PPT-Cost-effective Outbreak Detection in Networks

Jure Leskovec Andreas Krause Carlos Guestrin Christos Faloutsos Jeanne VanBriesen Natalie Glance Scenario 1 Water network Given a real city water distribution network And data on how contaminants spread in the network. We present a general methodology for near optimal sensor placement in these and related problems We demonstrate that many realistic outbreak detection objectives eg de tection likelihood population a64256ected exhibit the prop erty of submodularity Ad-hoc and sensor networks are emerging as promising platforms for a variety of application areas in both military and civilian domains. These networks are especially attractive for scenarios where it MIMO TDMA ProtocolUbiquiti Networks Inc., 91 E. Tasman Dr., San Jose, CA 95134 www.ubnt.com DataRateAvg. TXToleranceDataRateSensitivityTolerance1-24Mbps23 dBm+/-2dB24Mbps-83 dBm+/-2dB36Mbps21 dBm+/- inWireless. Sensor Networks. Abstract. Wireless sensor networks are vulnerable to the node clone, and several distributed protocols have been proposed to . de¬tect. this attack. . However, they require too strong assumptions to be practical for large-scale, randomly deployed sensor networks. . Jure . Leskovec. Machine Learning Department, CMU. Joint work with Andreas . Krause, Carlos . Guestrin. , Christos . Faloutsos. , Jeanne . VanBriesen. , . and Natalie . Glance. Networks as data. Internet (a). Generally a DAG, directed acyclic graph. VisGraph, HKUST. LeNet. AlexNet. ZF Net. GoogLeNet. VGGNet. ResNet. Learned convolutional filters: Stage 1. Visualizing and understanding convolutional neural networks.. January, 2014. Objectives. Summarize clinical and epidemiological information on common skin infections:. Fungal (. Tinea. ). Bacterial (. Staphylococcus, Streptococcus. ). Viral (. M. olluscum. . contagiosum. SITORY – COPYRIGHT NOTICE This is the peer - reviewed, manuscript version of the following article: Jemberu, W. T., Mourits - benefit analysis of foot and mouth disease control in Ethiopia', Preve Module 2. Module Objectives. By the end of this module, participants will be able to identify the agents of foodborne illness and the associated burden on the United States.. Explain . the burden . of foodborne . using Channel Dependent Posteriors. Presented By:. Vinit Shah. Neural Engineering Data Consortium,. Temple University. 1. Abstract. An important factor of seizure detection problem, known as segmentation: defined as the ability to detect start and stop times within a fraction of a second, is a challenging and under-researched problem.. esponse: . H. ealthcare-Associated Infections . A. ntibiotic-Resistant Pathogens. Joe Perz, . DrPH. MA. CDC/DHQP/PRB . CORHA Governance Committee Member. ICEID 2018. Problem. Despite significant progress, . Source:. TG-Outbreaks . topic driver. Title:. TDD (new): TG-Outbreaks (Outbreak detection) - Att.2 - Data & Benchmarking challenges. Purpose:. Discussion. Contact:. Dr. . Stéphane. Ghozzi. Dr. Martina Fischer. San Francisco Bureau C. SHEPARD Mumps Can Occur Even Among theVaccinated, Recent Outbreak ProvesBY TIMOTHY F.KIRN umps outbreaks can occur even amonghighly vaccinated populations, accordingto a report Advanced Computer Networks. C13. . Term C10. Transmission Errors Outline. Error Detection versus Error Correction. Hamming Distances and Codes. Linear Codes - Parity. Internet Checksum. Polynomial Codes.