Microstructure and Microchemistry of Polymer-Derived Crystalline SiC Fibers

Stoichiometric and near-stoichiometric silicon carbide fibers, derived from the polymer polycarbosilane, have been characterized by scanning Auger and transmission electron microscope techniques. Excess carbon usually appeared in the fibers, but it could be eliminated by controlling processing conditions. The fibers consisted mainly of submicrometer-sized SiC microcrystals, with a considerably twinned and stacking-faulted β-SiC structure. Free carbon, which appeared in the form of graphite, was the only detectable intergranular phase in the nonstoichioinetric SiC fibers, while the stoichiometric SiC fibers contained no detectable second phase whatsoever.     

Aggregation behaviour of well defined amphiphilic diblock copolymers with poly(N-isopropylacrylamide) and hydrophobic blocks

Series of amphiphilic diblock copolymers with poly(N-isopropylacrylamide) as a hydrophilic block and a hydrophobic block consisting of either polystyrene or poly(tert-butyl methacrylate) were synthesised using RAFT polymerisations. Differential scanning calorimetry showed the chemically different blocks being phase separated in dry polymers. Light scattering and microcalorimetry studies were performed on aqueous solutions to investigate the phase behavior of the diblock copolymers. By carefully transferring the polymers from an organic solvent to water, either micellar particles or large aggregates were obtained depending on the relative lengths of the blocks. Large aggregates collapsed upon heating, whereas collapse occurred slowly within a broad temperature range in the case of micelle like structures. However, microcalorimetrically the collapse of the PNIPAM chains was observed to take place in all samples, suggesting that the shells of the micellar particles are crowded in a way which hinders the compression of the poly(N-isopropylacrylamide) chains.     

Bayesian estimation of Dirichlet mixture model with variational inference

In statistical modeling, parameter estimation is an essential and challengeable task. Estimation of the parameters in the Dirichlet mixture model (DMM) is analytically intractable, due to the integral expressions of the gamma function and its corresponding derivatives. We introduce a Bayesian estimation strategy to estimate the posterior distribution of the parameters in DMM. By assuming the gamma distribution as the prior to each parameter, we approximate both the prior and the posterior distribution of the parameters with a product of several mutually independent gamma distributions. The extended factorized approximation method is applied to introduce a single lower-bound to the variational objective function and an analytically tractable estimation solution is derived. Moreover, there is only one function that is maximized during iterations and, therefore, the convergence of the proposed algorithm is theoretically guaranteed. With synthesized data, the proposed method shows the advantages over the EM-based method and the previously proposed Bayesian estimation method. With two important multimedia signal processing applications, the good performance of the proposed Bayesian estimation method is demonstrated.     

Organic Nanoparticles with Polypropyleneoxide Chains as Support for Metallocene Catalysts: Ethylene Homopolymerization and Ethylene/α-olefin Copolymerization

Summary Novel organic nanoparticles functionalized with nucleophilic polypropyleneoxide (PPO) chains on their surfaces for supporting metallocene catalysts in heterogeneous olefin polymerization are presented. The nanoparticles (60–100 nm) were obtained by miniemulsion polymerization of styrene, divinylbenzene and PPO functionalized styrene. It is demonstrated that Me₂Si(2MeBenzlnd)₂ZrCl₂/MAO supported on these nanoparticles is suitable for the homopolymerization of ethylene, resulting in excellent product morphologies and high activities. lt is shown that by varying the MAO/Zr ratios and Zr concentrations the activities and productivities of the catalysts as well as the qualities of the polyethylene products can be tuned. These new supported catalysts are also suitable for the copolymerization of ethylene with several comonomers (1-hexene, 1-octene, 1-decene or norbornene). As the obtained product properties like crystallinity, melting temperature or bulk density match the results of silica supported systems, these organic nanoparticles can be considered as alternative carriers in comparison to the established inorganic ones.     

Branching-time logics with path relativisation

We define extensions of the full branching-time temporal logic CTL^? in which the path quantifiers are relativised by formal languages of infinite words, and consider its natural fragments obtained by extending the logics CTL and CTL⁺ in the same way. This yields a small and two-dimensional hierarchy of temporal logics parametrised by the class of languages used for the path restriction on one hand, and the use of temporal operators on the other. We motivate the study of such logics through two application scenarios: in abstraction and refinement they offer more precise means for the exclusion of spurious traces; and they may be useful in software synthesis where decidable logics without the finite model property are required. We study the relative expressive power of these logics as well as the complexities of their satisfiability and model-checking problems.     

Spatio‐temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces

Multi‐view reconstruction aims at computing the geometry of a scene observed by a set of cameras. Accurate 3D reconstruction of dynamic scenes is a key component for a large variety of applications, ranging from special effects to telepresence and medical imaging. In this paper we propose a method based on Moving Least Squares surfaces which robustly and efficiently reconstructs dynamic scenes captured by a calibrated set of hybrid color+depth cameras. Our reconstruction provides spatio‐temporal consistency and seamlessly fuses color and geometric information. We illustrate our approach on a variety of real sequences and demonstrate that it favorably compares to state‐of‐the‐art methods.     

On‐the‐fly generation and rendering of infinite cities on the GPU

In this paper, we present a new approach for shape‐grammar‐based generation and rendering of huge cities in real‐time on the graphics processing unit (GPU). Traditional approaches rely on evaluating a shape grammar and storing the geometry produced as a preprocessing step. During rendering, the pregenerated data is then streamed to the GPU. By interweaving generation and rendering, we overcome the problems and limitations of streaming pregenerated data. Using our methods of visibility pruning and adaptive level of detail, we are able to dynamically generate only the geometry needed to render the current view in real‐time directly on the GPU. We also present a robust and efficient way to dynamically update a scene's derivation tree and geometry, enabling us to exploit frame‐to‐frame coherence. Our combined generation and rendering is significantly faster than all previous work. For detailed scenes, we are capable of generating geometry more rapidly than even just copying pregenerated data from main memory, enabling us to render cities with thousands of buildings at up to 100 frames per second, even with the camera moving at supersonic speed.     

IISPH‐FLIP for incompressible fluids

We propose to use Implicit Incompressible Smoothed Particle Hydrodynamics (IISPH) for pressure projection and boundary handling in Fluid‐Implicit‐Particle (FLIP) solvers for the simulation of incompressible fluids. This novel combination addresses two issues of existing SPH and FLIP solvers, namely mass preservation in FLIP and efficiency and memory consumption in SPH. First, the SPH component enables the simulation of incompressible fluids with perfect mass preservation. Second, the FLIP component efficiently enriches the SPH component with detail that is comparable to a standard SPH simulation with the same number of particles, while improving the performance by a factor of 7 and significantly reducing the memory consumption. We demonstrate that the proposed IISPH‐FLIP solver can simulate incompressible fluids with a quantifiable, imperceptible density deviation below 0.1%. We show large‐scale scenarios with up to 160 million particles that have been processed on a single desktop PC using only 15GB of memory. One‐ and two‐way coupled solids are illustrated.     

Foresight for a responsible future with ICT

The ACM Code of Ethics asserts that computing professionals have an ethical responsibility to minimize the negative consequences of information and communication technologies (ICT). Negative consequences are rarely intended, but they can often be foreseen with careful sociotechnical analysis in advance of system building. Motivated by an interest in extremely complex sociotechnical contexts (e.g., mortgage lending and automated trading) where ICT appears to be having negative consequences in addition to many benefits, this paper identifies and evaluates future-oriented problem analysis and solution design tools in three potentially relevant literatures: 1) ICT ethics, 2) environmental sustainability, and 3) technology hazards. Several promising future-oriented technology analysis techniques (e.g., anticipatory technology ethics, technology roadmapping, morphological analysis, and control structure analysis) were found and are discussed in this paper, but much work remains to be done to customize them, integrate them, and codify them for use in education and high-quality IS research on very complex sociotechnical contexts like the global financial network.     

Automatic classifier selection for non-experts

Choosing a suitable classifier for a given dataset is an important part of developing a pattern recognition system. Since a large variety of classification algorithms are proposed in literature, non-experts do not know which method should be used in order to obtain good classification results on their data. Meta-learning tries to address this problem by recommending promising classifiers based on meta-features computed from a given dataset. In this paper, we empirically evaluate five different categories of state-of-the-art meta-features for their suitability in predicting classification accuracies of several widely used classifiers (including Support Vector Machines, Neural Networks, Random Forests, Decision Trees, and Logistic Regression). Based on the evaluation results, we have developed the first open source meta-learning system that is capable of accurately predicting accuracies of target classifiers. The user provides a dataset as input and gets an automatically created high-performance ready-to-use pattern recognition system in a few simple steps. A user study of the system with non-experts showed that the users were able to develop more accurate pattern recognition systems in significantly less development time when using our system as compared to using a state-of-the-art data mining software.