Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

ALIGNMENT-FREE METHODS AND ITS APPLICATIONS

Comparing biological sequences remains one of the most vital activities in Bioinformatics. Comparing biological sequences would address the relatedness between species, and find similar structures that might lead to similar functions. Sequence alignment is the default method, and has been used in the domain for over four decades. It gained a lot of trust, but limitations and even failure has been reported, especially with the new generated genomes. These new generated genomes have bigger size, and to some extent suffer errors. Such errors come mainly as a result from the sequencing machine. These sequencing errors should be considered when submitting sequences to GenBank, for sequence comparison, it is often hard to address or even trace this problem. Alignment-based methods would fail with such errors, and even if biologists still trust them, reports showed failure with these methods. The poor results of alignment-based methods with erratic sequences, motivated researchers in the domain to look for alternatives. These alternative methods are alignment-free, and would overcome the shortcomings of alignment-based methods. The work of this thesis is based on alignment-free methods, and it conducts an in-depth study to evaluate these methods, and find the right domain’s application for them. The right domain for alignment-free methods could be by applying them to data that were subjected to manufactured errors, and test the methods provide better comparison results with data that has naturally severe errors. The two techniques used in this work are compression-based and motif-based (or k-mer based, or signal based). We also addressed the selection of the used motifs in the second technique, and how to progress the results by selecting specific motifs that would enhance the quality of results. In addition, we applied an alignment-free method to a different domain, which is gene prediction. We are using alignment-free in gene prediction to speed up the process of providing high quality results, and predict accurate stretches in the DNA sequence, which would be considered parts of genes

Depth map compression via 3D region-based representation

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.Postprint (author's final draft

Ship Detection and Segmentation using Image Correlation

There have been intensive research interests in ship detection and segmentation due to high demands on a wide range of civil applications in the last two decades. However, existing approaches, which are mainly based on statistical properties of images, fail to detect smaller ships and boats. Specifically, known techniques are not robust enough in view of inevitable small geometric and photometric changes in images consisting of ships. In this paper a novel approach for ship detection is proposed based on correlation of maritime images. The idea comes from the observation that a fine pattern of the sea surface changes considerably from time to time whereas the ship appearance basically keeps unchanged. We want to examine whether the images have a common unaltered part, a ship in this case. To this end, we developed a method - Focused Correlation (FC) to achieve robustness to geometric distortions of the image content. Various experiments have been conducted to evaluate the effectiveness of the proposed approach.Comment: 8 pages, to be published in proc. of conference IEEE SMC 201

3D oceanographic data compression using 3D-ODETLAP

This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit