Controllability of control systems on complex simple lie groups and the topology of flag manifolds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reversibility properties of semigroup actions on homogeneous spaces

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

Flag state responsibility for maritime terrorism

Modern international shipping is largely a flag state-based system. Only the flag state has complete authority over the vessels that fly its flag, and as a result, other states’ jurisdiction over these vessels is very limited. Against this backdrop, this article examines the flag state’s responsibility for maritime terrorism, a major security issue and vulnerability in the global supply chain. It is not an exaggeration that the global community’s repeated statements regarding the illegality of terrorism have created a customary international law obligation for states to take all possible steps for the prevention of terrorism. This article argues that providing flags to suspicious entities in an obscure registration system is not compatible with this obligation.

Energy-based positioning control of underactuated vehicles using manifold regulation

In this paper, we consider the problem of position regulation of a class of underactuated rigid-body vehicles that operate within a gravitational field and have fully-actuated attitude. The control objective is to regulate the vehicle position to a manifold of dimension equal to the underactuation degree. We address the problem using Port-Hamiltonian theory, and reduce the associated matching PDEs to a set of algebraic equations using a kinematic identity. The resulting method for control design is constructive. The point within the manifold to which the position is regulated is determined by the action of the potential field and the geometry of the manifold. We illustrate the performance of the controller for an unmanned aerial vehicle with underactuation degree two-a quadrotor helicopter.

Manoeuvring control of underactuated surface vessels using manifold regulation for Port-Hamiltonian systems

This paper considers the manoeuvring of underactuated surface vessels. The control objective is to steer the vessel to reach a manifold which encloses a waypoint. A transformation of configuration variables and a potential field are used in a Port-Hamiltonian framework to design an energy-based controller. With the proposed controller, the geometric task associated with the manoeuvring problem depends on the desired potential energy (closed-loop) and the dynamic task depends on the total energy and damping. Therefore, guidance and motion control are addressed jointly, leading to model-energy-based trajectory generation.

Cidade é uma palavra plural [City is a manifold word]

This text elaborates on the city as cultural construct and representation and Lisbocópio, the installation by Pancho Guedes and Ricardo Jacinto in the context of the Official Representation of Portugal at the 10. Mostra Internazionale di Architettura-La Biennale di Venezia.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.

Japanese naval forces parade along Nanjing Road in Shanghai; Nazi flag in the background Street Scenes

Digital Image

Under Swastika and the French flag.

The memoirs were originally written for the Harvard University competition in 1940 and were translated by the author in 2001. Reflections on his childhood in Germany and Austria. His parents were both from Poland. They moved to Vienna in 1921, where his father opened a haberdashery store in the Second district (Leopoldstadt). Otto attended primary school in Czerningasse. Birth of his sister Cecile in 1924. After his failing business endeavors his father decided to move back to Germany, where the family opened a department store in Elbing, East Prussia. Otto attended Gymnasium, where he was one of only two Jewish students in his class. Growing Nazi movement among students. Summer vacations on the Baltic Sea. Private piano lessons. Hitler’s rise in Germany and life under National Socialism. Bar mitzvah in 1933. Anti-Jewish boycotts. His father fled to Vienna in order to escape a rounding up of Jews. The family followed soon after to Austria. Otto attended Gymnasium in the Zirkusgasse and started to work as a tutor. Member of a youth group and hiking tours in the mountains. Recollections of the Anschluss in 1938. Fervent attempts to obtain an exit visa for the United States, where they had a relative in New York. Description of discriminations and frequent attacks on Jewish friends and relatives in the weeks after the Anschluss. Otto was picked up by Nazi stormtroops. He was forced to hold up an anti-Jewish sign and was walked up and down, receiving beatings and spittings in front of a jeering crowd. Detailed account of the atmosphere within the Jewish population. The Gymnasium Zirkusgasse was transferred into a Jewish school. Frequent attacks of Hitler Youths on the students. Preparations for the “Matura” despite the turmoil. In June of 1938 his father was arrested and sent to Dachau concentration camp. After passing the final exams, Otto planned on leaving the country illegally, since he was subject to the Polish quota for the United States with

Newspaper clipping regarding presentation of flag by Mrs. Commodore Levy

Digital image