PPT-Dynamics of networks Jure

Leskovec Computer Science Department Cornell University Stanford University Today Rich data L arge online systems have detailed records of human activity Online communities Facebook 64 million users billion dollar business. cornelledu larscscornelledu kleinbercscornelledu ABSTRACT Tracking new topics ideas and memes across the Web has been an issue of considerable interest Recent work has developed meth ods for tracking topic shifts over long time scales as well as abru edu Jure Leskovec CS Department Stanford University jurecsstanfordedu Abstract Social media forms a central domain for the production and dissemination of realtime information Even though such 64258ows of information have traditionally been thought o It is in fact a generational shift in business software delivering new levels of capability unmatched agility and a compelling and empowering user experience At the same time Microsoft Dynami cs AX 2012 sets a new standard for simplicity This paper Building Business Process into your Application.  Josh . Honeyman. Sr. Development Lead. Microsoft Corporation. BB57. Agenda. Dynamics AX Workflow Overview. Workflow in Dynamics AX Today. Workflow in Dynamics AX Tomorrow. Computational Neuroscience. of Single Neurons. Week 1 – neurons and mathematics:. . a first simple neuron model. Wulfram. Gerstner. EPFL, Lausanne, Switzerland. 1.1 . Neurons. and Synapses: . Index. Introduction to Dynamics 365. Architecture of Dynamics 365. Editions in Dynamics 365. Features of Dynamics 365. Introduction To Dynamics 365. Microsoft Dynamics 365 is the all-new next level of business applications that facilitates intelligence enabling organizations to evolve, grow, and completely transform. . Brian Aronson. Review of ego networks. Ego network (personal network). Ego: Focal node/respondent. Alter: Actors ego has ties with. Dyad: Pair of individuals. Ties. (Ego). D. C. B. Tie types. Friends. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. De Facto Segregation. De Jure Segregation. The Two Black Americas. De Jure Segregation & De Facto Segregation. De Jure. Segregation by . law. Example?. Jim Crow Laws. De Facto. Segregation in . practice. JUSTICE REFORM (DE JURE) Data and Evidence for Justice Reform (DE JURE) The Data and Evidence for Justice Reform (DE JURE) program aims to revolutionize the way we measure, understand and enhance l Jiang. Feb 17. Model formulation.  .  .  .  .  .  . …. Recall the model of fully-connected neural networks.  . When .  . Linear Networks. In the following slides, we only consider linear networks without bias:.