http://hdl.handle.net/1721.1/29808 http://dspace.mit.edu:80/retrieve/261803 http://hdl.handle.net/1721.1/29808 http://hdl.handle.net/1721.1/46710 Code for LOLCAT Method (Variant of Gillespie Algorithm) Beal, Jacob Indurkhya, Sagar This code and data is publicly listed code for the LOLCAT Method developed by Sagar Indurkhya and Jacob Beal, in the paper: "Reaction factoring and bipartite update graphs accelerate the Gillespie algorithm for large-scale biochemical systems." Thu, 03 Sep 2009 22:58:59 GMT http://hdl.handle.net/1721.1/45565 Simple LCD Transmitter Camera Receiver Data Link Katabi, Dina Raskar, Ramesh Mohan, Ankit Woo, Grace We demonstrate a freespace optical system using a consumer camera and projector in indoor environments using available devices for visual computing. Through design, prototype and experimentation with this commodity hardware, we analyze a practical optical solution as well as the drawbacks for current wireless challenges unmet by classic RF wireless communication. We summarize and introduce some new applications enabled by such similar setups. Sun, 14 Jun 2009 22:58:59 GMT http://hdl.handle.net/1721.1/45548 Sepia: a Framework for Natural Language Semantics Marton, Gregory Adam Westrick, Linda Brown To help explore linguistic semantics in the context of computational natural language understanding, Sepia provides a realization the central theoretical idea of categorial grammar: linking words and phrases to compositional lambda semantics. The Sepia framework provides a language in which to express complex transformations from text to data structures, and tools surrounding that language for parsing and machine learning. Lambda semantics are expressed as arbitrary Scheme programs, unlimited in the semantic representations they may build, and the rules for transformation are expressed in Combinatory Categorial Grammar, though the details of grammar formalism may be easily changed. This report explains the major design decisions, and is meant to teach the reader how to understand Sepia semantics and how to create lexical items for a new language understanding task. Source code and technical description Wed, 27 May 2009 22:58:59 GMT http://hdl.handle.net/1721.1/42004 Style Translation for Human Motion (Supplemental Material) Hsu, Eugene Pulli, Kari Popovic, Jovan Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications, which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions, and a linear time-invariant model to represent stylistic differences. Once the model is estimated with system identification, our system is capable of translating streaming input with simple linear operations at each frame. Sun, 31 Jul 2005 22:58:59 GMT