The Square Root Depth Wave Equations

We introduce a set of coupled equations for multilayer water waves that removes the ill-posedness of the multilayer Green-Naghdi (MGN) equations in the presence of shear. The new well-posed equations are Hamiltonian and in the absence of imposed background shear they retain the same travelling wave solutions as MGN. We call the new model the Square Root Depth equations, from the modified form of their kinetic energy of vertical motion. Our numerical results show how the Square Root Depth equations model the effects of multilayer wave propagation and interaction, with and without shear.Comment: 10 pages, 5 figure

LUNASKA simultaneous neutrino searches with multiple telescopes

The most sensitive method for detecting neutrinos at the very highest energies is the lunar Cherenkov technique, which employs the Moon as a target volume, using conventional radio telescopes to monitor it for nanosecond-scale pulses of Cherenkov radiation from particle cascades in its regolith. Multiple-antenna radio telescopes are difficult to effectively combine into a single detector for this purpose, while single antennas are more susceptible to false events from radio interference, which must be reliably excluded for a credible detection to be made. We describe our progress in excluding such interference in our observations with the single-antenna Parkes radio telescope, and our most recent experiment (taking place the week before the ICRC) using it in conjunction with the Australia Telescope Compact Array, exploiting the advantages of both types of telescope.Comment: 4 pages, 4 figures, in Proceedings of the 32nd International Cosmic Ray Conference (Beijing 2011

Diversity-induced resonance

We present conclusive evidence showing that different sources of diversity, such as those represented by quenched disorder or noise, can induce a resonant collective behavior in an ensemble of coupled bistable or excitable systems. Our analytical and numerical results show that when such systems are subjected to an external subthreshold signal, their response is optimized for an intermediate value of the diversity. These findings show that intrinsic diversity might have a constructive role and suggest that natural systems might profit from their diversity in order to optimize the response to an external stimulus.Comment: 4 pages, 3 figure

The theory of heating of the quantum ground state of trapped ions

Using a displacement operator formalism, I analyse the depopulation of the vibrational ground state of trapped ions. Two heating times, one characterizing short time behaviour, the other long time behaviour are found. The short time behaviour is analyzed both for single and multiple ions, and a formula for the relative heating rates of different modes is derived. The possibility of correction of heating via the quantum Zeno effect, and the exploitation of the suppression of heating of higher modes to reduce errors in quantum computation is considered.Comment: 9 pages, 2 figure

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure