On merging the fields of neural networks and adaptive data structures to yield new pattern recognition methodologies

The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established fields of Neural Networks (NNs) and Adaptive Data Structures (ADS) respectively. The field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. This structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data

Locally Self-Adjusting Skip Graphs

We present a distributed self-adjusting algorithm for skip graphs that minimizes the average routing costs between arbitrary communication pairs by performing topological adaptation to the communication pattern. Our algorithm is fully decentralized, conforms to the $\mathcal{CONGEST}$ model (i.e. uses $O(\log n)$ bit messages), and requires $O(\log n)$ bits of memory for each node, where $n$ is the total number of nodes. Upon each communication request, our algorithm first establishes communication by using the standard skip graph routing, and then locally and partially reconstructs the skip graph topology to perform topological adaptation. We propose a computational model for such algorithms, as well as a yardstick (working set property) to evaluate them. Our working set property can also be used to evaluate self-adjusting algorithms for other graph classes where multiple tree-like subgraphs overlap (e.g. hypercube networks). We derive a lower bound of the amortized routing cost for any algorithm that follows our model and serves an unknown sequence of communication requests. We show that the routing cost of our algorithm is at most a constant factor more than the amortized routing cost of any algorithm conforming to our computational model. We also show that the expected transformation cost for our algorithm is at most a logarithmic factor more than the amortized routing cost of any algorithm conforming to our computational model

Community Development: A Guide for Grantmakers on Fostering Better Outcomes Through Good Process

Focuses on participation and collaboration as major elements of processes that are effective. Provides examples of, and offers tools for overcoming, challenges to collaboration. Includes strategies and resources for evaluation and collaboration

Data Structures & Algorithm Analysis in C++

This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp

A friendly notebook on Data Structures and Algorithms

The purpose of this document is to provide study material that can be used for independent study by the students of the subject ’Data Structures and Algorithms’. We have tried to write it in a student-friendly way that encourages students to learn as well as enjoy. The document reviews the main concepts of the subject providing clear examples to help students. Each chapter also proposes a set of exercises to reinforce students’ knowledge

Building an application for the writing process

The idea that writing is a process and not a product is now generally accepted in writing education, but discussions of digital scholarly communication often neglect the idea, in theory and in practice. This thesis report introduces a Mac OS X software package to support the early stages of the writing process, called Brouillon. Brouillon’s features include: the concatenation of discrete note files into notebooks; notes appearing in multiple notebooks; note intake from mobile devices via Dropbox; and an open standard file format. The report also provides a model of the organization of products of the writing process, with a focus on Brouillon’s most unusual feature, multi-notebook notes. It discusses difficulties in implementation and identifies possibilities for future improvement

Self organizing maps for outlier detection

In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data