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How Different is 'Different': A Perspective on Music Similarity in Indian Art Music
How Different is 'Different': A Perspective on Music Similarity in Indian Art Music
Kaustuv Kanti Ganguli
00:48:24 · 11 June 2015

The characteristic melodic motifs (lit. pakad) of a raga in Indian classical music are an important cue to its identity. Artists, however, incorporate considerable creative variation within a raga phrase during performance while still preserving its identity in the ears of the listeners. It is of interest therefore to explore the boundaries of this categorization of the phrase identity, given the space of musical variations in the pitch interval and duration dimensions. Such an endeavour can help better model musical similarity for music retrieval and pedagogy applications. Our primary research goals lie in modelling the melodic shape corresponding to a raga phrase in a perceptually relevant manner. This work can help develop methods for the automatic discovery of musically meaningful melodic patterns from audio. In this talk, I shall motivate the relevance of studying music perception, in a computational framework. As a signal processing engineer, I started off with modelling melodic motifs with a view to being able to 'classify' and 'discover' characteristic phrases in a raga audio. Being a musician myself, I could perceive the similar patterns in a concert audio, the pitch contours of which looked quite different visually. Seeing these broad differences in vision and audition, the immediate question that came to my mind was: 'How different is 'different'?' That was the point I started reverse engineering and noted human responses to capture the correlation between subjective and objective behaviors. The broad goal is to redefine a distance measure best suited for Indian raga music that will take care of 'microtonality' and 'improvisation', the crux of this music tradition.

WIKIBHASHA NEW
WIKIBHASHA NEW
00:01:27 · 10 June 2015

WikiBhasha beta is a multilingual content creation tool for Wikipedia. Developed by Microsoft Research, WikiBhasha beta enables contributors to Wikipedia to find content from other Wikipedia articles, translate the content into other languages, and then either compose new articles or enhance existing articles in multilingual Wikipedias.

WikiBhasha beta is a powerful content creator and has an easy to use, intuitive user interface. It enables easy and quick translation of articles from English to more than 30 other languages supported by Microsoft’s Machine Translator and Microsoft’s Collaborative Translation Framework (CTF).

​WikiBhasha beta is available at www.wikibhasha.org for contributors to use and create content on Wikipedia.

99DOTS: low-cost monitoring and improving medication adherence
99DOTS: low-cost monitoring and improving medication adherence
00:01:37 · 26 May 2015

99DOTS is a new model of delivering tuberculosis medications that utilizesbasic mobile phones and augmented blister packaging to provide real-timeadherence monitoring without the burden of direct observation.
By far three pilots have been conducted, spanning over 40 patients inIndia. Results from the pilots are very encouraging. Over 90% of all doses werereported correctly using 99DOTS. All patients were able to use the system, evenif they had not received any formal education.

We believe that 99DOTS represents a promising approach for improving thedelivery of tuberculosis medications. If a patient shows regular adherenceunder traditional DOTS, they could gradually transition to 99DOTS, improvingconvenience to patients and also allowing providers to focus their attention onless adherent patients.

DebugAdvisor- a recommendation system for debugging
DebugAdvisor- a recommendation system for debugging
00:01:34 · 11 May 2015

DebugAdvisor is a recommendation system for debugging that has two specialities:
a) It supports fat queries - allows for the query to be the full context of a bug without the need for cleanup
b) It uses relationships between development artifacts to find people, sources and binaries (and other artifacts) that are related to the query. DebugAdvisor is a collaboration between MSR India, WinSE (IDC) and WinSE(Redmond).

For more information about this project, please visit: http://research.microsoft.com/en-us/projects/debugadvisor

Rich Interactive Narratives- Microsoft Research India
Rich Interactive Narratives- Microsoft Research India
00:02:19 · 4 May 2015

The Microsoft Rich Interactive Narratives (RIN) research project combines traditional forms of storytelling with new visualization technologies to create compelling interactive digital narratives. The RIN project is an undertaking by Microsoft Research India in collaboration with the Interactive Visual Experience group in Microsoft Research Redmond and the Microsoft Research Connections.
Here's how you can be part of RIN;

If you are a content developer and have a lot of media content you can start building RIN right from today. You can download RIN studio from MSR India website here: http://research.microsoft.com/en-us/downloads/32f330bc-fd4d-4170-8bbe-b077bc25f4c7/default.aspx

If you are a developer, you can use RIN JavaScript library to enrich your website. You can go to: http://rinjs.org/ and use step by step instruction on how to use RIN JS library to embed any frame and also how to extend RIN JS library to include your own interactive experiences.

P: Safe Asynchronous Event-Driven Programming
P: Safe Asynchronous Event-Driven Programming
00:01:29 · 3 May 2015

P: a domain specific language for writing asynchronousevent-driven programs. This asynchronouslanguage promotes a discipline of programmingwhere deferrals need to be declared explicitly, and consequentlyleads to responsive systems.

The main technicalcontribution of this work is an asynchronous model which forceseach event in the queue to be handled as soon as the machine associatedwith the queue is scheduled, and has a chance to de-queue theevent. The system's verifier systematically explores the state space of machinesand ensures that there are no unhandled events. In certaincircumstances, such as processing a high priority event, or processinga sequence of event exchanges during a transaction, some otherlower priority events may have to be queued temporarily. P has featuressuch as deferred events for a programmer to explicitly specifysuch deferrals.

The Solution of the Kadison-Singer Problem
The Solution of the Kadison-Singer Problem
00:02:05 · 2 May 2015

The Kadison-Singer problem is a question in operator theory whicharose in 1959 while trying to make Dirac's axioms for quantum mechanicsmathematically rigorous in the context of von Neumann algebras.
A positive solution to the problem is given by Nikhil Srivastava by proving essentially the strongestpossible partitioning theorem of this type. The proof is based on two significantingredients: a new existence argument, which reduces the problem to boundingthe roots of the expected characteristic polynomials of certain randommatrices, and a general method for proving upper bounds on the roots of suchpolynomials. The techniques are elementary, mostly based on tools from thetheory of real stable polynomials

Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks
Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks
Sanchit Misra
00:44:11 · 18 February 2015

Learning Bayesian networks is NP-hard. Even with recent progress in heuristic and parallel algorithms, modeling capabilities still fall short of the scale of the problems encountered. In this work, we present a massively parallel method for Bayesian network structure learning, and demonstrate its capability by constructing genome-scale gene networks of the model plant Arabidopsis thaliana from over 168.5 million gene expression values. We report strong scaling efficiency of 75% and demonstrate scaling to 1.57 million cores of the Tianhe-2 supercomputer. Our results constitute three and five orders of magnitude increase over previously published results in the scale of data analyzed and computations performed, respectively. We achieve this through algorithmic innovations, using efficient techniques to distribute work across all compute nodes, all available processors and coprocessors on each node, all available threads on each processor and coprocessor, and vectorization techniques to maximize single thread performance.

Computing on Encryption Data: Functional Encryption and More
Computing on Encryption Data: Functional Encryption and More
Vipul Goyal
00:42:56 · 27 January 2015
Q and A - Session 2
Q and A - Session 2
Butler Lampson, Jeannette Wing, and Siddharth Prakash
00:15:46 · 23 January 2015

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