An Account of the Loss of the Country Ship Forbes and Frazer Sinclair, Her Late Commander

This paper reports on the life of the English Country trader Captain Frazer Sinclair leading up to and following the loss of the Forbes in the Karimata Strait in 1806. It examines the adventure and tenuous times of trading around the Indonesian archipelago after the fall of the VOC and subsequent transfer to the British. Included are the details of Captain Sinclair\u27s trading history, multiple prizes as a privateer, and shipwrecks

A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201

Primary task event-related potentials related to different aspects of information processing

The results of two studies which investigated the relationships between cognitive processing and components of transient event-related potentials (ERPs) are presented in a task in which mental workload was manipulated. The task involved the monitoring of an array of discrete readouts for values that went out of bounds, and was somewhat analogous to tasks performed in cockpits. The ERPs elicited by the changing readouts varied with the number of readouts being monitored, the number of monitored readouts that were close to going out of bounds, and whether or not the change took a monitored readout out of bounds. Moreover, different regions of the waveform differentially reflected these effects. The results confirm the sensitivity of scalp-recorded ERPs to the cognitive processes affected by mental workload and suggest the possibility of extracting useful ERP indices of primary task performance in a wide range of man-machine settings

Towards health promotion indicators

This article examines the state of the art in health promotion indicator development over the past few years and presents five conclusions from this review, The authors put forward proposals for the development of health promotion indicators based on the question, "What research in health promotion will lead to appropriate indicators?”. The authors illustrate some areas of concern both for researchers and policymakers and suggest a number of indicators, appropriate to each of these groups, for important dimensions of health, health-related processes and health resources. The dialogue between health researchers and health decision-makers is growing and links are being made; it is important to continue this proces

GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data

Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is mostly due to architectural design choices, which were often adopted from the 2D image domain, where geometric context is rarely available. In 3D, however, considering the object properties and its surroundings in a holistic way is important to distinguish between true and false positive detections, e.g. occluded pedestrians in a group. To address this, we present GACE, an intuitive and highly efficient method to improve the confidence estimation of a given black-box 3D object detector. We aggregate geometric cues of detections and their spatial relationships, which enables us to properly assess their plausibility and consequently, improve the confidence estimation. This leads to consistent performance gains over a variety of state-of-the-art detectors. Across all evaluated detectors, GACE proves to be especially beneficial for the vulnerable road user classes, i.e. pedestrians and cyclists.Comment: ICCV 2023, code is available at https://github.com/dschinagl/gac