1,892 research outputs found
Realisations of Symmetry
We perform a systematic investigation of free-scalar realisations of the
Za\-mo\-lod\-chi\-kov 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 algebra both in terms of two scalars as well
as in terms of an arbitrary number 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
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