1. John Heckle LIVE at Midnight Shift x Zouk: Άλφα Jan 2013


    from Midnight Shift / Added

    254 Plays / / 0 Comments

    Midnight Shift: www.mnshift.com John Heckle: www.johnheckle.com Facebook: www.facebook.com/mnshift

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    • Linear Combination of Random Variables: S2 May/June 2012/73 - Q5


      from Sherry / Added

      212 Plays / / 0 Comments

      A-Level S2 Maths: solving a problem relating Linear Combination of Random Variables.

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      • The Grand Geometry of Nature


        from Science for the Public / Added

        74 Plays / / 0 Comments

        Science for the Public Sept 18,2012. Tom Mrowka, Professor of Mathematics and Gigliola Staffilani,Professor of Mathematics, (both) Massachusetts Institute of Technology. Professors Tom Mrowka and Gigliola Staffilani explain how an understanding of Nature inevitably depends on mathematics. Forces, processes, patterns —they are all expressed in the unique and universal language of mathematics, and particularly in geometry. These mathematicians decode for a general public the deep aesthetics of these structures, and they explain how mathematics reveals the core of Nature. No need to fear math —it is a whole new way to experience reality.s, (both) Massachusetts Institute of Technology.

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        • Big Data Science: The Algorithmic Advances that Lie Underneath - Anna Gilbert, University of Michigan


          from Kavli Frontiers of Science / Added

          24 Plays / / 0 Comments

          The Efficient and Effective Transmission, Storage, and Retrieval of Information on a Large-Scale are among the Core Technical Problems in the Modern Digital Revolution Anna Gilbert, University of Michigan Even areas of science and technology that traditionally generated and analyzed small ``analog'' data sets, such as biology, now routinely handle much larger, discrete data with sophisticated algorithmic processing and generation. The massive volume of data necessitates the quest for mathematical and algorithmic methods for efficiently describing, summarizing, synthesizing, and, increasingly more critical, deciding when and how to discard data before storing or transmitting it. The mathematical and algorithmic techniques used to describe data, to capture its inherent information, and to encode it for transmission and analysis are fundamentally different from those used in the analysis of small data sets. They include using randomness to take random snapshots of data sets and running algorithms whose success is only approximate and correct with high probability. I will discuss several of these techniques, what implications they have for scientific analysis, and how these techniques are changing not only how scientists collect data but the devices with which they measure physical or biological phenomena.

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          • Big Data Science: The Algorithmic Advances that Lie Underneath - Michael W. Mahoney, Stanford University


            from Kavli Frontiers of Science / Added

            85 Plays / / 0 Comments

            Sensors, Networks, and Massive Data Michael W. Mahoney, Department of Mathematics, Stanford University Massive quantities of data are routinely generated in many scientific and non-scientific domains, and developing tools to deal with these data leads to very fundamental algorithmic and statistical challenges. At root, these data are generated in such quantities because technological developments permit us to measure or monitor or sense the world very inexpensively at unprecedented levels of granularity; and, relatedly, a common theme in many of these applications is that, since the data are generated in relatively-uncontrolled ways, "noise" is often a dominant property of the data, with interesting "signal" being a second order effect. A good "hydrogen atom" for addressing these issues and for developing algorithmic and statistical methods for massive data more generally can be found in large social and information networks, basically since nearly any "niceness" assumption, e.g., about how the data are generated or structured, is severely violated. Using this as a case study, I will describe some of the challenges that scientists will face as they deal more and more with increasingly massive data.

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            • Big Data Science: The Algorithmic Advances that Lie Underneath - Alexander Gray, Georgia Institute of Technology


              from Kavli Frontiers of Science / Added

              37 Plays / / 0 Comments

              Big Data Science: The Algorithmic Advances that Lie Underneath Alexander Gray, Georgia Institute of Technology Virtually every area of science and engineering is either experiencing its own version of a "big data" revolution that is yielding unprecedented new insights, or has such opportunities on the horizon. I will survey seven main types of data analysis methods that can be used across all areas, and give examples in astrophysics of new science that can be achieved by applying such methods to massive data sources. The central difficulty is in the computation of such analyses. I'll discuss seven main types of computational bottlenecks and how difficult they are, as well as seven main types of algorithmic concepts that can be used to tackle them, drawing from the fastest practical algorithms across many areas of applied mathematics and computer science.

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              • Evolutionary game theory 1b: Cursory comparison with tabular game theory


                from David Liao / Added

                58 Plays / / 0 Comments

                (C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Pairwise business transactions and payoffs Prisoner's dilemma

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                • The future of glaciers


                  from IMAGINARY / Added

                  5,690 Plays / / 1 Comment

                  Author: Guillaume Jouvet Institution: Department of Mathematics and Computer Science, Freie Univerisität Berlin, Germany Support: Deutsche Forschungsgemeinschaft (project KL 1806 5-1) Licence: Creative Commons BY-NC-ND Entry for the project "Mathematics of Planet Earth". Part of the open source exhibition Mathematics of Planet Earth, hosted by www.imaginary.org.

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                  • How Maths Illuminates Our Lives


                    from The RSA / Added

                    74 Plays / / 0 Comments

                    Acclaimed author and one of the world’s most extraordinary minds, Daniel Tammet visits the RSA to give us a unique perspective on how mathematics can help us to make sense of the world and our place in it. With Simon Ings, novelist, reviewer and editor of Arc, magazine of futures and fiction from the New Scientist. Listen to the podcast of the full event including audience Q&A: http://www.thersa.org/events/audio-and-past-events/2012/how-maths-illuminates-our-lives Our events are made possible with the support of our Fellowship. Support us by donating or applying to become a Fellow. Donate: http://www.thersa.org/support-the-rsa Become a Fellow: http://www.thersa.org/fellowship/apply

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                    • Interview with Thelma Perso (2011)


                      from AAMT Inc. / Added

                      64 Plays / / 0 Comments

                      An interview with Thelma Perso about numeracy and a balanced numeracy program at the AAMT-MERGA conference, Mathematics: Traditions and [New] Practices, in Alice Springs, July 2011.

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