Generation of spherical and monodisperse particles of poly(styrene) and poly(methyl methacrylate) by atomization of monomers and dissolved polymer precursors

A technique to produce spherical and monodisperse particles of selected polymers is presented. Liquid precursors of either mixtures of organic monomers and initiator catalysts or polymers dissolved in organic solvents were sprayed inside a vertical thermal reactor. The temperature range in the reactor was 400–670 K and the experiments were conducted in a nitrogen atmosphere. Atomization was achieved by an acoustically excited aerosol generator. Batches of equal size particles of two thermoplastic materials, poly(styrene) and poly(methyl methacrylate), were obtained in the range of 30–60 μm in diameter. Elemental analysis showed that the C and H composition of the produced particles was very close to theoretically expected values. The thermal environment, atomization conditions, and residence times the particles experienced in the reactor were explored using numerical techniques; residence times in the order of 4–10 s were estimated.     

New Mononuclear Cu(II) Complexes and 1D Chains with 4-Amino-4H-1,2,4-triazole

The crystal structures of two mononuclear Cu(II) NH₂trz complexes [Cu(NH₂trz)₄(H₂O)](AsF₆)₂ (I) and [Cu(NH₂trz)₄(H₂O)](PF₆)₂ (II) as well as two coordination polymers [Cu(μ₂-NH₂trz)₂Cl]Cl·H₂O (III) and [Cu(μ₂-NH₂trz)₂Cl] (SiF₆)_0.5·1.5H₂O (IV) are presented. Cationic 1D chains with bridging bis-monodentate μ₂-coordinated NH₂trz and bridging μ₂-coordinated chloride ligands are present in III and IV. In these coordination polymers, the Cu(II) ions are strongly antiferromagnetically coupled with J = −128.4 cm⁻¹ for III and J = −143 cm⁻¹ for IV (H = −J∑S_iS_i+1), due to the nature of the bridges between spin centers. Inter-chain interactions present in the crystal structures were taken into consideration, as well as g factors, which were determined experimentally, for the quantitative modeling of their magnetic properties.     

A computational analysis of the impact of mass transport and shear on three-dimensional stem cell cultures in perfused micro-bioreactors

In this study, Computational Fluid Dynamics(CFD) is used to investigate and compare the impact of bioreactor parameters(such as its geometry, medium flow-rate, scaffold configuration) on the local transport phenomena and, hence, their impact on human mesenchymal stem cell(hM SC) expansion. The geometric characteristics of the TissueFlex174;(Zyoxel Limited, Oxford, UK) microbioreactor were considered to set up a virtual bioreactor containing alginate(in both slab and bead configuration) scaffolds. The bioreactor and scaffolds were seeded with cells that were modelled as glucose consuming entities. The widely used glucose medium, Dulbecco's Modified Eagle Medium(DMEM), supplied at two inlet flow rates of 25 and 100 μl·h~(-1), was modelled as the fluid phase inside the bioreactors. The investigation, based on applying dimensional analysis to this problem, as well as on detailed three-dimensional transient CFD results, revealed that the default bioreactor design and boundary conditions led to internal and external glucose transport, as well as shear stresses, that are conducive to h MSC growth and expansion. Furthermore, results indicated that the ‘top-inout' design(as opposed to its symmetric counterpart) led to higher shear stress for the same media inlet rate(25 μl·h~(-1)), a feature that can be easily exploited to induce shear-dependent differentiation. These findings further confirm the suitability of CFD as a robust design tool.     

An approach to optimization of fault tolerant architectures using HiP‐HOPS

New processes for the design of dependable systems must address both cost and dependability concerns. They should also maximize the potential for automation to address the problem of increasing technological complexity and the potentially immense design spaces that need to be explored. In this paper we show a design process that integrates system modelling, automated dependability analysis and evolutionary optimization techniques to achieve the optimization of designs with respect to dependability and cost from the early stages. Computerized support is provided for difficult aspects of fault tolerant design, such as decision making on the type and location of fault detection and fault tolerant strategies. The process is supported by HiP‐HOPS, a scalable automated dependability analysis and optimization tool. The process was applied to a Pre‐collision system for vehicles at an early stage of its design. The study shows that HiP‐HOPS can overcome the limitations of earlier work based on Reliability Block Diagrams by enabling dependability analysis and optimization of architectures that may have a network topology and exhibit multiple failure modes. Copyright © 2011 John Wiley & Sons, Ltd.     

Big data regression with parallel enhanced and convex incremental extreme learning machines

This work considers scalable incremental extreme learning machine (I‐ELM) algorithms, which could be suitable for big data regression. During the training of I‐ELMs, the hidden neurons are presented one by one, and the weights are based solely on simple direct summations, which can be most efficiently mapped on parallel environments. Existing incremental versions of ELMs are the I‐ELM, enhanced incremental ELM (EI‐ELM), and convex incremental ELM (CI‐ELM). We study the enhanced and convex incremental ELM (ECI‐ELM) algorithm, which is a combination of the last 2 versions. The main findings are that ECI‐ELM is fast, accurate, and fully scalable when it operates in a parallel system of distributed memory workstations. Experimental simulations on several benchmark data sets demonstrate that the ECI‐ELM is the most accurate among the existing I‐ELM, EI‐ELM, and CI‐ELM algorithms. We also analyze the convergence as a function of the hidden neurons and demonstrate that ECI‐ELM has the lowest error rate curve and converges much faster than the other algorithms in all of the data sets. The parallel simulations also reveal that the data parallel training of the ECI‐ELM can guarantee simplicity and straightforward mappings and can deliver speedups and scale‐ups very close to linear.     

Building and evaluating resources for sentiment analysis in the Greek language

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.     

Compression techniques for fast external sorting

External sorting of large files of records involves use of disk space to store temporary files, processing time for sorting, and transfer time between CPU, cache, memory, and disk. Compression can reduce disk and transfer costs, and, in the case of external sorts, cut merge costs by reducing the number of runs. It is therefore plausible that overall costs of external sorting could be reduced through use of compression. In this paper, we propose new compression techniques for data consisting of sets of records. The best of these techniques, based on building a trie of variable-length common strings, provides fast compression and decompression and allows random access to individual records. We show experimentally that our trie-based compression leads to significant reduction in sorting costs; that is, it is faster to compress the data, sort it, and then decompress it than to sort the uncompressed data. While the degree of compression is not quite as great as can be obtained with adaptive techniques such as Lempel-Ziv methods, these cannot be applied to sorting. Our experiments show that, in comparison to approaches such as Huffman coding of fixed-length substrings, our novel trie-based method is faster and provides greater size reductions. Preliminary versions of parts of this paper, not including the work on vargram compression” [41]     

Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control

The problem of force/position tracking for a robotic manipulator in compliant contact with a surface under non-parametric uncertainties is considered. In particular, structural uncertainties are assumed to characterize the compliance and surface friction models, as well as the robot dynamic model. A novel neuro-adaptive controller is proposed, that exploits the approximation capabilities of the linear in the weights neural networks, guaranteeing the uniform ultimate boundedness of force and position error with respect to arbitrarily small sets, plus the boundedness of all signals in the closed loop. Simulations highlight the approach.     

Qualitative egomotion

Due to the aperture problem, the only motion measurement in images, whose computation does not require any assumptions about the scene in view, is normal flow—the projection of image motion on the gradient direction. In this paper we show how a monocular observer can estimate its 3D motion relative to the scene by using normal flow measurements in a global and qualitative way. The problem is addressed through a search technique. By checking constraints imposed by 3D motion parameters on the normal flow field, the possible space of solutions is gradually reduced. In the four modules that comprise the solution, constraints of increasing restriction are considered, culminating in testing every single normal flow value for its consistency with a set of motion parameters. The fact that motion is rigid defines geometric relations between certain values of the normal flow field. The selected values form patterns in the image plane that are dependent on only some of the motion parameters. These patterns, which are determined by the signs of the normal flow values, are searched for in order to find the axes of translation and rotation. The third rotational component is computed from normal flow vectors that are only due to rotational motion. Finally, by looking at the complete data set, all solutions that cannot give rise to the given normal flow field are discarded from the solution space.Research supported in part by NSF (Grant IRI-90-57934), ONR (Contract N00014-93-1-0257) and ARPA (Order No. 8459).     

Vision and action

Our work on active vision has recently focused on the computational modelling of navigational tasks, where our investigations were guided by the idea of approaching vision for behavioural systems in the form of modules that are directly related to perceptual tasks. These studies led us to branch in various directions and inquire into the problems that have to be addressed in order to obtain an overall understanding of perceptual systems. In this paper, we present our views about the architecture of vision systems, about how to tackle the design and analysis of perceptual systems, and promising future research directions. Our suggested approach for understanding behavioural vision to realize the relationships of perception and action builds on two earlier approaches, the Medusa philosophy¹ and the Synthetic approach². The resulting framework calls for synthesizing an artificial vision system by studying vision competences of increasing complexity and, at the same time, pursuing the integration of the perceptual components with action and learning modules. We expect that computer vision research in the future will progress in tight collaboration with many other disciplines that are concerned with empirical approaches to vision, i.e. the understanding of biological vision. Throughout the paper, we describe biological findings that motivate computational arguments which we believe will influence studies of computer vision in the near future.