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I've started to work with Neo4j. I know there is an opportunity to extend its API with new functionality. Also, I'm interested in algorithms for large-scale graphs.
My question is: Does anyone know any sites or other resources with the latest improvements for large-scale graphs algorithms? Or maybe you can advise me the most effective solutions for some kind of operations, like: finding the shortest path algorithms, clusterization algorithms, nearest neighbour, radius/diameter computing etc.
Thanks a lot!
Much of modern applied graph theory centers on applying computational linear algebra to graph theoretic algorithms. One prominent group involved in such work is John Gilbert's at UCSB: his group put out a piece of software called Combinatorial BLAS for efficiently executing graph algorithms using methods in computational linear algebra.
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I am looking for a book that contains descriptions of the topics: Shor's algorithm, McEliece cryptosystem, Lattice-based cryptography, Discrete logarithm; and possibly others.
Does anyone know a single book or a few that would contain there topics?
There is "Post-Quantum Cryptography" published by Daniel J. Bernstein. The book is more of a general overview and doesn't go that far into details.
It contains sections about lattice based, hash based and code based cryptography.
Shor's algorithm as well as discrete logarithm aren't handled in depth, but there is a general overview.
I think the best way to get into the details is by studying the respective scientific papers.
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I am looking for an optimized 4-connectivity or 8-connectivity Connected Component Labelling source code in MATLAB or C++. I saw many implementation of connected component labelling (4-connectivity) in MATLAB.
One of the implementations that works faster is the recursive implementation explained here: http://www.mathworks.com/matlabcentral/fileexchange/38010-connected-component-labeling-like-bwlabel
MATLAB has a built-in bwlabeln or bwlabel, which is far more optimized. They claim to use union-find method from two-pass algorithm described in Sedgewick's Algorithms in C, Addison-Wesley. However, it is hard to find any source code of it. Does anyone have idea about it? An optimized code is really needed.
You can indeed work by scanning the image in scanline order and when you meet a component seed-fill it.
You will find two efficient (and very similar) algorithms in Graphics GEMS 1:
A SEED FILL ALGORITHM, Paul S. Heckbert
FILLING A REGION IN A FRAME BUFFER, Ken Fishkin
and with a little effort some implementations. (The papers give Pascal-like code which is easy to translate.)
They run in linear time, use an explicit stack and don't require union-find.
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Currently I was studying the genetic algorithm and I got it very interesting area to work in but as per my new field I am not getting any idea about the topic. So I want to know that "What are the current problems yet to be solved in Genetic Algorithms?".
Thanks!
PS: I read the concepts and still working to improve knowledge in it still ideas and problems are most welcomed.
If I understand your question correctly, I personally think you should rephrase your question to "What are the current problems yet to be solved in Genetic Algorithms?".
In regards to your answer, I'm also studying Genetic Algorithms (GA) and I can't say I'm an an expert, however there some of the things that plagues GAs:
finding the absolute optimal when searching through an in-complete search space. If you need to guarantee absolute best, it is best to user other methods.
Then there is the encoding problem, there are many problems where you can't really encode your problem into a GA.
Defining a sound evaluation function, how you define the best.
Converging in a dynamic environment, GA assumes the environment or search landscape is static
Perform poorly with Boolean evaluation functions (Yes/No, On/Off functions)
For more information of what problems are hard for GAs is this paper, "What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation"
As for applications of GA, the list is huge, see here
1.study The problem
2.analysis the problem
3.make possible steps to draw flow chart
4.draw flowchart
5.end
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I'm doing some research on software cost estimation models and methods with a few colleagues of mine, and I was just wondering if you guys knew any specifically other than the ones listed below. Thanks!
Models:
The Planning Game
COCOMO / COCOMO II
SLIM
SEER-SEM
Weighted Micro Function Points
Program Evaluation and Review Technique
PRICE Systems
Wideband Delphi
Function Point Analysis
Evidence-based Scheduling
Methods:
Expert Judgment Method
Analogy Method
Parametric / Algorithmic Method
Bottom-Up/Work Breakdown Structure Method
Top-Down Method
Analysis Effort Method
Proxy-Based Estimation Method
There's also the REVIC model mainly used for government projects (as used by http://sepo.spawar.navy.mil/Estimation.html), I've found the Weighted Micro Function Points cost model (as used by http://www.projectcodemeter.com) to be the most useful since it's more suitable for measuring small increments in code development.
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Are there any open-source implementations of an graph-cut algorithm similar to that used for Photoshop's quick select tool? I'm researching for a personal project of making a GIMP tool\plugin similar to PS's tool, but I'm wondering if it's been done before.
I'm not sure How exactly quick select works, but if i had to implement such task - my first starting approach would be to detect area of similar colors. For this one should:
Convert RGB color space to HSV or HSL.
Run edge detection algorithm on Hue values.
Sample edge points to reduce data size.
Run points clustering algorithm.
Filter-out points which doesn't belongs to nearest cluster.
Run convex hull algorithm to dismiss cluster inner edges.
I'm not saying that this approach will work, but still i think that this is an interesting idea to explore.
Good luck!
There is a paper called "Paint Selection" published at Siggraph 2009. It describes a method very similar to Photoshop's quick select tool. You can access the paper from https://www.microsoft.com/en-us/research/wp-content/uploads/2009/08/PaintSelection_SIGGRAPH09.pdf