Extracting inner‐heliosphere solar wind speed information from Heliospheric Imager observations

We present evidence that variability in the STEREO‐A Heliospheric Imager (HI) data is correlated with in situ solar wind speed estimates from WIND, STEREO‐A, and STEREO‐B. For 2008–2012, we compute the variability in HI differenced images in a plane‐of‐sky shell between 20 to 22.5 solar radii and, for a range of position angles, compare daily means of HI variability and in situ solar wind speed estimates. We show that the HI variability data and in situ solar wind speeds have similar temporal autocorrelation functions. Carrington rotation periodicities are well documented for in situ solar wind speeds, but, to our knowledge, this is the first time they have been presented in statistics computed from HI images. In situ solar wind speeds from STEREO‐A, STEREO‐B, and WIND are all are correlated with the HI variability, with a lag that varies in a manner consistent with the longitudinal separation of the in situ monitor and the HI instrument. Unlike many approaches to processing HI observations, our method requires no manual feature tracking; it is automated, is quick to compute, and does not suffer the subjective biases associated with manual classifications. These results suggest we could possibly estimate solar wind speeds in the low heliosphere directly from HI observations. This motivates further investigation, as this could be a significant asset to the space weather forecasting community; it might provide an independent observational constraint on heliospheric solar wind forecasts, through, for example, data assimilation. Finally, these results are another argument for the potential utility of including a HI on an operational space weather mission

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.Comment: Accepted for publication at German Conference on Pattern Recognition (GCPR) 2017. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualisation and Social Computing

Microwave-frequency scanning gate microscopy of a Si/SiGe double quantum dot

Conventional quantum transport methods can provide quantitative information on spin, orbital, and valley states in quantum dots, but often lack spatial resolution. Scanning tunneling microscopy, on the other hand, provides exquisite spatial resolution of the local electronic density of states, but often at the expense of speed. Working to combine the spatial resolution and energy sensitivity of scanning probe microscopy with the speed of microwave measurements, we couple a metallic probe tip to a Si/SiGe double quantum dot that is integrated with a local charge detector. We first demonstrate that a dc-biased tip can be used to change the charge occupancy of the double dot. We then apply microwave excitation through the scanning tip to drive photon-assisted tunneling transitions in the double dot. We infer the double dot energy level diagram from the frequency and detuning dependence of the photon-assisted tunneling resonance condition. These measurements allow us to resolve $\sim$65 $\mu$eV excited states, an energy scale consistent with typical valley splittings in Si/SiGe. Future extensions of this approach may allow spatial mapping of the valley splitting in Si devices, which is of fundamental importance for spin-based quantum processors

GRB 070201: A possible Soft Gamma Ray Repeater in M31

The gamma-ray burst (GRB) 070201 was a bright short-duration hard-spectrum GRB detected by the Inter-Planetary Network (IPN). Its error quadrilateral, which has an area of 0.124 sq. deg, intersects some prominent spiral arms of the nearby M31 (Andromeda) galaxy. Given the properties of this GRB, along with the fact that LIGO data argues against a compact binary merger origin in M31, this GRB is an excellent candidate for an extragalactic Soft Gamma-ray Repeater (SGR) giant flare, with energy of 1.4x10^45 erg. Analysis of ROTSE-IIIb visible light observations of M31, taken 10.6 hours after the burst and covering 42% of the GRB error region, did not reveal any optical transient down to a limiting magnitude of 17.1. We inspected archival and proprietary XMM-Newton X-ray observations of the intersection of the GRB error quadrilateral and M31, obtained about four weeks prior to the outburst, in order to look for periodic variable X-ray sources. No SGR or Anomalous X-ray Pulsar (AXP) candidates (periods in range 1 to 20 s) were detected. We discuss the possibility of detecting extragalactic SGRs/AXPs by identifying their periodic X-ray light curves. Our simulations suggest that the probability of detecting the periodic X-ray signal of one of the known Galactic SGRs/AXPs, if placed in M31, is about 10% (50%), using 50 ks (2 Ms) XMM-Newton exposures.Comment: 7 pages, submitted to ApJ (Fig. 2 resolution reduced

Prediction and measurement of the size-dependent stability of fluorescence in diamond over the entire nanoscale

Fluorescent defects in non-cytotoxic diamond nanoparticles are candidates for qubits in quantum computing, optical labels in biomedical imaging and sensors in magnetometry. For each application these defects need to be optically and thermodynamically stable, and included in individual particles at suitable concentrations (singly or in large numbers). In this letter, we combine simulations, theory and experiment to provide the first comprehensive and generic prediction of the size, temperature and nitrogen-concentration dependent stability of optically active NV defects in nanodiamonds.Comment: Published in Nano Letters August 2009 24 pages, 6 figure

‘O sibling, where art thou?’ – a review of avian sibling recognition with respect to the mammalian literature

Avian literature on sibling recognition is rare compared to that developed by mammalian researchers. We compare avian and mammalian research on sibling recognition to identify why avian work is rare, how approaches differ and what avian and mammalian researchers can learn from each other. Three factors: (1) biological differences between birds and mammals, (2) conceptual biases and (3) practical constraints, appear to influence our current understanding. Avian research focuses on colonial species because sibling recognition is considered adaptive where ‘mixing potential’ of dependent young is high; research on a wider range of species, breeding systems and ecological conditions is now needed. Studies of acoustic recognition cues dominate avian literature; other types of cues (e.g. visual, olfactory) deserve further attention. The effect of gender on avian sibling recognition has yet to be investigated; mammalian work shows that gender can have important influences. Most importantly, many researchers assume that birds recognise siblings through ‘direct familiarisation’ (commonly known as associative learning or familiarity); future experiments should also incorporate tests for ‘indirect familiarisation’ (commonly known as phenotype matching). If direct familiarisation proves crucial, avian research should investigate how periods of separation influence sibling discrimination. Mammalian researchers typically interpret sibling recognition in broad functional terms (nepotism, optimal outbreeding); some avian researchers more successfully identify specific and testable adaptive explanations, with greater relevance to natural contexts. We end by reporting exciting discoveries from recent studies of avian sibling recognition that inspire further interest in this topic

Delivery of stable ultra-thin liquid sheets in vacuum for biochemical spectroscopy

The development of ultra-thin flat liquid sheets capable of running in vacuum has provided an exciting new target for X-ray absorption spectroscopy in the liquid and solution phases. Several methods have become available for delivering in-vacuum sheet jets using different nozzle designs. We compare the sheets produced by two different types of nozzle; a commercially available borosillicate glass chip using microfluidic channels to deliver colliding jets, and an in-house fabricated fan spray nozzle which compresses the liquid on an axis out of a slit to achieve collision conditions. We find in our tests that both nozzles are suitable for use in X-ray absorption spectroscopy with the fan spray nozzle producing thicker but more stable jets than the commercial nozzle. We also provide practical details of how to run these nozzles in vacuum

Platelet activation in cystic fibrosis

Cystic fibrosis (CF) is caused by a mutation of the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR). We examined platelet function in CF patients because lung inflammation is part of this disease and platelets contribute to inflammation. CF patients had increased circulating leukocyte-platelet aggregates and increased platelet responsiveness to agonists compared with healthy controls. CF plasma caused activation of normal and CF platelets; however, activation was greater in CF platelets. Furthermore, washed CF platelets also showed increased reactivity to agonists. CF platelet hyperreactivity was incompletely inhibited by prostaglandin E(1) (PGE(1)). As demonstrated by Western blotting and reverse-transcriptase-polymerase chain reaction (RT-PCR), there was neither CFTR nor CFTR-specific mRNA in normal platelets. There were abnormalities in the fatty acid composition of membrane fractions of CF platelets. In summary, CF patients have an increase in circulating activated platelets and platelet reactivity, as determined by monocyte-platelet aggregation, neutrophil-platelet aggregation, and platelet surface P-selectin. This increased platelet activation in CF is the result of both a plasma factor(s) and an intrinsic platelet mechanism via cyclic adenosine monophosphate (cAMP)/adenylate cyclase, but not via platelet CFTR. Our findings may account, at least in part, for the beneficial effects of ibuprofen in CF

Looking for pulsations in HgMn stars through CoRoT lightcurves

HgMn Chemically Peculiar stars are among the quietest stars of the main-sequence. However, according to theoretical predictions, these stars could have pulsations related to the very strong overabundances of iron peak elements, which are produced by atomic diffusion in upper layers. Such pulsations have never been detected from ground based observations. Our aim is to search for signatures of pulsations in HgMn stars using the high quality lightcurves provided by the CoRoT satellite. We identified three faint stars (V>12), from VLT-GIRAFFE multiobject spectrograph survey in a field which was planned for observation by CoRoT. They present the typical characteristics of HgMn stars. They were observed by the CoRoT satellite during the long run (131 days) which started from the 24th of October 2007, with the exoplanets CCD's (Additional Programme). In the present work, we present the analysis of the ground based spectra of these three stars and the analysis of the corresponding CoRoT lightcurves. Two of these three HgMn candidates show low amplitude (less than 1.6 mmag) periodic variations (4.3 and 2.53 days respectively, with harmonics) which are compatible with periods predicted by theoretical models.Comment: Accepted paper in A&A (7 May 2009

CubeNet: Equivariance to 3D Rotation and Translation

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.Comment: Preprin