 | "Early Abstractions" (1946-57), Pt. 1 Short animations by Harry Smith. No. 1: A Strange Dream (l946) No. 2: Message from the Sun (1946-48) No. 3: Interwoven (1947-49) (Part 1) |
 | The Danny Carey Mandala Drum - Preset 62 - Drawn&Quartered This is one of the more abstract presets. The Mandala is extremely sensitive to where and how hard it is hit and this preset applies that aspect very well to tonal scales and key centers. One preset on one Mandala Pad can generate more than 16,000 alterations of sound. Add a control pedal (like in this example) and and that number goes past 2 million. |
 | Acrylic nail modelling (Simple Abstraction) If you like this video and find it useful, you can order video DVDs with more difficult and interesting designs. Videos from DVD never was published at internet. Contact me at liliya.od.ua@gmail.com www.liliya.od.ua |
 | Lec 1 | MIT 6.002 Circuits and Electronics, Spring 2007 Introduction and lumped abstraction View the complete course: http://ocw.mit.edu/6-002S07 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu |
 | Upcoming Changes to the JavaScript Language Google Tech Talks November, 14 2007 ABSTRACT After eight years of work in the standards committee, JavaScript will soon get an update. We present the highlights and rationales of the proposed changes to JavaScript. The fourth edition of the ECMAScript (JavaScript) language represents a significant evolution of the third edition language, which was standardized in 1999. ES4 is compatible with ES3 and adds important facilities for programming in the large (classes, interfaces, namespaces, packages, program units, optional type annotations, and optional static type checking and verification), evolutionary programming and scripting, data structure construction, control abstraction (proper tail calls, iterators, and generators), and introspection. Improved support for regular expressions and Unicode, richer libraries, and proper block scoping are also added. Speaker: Waldemar Horwat Speaker: Pascal-Louis Perez |
 | Lecture 1 | Programming Abstractions (Stanford) The first lecture by Julie Zelenski for the Programming Abstractions Course (CS106B) in the Stanford Computer Science Department. Julie Zelenski gives an introduction to the course, recursion, algorithms, dynamic data structures and data abstraction; she also introduced the significance of programming and gives her opinion of what makes 106B "great;" C++ is introduced, too. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=FE6E58F856038C69 CS 106B Course Website: http://cs106b.stanford.edu Stanford Center for Professional Development: http://scpd.stanford.edu/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanforduniversity/ |
 | Virtual Machine-Based Replay Debugging Google Tech Talks October 30, 2008 ABSTRACT Replay debugging allows developers to debug recordings of programs running in virtual machines. This simple (and old) idea addresses some of the greatest challenges of software development, because a recording containing the manifestation of a bug represents an unambiguous encapsulation of that bug. Whether the bug is non-deterministic, difficult to reproduce, or just difficult to describe, the program containing the bug will behave identically each time it is replayed, allowing the developer to more easily debug it. In addition, replay debugging is non-invasive, because while debugging a replayed program behaves as it did during recording. The developer need not worry about how debugging may change the scheduling of threads, the order in which locks are acquired, or connections with external processes/machines timing out. Finally, an abstraction of reverse execution can easily be built on top of replay debugging, allowing developers to directly move (backward) from the manifestation of a bug to its origin. VMware Workstation 6.5 includes an experimental form of replay debugging for C/C++ Windows developers using Visual Studio. We hope you will give this feature a try, and we are very interested in feedback concerning the direction it should take in future releases. This presentation motivates and introduces replay debugging and summarizes our implementation in VMware Workstation 6.5. More information is available at http://www.replaydebugging.com/. Speaker: E Christopher Lewis E Lewis has been an engineer in the Advanced Development Group at VMware since 2007 where he explores novel applications of virtualization. Before joining VMware, E was a professor (University of Pennsylvania), student (Cornell University and the University of Washington), and lay about (growing up in Vermont and North Carolina). To get away from it all, E plays guitar, knits, and teaches empathy to the unsuspecting. Speaker: Prashant Dhamdhere Prashant is MTS at VMware, where he is actively working on Replay Debugging technology. Before joining VMware, he worked on various aspects of storage solutions at Veritas that is now part of Symantec. Prashant brings key windows kernel expertise to team. He holds BS in Computer Science from University of Pune, India. Speaker: Eric Xiaojian Chen Eric is an engineering manager at VMware. He manages several projects on VM kernel driver development. Before VMware, Eric has worked at Cisco on a high end switch product for eight years. Eric holds a MBA degree from UC Berkeley and a master degree of Computer Science from Fudan University in Shanghai, China. |
 | Seattle Conference on Scalability 2008: Chapel Google Tech Talks June 14, 2008 ABSTRACT Chapel: Productive Parallel Programming at Scale Chapel is a new programming language being developed by Cray Inc. as part of the DARPA-led High Productivity Computing Systems Program (HPCS). Chapel strives to increase parallel programmability for supercomputer users by raising the level of abstraction compared to current parallel programming models. Language concepts that support this goal include abstractions for globally distributed data aggregates and anonymized task-based parallelism. Since locality is crucial when computing at large scales, Chapel also supports language concepts for reasoning about architectural locality on the target machine, including control over data placement and affinity between tasks and data. In contrast to previous higher-level parallel languages, Chapel is designed to be a "multi-resolution language", in which users can start by writing very abstract code and then incrementally add more detail until they are as close to the machine as that portion of their code requires. Although Chapel was not specifically designed for datacenter-oriented applications, many of its concepts should also be quite suitable for this domain given the importance of distributed data, concurrency, and affinity. In this talk, I will provide an overview of Chapel, explain how it was designed to help the HPC community, and describe its status. I will also attempt to make ties between its concepts and how they might be useful in a datacenter-based programming environment. Speaker: Bradford Chamberlain Bradford Chamberlain is a Principal Engineer at Cray Inc., where he works on parallel programming models, focusing primarily on the design and implementation of the Chapel parallel language in his role as technical lead for that project. Before starting at Cray in 2002, he spent a year at a start-up working at the opposite end of the hardware spectrum to design a parallel language (SilverC) for reconfigurable embedded hardware. Brad received his Ph.D. in Computer Science & Engineering from the University of Washington in 2001 where his work focused on the design and implementation of the ZPL parallel array language, particularly on implementing and generalizing its region concept -- --a first-class index set representation for programming with distributed arrays. While at UW, he also dabbled in algorithms for accelerating the rendering of complex 3D scenes. Brad remains associated with the University of Washington as an affiliate faculty member and most recently co-led a seminar there that focused on the design of Chapel. He received his Bachelor's degree in Computer Science from Stanford University with honors in 1992. Slides for this talk are available at http://groups.google.com/group/seattle-scalability-conference |
 | Disk-Based Parallel Computation, Rubik's Cube, and Checkpointing Google Tech Talks March, 24 2008 ABSTRACT This talk takes us on a journey through three varied, but interconnected topics. First, our research lab has engaged in a series of disk-based computations extending over five years. Disks have traditionally been used for filesystems, for virtual memory, and for databases. Disk-based computation opens up an important fourth use: an abstraction for multiple disks that allows parallel programs to treat them in a manner similar to RAM. The key observation is that 50 disks have approximately the same parallel bandwidth as a _single_ RAM subsystem. This leaves latency as the primary concern. A second key is the use of techniques like delayed duplicate detection to avoid latency. For example, hash accesses accesses can be saved (even saved on disk), until there are sufficiently many pending accesses to use standard streaming techniques. We have designed a library for search problems that exploits the high parallel bandwidth while hiding the latency. We build abstractions for search that employ parallel disk-based hash arrays with the same speed as a single hash array in a single RAM subsystem. In the case of Rubik's cube, we exploited this mechanism by using seven terabytes of distributed disk in a search problem that showed that 26 moves suffice to solve Rubik's cube. Our initial efforts emphasize idempotent operations, so that we can easily recover from hardware or software faults. We next intend to apply a more general solution for fault recovery: checkpointing. This separate effort in our lab has now produced a mature, robust user-level checkpointing program has now matured. The package works successfully in tests on OpenMPI, MPICH-2, OpenMP, and parallel iPython (used in SciPy and NumPy). Our DMTCP package transparently checkpoints parallel, multi-threaded processes, with no modification either to the operating system or to the application binaries. Extrapolating from current experiments, we estimate that we can checkpoint a 1,000 node parallel computation in a matter of minutes. We are currently searching for a testbed on which to demonstrate this scalability. Speaker: Gene Cooperman |
 | Visual Perception with Deep Learning Google Tech Talks April, 9 2008 ABSTRACT A long-term goal of Machine Learning research is to solve highy complex "intelligent" tasks, such as visual perception auditory perception, and language understanding. To reach that goal, the ML community must solve two problems: the Deep Learning Problem, and the Partition Function Problem. There is considerable theoretical and empirical evidence that complex tasks, such as invariant object recognition in vision, require "deep" architectures, composed of multiple layers of trainable non-linear modules. The Deep Learning Problem is related to the difficulty of training such deep architectures. Several methods have recently been proposed to train (or pre-train) deep architectures in an unsupervised fashion. Each layer of the deep architecture is composed of an encoder which computes a feature vector from the input, and a decoder which reconstructs the input from the features. A large number of such layers can be stacked and trained sequentially, thereby learning a deep hierarchy of features with increasing levels of abstraction. The training of each layer can be seen as shaping an energy landscape with low valleys around the training samples and high plateaus everywhere else. Forming these high plateaus constitute the so-called Partition Function problem. A particular class of methods for deep energy-based unsupervised learning will be described that solves the Partition Function problem by imposing sparsity constraints on the features. The method can learn multiple levels of sparse and overcomplete representations of data. When applied to natural image patches, the method produces hierarchies of filters similar to those found in the mammalian visual cortex. An application to category-level object recognition with invariance to pose and illumination will be described (with a live demo). Another application to vision-based navigation for off-road mobile robots will be described (with videos). The system autonomously learns to discriminate obstacles from traversable areas at long range. This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell, Fu-Jie Huang, Koray Kavakcuoglu, and Marc'Aurelio Ranzato. Speaker: Yann Le Cun Computational and Biological Learning Lab, Courant Institute of Mathematical Sciences, New York University. |
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