Powder Compaction: Compression Properties of Cellulose Ethers

Effective development of matrix tablets requires a comprehensive understanding of different raw material attributes and their impact on process parameters. Cellulose ethers (CE) are the most commonly used pharmaceutical excipients in the fabrication of hydrophilic matrices. The innate good compression and binding properties of CE enable matrices to be prepared using economical direct compression (DC) techniques. However, DC is sensitive to raw material attributes, thus, impacting the compaction process. This article critically reviews prior knowledge on the mechanism of powder compaction and the compression properties of cellulose ethers, giving timely insight into new developments in this field

Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations

Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manipulate large, dense and unstructured kernel matrices. Despite the fact that at the core of training a SVM there is a \textit{simple} convex optimization problem, the presence of kernel matrices is responsible for dramatic performance reduction, making SVMs unworkably slow for large problems. Aiming to an efficient solution of large-scale nonlinear SVM problems, we propose the use of the \textit{Alternating Direction Method of Multipliers} coupled with \textit{Hierarchically Semi-Separable} (HSS) kernel approximations. As shown in this work, the detailed analysis of the interaction among their algorithmic components unveils a particularly efficient framework and indeed, the presented experimental results demonstrate a significant speed-up when compared to the \textit{state-of-the-art} nonlinear SVM libraries (without significantly affecting the classification accuracy)