CS 400/600-02: Data Structures and Algorithms

Study of the implementation of data structures and control structures in professional computer programs. Introduction to the fundamentals of complexity and analysis. Study of common standard problems and solutions (e.g., transitive closure and critical path). Emphasis on high-level language software design

Dealing with Complexity: A Method to Adapt and Implement a Maturity Model for Corporate Data Quality Management

Reference models usually serve as starting points for developing company specific models. Unfortunately, successful usage of reference models is often impeded by various aspects, such as a lack of acceptance among employees, incorrect model implementation, or high project costs - all of which more often than not are resulting from an imbalance between the model\u27s complexity and the complexity of a company\u27s specific structures. The paper at hand develops a methodical approach for taking a given reference model (the Maturity Model for Corporate Data Quality Management) and transforming it into a company specific model, with a particular focus on the specific complexity of a company\u27s structures. Corporate Data Quality Management describes the quality oriented organization and control of a company\u27s key data assets such as material, customer, and vendor data. Two case studies show how the method has been successfully implemented in real-world scenarios

Status of the Mast experiment

Many sophisticated mathematical control techniques for flexible structures have been devised. The basic problem is that most of them require a relatively accurate mathematical model of the system under control including the dynamics of both the structure and the control system components. Obtaining such a model for either subsystem traditionally has required great effort including a significant validation step based on test data. Because of the quantum increase in complexity over proven methods, promising techniques for the control of flexible structures must be validated in actual hardware experiments before committing to their use in actual spacecraft missions. The Mast experiment system serves as a focus for such validation. It is the first in a series of experiments under the Control of Flexible Structures (COFS) Program at the NASA Langley Research Center. The Mast experiment is a combination of ground tests, orbital flight test, and analysis of a deployable beam under the COFS program. It provides a vehicle for research in structures, structural dynamics, and control issues

The structural analysis of programming languages

A language's structures are some of its most important characteristics. These include the data structures -- those mechanisms that the language provides for organizing elementary data values. They also include the control structures, which organize the control flow. Less obviously, they include the same structures, which partition and organize the name space. Languages can be compared relative to their structures in the data, control, and name domains. This report describes a syntax-independent method of representing the structures of a language which facilitates visual complexity comparisons and is amenable to measurement. The data, control, and name structures of a number of languages are analyzed, including Pascal, LISP, Algol-60, Algol-68, the lambda calculus, FORTRAN, and Basic. (Author)Prepared for: Chief of Naval Research; Arlington, VA 22217.http://archive.org/details/structuralanalys00maclfunds provided by the Chief of Naval Researc

Blind Detection of Polar Codes

Polar codes were recently chosen to protect the control channel information in the next-generation mobile communication standard (5G) defined by the 3GPP. As a result, receivers will have to implement blind detection of polar coded frames in order to keep complexity, latency, and power consumption tractable. As a newly proposed class of block codes, the problem of polar-code blind detection has received very little attention. In this work, we propose a low-complexity blind-detection algorithm for polar-encoded frames. We base this algorithm on a novel detection metric with update rules that leverage the a priori knowledge of the frozen-bit locations, exploiting the inherent structures that these locations impose on a polar-encoded block of data. We show that the proposed detection metric allows to clearly distinguish polar-encoded frames from other types of data by considering the cumulative distribution functions of the detection metric, and the receiver operating characteristic. The presented results are tailored to the 5G standardization effort discussions, i.e., we consider a short low-rate polar code concatenated with a CRC.Comment: 6 pages, 8 figures, to appear at the IEEE Int. Workshop on Signal Process. Syst. (SiPS) 201

Similarity Learning for High-Dimensional Sparse Data

A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data. In this paper, we propose a method that can learn efficiently similarity measure from high-dimensional sparse data. The core idea is to parameterize the similarity measure as a convex combination of rank-one matrices with specific sparsity structures. The parameters are then optimized with an approximate Frank-Wolfe procedure to maximally satisfy relative similarity constraints on the training data. Our algorithm greedily incorporates one pair of features at a time into the similarity measure, providing an efficient way to control the number of active features and thus reduce overfitting. It enjoys very appealing convergence guarantees and its time and memory complexity depends on the sparsity of the data instead of the dimension of the feature space. Our experiments on real-world high-dimensional datasets demonstrate its potential for classification, dimensionality reduction and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015). Matlab code: https://github.com/bellet/HDS