Realisations of W3W_3 Symmetry

We perform a systematic investigation of free-scalar realisations of the Za\-mo\-lod\-chi\-kov $W_3$ algebra in which the operator product of two spin-three generators contains a non-zero operator of spin four which has vanishing norm. This generalises earlier work where such an operator was required to be absent. By allowing this spin-four null operator we obtain several realisations of the $W_3$ algebra both in terms of two scalars as well as in terms of an arbitrary number $n$ of free scalars. Our analysis is complete for the case of two-scalar realisations.Comment: 14 pages, LATEX, UG-6/9

Validation of a smartphone app to map social networks of proximity

Social network analysis is a prominent approach to investigate interpersonal relationships. Most studies use self-report data to quantify the connections between participants and construct social networks. In recent years smartphones have been used as an alternative to map networks by assessing the proximity between participants based on Bluetooth and GPS data. While most studies have handed out specially programmed smartphones to study participants, we developed an application for iOS and Android to collect Bluetooth data from participants own smartphones. In this study, we compared the networks estimated with the smartphone app to those obtained from sociometric badges and self-report data. Participants (n=21) installed the app on their phone and wore a sociometric badge during office hours. Proximity data was collected for 4 weeks. A contingency table revealed a significant association between proximity data (rho = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%) than for the badges (1.3%), indicating that dyads were more often detected by the app. We then compared the networks that were estimated using the proximity and self-report data. All three networks were significantly correlated, although the correlation with self-reported data was lower for the app (rho = 0.25) than for badges (rho = 0.67). The scanning rates of the app varied considerably between devices and was lower on iOS than on Android. The association between the app and the badges increased when the network was estimated between participants whose app recorded more regularly. These findings suggest that the accuracy of proximity networks can be further improved by reducing missing data and restricting the interpersonal distance at which interactions are detected.Comment: 20 pages, 5 figure

Using Bluetooth Low Energy in smartphones to map social networks

Social networks have an important role in an individual's health, with the propagation of health-related features through a network, and correlations between network structures and symptomatology. Using Bluetooth-enabled smartphones to measure social connectivity is an alternative to traditional paper-based data collection; however studies employing this technology have been restricted to limited sets of homogenous handsets. We investigated the feasibility of using the Bluetooth Low Energy (BLE) protocol, present on users' own smartphones, to measure social connectivity. A custom application was designed for Android and iOS handsets. The app was configured to simultaneously broadcast via BLE and perform periodic discovery scans for other nearby devices. The app was installed on two Android handsets and two iOS handsets, and each combination of devices was tested in the foreground, background and locked states. Connectivity was successfully measured in all test cases, except between two iOS devices when both were in a locked state with their screens off. As smartphones are in a locked state for the majority of a day, this severely limits the ability to measure social connectivity on users' own smartphones. It is not currently feasible to use Bluetooth Low Energy to map social networks, due to the inability of iOS devices to detect another iOS device when both are in a locked state. While the technology was successfully implemented on Android devices, this represents a smaller market share of partially or fully compatible devices.Comment: 6 pages, 1 tabl

Cofactor regeneration by a soluble pyridine nucleotide transhydrogenase for biological production of hydromorphone

We have applied the soluble pyridine nucleotide transhydrogenase of Pseudomonas fluorescens to a cell-free system for the regeneration of the nicotinamide cofactors NAD and NADP in the biological production of the important semisynthetic opiate drug hydromorphone. The original recombinant whole-cell system suffered from cofactor depletion resulting from the action of an NADP(+)-dependent morphine dehydrogenase and an NADH-dependent morphinone reductase. By applying a soluble pyridine nucleotide transhydrogenase, which can transfer reducing equivalents between NAD and NADP, we demonstrate with a cell-free system that efficient cofactor cycling in the presence of catalytic amounts of cofactors occurs, resulting in high yields of hydromorphone. The ratio of morphine dehydrogenase, morphinone reductase, and soluble pyridine nucleotide transhydrogenase is critical for diminishing the production of the unwanted by-product dihydromorphine and for optimum hydromorphone yields. Application of the soluble pyridine nucleotide transhydrogenase to the whole-cell system resulted in an improved biocatalyst with an extended lifetime. These results demonstrate the usefulness of the soluble pyridine nucleotide transhydrogenase and its wider application as a tool in metabolic engineering and biocatalysis

Immune mechanisms of vaccine induced protection against chronic hepatitis C virus infection in chimpanzees

Hepatitis C virus (HCV) infection is characterized by a high propensity for development of life-long viral persistence. An estimated 170 million people suffer from chronic hepatitis caused by HCV. Currently, there is no approved prophylactic HCV vaccine available. With the near disappearance of the most relevant animal model for HCV, the chimpanzee, we review the progression that has been made regarding prophylactic vaccine development against HCV. We describe the results of the individual vaccine evaluation experiments in chimpanzees, in relation to what has been observed in humans. The results of the different studies indicate that partial protection against infection can be achieved, but a clear correlate of protection has thus far not yet been defined.</p

Non-extremal D-instantons

We construct the most general non-extremal deformation of the D-instanton solution with maximal rotational symmetry. The general non-supersymmetric solution carries electric charges of the SL(2,R) symmetry, which correspond to each of the three conjugacy classes of SL(2,R). Our calculations naturally generalise to arbitrary dimensions and arbitrary dilaton couplings. We show that for specific values of the dilaton coupling parameter, the non-extremal instanton solutions can be viewed as wormholes of non-extremal Reissner-Nordstr\"om black holes in one higher dimension. We extend this result by showing that for other values of the dilaton coupling parameter, the non-extremal instanton solutions can be uplifted to non-extremal non-dilatonic p-branes in p+1 dimensions higher. Finally, we attempt to consider the solutions as instantons of (compactified) type IIB superstring theory. In particular, we derive an elegant formula for the instanton action. We conjecture that the non-extremal D-instantons can contribute to the R^8-terms in the type IIB string effective action.Comment: 31 pages, 4 figures. v3: minor correction and reference adde

Improving novelty detection using the reconstructions of nearest neighbours

We show that using nearest neighbours in the latent space of autoencoders (AE) significantly improves performance of semi-supervised novelty detection in both single and multi-class contexts. Autoencoding methods detect novelty by learning to differentiate between the non-novel training class(es) and all other unseen classes. Our method harnesses a combination of the reconstructions of the nearest neighbours and the latent-neighbour distances of a given input's latent representation. We demonstrate that our nearest-latent-neighbours (NLN) algorithm is memory and time efficient, does not require significant data augmentation, nor is reliant on pre-trained networks. Furthermore, we show that the NLN-algorithm is easily applicable to multiple datasets without modification. Additionally, the proposed algorithm is agnostic to autoencoder architecture and reconstruction error method. We validate our method across several standard datasets for a variety of different autoencoding architectures such as vanilla, adversarial and variational autoencoders using either reconstruction, residual or feature consistent losses. The results show that the NLN algorithm grants up to a 17% increase in Area Under the Receiver Operating Characteristics (AUROC) curve performance for the multi-class case and 8% for single-class novelty detection

Learning to detect radio frequency interference in radio astronomy without seeing it

Radio Frequency Interference (RFI) corrupts astronomical measurements, thus affecting the performance of radio telescopes. To address this problem, supervised segmentation models have been proposed as candidate solutions to RFI detection. However, the unavailability of large labelled datasets, due to the prohibitive cost of annotating, makes these solutions unusable. To solve these shortcomings, we focus on the inverse problem; training models on only uncontaminated emissions thereby learning to discriminate RFI from all known astronomical signals and system noise. We use Nearest-Latent-Neighbours (NLN) - an algorithm that utilises both the reconstructions and latent distances to the nearest-neighbours in the latent space of generative autoencoding models for novelty detection. The uncontaminated regions are selected using weak-labels in the form of RFI flags (generated by classical RFI flagging methods) available from most radio astronomical data archives at no additional cost. We evaluate performance on two independent datasets, one simulated from the HERA telescope and another consisting of real observations from LOFAR telescope. Additionally, we provide a small expert-labelled LOFAR dataset (i.e., strong labels) for evaluation of our and other methods. Performance is measured using AUROC, AUPRC and the maximum F1-score for a fixed threshold. For the simulated data we outperform the current state-of-the-art by approximately 1% in AUROC and 3% in AUPRC for the HERA dataset. Furthermore, our algorithm offers both a 4% increase in AUROC and AUPRC at a cost of a degradation in F1-score performance for the LOFAR dataset, without any manual labelling

Seven-branes and Supersymmetry

We re-investigate the construction of half-supersymmetric 7-brane solutions of IIB supergravity. Our method is based on the requirement of having globally well-defined Killing spinors and the inclusion of SL(2,Z)-invariant source terms. In addition to the well-known solutions going back to Greene, Shapere, Vafa and Yau we find new supersymmetric configurations, containing objects whose monodromies are not related to the monodromy of a D7-brane by an SL(2,Z) transformation.Comment: 31 pages, 3 figure