Modelling of a novel high-impedance matching layer for high frequency (>30 MHz) ultrasonic transducers

This work describes a new approach to impedance matching for ultrasonic transducers. A single matching layer with high acoustic impedance of 16 MRayls is demonstrated to show a bandwidth of around 70%, compared with conventional single matching layer designs of around 50%. Although as a consequence of this improvement in bandwidth, there is a loss in sensitivity, this is found to be similar to an equivalent double matching layer design. Designs are calculated by using the KLM model and are then verified by FEA simulation, with very good agreement Considering the fabrication difficulties encountered in creating a high-frequency double matched design due to the requirement for materials with specific acoustic impedances, the need to accurately control the thickness of layers, and the relatively narrow bandwidths available for conventional single matched designs, the new approach shows advantages in that alternative (and perhaps more practical) materials become available, and offers a bandwidth close to that of a double layer design with the simplicity of a single layer design. The disadvantage is a trade-off in sensitivity. A typical example of a piezoceramic transducer matched to water can give a 70% fractional bandwidth (comparable to an ideal double matched design of 72%) with a 3 dB penalty in insertion loss.<br/

High functional coherence in k-partite protein cliques of protein interaction networks

We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI&rsquo;s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph&rsquo;s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques&mdash;most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.<br /

Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

Function signature recovery is important for many binary analysis tasks such as control-flow integrity enforcement, clone detection, and bug finding. Existing works try to substitute learning-based methods with rule-based methods to reduce human effort.They made considerable efforts to enhance the system's performance, which also bring the side effect of higher resource consumption. However, recovering the function signature is more about providing information for subsequent tasks, and both efficiency and performance are significant. In this paper, we first propose a method called Nimbus for efficient function signature recovery that furthest reduces the whole-process resource consumption without performance loss. Thanks to information bias and task relation (i.e., the relation between parameter count and parameter type recovery), we utilize selective inputs and introduce multi-task learning (MTL) structure for function signature recovery to reduce computational resource consumption, and fully leverage mutual information. Our experimental results show that, with only about the one-eighth processing time of the state-of-the-art method, we even achieve about 1% more prediction accuracy over all function signature recovery tasks