Selecting surface features for accurate multi-camera surface reconstruction

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector

Multi-frame scene-flow estimation using a patch model and smooth motion prior

This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth

Driver and Passenger Identification from Smartphone Data

The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics and Electronics (CAPE) agreement

Magnetic Stress at the Marginally Stable Orbit: Altered Disk Structure, Radiation, and Black Hole Spin Evolution

Magnetic connections to the plunging region can exert stresses on the inner edge of an accretion disk around a black hole. We recompute the relativistic corrections to the thin-disk dynamics equations when these stresses take the form of a time-steady torque on the inner edge of the disk. The additional dissipation associated with these stresses is concentrated relatively close outside the marginally stable orbit, scaling as r to the -7/2 at large radius. As a result of these additional stresses: spin-up of the central black hole is retarded; the maximum spin-equilibrium accretion efficiency is 36%, and occurs at a/M=0.94; the disk spectrum is extended toward higher frequencies; line profiles (such as Fe K-alpha) are broadened if the line emissivity scales with local flux; limb-brightening, especially at the higher frequencies, is enhanced; and the returning radiation fraction is substantially increased, up to 58%. This last effect creates possible explanations for both synchronized continuum fluctuations in AGN, and polarization rises shortward of the Lyman edge in quasars. We show that no matter what additional stresses occur, when a/M < 0.36, the second law of black hole dynamics sets an absolute upper bound on the accretion efficiency.Comment: 11 pages, 15 figures, accepted for publication in the Astrophysical Journa

Warwick-JLR driver monitoring dataset (DMD) : statistics and early findings

Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced

Data mining for vehicle telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height

Raising standards in American schools: the case of No Child Left Behind

In January 2002, President George W Bush signed into law what is arguably the most important piece of US educational legislation for the past 35 years. For the first time, Public Law 107-110 links high stakes testing with strict accountability measures designed to ensure that, at least in schools that receive government funding, no child is left behind. The appropriately named No Child Left Behind Act (NCLB) links government funding to strict improvement policies for America’s public schools. Much of what is undertaken in NCLB is praiseworthy, the Act is essentially equitable for it ensures that schools pay due regard to the progress of those sections of the school population who have traditionally done less well in school, in particular, students from economically disadvantaged homes, as well as those from ethnic minority backgrounds and those who have limited proficiency to speak English. However, this seemingly salutatory aspect of the Act is also the one that has raised the most objections. This paper describes the key features of this important piece of legislation before outlining why it is that a seemingly equitable Act has produced so much consternation in US education circles. Through an exploration of school level data for the state of New Jersey, the paper considers the extent to which these concerns have been justified during the early days of No Child Left Behind

Resolving the Hard X-ray Emission of GX 5-1 with INTEGRAL

We present the study of one year of INTEGRAL data on the neutron star low mass X-ray binary GX 5-1. Thanks to the excellent angular resolution and sensitivity of INTEGRAL, we are able to obtain a high quality spectrum of GX 5-1 from ~5 keV to ~100 keV, for the first time without contamination from the nearby black hole candidate GRS 1758-258 above 20 keV. During our observations, GX 5-1 is mostly found in the horizontal and normal branch of its hardness intensity diagram. A clear hard X-ray emission is observed above ~30 keV which exceeds the exponential cut-off spectrum expected from lower energies. This spectral flattening may have the same origin of the hard components observed in other Z sources as it shares the property of being characteristic to the horizontal branch. The hard excess is explained by introducing Compton up-scattering of soft photons from the neutron star surface due to a thin hot plasma expected in the boundary layer. The spectral changes of GX 5-1 downward along the "Z" pattern in the hardness intensity diagram can be well described in terms of monotonical decrease of the neutron star surface temperature. This may be a consequence of the gradual expansion of the boundary layer as the mass accretion rate increases.Comment: 10 pages, 17 figures, accepted for publication in A&

CHILLI : a data context-aware perturbation method for XAI

The trustworthiness of Machine Learning (ML) models can be difficult to assess, but is critical in high-risk or ethically sensitive applications. Many models are treated as a ‘black-box’ where the reasoning or criteria for a final decision is opaque to the user. To address this, some existing Explainable AI (XAI) approaches approximate model behaviour using perturbed data. However, such methods have been criticised for ignoring feature dependencies, with explanations being based on potentially unrealistic data. We propose a novel framework, CHILLI, for incorporating data context into XAI by generating contextually aware perturbations, which are faithful to the training data of the base model being explained. This is shown to improve both the soundness and accuracy of the explanations

Distributive Learning in Introductory Chemical Engineering: University Students' Learning, Motivation, and Attitudes Using a CD-ROM

This article reports a study in which student performance and approaches to study ina CD-ROM version of a chemical engineering course were examined. The study consists of three phases. The purpose of phase 1 was to evaluate of the efficacy of CD-ROM for this content and student population. Therefore, we compared the performance of students who participated in a traditional classroom offering with those who participated in the CD-ROM version. The results supported the soundness of the CD-ROM based instruction. In phase 2, we interviewed students who were successful and less successful in the course to examine any differences in the strategies they used for learning the content. Differences consistent with a surface versus deep approach to studying were found. Prior to the third phase, the CD-ROM and approaches to learning instrument were modified and then a new group of students was examined to determine the factors that contribute to success in the CD-ROM version. Results showed that deep cognitive engagement and motivation, defined in terms of goals and self-efficacy, were significant predictors of success uses two indices of course performance. The results suggest that although technology provides opportunities for learners to learn in increasingly independent environments, educators need to prepare students to learn independently using newer electronic technologies.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline