Signatures of Coronal Heating Mechanisms

Alfven waves created by sub-photospheric motions or by magnetic reconnection in the low solar atmosphere seem good candidates for coronal heating. However, the corona is also likely to be heated more directly by magnetic reconnection, with dissipation taking place in current sheets. Distinguishing observationally between these two heating mechanisms is an extremely difficult task. We perform 1.5-dimensional MHD simulations of a coronal loop subject to each type of heating and derive observational quantities that may allow these to be differentiated.Comment: To appear in "Magnetic Coupling between the Interior and the Atmosphere of the Sun", eds. S.S. Hasan and R.J. Rutten, Astrophysics and Space Science Proceedings, Springer-Verlag, Heidelberg, Berlin, 200

Guided Unfoldings for Finding Loops in Standard Term Rewriting

In this paper, we reconsider the unfolding-based technique that we have introduced previously for detecting loops in standard term rewriting. We improve it by guiding the unfolding process, using distinguished positions in the rewrite rules. This results in a depth-first computation of the unfoldings, whereas the original technique was breadth-first. We have implemented this new approach in our tool NTI and compared it to the previous one on a bunch of rewrite systems. The results we get are promising (better times, more successful proofs).Comment: Pre-proceedings paper presented at the 28th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2018), Frankfurt am Main, Germany, 4-6 September 2018 (arXiv:1808.03326

Porcine In Vivo Validation of a Virtual Contrast Model: The Influence of Contrast Agent Properties and Vessel Flow Rates

BACKGROUND AND PURPOSE: Accurately and efficiently modeling the transport of angiographic contrast currently offers the best method of verifying computational fluid dynamics simulations and, with it, progress toward the lofty goal of prediction of aneurysm treatment outcome a priori. This study specifically examines the influence of estimated flow rate and contrast properties on such in silico predictions of aneurysm contrast residence and decay. MATERIALS AND METHODS: Four experimental sidewall aneurysms were created in swine, with aneurysm contrast flow patterns and decay rates observed under angiography. A simplified computational fluid dynamics model of the experimental aneurysm was constructed from 3D angiography and contrast residence predicted a priori. The relative influence of a number of estimated model parameters (contrast viscosity, contrast density, and blood flow rate) on contrast residence was then investigated with further simulations. RESULTS: Contrast infiltration and washout pattern were accurately predicted by the a priori computational fluid dynamics model; however, the contrast decay rate was underestimated by ∼25%. This error was attributed to the estimated parent vessel flow rate alone, and the effects of contrast viscosity and density on the decay rate were found to be inconsequential. A linear correlation between the parent vessel flow rate and the corresponding contrast decay rate was observed. CONCLUSIONS: In experimental sidewall aneurysms, contrast fluid properties (viscosity and density) were shown to have a negligible effect on variation in the modeled contrast decay rate. A strong linear correlation was observed between parent vessel flow rate and contrast decay over a physiologically reasonable range of flow rates

SALIC: Social Active Learning for Image Classification

In this paper, we present SALIC, an active learning method for selecting the most appropriate user tagged images to expand the training set of a binary classifier. The process of active learning can be fully automated in this social context by replacing the human oracle with the images' tags. However, their noisy nature adds further complexity to the sample selection process since, apart from the images' informativeness (i.e., how much they are expected to inform the classifier if we knew their label), our confidence about their actual label should also be maximized (i.e., how certain the oracle is on the images' true contents). The main contribution of this work is in proposing a probabilistic approach for jointly maximizing the two aforementioned quantities. In the examined noisy context, the oracle's confidence is necessary to provide a contextual-based indication of the images' true contents, while the samples' informativeness is required to reduce the computational complexity and minimize the mistakes of the unreliable oracle. To prove this, first, we show that SALIC allows us to select training data as effectively as typical active learning, without the cost of manual annotation. Finally, we argue that the speed-up achieved when learning actively in this social context (where labels can be obtained without the cost of human annotation) is necessary to cope with the continuously growing requirements of large-scale applications. In this respect, we demonstrate that SALIC requires ten times less training data in order to reach the same performance as a straightforward informativeness-agnostic learning approach

Real-time 3D human tracking for mobile robots with multisensors

© 2017 IEEE. Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness

Media coverage and public understanding of sentencing policy in relation to crimes against children

This research examines how the media report on sentences given to those who commit serious crimes against children and how this impacts on public knowledge and attitudes. Three months of press and television coverage were analysed in order to establish the editorial lines that are taken in different sections of the media and how they are promoted by selective reporting of sentencing. Results indicate that a small number of very high profile crimes account for a significant proportion of reporting in this area and often, particularly in the tabloid press, important information regarding sentencing rationale is sidelined in favour of moral condemnation and criticism of the judiciary. Polling data indicate that public attitudes are highly critical of sentencing but also confused about the meaning of tariffs. The article concludes by discussing what can be done to promote a more informed public debate over penal policy in this area

Environment-adaptive interaction primitives for human-robot motor skill learning

© 2016 IEEE. In complex environments where robots are expected to co-operate with human partners, it is vital for the robot to consider properties of their collaborative activity in addition to the behavior of its partner. In this paper, we propose to learn such complex interactive skills by observing the demonstrations of a human-robot team with additional external attributes. We propose Environment-adaptive Interaction Primitives (EalPs) as an extension of Interaction Primitives. In cooperation tasks between human and robot with different environmental conditions, EalPs not only improve the predicted motor skills of robot within a brief observed human motion, but also obtain the generalization ability to adapt to new environmental conditions by learning the relationships between each condition and the corresponding motor skills from training samples. Our method is validated in the collaborative task of covering objects by plastic bag with a humanoid Baxter robot. To achieve the task successfully, the robot needs to coordinate itself to its partner while also considering information about the object to be covered

Environment-adaptive interaction primitives through visual context for human–robot motor skill learning

© 2018, The Author(s). In situations where robots need to closely co-operate with human partners, consideration of the task combined with partner observation maintains robustness when partner behavior is erratic or ambiguous. This paper documents our approach to capture human–robot interactive skills by combining their demonstrative data with additional environmental parameters automatically derived from observation of task context without the need for heuristic assignment, as an extension to overcome shortcomings of the interaction primitives framework. These parameters reduce the partner observation period required before suitable robot motion can commence, while also enabling success in cases where partner observation alone was inadequate for planning actions suited to the task. Validation in a collaborative object covering exercise with a humanoid robot demonstrate the robustness of our environment-adaptive interaction primitives, when augmented with parameters directly drawn from visual data of the task scene