Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Automatic Macro- and Micro-Facial Expression Spotting and Applications

Automatically determining the temporal characteristics of facial expressions has extensive application domains such as human-machine interfaces for emotion recognition, face identification, as well as medical analysis. However, many papers in the literature have not addressed the step of determining when such expressions occur. This dissertation is focused on the problem of automatically segmenting macro- and micro-expressions frames (or retrieving the expression intervals) in video sequences, without the need for training a model on a specific subset of such expressions. The proposed method exploits the non-rigid facial motion that occurs during facial expressions by modeling the strain observed during the elastic deformation of facial skin tissue. The method is capable of spotting both macro expressions which are typically associated with emotions such as happiness, sadness, anger, disgust, and surprise, and rapid micro- expressions which are typically, but not always, associated with semi-suppressed macro-expressions. Additionally, we have used this method to automatically retrieve strain maps generated from peak expressions for human identification. This dissertation also contributes a novel 3-D surface strain estimation algorithm using commodity 3-D sensors aligned with an HD camera. We demonstrate the feasibility of the method, as well as the improvements gained when using 3-D, by providing empirical and quantitative comparisons between 2-D and 3-D strain estimations

Label-Noise Reduction with Support Vector Machines

The problem of detection of label-noise in large datasets is investigated. We consider applications where data are susceptible to label error and a human expert is available to verify a limited number of such labels in order to cleanse the data. We show the support vectors of a Support Vector Machine (SVM) contain almost all of these noisy labels. Therefore, the verification of support vectors allows efficient cleansing of the data. Empirical results are presented for two experiments. In the first experiment, two datasets from the character recognition domain are used and artificial random noise is applied in their labeling. In the second experiment, a large dataset of plankton images, that contains inadvertent human label error, is considered. It is shown that up to 99% of all label-noise from such datasets can be detected by verifying just the support vectors of the SVM classifier