Visual cues in estimation of part-to-whole comparison

Pie charts were first published in 1801 by William Playfair and have caused some controversy since. Despite the suggestions of many experts against their use, several empirical studies have shown that pie charts are at least as good as alternatives. From Brinton to Few on one side and Eells to Kosara on the other, there appears to have been a hundred-year war waged on the humble pie. In this paper a set of experiments are reported that compare the performance of pie charts and horizontal bar charts with various visual cues. Amazon's Mechanical Turk service was employed to perform the tasks of estimating segments in various part-to-whole charts. The results lead to recommendations for data visualization professionals in developing dashboards.Comment: Camera-ready version of final accepted paper for IEEE VIS 2019 Short Papers trac

Estimating Lower Limb Kinematics using a Lie Group Constrained EKF and a Reduced Wearable IMU Count

This paper presents an algorithm that makes novel use of a Lie group representation of position and orientation alongside a constrained extended Kalman filter (CEKF) to accurately estimate pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. The algorithm iterates through the prediction update (kinematic equation), measurement update (pelvis height, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The paper also describes a novel Lie group formulation of the assumptions implemented in the said measurement and constraint updates. Evaluation of the algorithm on nine healthy subjects who walked freely within a $4 \times 4$ m$^2$ room shows that the knee and hip joint angle root-mean-square errors (RMSEs) in the sagittal plane for free walking were $10.5 \pm 2.8^\circ$ and $9.7 \pm 3.3^\circ$, respectively, while the correlation coefficients (CCs) were $0.89 \pm 0.06$ and $0.78 \pm 0.09$, respectively. The evaluation demonstrates a promising application of Lie group representation to inertial motion capture under reduced-sensor-count configuration, improving the estimates (i.e., joint angle RMSEs and CCs) for dynamic motion, and enabling better convergence for our non-linear biomechanical constraints. To further improve performance, additional information relating the pelvis and ankle kinematics is needed.Comment: 6 pages. arXiv admin note: text overlap with arXiv:1910.0091

Pseudo-Code and Data Appendices for Paper: A Technique to Interconnect and Control Co-Simulation Systems

Appendices for IET paper: A Technique to Interconnect and Control Co-Simulation Systems. Assigned DOI: 10.5258/SOTON/405667</span

Bistable emission of a black-body radiator

Bistable black-body emission is reported from resonantly excited Er3+,Yb3+:Y2O3Er3+,Yb3+:Y2O3 nanopowders. A simple model based on thermo-optic nonlinear response in the strongly scattering random medium explains the observed behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69863/2/APPLAB-85-23-5517-1.pd

Suitability of Native Milkweed (\u3cem\u3eAsclepias\u3c/em\u3e) Species versus Cultivars for Supporting Monarch Butterflies and Bees in Urban Gardens

Public interest in ecological landscaping and gardening is fueling a robust market for native plants. Most plants available to consumers through the horticulture trade are cultivated forms that have been selected for modified flowers or foliage, compactness, or other ornamental characteristics. Depending on their traits, some native plant cultivars seem to support pollinators, specialist insect folivores, and insect-based vertebrate food webs as effectively as native plant species, whereas others do not. There is particular need for information on whether native cultivars can be as effective as true or “wild-type” native species for supporting specialist native insects of conservation concern. Herein we compared the suitability of native milkweed species and their cultivars for attracting and supporting one such insect, the iconic monarch butterfly (Danaus plexippus L.), as well as native bees in urban pollinator gardens. Wild-type Asclepias incarnata L. (swamp milkweed) and Asclepias tuberosa L. (butterfly milkweed) and three additional cultivars of each that vary in stature, floral display, and foliage color were grown in a replicated common garden experiment at a public arboretum. We monitored the plants for colonization by wild monarchs, assessed their suitability for supporting monarch larvae in greenhouse trials, measured their defensive characteristics (leaf trichome density, latex, and cardenolide levels), and compared the proportionate abundance and diversity of bee families and genera visiting their blooms. Significantly more monarch eggs and larvae were found on A. incarnata than A. tuberosa in both years, but within each milkweed group, cultivars were colonized to the same extent as wild types. Despite some differences in defense allocation, all cultivars were as suitable as wild-type milkweeds in supporting monarch larval growth. Five bee families and 17 genera were represented amongst the 2,436 total bees sampled from blooms of wild-type milkweeds and their cultivars in the replicated gardens. Bee assemblages of A. incarnata were dominated by Apidae (Bombus, Xylocopa spp., and Apis mellifera), whereas A. tuberosa attracted relatively more Halictidae (especially Lasioglossum spp.) and Megachilidae. Proportionate abundance of bee families and genera was generally similar for cultivars and their respective wild types. This study suggests that, at least in small urban gardens, milkweed cultivars can be as suitable as their parental species for supporting monarch butterflies and native bees

Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: https://sites.google.com/view/incipient-slip-detection

Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency

Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards. Even though HER improves the sample efficiency of RL-based agents by learning from mistakes made in past experiences, it does not provide any guidance while exploring the environment. This leads to very large training times due to the volume of experience required to train an agent using this replay strategy. In this paper, we propose a method that uses primitive behaviours that have been previously learned to solve simple tasks in order to guide the agent toward more rewarding actions during exploration while learning other more complex tasks. This guidance, however, is not executed by a manually designed curriculum, but rather using a critic network to decide at each timestep whether or not to use the actions proposed by the previously-learned primitive policies. We evaluate our method by comparing its performance against HER and other more efficient variations of this algorithm in several block manipulation tasks. We demonstrate the agents can learn a successful policy faster when using our proposed method, both in terms of sample efficiency and computation time. Code is available at https://github.com/franroldans/qmp-her.Comment: 6 pages, 2 figures, 1 algorithm, 1 table. Version accepted to ICARA 202

Predicting school uptake of The Daily Mile in Northern Ireland- a data linkage study with School Census Data and Multiple Deprivation Measures

BACKGROUND: Participating in physical activity benefits health, yet a majority of children remain inactive. The Daily Mile™ (TDM) originated in Scotland in 2012 with the aim of increasing primary school children's physical fitness. Despite being a practically feasible and popular initiative, it remains unclear the extent to which schools implement TDM, and whether TDM core principles are adhered to (i.e., run or jog at least 3-days per week). In Northern Ireland it is unknown how many schools regularly participate in TDM, and whether there is an association between TDM participation with school type, school location, size, total number of children attending the school, school deprivation level, and/or motivation as measured by the COM-B model (Capabilities, Opportunities, Motivation model of behaviour). Therefore, this study aimed to quantify the uptake of TDM in Northern Ireland, assess whether schools are following the core principles, and analyse if there is an association between aforesaid demographic factors and TDM participation.METHODS: An online cross-sectional survey was sent to all primary and special education schools in Northern Ireland with the support of the Education Authority for Northern Ireland and the Public Health Agency for Northern Ireland. The survey was completed by the school principal or teacher, and was available from 31st August until 16th December 2022. Survey results were linked with the 2021/2022 Northern Ireland School Census Data and Northern Ireland Multiple Deprivation Measure 2017. Quantitative and qualitative questions were included in the survey to assess participation and implementation of TDM.RESULTS: The survey received 609 school responses. After data cleaning, and removal of duplicates from schools a sample of 358 primary schools (45%) and 19 special education schools (47.5%) was analysed. Over half (54.7%) of primary schools and 36.8% of special education schools reported taking part in TDM. More special education needs schools reported taking part in their own version of an 'active mile' rather than TDM formally, and qualitative findings showed TDM was not perceived as appropriate for many children in special educational settings. There was wide variation in adherence to TDM core principles. A multivariate binary logistic regression model was fitted to the data, but it was not statistically significant (χ2(17) = 22.689, p = .160). However, univariate effects showed that increasing levels on COM-B (Capability) was associated with increased likelihood of TDM participation (OR = 2.506), and Catholic Maintained schools were almost twice as likely as Controlled schools to be delivering TDM (OR = 1.919). There was no association found between deprivation and TDM uptake.CONCLUSION: Encouragingly over 50% of schools in Northern Ireland reported taking part in TDM. However, despite being a low-cost and practically feasible physical activity initiative, further intervention work with sound research methodology is needed to promote adherence to TDM core principles to maximise benefits to children's health. Furthermore, concerted efforts are required to adjust TDM so that it is inclusive for all educational settings, and children's abilities.</p