Ethylene-propylene copolymers by a supported Ziegler-Natta catalyst based on TiCl4/MgCl2

Ethylene-propylene copolymers have been prepared by using Ziegler-Natta catalysts based on TiCl₄, MgCl₂, PCl₃ and (n-Bu)₃PO₄. The catalysts TiCl₄/MgCl₂/PCl₃ and TiCl₄/MgCl₂/(n-Bu)₃PO₄ were prepared by reacting TiCl₄ with pretreated MgCl₂. The support was prepared by ball milling of MgCl₂ with varied amounts of PCl₃ or (n-Bu)₃PO₄. The addition of PCl₃ has remarkably increased the MgCl₂ surface area in comparison with (n-Bu)₃PO₄. The effects of PCl₃ and (n-Bu)₃PO₄ on ethylene homopolymerization, ethylene-propylene copolymerization and on copolymer properties were evaluated. The catalyst system containing PCl₃ permitted to synthesize propylene-ethylene copolymers with up to 75% (w/w) of propylene and provided control of copolymer crystallinity. The reduction of the copolymer molecular weight distribution suggested that PCl₃ acted as an internal donor, poisoning some active catalytic sites. Received: 2 April 1997/Revised: 6 June 1997/Accepted: 18 June 1997     

High styrene–rubber ionomers,an alternative to thermoplastic elastomers

High styrene rubber ionomers were prepared by sulfonating styrene–butadiene rubber of high styrene content (high styrene rubber) in 1,2‐dichloroethane using acetyl sulfate reagent, followed by neutralization of the precursor acids using methanolic zinc acetate. The ionomers were characterized using X‐ray fluorescence spectroscopy, Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), dynamic mechanical analysis (DMA), and also by the evaluation of mechanical properties. The FTIR studies of the ionomer reveal that the sulfonate groups are attached to the benzene ring. The NMR spectra give credence to this observation. Results of DMA show an ionic transition (T_i) in addition to glass–rubber transition (T_g). Incorporation of ionic groups results in improved mechanical properties as well as retention of properties after three cycles of processing. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2294–2300, 2002     

Optimizing BRDF orientations for the manipulation of anisotropic highlights

This paper introduces a system for the direct editing of highlights produced by anisotropic BRDFs, which we call anisotropic highlights. We first provide a comprehensive analysis of the link between the direction of anisotropy and the shape of highlight curves for arbitrary object surfaces. The gained insights provide the required ingredients to infer BRDF orientations from a prescribed highlight tangent field. This amounts to a non‐linear optimization problem, which is solved at interactive framerates during manipulation. Taking inspiration from sculpting software, we provide tools that give the impression of manipulating highlight curves while actually modifying their tangents. Our solver produces desired highlight shapes for a host of lighting environments and anisotropic BRDFs.     

Assessment of crowdsourcing and gamification loss in user-assisted object segmentation

There has been a growing interest in applying human computation – particularly crowdsourcing techniques – to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans. In this paper we focus on a specific problem – object segmentation within color images – and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game. We use Click’n’Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask’nSeek, a game with a purpose designed for object detection and segmentation. The two main contributions of this paper are: (i) We use the results of Click’n’Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we compare the quality of the results obtained with Click’n’Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task. We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that – on the other hand – the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.     

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Workload assignment for global real-time scheduling on unrelated clustered platforms

Real-Time Systems - Heterogeneous MPSoCs are being used more and more, from cellphones to critical embedded systems. Most of those systems offer heterogeneous sets of identical cores. In this...     

Healing Effect of Controlled Anti‐Electromigration on Conventional and High‐Tc Superconducting Nanowires

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti‐electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La_2?x Ce_x CuO₄), the electromigration process acts selectively on the oxygen atoms with no apparent modification of the structure. This allows to adjust the doping of this compound and switch from a superconducting to an insulating state in a nearly reversible fashion. In addition, the conditions needed to replace feedback controlled electromigration by a simpler technique of electropulsing are discussed. These findings have a direct practical application as a method to explore the dependence of the characteristic parameters on the exact oxygen content and pave the way for a reversible control of local properties of nanowires.