Hyderabad
The Hyderabad chapter of Papers We Love
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Object Space Visibility: Its applications to Games and Smart City(IoT)
Understanding Visibility: Its possible applications to Games and Smart City(IoT).
DESCRIPTION:Visibility is one of the fundamental, to applications such as graphics, games, architecture, design simulations, and many more.,
http://people.csail.mit.edu/teller/pubs/siggraph91.pdf
http://www.cs.virginia.edu/~luebke/publications/pdf/portals.pdf
Given an environment with set of geometric objects, we understand the intricacies of visibility concepts, and structures. Applications immediately be realized are games, and possibly design systems (software simulations, for Smart Cities).
Potential Attendees:1) Students; 2) Researchers …
Review of the FLP Impossibility result
FLP Impossibility result is a really cool one. It got an award in 2001 for being the most influential paper in distributed systems.
The link to the paper is here - https://groups.csail.mit.edu/tds/papers/Lynch/jacm85.pdf
We will go through a quick review of what consensus protocols are and then dive right into the paper.
…Parallel Tracking and Mapping for Small AR Workspaces: Vinay Babu
Driver-less cars is the next big thing in transportation systems using which we can reduce lot of accidents and also we can optimize travelling time. Somehow we perceive the environment and we will able to localize ourself within the perceived environment. The biggest challenge for Driver less Cars or Autonomous Cars is to Map the Environment and the localization of itself within the mapped environment This problem is called Simultaneous Localization and Mapping.
In this meetup we will talk about how these SLAM techniques are useful for robots which are used in different domains, recent development in SLAM Techniques, challenges in SLAM and opensource tools that are available.
As a pre-requisite read the following paper: http://www.robots.ox.ac.uk/~gk/publications/KleinMurray2007ISMAR.pdf
Puneeth: ImageNet Classification with Deep Convolutional Neural Networks
The paper we are going to discuss is the famous AlexNet paper: http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
Puneeth will begin by giving a brief introduction to Neural Networks and Deep Neural Networks, and then proceed to give an overview of the paper. Hopefully, there will also be some code/demo to show!
Abstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-co…
Hrishikesh Barua: The Road to Software Defined Networking
Speaker
Hrishikesh Barua is going to speak about Software Defined Networking discussing what it is, discuss the history of the technology and demo a Python SDN controller.
We hope that you'll read the paper before the meetup, but don't stress if you can't.
…
Nithin Movva: Exploiting Statistics on Query Expressions for Optimization
Speaker
Nithin Movva will be presenting the paper "Exploiting Statistics on Query Expressions for Optimization".
We hope that you'll read the paper before the meetup, but don't stress if you can't.
Abstract
Statistics play an important role in influencing the plans produced by a query optimizer. Traditionally, optimizers use statistics built over base tables and assume independence between attributes while propagating statistical information through the query plan. This approach can introduce large estimation errors, which may result in the optimizer choosing inefficient execution plans. In this paper, we show how to extend a generic optimizer so that it also exploits statistics built on expressions corresponding to intermediate nodes of query plans. We show that in some cases, the quality of the resulting plans is significantly better than when only base-table statistics are…
A Theoretician's Guide to the Experimental Analysis of Algorithms
Speaker
Uday will be presenting the paper A Theoretician’s Guide to theExperimental Analysis of Algorithms. The paper presents an informal discussion of issues in analyzing algorithms experimentally, with the goal of providing a useful guide to new experimentalists, and challenging current researchers to improve their work from a scientific point of view.
We hope that you'll read the paper before the meetup, but don't stress if you can't.
Abstract
This paper presents an informal discussion of issues that arise when one attempts to analyze algorithms experimentally. It is based on lessons learned by the author over the course of more than a decade of experimentation, survey paper writing, refereeing, and lively discussions with other experimen…
A Note on Distributed Computing
A Note on Distributed Computing is a seminal paper that argues that the distributed and the local models of computing are fundamentally different, and efforts to unify them are flawed and outlines the reasons behind this assertion. You can find it online at http://www.eecs.harvard.edu/~waldo/Readings/waldo-94.pdf or https://github.com/papers-we-love/papers-we-love/blob/master/distributed_systems/a-note-on-distributed-computing.pdf
Please go through the paper so that we can have an engaging discussion!
…
Should Computer Scientists Experiment More? -- Walter Tichy
Intro
I'm Puneeth, and I would like to talk about this paper
Should Computer Scientists Experiment More? -- Walter Tichy. I was listening to a wonderful podcast on Ruby Rogues -- What We Actually Know About Software Development and Why We Believe It's True with Greg Wilson and Andreas Stefik -- and there was a very interesting discussion on conducting experimental studies in computer science and basing our development and future work on empirical evidence, instead of guesstimates. This paper was mentioned in the podcast. It is a quite simple paper, and I picked it for it simplicity, since this is our first meetup and hopefully will lead to a good discussion on the topic.
Abstract
Computer scientists and practitioners defend their lack of experimentation with a wide range of arguments. Some arguments suggest that experimentation is inappropriate, too difficul…