A strong redshift dependence of the broad absorption line quasar fraction

We describe the application of non-negative matrix factorisation to generate compact reconstructions of quasar spectra from the Sloan Digital Sky Survey (SDSS), with particular reference to broad absorption line quasars (BALQSOs). BAL properties are measured for SiIV lambda1400, CIV lambda1550, AlIII lambda1860 and MgII lambda2800, resulting in a catalogue of 3547 BALQSOs. Two corrections, based on extensive testing of synthetic BALQSO spectra, are applied in order to estimate the intrinsic fraction of CIV BALQSOs. First, the probability of an observed BALQSO spectrum being identified as such by our algorithm is calculated as a function of redshift, signal-to-noise ratio and BAL properties. Second, the different completenesses of the SDSS target selection algorithm for BALQSOs and non-BAL quasars are quantified. Accounting for these selection effects the intrinsic CIV BALQSO fraction is 41+/-5 per cent. Our analysis of the selection effects allows us to measure the dependence of the intrinsic CIV BALQSO fraction on luminosity and redshift. We find a factor of 3.5+/-0.4 decrease in the intrinsic fraction from the highest redshifts, z~4.0, down to z~2.0. The redshift dependence implies that an orientation effect alone is not sufficient to explain the presence of BAL troughs in some but not all quasar spectra. Our results are consistent with the intrinsic BALQSO fraction having no strong luminosity dependence, although with 3-sigma limits on the rate of change of the intrinsic fraction with luminosity of -6.9 and 7.0 per cent dex^-1 we are unable to rule out such a dependence.Comment: MNRAS in press; 28 pages, 28 figures; full data table is available until Sep 2011 at www.ast.cam.ac.uk/~jta/papers/bal_nmf_table1.da

Local and Global Information in Obstacle Detection on Railway Tracks

Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do not have enough classes to account for all possible scenarios, and datasets featuring objects on railways are challenging to obtain. We propose utilizing a shallow network to learn railway segmentation from normal railway images. The limited receptive field of the network prevents overconfident predictions and allows the network to focus on the locally very distinct and repetitive patterns of the railway environment. Additionally, we explore the controlled inclusion of global information by learning to hallucinate obstacle-free images. We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles. Our proposed method outperforms other learning-based baseline methods

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection

Automatic registration of 3D models to laparoscopic video images for guidance during liver surgery

Laparoscopic liver interventions offer significant advantages over open surgery, such as less pain and trauma, and shorter recovery time for the patient. However, they also bring challenges for the surgeons such as the lack of tactile feedback, limited field of view and occluded anatomy. Augmented reality (AR) can potentially help during laparoscopic liver interventions by displaying sub-surface structures (such as tumours or vasculature). The initial registration between the 3D model extracted from the CT scan and the laparoscopic video feed is essential for an AR system which should be efficient, robust, intuitive to use and with minimal disruption to the surgical procedure. Several challenges of registration methods in laparoscopic interventions include the deformation of the liver due to gas insufflation in the abdomen, partial visibility of the organ and lack of prominent geometrical or texture-wise landmarks. These challenges are discussed in detail and an overview of the state of the art is provided. This research project aims to provide the tools to move towards a completely automatic registration. Firstly, the importance of pre-operative planning is discussed along with the characteristics of the liver that can be used in order to constrain a registration method. Secondly, maximising the amount of information obtained before the surgery, a semi-automatic surface based method is proposed to recover the initial rigid registration irrespective of the position of the shapes. Finally, a fully automatic 3D-2D rigid global registration is proposed which estimates a global alignment of the pre-operative 3D model using a single intra-operative image. Moving towards incorporating the different liver contours can help constrain the registration, especially for partial surfaces. Having a robust, efficient AR system which requires no manual interaction from the surgeon will aid in the translation of such approaches to the clinics

Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data

Context. The direct detection of exoplanets with high-contrast imaging requires advanced data processing methods to disentangle potential planetary signals from bright quasi-static speckles. Among them, angular differential imaging (ADI) permits potential planetary signals with a known rotation rate to be separated from instrumental speckles that are either statics or slowly variable. The method presented in this paper, called ANDROMEDA for ANgular Differential OptiMal Exoplanet Detection Algorithm is based on a maximum likelihood approach to ADI and is used to estimate the position and the flux of any point source present in the field of view. Aims. In order to optimize and experimentally validate this previously proposed method, we applied ANDROMEDA to real VLT/NaCo data. In addition to its pure detection capability, we investigated the possibility of defining simple and efficient criteria for automatic point source extraction able to support the processing of large surveys. Methods. To assess the performance of the method, we applied ANDROMEDA on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright stars and on which we added synthetic planets of known position and flux in the field. In order to accommodate the real data properties, it was necessary to develop additional pre-processing and post-processing steps to the initially proposed algorithm. We then investigated its skill in the challenging case of a well-known target, $\beta$ Pictoris, whose companion is close to the detection limit and we compared our results to those obtained by another method based on principal component analysis (PCA). Results. Application on VLT/NaCo data demonstrates the ability of ANDROMEDA to automatically detect and characterize point sources present in the image field. We end up with a robust method bringing consistent results with a sensitivity similar to the recently published algorithms, with only two parameters to be fine tuned. Moreover, the companion flux estimates are not biased by the algorithm parameters and do not require a posteriori corrections. Conclusions. ANDROMEDA is an attractive alternative to current standard image processing methods that can be readily applied to on-sky data

Studying bias in visual features through the lens of optimal transport

Computer vision systems are employed in a variety of high-impact applications. However, making them trustworthy requires methods for the detection of potential biases in their training data, before models learn to harm already disadvantaged groups in downstream applications. Image data are typically represented via extracted features, which can be hand-crafted or pre-trained neural network embeddings. In this work, we introduce a framework for bias discovery given such features that is based on optimal transport theory; it uses the (quadratic) Wasserstein distance to quantify disparity between the feature distributions of two demographic groups (e.g., women vs men). In this context, we show that the Kantorovich potentials of the images, which are a byproduct of computing the Wasserstein distance and act as “transportation prices", can serve as bias scores by indicating which images might exhibit distinct biased characteristics. We thus introduce a visual dataset exploration pipeline that helps auditors identify common characteristics across high- or low-scored images as potential sources of bias. We conduct a case study to identify prospective gender biases and demonstrate theoretically-derived properties with experiments on the CelebA and Biased MNIST datasets

Geophysical methods to detect tunnelling at a geological repository site : Applicability in safeguards

ABSTRACT Generating power with nuclear energy accumulates radioactive spent nuclear fuel, anticipated not to be diversified into any unknown purposes. Nuclear safeguards include bookkeeping of nuclear fuel inventories, frequent checking, and monitoring to confirm nuclear non-proliferation. Permanent isolation of radionuclides from biosphere by disposal challenges established practices, as opportunities for monitoring of individual fuel assemblies ceases. Different concepts for treatment and geological disposal of spent nuclear fuel exist. Spent nuclear fuel disposal facility is under construction in Olkiluoto in Southwest Finland. Posiva Oy has carried out multidisciplinary bedrock characterization of crystalline bedrock for siting and design of the facility. Site description involved compilation of geological models from investigations at surface level, from drillholes and from underground rock characterization facility ONKALO. Research focused on long term safety case (performance) of engineered and natural barriers in purpose to minimize risks of radionuclide release. Nuclear safeguards include several concepts. Containment and surveillance (C/S) are tracking presence of nuclear fuel through manufacturing, energy generation, cooling, transfer, and encapsulation. Continuity of knowledge (CoK) ensures traceability and non-diversion. Design information provided by the operator to the state and European Commission (Euratom), and further to IAEA describes spent nuclear fuel handling in the facility. Design information verification (DIV) using timely or unannounced inspections, provide credible assurance on absence of any ongoing undeclared activities within the disposal facility. Safeguards by design provide information applicable for the planning of safeguards measures, e.g., surveillance during operation of disposal facility. Probability of detection of an attempt to any undeclared intrusion into the repository containment needs to be high. Detection of such preparations after site closure would require long term monitoring or repeated geophysical measurements within or at proximity of the repository. Bedrock imaging (remote sensing, geophysical surveys) would serve for verifying declarations where applicable, or for characterization of surrounding rock mass to detect undeclared activities. ASTOR working group has considered ground penetrating radar (GPR) for DIV in underground constructed premises during operation. Seismic reflection survey and electrical or electromagnetic imaging may also apply. This report summarizes geophysical methods used in Olkiluoto, and some recent development, from which findings could be applied also for nuclear safeguards. In this report the geophysical source fields, involved physical properties, range of detection, resolution, survey geometries, and timing of measurements are reviewed for different survey methods. Useful interpretation of geophysical data may rely on comparison of results to declared repository layout, since independent understanding of the results may not be successful. Monitoring provided by an operator may enable alarm and localization of an undeclared activity in a cost-effective manner until closure of the site. Direct detection of constructed spaces, though possible, might require repeated effort, have difficulties to provide spatial coverage, and involve false positive alarms still requiring further inspection