Scouting algorithms for field robots using triangular mesh maps

Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method

Multiple malignancies amongst cancer survivors in the Netherlands since 1989 - implications for surveillance

Early detection of cancer as well as advances in therapy and supportive care have resulted a prolonged survival period of time after cancer. In the Netherlands the 5-year relative survival for all types of cancers combined increased from 47% in 1989-1993 to 59% in 2004-2008. Once patients have survived long enough (i.e. 10 years) since diagnosis of their cancer, their life expectancy usually becomes almost the same as people without a cancer (conditional 5-year relative survival>95%). A Netherlands Cancer scenario Report expected the prevalence of second cancer patients to increase from 14,000 in 1985 to 24,000 (excl. skin cancer) in 2000 when assuming an average increase of duration of survival by 1% per annum. The Signallling report in 2004 from the Dutch Cancer Society estimated the prevalence of multiple malignancies (MMs) to reach around 100,000 cases in 2015 in the Netherlands due to a twofold number of cancer survivors since 2005. Since a second cancer diagnosis may impair survival and is likely to affect quality of life amongst cancer survivors we should be interested in prevention and early detection and its undoubtedly more complex treatment. MMs are defined as two or more primary cancers occurring in an individual that are neither an extension, nor a recurrence, nor a metastasis of the first tumor1

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.Comment: 10 pages, 7 figures, Appear in AAAI 202