Antimicrobial peptides (AMPs) are lately receiving significant attention as targets for antibacterial drug research. While many machine learning techniques are shown effective for AMP recognition, their utility for the rational design of novel AMP-based drugs in the wet laboratory is questionable. In this paper we seek to elucidate determinants of antimicrobial activity in a well-studied class ...

Understanding the impact of halo properties beyond mass on clustering galaxies (namely galaxy assembly bias) remains a challenge for contemporary models clustering. We explore use machine learning to predict occupations and recover bias in semi-analytic formation model. For stellar-mass selected samples, we train Random Forest algorithm number central satellite each dark matter halo. With predi...

Performance-critical components of virtual machines are often implemented in assembly language. Traditionally, this code is compiled with an assembler and linked statically with the rest of the system. Generating this code at startup time has significant engineering advantages over the conventional approach: The generated machine code is much safer, easier to maintain, and has better performanc...

Fault detection and classification in an automated assembly machine using machine vision (MV) based inspection methods is the subject of this paper. A high speed automated assembly machine is used as the test apparatus. The machine is designed to assemble circular O-rings, from a bulk supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Video data was co llecte...