SPC: Fast and Efficient Scalable Predictive Coding of Animated Meshes

Animated meshes are often represented by a sequence of static meshes with constant connectivity. Due to their frame-based representation they usually occupy a vast amount of bandwidth or disk space. We present a fast and efficient scalable predictive coding (SPC) scheme for frame-based representations of animated meshes. SPC decomposes animated meshes in spatial and temporal layers which are efficiently encoded in one pass through the animation. Coding is performed in a streamable and scalable fashion. Dependencies between neighbouring spatial and temporal layers are predictively exploited using the already encoded spatio-temporal neighbourhood. Prediction is performed in the space of rotation-invariant coordinates compensating local rigid motion. SPC supports spatial and temporal scalability, and it enables efficient compression as well as fast encoding and decoding. Parts of SPC were adopted in the MPEG-4 FAMC standard. However, SPC significantly outperforms the streaming mode of FAMC with coding gains of over 33%, while in comparison to the scalable FAMC, SPC achieves coding gains of up to 15%. SPC has the additional advantage over FAMC of achieving real-time encoding and decoding rates while having only low memory requirements. Compared to some other non-scalable state-of-the-art approaches, SPC shows superior compression performance with gains of over 16% in bit-rate.     

Local ISS of large-scale interconnections and estimates for stability regions

We consider interconnections of locally input-to-state stable (LISS) systems. The class of LISS systems is quite large, in particular it contains input-to-state stable (ISS) and integral input-to-state stable (iISS) systems.Local small-gain conditions both for LISS trajectory and Lyapunov formulations guaranteeing LISS of the composite system are provided in this paper. Notably, estimates for the resulting stability region of the composite system are also given. This in particular provides an advantage over the linearization approach, as will be discussed.     

On notions of regularity for data languages

With motivation from considerations in XML database theory and model checking, data strings have been introduced as an extension of finite alphabet strings which carry, at each position, a symbol and a data value from an infinite domain. Previous work has shown that it is difficult to come up with an expressive yet decidable automaton model for data languages. Recently, such a model, data automata, was introduced. This paper introduces a simpler but equivalent model and investigates its expressive power, algorithmic and closure properties, and some extensions.     

DYFRAM: dynamic fragmentation and replica management in distributed database systems

In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.     

Domain science and engineering from computer science to the sciences of informatics. Part I: Engineering

In this paper, we wish to advocate that departments of computer science put emphasis on teaching programming and software engineering based on formal methods; and more emphasis on research into formal methods for the trustworthy development of software. We also wish to advocate that the concepts of domain science and domain engineering become an indispensable part of the science of informatics and of software engineering.     

Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model

We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse N×N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number of nonzero entries is kN, i.e., where the average number of nonzero entries per column is k.     

Bond graph based control of a three-phase inverter with LC filter – Connection to passive and active loads

This paper proposes a model of a three phase electrical inverter with a LC output filter in delta connection used in a renewable energy supply system. The concept of inverse bond graph via bicausality is used for the control law design. The control law robustness is tested by connecting passive and active (induction machine) loads.     

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset.     

Surface plasmon resonance imaging (SPRi) as an alternative technique for rapid and quantitative screening of small molecules, useful in drug discovery

Surface plasmon resonance imaging (SPRi) is a label free technology for biomolecular interaction, which gives access to binding kinetic parameters from real time acquisition. It offers the possibility to test in a single run a large number of interactions, allowing rapid identification of the most suitable compounds toward a given biological entity. Until now, this technique has proven to be relevant for interaction between relatively large molecules (protein, antibodies, DNA) but has not been challenged yet for the screening of small molecules that can be of interested in the field of drug discovery. As a proof a principle, we have used SPRi to screen for interaction of several small molecules, referred to as G4-ligands on G-quadruplex DNA. This technology allowed to easy discrimination of the binding properties of four G4-ligands on quadruplex DNA models.     

Defining syntax-directed translations by tree bimorphisms

We introduce a class of tree bimorphisms that define exactly the translations performed by syntax-directed translation schemata. We also show that these “quasi-alphabetic” tree bimorphisms preserve recognizability, and that their class is closed under composition and inverses.