay2015.html<\/a><\/h5>\n
Fast solvers and randomized linear algebra <\/b><\/span><\/h5>\n
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Accessing and computing with big data<\/span> <\/b><\/h5>\n
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\nand Maximilien Servajean, Inria: Increasing Coverage in Distributed Search and Recommendation with Profile Diversity – abstract<\/a><\/li>\nUncertainty quantification<\/span> <\/b><\/h5>\n
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<\/a>Working session on Smart City & Mobility<\/span><\/span> <\/strong><\/h1>\n
Session Chairs<\/span><\/strong><\/h3>\n
Participating teams<\/span><\/strong><\/h3>\n
Overview<\/span><\/strong><\/h3>\n
Programme<\/span><\/strong><\/h3>\n
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