ISICSoo: a class for the calculation of ionization cross sections from ECPSSR and PWBA theory

ISICS, originally a C language program for calculating K-, L- and M-shell ionization and X-ray production cross sections from ECPSSR and PWBA theory, has been reengineered into a C++ language class, named ISICSoo. The new software design enables the use of ISICS functionality in other software systems. The code, originally developed for Microsoft Windows operating systems, has been ported to Linux and Mac OS platforms to facilitate its use in a wider scientific environment. The reengineered software also includes some fixes to the original implementation, which ensure more robust computational results and a review of some physics parameters used in the computation. The paper describes the software design and the modifications to the implementation with respect to the previous version; it also documents the test process and provides some indications about the software performance.Comment: Preprint submitted to Computer Physics Communication

Nuclear burst plasma injection into the magnetosphere and resulting spacecraft charging

The passage of debris from a high altitude ( 400 km) nuclear burst over the ionospheric plasma is found to be capable of exciting large amplitude whistler waves which can act to structure a collisionless shock. This instability will occur in the loss cone exits of the nuclear debris bubble, and the accelerated ambient ions will freestream along the magnetic field lines into the magnetosphere. Using Starfish-like parameters and accounting for plasma diffusion and thermalization of the propagating plasma mass, it is found that synchronous orbit plasma fluxes of high temperature electrons (near 10 keV) will be significantly greater than those encountered during magnetospheric substorms. These fluxes will last for sufficiently long periods of time so as to charge immersed bodies to high potentials and arc discharges to take place

Effect of PPARγ Inhibition during Pregnancy on Posterior Cerebral Artery Function and Structure

Peroxisome proliferator-activated receptor-γ (PPARγ), a ligand-activated transcription factor, has protective roles in the cerebral circulation and is highly activated during pregnancy. Thus, we hypothesized that PPARγ is involved in the adaptation of cerebral vasculature to pregnancy. Non-pregnant (NP) and late-pregnant (LP) rats were treated with a specific PPARγ inhibitor GW9662 (10 ]mg/kg/day, in food) or vehicle for 10 days and vascular function and structural remodeling were determined in isolated and pressurized posterior cerebral arteries (PCA). Expression of PPARγ and angiotensin type 1 receptor (AT1R) in cerebral (pial) vessels was determined by real-time RT-PCR. PPARγ inhibition decreased blood pressure and increased blood glucose in NP rats, but not in LP rats. PPARγ inhibition reduced dilation to acetylcholine and sodium nitroprusside in PCA from NP (p < 0.05 vs. LP-GW), but not LP rats. PPARγ inhibition tended to increase basal tone and myogenic activity in PCA from NP rats, but not LP rats. Structurally, PPARγ inhibition increased wall thickness in PCA from both NP and LP rats (p < 0.05), but increased distensibility only in PCA from NP rats. Pregnancy decreased expression of PPARγ and AT1R (p < 0.05) in cerebral arteries that was not affected by GW9662 treatment. These results suggest that PPARγ inhibition had significant effects on the function and structure of PCA in the NP state, but appeared to have less influence during pregnancy. Down-regulation of PPARγ and AT1R in cerebral arteries may be responsible for the lack of effect of PPARγ in cerebral vasculature and may be part of the vascular adaptation to pregnancy

X-MAN: Explaining multiple sources of anomalies in video

Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions

Real-time factored ConvNets: Extracting the x factor in human parsing

© 2017. The copyright of this document resides with its authors. We propose a real-time and lightweight multi-task style ConvNet (termed a Factored ConvNet) for human body parsing in images or video. Factored ConvNets have isolated areas which perform known sub-tasks, such as object localization or edge detection. We call this area and sub-task pair an X factor. Unlike multi-task ConvNets which have independent tasks, the Factored ConvNet’s sub-task has direct effect on the main task outcome. In this paper we show how to isolate the X factor of foreground/background (f/b) subtraction from the main task of segmenting human body images into 31 different body part types. Knowledge of this X factor leads to a number of benefits for the Factored ConvNet: 1) Ease of network transfer to other image domains, 2) ability to personalize to humans in video and 3) easy model performance boosts. All achieved by either efficient network update or replacement of the X factor whilst avoiding catastrophic forgetting of previously learnt body part dependencies and structure. We show these benefits on a large dataset of images and also on YouTube videos.SeeQuesto

Scaling digital screen reading with one-shot learning and re-identification

Using only a mobile phone app, our objective is to cheaply retro-fit digital meters (e.g blood pressure, blood glucose or industrial gauges) with 'smart' data transfer capabilities. Using the mobile phone camera we build an app to securely and accurately transcribe information from digital meter screens. Only a single labelled training image of a target meter is required to build a custom screen reading module. Here we show how this can scale to potentially hundreds of different meters by learning to recognising the meter type so that the reading module can be automatically selected. This makes the system very easy for a user who would need to scan multiple different meter types. To this end, we build a CNN based system which runs in real-time on mobile device with very high read accuracy and meter recognition. Our contributions include (i) a method of one-shot training by synthesis through domain shift reduction, (ii) a deep embedding network for scale, translation and rotation invariant re-identification of digital meters, (iii) a highly accurate and efficient mobile phone app for recognising and parsing digital meter screens and (iv) release of a new digital meter re-identification dataset

Real-time analogue gauge transcription on mobile phone

The objective of this paper is to automatically read any circular single pointer analogue gauge in real-time on mobile phone. We make the following contributions: (i) we show how to efficiently and accurately read gauges on mobile phones using a convolutional neural network (CNN) system which accepts both a high and low resolution gauge image; (ii) we introduce a large synthetic image dataset (far superior in size to prior works) with ground truth gauge readings, pointer layout and scale face homographies that is suitable for training a CNN for real world application; (iii) we also release a new real world analogue gauge dataset (larger meter variation than any previous) with annotation suitable for testing three different types of tasks and finally (iv) we beat state of the art performance for gauge reading on this dataset and an existing public dataset in multiple metrics by large margins, notably with pointer angle error less than 1 degree. Our method is fast and lightweight and runs up to 25fps on mobile devices

Generalised epipolar constraints

The frontier of a curved surface is the envelope of contour generators showing the boundary, at least locally, of the visible region swept out under viewer motion. In general, the outlines of curved surfaces (apparent contours) from different viewpoints are generated by different contour generators on the surface and hence do not provide a constraint on viewer motion. Frontier points, however, have projections which correspond to a real point on the surface and can be used to constrain viewer motion by the epipolar constraint. We show how to recover viewer motion from frontier points and generalise the ordinary epipolar constraint to deal with points, curves and apparent contours of surfaces. This is done for both continuous and discrete motion, known or unknown orientation, calibrated and uncalibrated, perspective, weak perspective and orthographic cameras. Results of an iterative scheme to recover the epipolar line structure from real image sequences using only the outlines of curved surfaces, is presented. A statistical evaluation is performed to estimate the stability of the solution. It is also shown how the full motion of the camera from a sequence of images can be obtained from the relative motion between image pairs