Robust Recognition using L1-Principal Component Analysis

The wide availability of visual data via social media and the internet, coupled with the demands of the security community have led to an increased interest in visual recognition. Recent research has focused on improving the accuracy of recognition techniques in environments where variability is well controlled. However, applications such as identity verification often operate in unconstrained environments. Therefore there is a need for more robust recognition techniques that can operate on data with considerable noise. Many statistical recognition techniques rely on principal component analysis (PCA). However, PCA suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. In this thesis we address this problem by using L1-PCA to minimize the effect of outliers in data. L1-PCA is applied to several statistical recognition techniques including eigenfaces and Grassmannian learning. Several popular face databases are used to show that L1-Grassmann manifolds not only outperform, but are also more robust to noise and occlusions than traditional L2-Grassmann manifolds for face and facial expression recognition. Additionally a high performance GPU implementation of L1-PCA is developed using CUDA that is several times faster than CPU implementations

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems

Modern operating systems and microarchitectures provide a myriad of mechanisms for monitoring and affecting system operation and resource utilization at runtime. Dynamic runtime control of these mechanisms can tailor system operation to the characteristics and behavior of the current workload, resulting in improved performance. However, developing effective models for system control can be challenging. Existing methods often require extensive manual effort, computation time, and domain knowledge to identify relevant low-level performance metrics, relate low-level performance metrics and high-level control decisions to workload performance, and to evaluate the resulting control models. This dissertation develops a general framework, based on the contextual bandit, for describing and learning effective models for runtime system control. Random profiling is used to characterize the relationship between workload behavior, system configuration, and performance. The framework is evaluated in the context of two applications of progressive complexity; first, the selection of paging modes (Shadow Paging, Hardware-Assisted Page) in the Xen virtual machine memory manager; second, the utilization of hardware memory prefetching for multi-core, multi-tenant workloads with cross-core contention for shared memory resources, such as the last-level cache and memory bandwidth. The resulting models for both applications are competitive in comparison to existing runtime control approaches. For paging mode selection, the resulting model provides equivalent performance to the state of the art while substantially reducing the computation requirements of profiling. For hardware memory prefetcher utilization, the resulting models are the first to provide dynamic control for hardware prefetchers using workload statistics. Finally, a correlation-based feature selection method is evaluated for identifying relevant low-level performance metrics related to hardware memory prefetching

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex