End-to-End Data-Flow Parallelism for Throughput Optimization in High-Speed Networks

The increase in the data produced by large-scale scientific applications necessitates innovative solutions for efficient transfer of data. Although the current optical networking technology reached theoretical speeds of 100?Gbps, applications still suffer from inefficient transport protocols and bottlenecks on the end-systems (e.g. disk, CPU, NIC). High-performance systems provide us with parallel disks, processors and network interfaces. However the lack of orchestration of these end-system resources with the available network capacity results in underutilization of the network bandwidth. In this study, a model and two algorithms that use ‘end-to-end data-flow parallelism’ to optimize the use of network and end-system resources are proposed. This is achieved by using multiple parallel streams over the network; and multiple parallel disks and CPUs at the end systems. Our model predicts the optimal number of streams and disk/CPU stripes that maximizes the data transfer speed for any setting. Our algorithms use GridFTP parallel samplings and calculate the optimal level of parallelism based on our prediction model. The experiments conducted by using actual GridFTP transfers show that the predictions performed by our model and algorithms provide close-to-optimal performances with negligible overhead and use minimal number of resources. The end-to-end data transfer throughput is improved dramatically in existence of end-system bottlenecks compared to the non-optimized transfers.     

Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

A low energy adaptive motion estimation hardware for H.264 multiview video coding

Multiview video coding (MVC) is the process of efficiently compressing stereo (two views) or multiview video signals. The improved compression efficiency achieved by H.264 MVC comes with a significant increase in computational complexity. Temporal prediction and inter-view prediction are the most computationally intensive parts of H.264 MVC. Therefore, in this paper, we propose novel techniques for reducing the amount of computations performed by temporal and inter-view predictions in H.264 MVC. The proposed techniques reduce the amount of computations performed by temporal and inter-view predictions significantly with very small PSNR loss and bit rate increase. We also propose a low energy adaptive H.264 MVC motion estimation hardware for implementing the temporal and inter-view predictions including the proposed computation reduction techniques. The proposed hardware is implemented in Verilog HDL and mapped to a Xilinx Virtex-6 FPGA. The FPGA implementation is capable of processing 30 × 8 = 240 frames per second (fps) of CIF (352 × 288) size eight view video sequence or 30 × 2 = 60 fps of VGA (640 × 480) size stereo (two views) video sequence. The proposed techniques reduce the energy consumption of this hardware significantly.     

Correction to: 3D SASHA myocardial T1 mapping with high accuracy and improved precision

Purpose

To improve the precision of a free-breathing 3D saturation-recovery-based myocardial T1 mapping sequence using a post-processing 3D denoising technique.

Methods

A T1 phantom and 15 healthy subjects were scanned on a 1.5 T MRI scanner using 3D saturation-recovery single-shot acquisition (SASHA) for myocardial T1 mapping. A 3D denoising technique was applied to the native T1-weighted images before pixel-wise T1 fitting. The denoising technique imposes edge-preserving regularity and exploits the co-occurrence of 3D spatial gradients in the native T1-weighted images by incorporating a multi-contrast Beltrami regularization. Additionally, 2D modified Look-Locker inversion recovery (MOLLI) acquisitions were performed for comparison purposes. Accuracy and precision were measured in the myocardial septum of 2D MOLLI and 3D SASHA T1 maps and then compared. Furthermore, the accuracy and precision of the proposed approach were evaluated in a standardized phantom in comparison to an inversion-recovery spin-echo sequence (IRSE).

Results

For the phantom study, Bland–Altman plots showed good agreement in terms of accuracy between IRSE and 3D SASHA, both on non-denoised and denoised T1 maps (mean difference −1.4 ± 18.9 ms and −4.4 ± 21.2 ms, respectively), while 2D MOLLI generally underestimated the T1 values (69.4 ± 48.4 ms). For the in vivo study, there was a statistical difference between the precision measured on 2D MOLLI and on non-denoised 3D SASHA T1 maps (P = 0.005), while there was no statistical difference after denoising (P = 0.95).

Conclusion

The precision of 3D SASHA myocardial T1 mapping was substantially improved using a 3D Beltrami regularization based denoising technique and was similar to that of 2D MOLLI T1 mapping, while preserving the higher accuracy and whole-heart coverage of 3D SASHA.

     

Effect of severe plastic deformation on tensile properties and impact toughness of two-phase Zn–40Al alloy

Tensile properties and impact toughness of the severe plastically deformed Zn–40Al alloy were investigated. The material billets were subjected to equal-channel angular extrusion (ECAE). After processing, elongation to failure increased significantly with the increasing number of ECAE passes. ECAE also increased the strength levels after one pass, however, they were reduced with the higher number of passes. The observed softening of the alloy upon multiple ECAE passes was shown to be due to the deformation-induced homogenization and the continuous change in the composition of the constituting phases with the number of passes. In addition, the volume fraction of the hard phase decreased due to dissolution and/or breakage. The impact toughness of the alloy was improved by multi-pass ECAE due to the significant increase in ductility. These findings demonstrate that multi-pass ECAE effectively transforms brittle Zn–Al cast alloys into tougher materials with ductile fracture behavior.     

Effect of equal-channel angular extrusion on mechanical and wear properties of eutectic Al–12Si alloy

Mechanical and wear properties of severely deformed Al–12Si alloy by equal-channel angular extrusion/pressing (ECAE/P) were investigated. Multi-pass ECAE processing of the as-cast alloy substantially increased both its strength and ductility. The increase in the tensile and yield strength values after six ECAE passes were about 48% and 87%, respectively. The sample after six ECAE passes exhibited 10% elongation before rupture, which was about five times higher than that of the as-cast one. The improvement in both strength and ductility was mainly attributed to the changes of the shape, size and distribution of the eutectic silicon particles along with the breakage and refined of the large α-Al grains during multi-pass ECAE processing. However, the wear test results surprisingly showed that the ECAE process decreased the wear resistance of the alloy, although there was improvement in strength and ductility values. This was mainly attributed to the tribochemical reaction leading to oxidative wear with the abrasive effect in Al–Si alloys during sliding. The oxide layer played a dominant role in determining the wear resistance of the sample in both as-cast and ECAE-processed states, and it masked the effect of strengthening of alloy structure on the wear resistance.     

Progress in deep-UV resists using CARL technology

Three positive working Si-CARL resists for bilayer applications with oxygen-RIE pattern transfer were investigated, and their lithographic performance at deep-UV exposure was compared. With all three we obtained good focus latitudes for 0.35 μm lines and spaces ranging from 1.6 to 2.2 μm exposed with an 0.37 NA KrF-excimer laser stepper. The zero bias exposure dose required for resist R1, of the diazodiketone type, is relatively high (70 mJ/cm²), but R1 has the advantage of not suffering from linewidth fluctuations caused by post-exposure delay time effects. Processing for resists R2 and R3, which are based on acid catalyzed deprotection of t-BOC-imide and t-butylester, respectively, had to be optimized to avoid bridging of isolated spaces after Chemical Amplification of Resist Lines (CARL). This bridging is caused indirectly by evaporation of triflate acid during PEB. Resist R2 needs only 8.5 mJ/cm² for exposure but has a poor linewidth increase after CARL, which seems to be the reason for eroded 0.25 μm patterns after oxygen-RIE. Resist R3 shows the steepest resist slopes and the best overall performance. The ultimate resolution for resists R1 and R3 is 0.25 μm, which, according to the Rayleigh equation for resolution, corresponds to a k-factor of 0.37.