Alternating quaternary algebra structures on irreducible representations of sl(2,C)

We determine the multiplicity of the irreducible representation V(n) of the simple Lie algebra sl(2,C) as a direct summand of its fourth exterior power $\Lambda^4 V(n)$. The multiplicity is 1 (resp. 2) if and only if n = 4, 6 (resp. n = 8, 10). For these n we determine the multilinear polynomial identities of degree $\le 7$ satisfied by the sl(2,C)-invariant alternating quaternary algebra structures obtained from the projections $\Lambda^4 V(n) \to V(n)$. We represent the polynomial identities as the nullspace of a large integer matrix and use computational linear algebra to find the canonical basis of the nullspace.Comment: 26 pages, 13 table

Model-based target sonification on mobile devices

We investigate the use of audio and haptic feedback to augment the display of a mobile device controlled by tilt input. We provide an example of this based on Doppler effects, which highlight the user's approach to a target, or a target's movement from the current state, in the same way we hear the pitch of a siren change as it passes us. Twelve participants practiced navigation/browsing a state-space that was displayed via audio and vibrotactile modalities. We implemented the experiment on a Pocket PC, with an accelerometer attached to the serial port and a headset attached to audio port. Users navigated through the environment by tilting the device. Feedback was provided via audio displayed via a headset, and by vibrotactile information displayed by a vibrotactile unit in the Pocket PC. Users selected targets placed randomly in the state-space, supported by combinations of audio, visual and vibrotactile cues. The speed of target acquisition and error rate were measured, and summary statistics on the acquisition trajectories were calculated. These data were used to compare different display combinations and configurations. The results in the paper quantified the changes brought by predictive or 'quickened' sonified displays in mobile, gestural interaction

Reply to “Comments on “Consensus and Cooperation in Networked Multi-Agent Systems””

[No abstract

Superhumps in Low-Mass X-Ray Binaries

We propose a mechanism for the superhump modulations observed in optical photometry of at least two black hole X-ray transients (SXTs). As in extreme mass-ratio cataclysmic variables (CVs), superhumps are assumed to result from the presence of the 3:1 orbital resonance in the accretion disc. This causes the disc to become non-axisymmetric and precess. However the mechanism for superhump luminosity variations in low mass X-ray binaries (LMXBs) must differ from that in CVs, where it is attributed to a tidally-driven modulation of the disc's viscous dissipation, varying on the beat between the orbital and disc precession period. By contrast in LMXBs, tidal dissipation in the outer accretion disc is negligible: the optical emission is overwhelming dominated by reprocessing of intercepted central X-rays. Thus a different origin for the superhump modulation is required. Recent observations and numerical simulations indicate that in an extreme mass-ratio system the disc area changes on the superhump period. We deduce that the superhumps observed in SXTs arise from a modulation of the reprocessed flux by the changing area. Therefore, unlike the situation in CVs, where the superhump amplitude is inclination-independent, superhumps should be best seen in low-inclination LMXBs, whereas an orbital modulation from the heated face of the secondary star should be more prominent at high inclinations. Modulation at the disc precession period (10s of days) may indicate disc asymmetries such as warping. We comment on the orbital period determinations of LMXBs, and the possibility and significance of possible permanent superhump LMXBs.Comment: 6 pages, 1 encapsulated figure. MNRAS in press; replaced to correct typographical error

Mem Tri: Memory Forensics Triage Tool

This work explores the development of MemTri. A memory forensics triage tool that can assess the likelihood of criminal activity in a memory image, based on evidence data artefacts generated by several applications. Fictitious illegal suspect activity scenarios were performed on virtual machines to generate 60 test memory images for input into MemTri. Four categories of applications (i.e. Internet Browsers, Instant Messengers, FTP Client and Document Processors) are examined for data artefacts located through the use of regular expressions. These identified data artefacts are then analysed using a Bayesian Network, to assess the likelihood that a seized memory image contained evidence of illegal activity. Currently, MemTri is under development and this paper introduces only the basic concept as well as the components that the application is built on. A complete description of MemTri coupled with extensive experimental results is expected to be published in the first semester of 2017

MemTri: A Memory Forensics Triage Tool using Bayesian Network and Volatility

This work explores the development of MemTri. A memory forensics triage tool that can assess the likelihood of criminal activity in a memory image, based on evidence data artefacts generated by several applications. Fictitious illegal suspect activity scenarios were performed on virtual machines to generate 60 test memory images for input into MemTri. Four categories of applications (i.e. Internet Browsers, Instant Messengers, FTP Client and Document Processors) are examined for data artefacts located through the use of regular expressions. These identified data artefacts are then analysed using a Bayesian Network, to assess the likelihood that a seized memory image contained evidence of illegal firearms trading activity. MemTri's normal mode of operation achieved a high artefact identification accuracy performance of 95.7% when the applications' processes were running. However, this fell significantly to 60% as applications processes' were terminated. To explore improving MemTri's accuracy performance, a second mode was developed, which achieved more stable results of around 80% accuracy, even after applications processes' were terminated

Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark