A line search improvement of efficient MPC

A recent efficient Model Predictive Control (MPC) strategy uses a univariate Newton-Raphson procedure to solve a dual problem, but is not amenable to warm starting or early termination. By solving a primal problem, the current note proposes a strategy which is more efficient than the Newton-Raphson method and which enables warm starting and early termination. Performance improvements are demonstrated over the Newton-Raphson method and alternative approaches based on quadratic programming or semidefinite programming.     

Note on Max Lin-2 above Average

In the Max Lin-2 problem we are given a system S of m linear equations in n variables over F₂ in which equation j is assigned a positive integral weight wj for each j. We wish to find an assignment of values to the variables which maximizes the total weight of satisfied equations. This problem generalizes Max Cut. The expected weight of satisfied equations is W/2, where W=w₁+?+wm; W/2 is a tight lower bound on the optimal solution of Max Lin-2.Mahajan et al. (Parameterizing above or below guaranteed values, J. Comput. Syst. Sci. 75 (2009) 137-153) stated the following parameterized version of Max Lin-2: decide whether there is an assignment of values to the variables that satisfies equations of total weight at least W/2+k, where k is the parameter. They asked whether this parameterized problem is fixed-parameter tractable, i.e., can be solved in time f(k)(nm)^O(1), where f(k) is an arbitrary computable function in k only. Their question remains open, but using some probabilistic inequalities and, in one case, a Fourier analysis inequality, Gutin et al. (A probabilistic approach to problems parameterized above tight lower bound, in: Proc. IWPEC'09, in: Lect. Notes Comput. Sci., vol. 5917, 2009, pp. 234-245) proved that the problem is fixed-parameter tractable in three special cases.In this paper we significantly extend two of the three special cases using only tools from combinatorics. We show that one of our results can be used to obtain a combinatorial proof that another problem from Mahajan et al. (Parameterizing above or below guaranteed values, J. Comput. Syst. Sci. 75 (2009) 137-153), Max r-SAT above Average, is fixed-parameter tractable for each r?2. Note that Max r-SAT above Average has been already shown to be fixed-parameter tractable by Alon et al. (Solving MAX-r-SAT above a tight lower bound, in: Proc. SODA 2010, pp. 511-517), but the paper used the approach of Gutin et al. (A probabilistic approach to problems parameterized above tight lower bound, in: Proc. IWPEC'09, in: Lect. Notes Comput. Sci., vol. 5917, 2009, pp. 234-245).     

Shape recognition and pose estimation for mobile Augmented Reality

Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class library. Nestor performs shape recognition by analyzing contour structures and generating projective-invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone.     

Evaluation of trend localization with multi-variate visualizations

Multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multivariate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task.     

Particle swarm optimisation based AdaBoost for object detection

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only, and the weak classifiers use a simple decision stump. The second uses PSO for both selecting good features and evolving weak classifiers in parallel. These two methods are examined and compared on two challenging object detection tasks in images: detection of individual pasta pieces and detection of a face. The experimental results suggest that both approaches can successfully detect object positions and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only. We also show that PSO can evolve and select meaningful features in the face detection task.     

Map‐aided localization in sparse global positioning system environments using vision and particle filtering

A map‐aided localization approach using vision, inertial sensors when available, and a particle filter is proposed and empirically evaluated. The approach, termed PosteriorPose, uses a Bayesian particle filter to augment global positioning system (GPS) and inertial navigation solutions with vision‐based measurements of nearby lanes and stop lines referenced against a known map of environmental features. These map‐relative measurements are shown to improve the quality of the navigation solution when GPS is available, and they are shown to keep the navigation solution converged in extended GPS blackouts. Measurements are incorporated with careful hypothesis testing and error modeling to account for non‐Gaussian and multimodal errors committed by GPS and vision‐based detection algorithms. Using a set of data collected with Cornell's autonomous car, including a measure of truth via a high‐precision differential corrections service, an experimental investigation of important design elements of the PosteriorPose estimator is conducted. The algorithm is shown to statistically outperform a tightly coupled GPS/inertial navigation solution both in full GPS coverage and in extended GPS blackouts. Statistical performance is also studied as a function of road type, filter likelihood models, bias models, and filter integrity tests. © 2011 Wiley Periodicals, Inc.     

Infinite factorization of multiple non-parametric views

Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale to the generative and non-parametric clustering setting by introducing a novel non-parametric hierarchical mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the sources. The commonalities between the sources are modeled by an infinite component model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views. We discover complex relationships between the marginals (that are multimodal in both marginals) that would remain undetected by simpler models. Cluster analysis of co-expression is a standard method of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation.     

Influence of Matrices on Oxygen Sensing of Three Sensing Films with Chemically Conjugated Platinum Porphyrin Probes and Preliminary Application for Monitoring of Oxygen Consumption of Escherichia coli (E. coli)

Oxygen sensing films were synthesized by a chemical conjugation of functional platinum porphyrin probes in silica gel, polystyrene (PS), and poly(2-hydroxyethyl methacrylate) (PHEMA) matrices. Responses of the sensing films to gaseous oxygen and dissolved oxygen were studied and the influence of the matrices on the sensing behaviors was investigated. Silica gel films had the highest fluorescence intensity ratio from deoxygenated to oxygenated environments and the fastest response time to oxygen. PHEMA films had no response to gaseous oxygen, but had greater sensitivity and a faster response time for dissolved oxygen than those of PS films. The influence of matrices on oxygen response, sensitivity and response time was discussed. The influence is most likely attributed to the oxygen diffusion abilities of the matrices. Since the probes were chemically immobilized in the matrices, no leaching of the probes was observed from the sensing films when applied in aqueous environment. One sensing film made from the PHEMA matrix was used to preliminarily monitor the oxygen consumption of Escherichia coli (E. coli) bacteria. E. coli cell density and antibiotics ampicillin concentration dependent oxygen consumption was observed, indicating the potential application of the oxygen sensing film for biological application.     

Birds of a feather: How personality influences blog writing and reading

The rapid development of blogs as a social networking tool has greatly increased the number of people who are expressing themselves in text published online. As yet little is known about the social psychology of online interaction using recently developed Web 2.0 functionalities. To what extent do principles of social psychology carry over into the online domain and how can appropriate use of those principles assist in activities such as community building, e-commerce, marketing, and personalization of services? There would seem to be a large number of novel research questions that can be posed with respect to online interaction. One pertinent question concerns the extent to which compatibility of personality influences online interaction. We investigated this question in terms of hypotheses derived from the personality research literature, using a two-part experiment. In the first study, eight participants wrote blogs in two distinct genres (personal diaries and commentaries) and rated their own personalities. In the second study 12 different participants judged the personality of authors as implied in the blog texts created in the first study. Those participants also completed a personality questionnaire and rated their attraction to the blog text author. Readers of the blog corpus were able to consistently judge the personality of the writers based solely on the text that they wrote. Moreover, they followed a well-established social rule regarding interpersonal attraction in real-life interaction. Blog readers were significantly more attracted to blog writers with more similar personalities—offering support that in a blog environment, “birds of a feather flock together.” Emotion word use in the blog corpus correlated with writer’s personality, suggesting that online personality may be signalled by linguistic cues. The two most popular genres of blog writing, personal journal and commentary, differed in how accurately readers judged author personality traits. Based on the results obtained it is suggested that personality is an important determinant and that further research on how people communicate with blogs will be relevant to social network analysis and to marketing.