Near-infrared transmission spectrum of the warm-uranus GJ 3470b with the Wide Field Camera-3 on the Hubble Space Telescope

The atmospheric composition of low-mass exoplanets is the object of intense observational and theoretical investigations. GJ3470b is a warm uranus recently detected in transit across a bright late-type star. The transit of this planet has already been observed in several band passes from the ground and space, allowing observers to draw an intriguing yet incomplete transmission spectrum of the planet atmospheric limb. In particular, published data in the visible suggest the existence of a Rayleigh scattering slope, making GJ3470b a unique case among the known neptunes, while data obtained beyond 2 um are consistent with a flat infrared spectrum. The unexplored near-infrared spectral region between 1 and 2 um, is thus key to undertanding the atmospheric nature of GJ3470b. Here, we report on the first space-borne spectrum of GJ3470, obtained during one transit of the planet with WFC3 on board HST, operated in stare mode. The spectrum covers the 1.1--1.7-um region with a resolution of about 300. We retrieve the transmission spectrum of GJ3470b with a chromatic planet-to-star radius ratio precision of 0.15% (about one scale height) per 40-nm bins. At this precision, the spectrum appears featureless, in good agreement with ground-based and Spitzer infrared data at longer wavelengths, pointing to a flat transmission spectrum from 1 to 5 um. We present new simulations of transmission spectra for GJ3470b, which allow us to show that the HST/WFC3 observations rule out cloudless hydrogen-rich atmospheres (>10 sigma) as well as hydrogen-rich atmospheres with tholin haze (>5 sigma). Adding our near-infrared measurements to the full set of previously published data from 0.3 to 5 um, we find that a cloudy, hydrogen-rich atmosphere can explain the full transmission spectrum if, at the terminator, the clouds are located at low pressures (<1 mbar) or the water mixing ratio is extremely low (<1 ppm).Comment: Astronomy & Astrophysics, in press. 19 figures. 2 table

Risky business: managing electronic payments in the 21st Century

On June 20 and 21, 2005, the Payment Cards Center of the Federal Reserve Bank of Philadelphia, in conjunction with the Electronic Funds Transfer Association (EFTA), hosted a day-and-a-half forum, “Risky Business: Managing Electronic Payments in the 21st Century.” The Center and EFTA invited participants from the financial services and processing sectors, law enforcement, academia, and policymakers to explore key topics associated with the challenge of effectively managing risk in a payments environment that is increasingly electronic. The meeting’s goal was to identify areas of potential risk and explore interindustry solutions. This paper provides highlights from the forum presentations and ensuing conversations.

Application of a novel tool for diagnosing bile acid diarrhoea

Bile acid diarrhoea (BAD) is a common disease that requires expensive imaging to diagnose. We have tested the efficacy of a new method to identify BAD, based on the detection of differences in volatile organic compounds (VOC) in urine headspace of BAD vs. ulcerative colitis and healthy controls. A total of 110 patients were recruited; 23 with BAD, 42 with ulcerative colitis (UC) and 45 controls. Patients with BAD also received standard imaging (Se75HCAT) for confirmation. Urine samples were collected and the headspace analysed using an AlphaMOS Fox 4000 electronic nose in combination with an Owlstone Lonestar Field Asymmetric Ion Mobility Spectrometer (FAIMS). A subset was also tested by gas chromatography, mass spectrometry (GCMS). Linear Discriminant Analysis (LDA) was used to explore both the electronic nose and FAIMS data. LDA showed statistical differences between the groups, with reclassification success rates (using an n-1 approach) at typically 83%. GCMS experiments confirmed these results and showed that patients with BAD had two chemical compounds, 2-propanol and acetamide, that were either not present or were in much reduced quantities in the ulcerative colitis and control samples. We believe that this work may lead to a new tool to diagnose BAD, which is cheaper, quicker and easier that current methods

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page

The Results of MINOS and the Future with MINOS+

The MINOS experiment took data from 2005 up until 2012. This was superseded by MINOS+, the continuation of the two-detector, on-axis, long-baseline experiment based at Fermilab, and at the Soudan Underground Laboratory in northern Minnesota. By searching for the deficit of muon neutrinos at the Far Detector, MINOS/MINOS+ is sensitive to the atmospheric neutrino oscillation parameters $\Delta m^{2}_{32}$ and $\theta_{23}$. By using the full MINOS data set looking at both \numu disappearance and \nue appearance in both neutrino and anti-neutrino configurations at the NuMI beam along with atmospheric neutrino data recorded at the FD, MINOS has made the most precise measurement of $\Delta m^{2}_{32}$. Using a full three-flavour framework and searching for \nue appearance MINOS/MINOS+ gains sensitivity to $\theta_{13}$, the mass hierarchy and the octant of $\theta_{23}$. Exotic phenomenon is also explored with the MINOS detectors looking for non-standard interactions and sterile neutrinos. The current MINOS+ era goals are to build on the previous MINOS results improving the precision on the three-flavour oscillation parameter measurements and strengthening the constraints placed on the sterile neutrino parameter space.Comment: Review for Advances in High Energy Physics. The special issue for which the paper is being processed is "Neutrino Masses and Oscillations 2015

(Machine) Learning to Do More with Less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in JHEP, discussion adde