FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Norwegian Armed Forces Personnel Recovery Network

In 2014, the Royal Norwegian Air Force (RNoAF) became the Executive Agent Office of Primary Responsibility (EA OPR) for Personnel Recovery (PR) for the Norwegian Armed Forces. This capstone project is sponsored by the commander RNoAF PR and SERE School to support the development of a whole-of-department approach to PR by answering this question: How can we design a Personnel Recovery (PR) system for the Norwegian Armed Forces that enables Norwegian commanders and staffs, forces and isolated personnel to collaborate and operate in a combined joint PR mission environment? This capstone project explores PR for the Norwegian Armed Forces through an inquiry of design and design thinking. As an initiation of the design process, the capstone begins the discovery phase with an examination of archival records centered on PR from WWII to the present and in-depth discussions with national and international PR subject-matter experts. This capstone describes the results from the design thinking process, its prototypes, and recommendations to the RNoAF. In brief, the capstone project recommends that the Norwegian Armed Forces, with the RNoAF as the EA OPR for PR, develop a network organization that coordinates the main actors in the PR system into well-functioning communities of practice.http://archive.org/details/norwegiarmedforc1094551672Major, Royal Norwegian Air ForceApproved for public release; distribution is unlimited

Connectionism, Learning and Linguistics Structure

Institute for Communicating and Collaborative SystemsThis thesis presents a connectionist theory of how infinite languages may fit within finite minds.Arguments are presented against the distinction between linguistic competence and observable language performance.It is suggested that certain kinds of finite state automata, i.e recurrent neural networks are likely to have suffcient computational power,and the necessary generalization capability,to serve as models for the procesing and acquisition of linguistic structure

The long road of statistical learning research: past, present and future

Published 21 November 2016 http://rstb.royalsocietypublishing.org/content/372/1711/20160047http://rstb.royalsocietypublishing.org/content/372/1711/20160047This paper was supported by the Israel Science Foundation (grant no. 217/14 awarded to R.F.), by the National Institute of Child Health and Human Development (RO1 HD 067364 awarded to Ken Pugh and R.F., PO1-HD 01994 awarded to Haskins Laboratories) and by the European Research Council (project ERC-ADG- 692502 awarded to R.F.)

Towards a theory of individual differences in statistical learning

Published 21 November 2016http://rstb.royalsocietypublishing.org/content/372/1711/20160059In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.This article was supported by the Israel Science Foundation (Grant No. 217/14, awarded to R.F.), by the National Institute of Child Health and Human Development (Grant Nos. RO1 HD 067364, awarded to Ken Pugh and R.F., and PO1-HD 01994, awarded to Haskins Laboratories), and by the ERC (project 692502, awarded to R.F.). L.B. is a research fellow of the Fyssen Foundation