A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images. Therefore, it is necessary to deploy an on-orbit semantic segmentation model to filter out useless images before data transmission. In this paper, we present a detailed comparison on the recent deep learning models. Considering the computing environment of satellites, we compare methods from accuracy, parameters and resource consumption on the same public dataset. And we also analyze the relation between them. Based on experimental results, we further propose a viable on-orbit semantic segmentation strategy. It will be deployed on the TianZhi-2 satellite which supports deep learning methods and will be lunched soon.Comment: 8 pages, 3 figures, ICNC-FSKD 201

Diverse Diversions: Youth Justice Reform, Localized Practices, and a ‘New Interventionist Diversion’?

The recent resurgence of practices aimed at ‘diverting’ young people from prosecution appears to suggest a sea change from the interventionism which characterized New Labour’s approach to young law-breakers. Drawing on interviews with youth justice practitioners at two sites in England, we argue this is overly simplistic, since the ‘interventionist diversion’ they describe reflects the continued influence of New Labour reforms, as well as older approaches. We conclude that more empirical research is needed to establish where such interventions sit within the broader – and increasingly localized – landscape of support provision, as well as the consequences of providing ‘welfare’ in this way

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

SSH adequacy to preimplantation mammalian development: Scarce specific transcripts cloning despite irregular normalisation

BACKGROUND: SSH has emerged as a widely used technology to identify genes that are differentially regulated between two biological situations. Because it includes a normalisation step, it is used for preference to clone low abundance differentially expressed transcripts. It does not require previous sequence knowledge and may start from PCR amplified cDNAs. It is thus particularly well suited to biological situations where specific genes are expressed and tiny amounts of RNA are available. This is the case during early mammalian embryo development. In this field, few differentially expressed genes have been characterized from SSH libraries, but an overall assessment of the quality of SSH libraries is still required. Because we are interested in the more systematic establishment of SSH libraries from early embryos, we have developed a simple and reliable strategy based on reporter transcript follow-up to check SSH library quality and repeatability when starting with small amounts of RNA. RESULTS: Four independent subtracted libraries were constructed. They aimed to analyze key events in the preimplantation development of rabbit and bovine embryos. The performance of the SSH procedure was assessed through the large-scale screening of thousands of clones from each library for exogenous reporter transcripts mimicking either tester specific or tester/driver common transcripts. Our results show that abundant transcripts escape normalisation which is only efficient for rare and moderately abundant transcripts. Sequencing 1600 clones from one of the libraries confirmed and extended our results to endogenous transcripts and demonstrated that some very abundant transcripts common to tester and driver escaped subtraction. Nonetheless, the four libraries were greatly enriched in clones encoding for very rare (0.0005% of mRNAs) tester-specific transcripts. CONCLUSION: The close agreement between our hybridization and sequencing results shows that the addition and follow-up of exogenous reporter transcripts provides an easy and reliable means to check SSH performance. Despite some cases of irregular normalisation and subtraction failure, we have shown that SSH repeatedly enriches the libraries in very rare, tester-specific transcripts, and can thus be considered as a powerful tool to investigate situations where small amounts of biological material are available, such as during early mammalian development

Local Volatility Calibration by Optimal Transport

The calibration of volatility models from observable option prices is a fundamental problem in quantitative finance. The most common approach among industry practitioners is based on the celebrated Dupire's formula [6], which requires the knowledge of vanilla option prices for a continuum of strikes and maturities that can only be obtained via some form of price interpolation. In this paper, we propose a new local volatility calibration technique using the theory of optimal transport. We formulate a time continuous martingale optimal transport problem, which seeks a martingale diffusion process that matches the known densities of an asset price at two different dates, while minimizing a chosen cost function. Inspired by the seminal work of Benamou and Brenier [1], we formulate the problem as a convex optimization problem, derive its dual formulation, and solve it numerically via an augmented Lagrangian method and the alternative direction method of multipliers (ADMM) algorithm. The solution effectively reconstructs the dynamic of the asset price between the two dates by recovering the optimal local volatility function, without requiring any time interpolation of the option prices

Degradation of metaldehyde in water by nanoparticle catalysts and powdered activated carbon

Metaldehyde, an organic pesticide widely used in the UK, has been detected in drinking water in the UK with a low concentration (<1 μg L−1) which is still above the European and UK standard requirements. This paper investigates the efficiency of four materials: powdered activated carbon (PAC) and carbon-doped titanium dioxide nanocatalyst with different concentrations of carbon (C-1.5, C-40, and C-80) for metaldehyde removal from aqueous solutions by adsorption and oxidation via photocatalysis. PAC was found to be the most effective material which showed almost over 90% removal. Adsorption data were well fitted to the Langmuir isotherm model, giving a qm (maximum/saturation adsorption capacity) value of 32.258 mg g−1 and a KL (Langmuir constant) value of 2.013 L mg−1. In terms of kinetic study, adsorption of metaldehyde by PAC fitted well with a pseudo-second-order equation, giving the adsorption rate constant k2 value of 0.023 g mg−1 min−1, implying rapid adsorption. The nanocatalysts were much less effective in oxidising metaldehyde than PAC with the same metaldehyde concentration and 0.2 g L−1 loading concentration of materials under UV light; the maximum removal achieved by carbon-doped titanium dioxide (C-1.5) nanocatalyst was around 15% for a 7.5 ppm metaldehyde solution

Supermassive black holes do not correlate with dark matter halos of galaxies

Supermassive black holes have been detected in all galaxies that contain bulge components when the galaxies observed were close enough so that the searches were feasible. Together with the observation that bigger black holes live in bigger bulges, this has led to the belief that black hole growth and bulge formation regulate each other. That is, black holes and bulges "coevolve". Therefore, reports of a similar correlation between black holes and the dark matter halos in which visible galaxies are embedded have profound implications. Dark matter is likely to be nonbaryonic, so these reports suggest that unknown, exotic physics controls black hole growth. Here we show - based in part on recent measurements of bulgeless galaxies - that there is almost no correlation between dark matter and parameters that measure black holes unless the galaxy also contains a bulge. We conclude that black holes do not correlate directly with dark matter. They do not correlate with galaxy disks, either. Therefore black holes coevolve only with bulges. This simplifies the puzzle of their coevolution by focusing attention on purely baryonic processes in the galaxy mergers that make bulges.Comment: 12 pages, 9 Postscript figures, 1 table; published in Nature (20 January 2011

A predicted astrometric microlensing event by a nearby white dwarf

We used the Tycho-Gaia Astrometric Solution catalogue, part of the Gaia Data Release 1, to search for candidate astrometric microlensing events expected to occur within the remaining lifetime of the Gaia satellite. Our search yielded one promising candidate. We predict that the nearby DQ type white dwarf LAWD 37 (WD 1142-645) will lens a background star and will reach closest approach on November 11th 2019 ($\pm$ 4 days) with impact parameter $380\pm10$ mas. This will produce an apparent maximum deviation of the source position of $2.8\pm0.1$ mas. In the most propitious circumstance, Gaia will be able to determine the mass of LAWD 37 to $\sim3\%$. This mass determination will provide an independent check on atmospheric models of white dwarfs with helium rich atmospheres, as well as tests of white dwarf mass radius relationships and evolutionary theory

Random qubit-states and how best to measure them

We consider the problem of measuring a single qubit, known to have been prepared in either a randomly selected pure state or a randomly selected real pure state. We seek the measurements that provide either the best estimate of the state prepared or maximise the accessible information. Surprisingly, any sensible measurement turns out to be optimal. We discuss the application of these ideas to multiple qubits and higher-dimensional systems

An Edgeworth expansion for finite population L-statistics

In this paper, we consider the one-term Edgeworth expansion for finite population L-statistics. We provide an explicit formula for the Edgeworth correction term and give sufficient conditions for the validity of the expansion which are expressed in terms of the weight function that defines the statistics and moment conditions.Comment: 14 pages. Minor revisions. Some explanatory comments and a numerical example were added. Lith. Math. J. (to appear