Spatio-temporal metadata filtering and synchronising invideo surveillance

International audienceThis paper presents an ongoing work that aims at assisting videoprotection agents in the search for particular video scenes of interest in transit network. The video-protection agent inputs a query in the form of date, time, location and a visual description of the scene. The query processing starts by selecting a set of cameras likely to have filmed the scene followed by an analysis of the video content obtained from these cameras. The main contribution of this paper is the innovative framework that is composed of: (1) a spatiotemporal filtering method based on a spatio-temporal modeling of the transit network and associated cameras, and (2)a content-based retrieval based method on visual features. The presented filtering framework is to be tested on real data acquired within a French National project in partnership with the French Interior Ministry and the French National Police. The project aims at setting up public demonstrators that will be used by researchers and commercials from the video-protection community

SURAT PENCATATAN CIPTAAN Karya Rekaman: Inovasi Pengajaran Gerak Dasar Tari Bali Dengan Bahasa Inggris Dalam Upaya Memperkokoh Kiprah ISI Denpasar di Dunia Internasional

Spatial joins are join operations that involve spatial data types and operators. Spatial access methods are often used to speed up the computation of spatial joins. This paper addresses the issue of benchmarking spatial join operations. For this purpose, we first present a WWW-based benchmark generator to produce sets of rectangles. Using a Web browser, experimenters can specify the number of rectangles in a sample, as well as the statistical distributions of their sizes, shapes, and locations. Second, using the generator and a well-defined set of statistical models we define several tests to compare the performance of three spatial join algorithms: nested loop, scan-and-index, and synchronized tree traversal. We also added a real-life data set from the Sequoia 2000 storage benchmark. Our results show that the relative performance of the different techniques mainly depends on two parameters: sample size, and selectivity of the join predicate. All of the statistical models and algorithms are available on the Web, which allows for easy verification and modification of our experiments.Peer Reviewe

Fetal Tracheal Occlusion Increases Lung Basal Cells via Increased Yap Signaling

Basal cell; Fetal tracheal occlusion; MechanotransductionCélula basal; Oclusión traqueal fetal; MecanotransducciónCèl·lula basal; Oclusió traqueal fetal; MecanotransduccióFetal endoscopic tracheal occlusion (FETO) is an emerging surgical therapy for congenital diaphragmatic hernia (CDH). Ovine and rabbit data suggested altered lung epithelial cell populations after tracheal occlusion (TO) with transcriptomic signatures implicating basal cells. To test this hypothesis, we deconvolved mRNA sequencing (mRNA-seq) data and used quantitative image analysis in fetal rabbit lung TO, which had increased basal cells and reduced ciliated cells after TO. In a fetal mouse TO model, flow cytometry showed increased basal cells, and immunohistochemistry demonstrated basal cell extension to subpleural airways. Nuclear Yap, a known regulator of basal cell fate, was increased in TO lung, and Yap ablation on the lung epithelium abrogated TO-mediated basal cell expansion. mRNA-seq of TO lung showed increased activity of downstream Yap genes. Human lung specimens with congenital and fetal tracheal occlusion had clusters of subpleural basal cells that were not present in the control. TO increases lung epithelial cell nuclear Yap, leading to basal cell expansion.Funding was obtained from NIH/NHLBI R01HL141229 (to BV)

The Random Hivemind: An Ensemble Deep Learner. A Case Study of Application to Solar Energetic Particle Prediction Problem

Deep learning has become a popular trend in recent years in the machine learning community and has even occasionally become synonymous with machine learning itself thanks to its efficiency, malleability, and ability to operate free of human intervention. However, a series of hyperparameters passed to a conventional neural network (CoNN) may be rather arbitrary, especially if there is no surefire way to decide how to program hyperparameters for a given dataset. The random hivemind (RH) alleviates this concern by having multiple neural network estimators make decisions based on random permutations of features. The learning rate and the number of epochs may be boosted or attenuated depending on how all features of a given estimator determine the class that the numerical feature data belong to, but all other hyperparameters remain the same across estimators. This allows one to quickly see whether consistent decisions on a given dataset can be made by multiple neural networks with the same hyperparameters, with random subsets of data chosen to force variation in how data are predicted by each, placing the quality of the data and hyperparameters into focus. The effectiveness of RH is demonstrated through experimentation in the predictions of dangerous solar energetic particle events (SEPs) by comparing it to that of using both CoNN and the traditional approach used by ensemble deep learning in this application. Our results demonstrate that RH outperforms the CoNN and a committee-based approach, and demonstrates promising results with respect to the ``all-clear'' prediction of SEPs.Comment: 10 pages, 2 figures, 5 table

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-Weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager onboard Solar and Heliospheric Observatory) Active Region Patches (SMARP) and Space-Weather HMI (Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory) Active Region Patches (SHARP), which are two currently available data products containing magnetic field characteristics of solar active regions. The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP with SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI-HMI dataset currently spans the period between April 4, 1996, and December 13, 2022, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites (GOES). Time-lagged cross-correlation indicates that a relationship may exist, where some magnetic field properties of active regions lead the flare index in time. Applying the rolling window technique makes it possible to see how this leader-follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle

Review of solar energetic particle models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.</p