The successful man in his manifold relations with life.

Mode of access: Internet.

When does a Bounded Domain Cover a Projective Manifold? (Survey)

* The research has been partially supported by Bulgarian Funding Organizations, sponsoring the Algebra Section of the Mathematics Institute, Bulgarian Academy of Sciences, a Contract between the Humboldt Univestit¨at and the University of Sofia, and Grant MM 412 / 94 from the Bulgarian Board of Education and Technology

On Affine Connections in a Riemannian Manifold with a Circulant Metric and two Circulant Affinor Structures

Ива Р. Докузова, Димитър Р. Разпопов - В настоящата статия е разгледан клас V оттримерни риманови многообразия M с метрика g и два афинорни тензора q и S. Дефинирана е и друга метрика ¯g в M. Локалните координати на всички тези тензори са циркулантни матрици. Намерени са: 1) зависимост между тензора на кривина R породен от g и тензора на кривина ¯R породен от ¯g; 2) тъждество за тензора на кривина R в случая, когато тензорът на кривина ¯R се анулира; 3) зависимост между секционната кривина на прозволна двумерна q-площадка {x, qx} и скаларната кривина на M.

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

Manifold learning and the quantum Jensen-Shannon divergence kernel

The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

Warped Product Semi-Slant Submanifolds of a Sasakian Manifold

2000 Mathematics Subject Classification: 53C40, 53C25.

Geometry of Warped Product Semi-Invariant Submanifolds of a Locally Riemannian Product Manifold

2000 Mathematics Subject Classification: 53C42, 53C15.

Totally Umbilical Pseudo-Slant Submanifolds of a Nearly Cosymplectic Manifold

2000 Mathematics Subject Classification: 53C40, 53B25.

Geometric properties of 2-dimensional minimal surfaces in a sub-Riemannian manifold which models the Visual Cortex

In this paper we study the notion of degree forsubmanifolds embedded in an equiregular sub-Riemannian manifold and we provide the definition of their associated area functional. In this setting we prove that the Hausdorff dimension of a submanifold coincides with its degree, as stated by Gromov. Using these general definitions we compute the first variation for surfaces embedded in low dimensional manifolds and we obtain the partial differential equation associated to minimal surfaces. These minimal surfaces have several applications in the neurogeometry of the visual cortex.

Curso de MANIFOLD 8.0

Curso de formación del software GIS MANIFOLD 8.0

Design and analysis of group key exchange protocols

A group key exchange (GKE) protocol allows a set of parties to agree upon a common secret session key over a public network. In this thesis, we focus on designing efficient GKE protocols using public key techniques and appropriately revising security models for GKE protocols. For the purpose of modelling and analysing the security of GKE protocols we apply the widely accepted computational complexity approach. The contributions of the thesis to the area of GKE protocols are manifold. We propose the first GKE protocol that requires only one round of communication and is proven secure in the standard model. Our protocol is generically constructed from a key encapsulation mechanism (KEM). We also suggest an efficient KEM from the literature, which satisfies the underlying security notion, to instantiate the generic protocol. We then concentrate on enhancing the security of one-round GKE protocols. A new model of security for forward secure GKE protocols is introduced and a generic one-round GKE protocol with forward security is then presented. The security of this protocol is also proven in the standard model. We also propose an efficient forward secure encryption scheme that can be used to instantiate the generic GKE protocol. Our next contributions are to the security models of GKE protocols. We observe that the analysis of GKE protocols has not been as extensive as that of two-party key exchange protocols. Particularly, the security attribute of key compromise impersonation (KCI) resilience has so far been ignored for GKE protocols. We model the security of GKE protocols addressing KCI attacks by both outsider and insider adversaries. We then show that a few existing protocols are not secure against KCI attacks. A new proof of security for an existing GKE protocol is given under the revised model assuming random oracles. Subsequently, we treat the security of GKE protocols in the universal composability (UC) framework. We present a new UC ideal functionality for GKE protocols capturing the security attribute of contributiveness. An existing protocol with minor revisions is then shown to realize our functionality in the random oracle model. Finally, we explore the possibility of constructing GKE protocols in the attribute-based setting. We introduce the concept of attribute-based group key exchange (AB-GKE). A security model for AB-GKE and a one-round AB-GKE protocol satisfying our security notion are presented. The protocol is generically constructed from a new cryptographic primitive called encapsulation policy attribute-based KEM (EP-AB-KEM), which we introduce in this thesis. We also present a new EP-AB-KEM with a proof of security assuming generic groups and random oracles. The EP-AB-KEM can be used to instantiate our generic AB-GKE protocol.

Preface [From The Third Knowledge City World Summit 2010 proceedings]

‘Knowledge’ is a resource, which relies on the past for a better future. In the 21st century, more than ever before, cities around the world depend on the knowledge of their citizens, their institutions and their firms and enterprises. The knowledge image, the human competence and the reputation of their public and private institutions and corporations profiles a city. It attracts investment, qualified labour and professionals, as well as students and researchers. And it creates local life spaces and professional milieus, which offer the quality of life to the citizens that are seeking to cope with the challenges of modern life in a competitive world. Integrating knowledge-based development in urban strategies and policies, beyond the provision of schools and locations for higher education, has become a new ambitious arena of city politics. Coming from theory to practice, and bringing together the manifold knowledge stakeholders in a city and preparing joint visions for the knowledge city is a new challenge for city managers, urban planners and leaders of the civic society . It requires visionary power, creativity, holistic thinking, the willingness to cooperate with all groups of the local civil society, and the capability to moderate communication processes to overcome conflicts and to develop joint action for a sustainable future.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.