A Vector Approach for the Analysis and Synthesis of Directional Modulation Transmitters

In order to formalize and extend on previous ad-hoc analysis and synthesis methods a theoretical treatment using vector representations of directional modulation (DM) systems is introduced and used to achieve DM transmitter characteristics. An orthogonal vector approach is proposed which allows the artificial orthogonal noise concept derived from information theory to be brought to bear on DM analysis and synthesis. The orthogonal vector method is validated and discussed via bit error rate (BER) simulations.

RAS testing of colorectal carcinoma—a guidance document from the Association of Clinical Pathologists Molecular Pathology and Diagnostics Group

Analysis of colorectal carcinoma (CRC) tissue for KRAS codon 12 or 13 mutations to guide use of anti-epidermal growth factor receptor (EGFR) therapy is now considered mandatory in the UK. The scope of this practice has been recently extended because of data indicating that NRAS mutations and additional KRAS mutations also predict for poor response to anti-EGFR therapy. The following document provides guidance on RAS (i.e., KRAS and NRAS) testing of CRC tissue in the setting of personalised medicine within the UK and particularly within the NHS. This guidance covers issues related to case selection, preanalytical aspects, analysis and interpretation of such RAS testing.

A Far-field Pattern Separation Approach for the Synthesis of Directional Modulation Transmitter Arrays (Invited)

In this paper a far-field power pattern separation approach is proposed for the synthesis of directional modulation (DM) transmitter arrays. Separation into information pattern and interference patterns is enabled by far-field pattern null steering. Compared with other DM synthesis methods, e.g., BER-driven DM optimization and orthogonal vector injection, this approach facilitates manipulation of artificial interference spatial distributions. With such capability more interference power can be projected into those most vulnerable to eavesdropping spatial directions in free space, i.e., the information sidelobes. In such a fashion information leaked through radiation sidelobes can be effectively mitigated in a transmitter power efficient manner. The proposed synthesis approach is further validated via bit error rate (BER) simulations.

Markievicz Commission Report: Gender equality document

Report on implementation of the candidate gender quota in the Fianna Fail Party.

Accelerating Large Scale Centroid-based Clustering with Locality Sensitive Hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing clustering algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing (LSH) to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.