Influences of Labeling Policy and Media Coverage On the Demand for Butter and Margarine

food labeling, regulation, media coverage, trans fat, consumer demand, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

Self-Perception of Weight and Health and Dietary Quality

perception, dietary quality, obesity, Food Consumption/Nutrition/Food Safety,

Weight Control Strategies and Diet Quality

obesity, diet quality, nhanes, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, I00,

CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly

Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end-effector, or limiting the distance between the target and the initial pose position, which prevents them from being deployed in real-world manufacturing. Thus, we present a Coarse-to-Fine Visual Servoing (CFVS) peg-in-hole method, achieving 6-DoF end-effector motion control based on 3D visual feedback. CFVS can handle arbitrary tilt angles and large initial alignment errors through a fast pose estimation before refinement. Furthermore, by introducing a confidence map to ignore the irrelevant contour of objects, CFVS is robust against noise and can deal with various targets beyond training data. Extensive experiments show CFVS outperforms state-of-the-art methods and obtains 100%, 91%, and 82% average success rates in 3-DoF, 4-DoF, and 6-DoF peg-in-hole, respectively

Knowledge, attitude and perception on climate change and dietary choices in a predominantly Chinese university students population in Klang Valley

Climate change is a public health threat that is aggravated by the food supply chain. A dietary shift to climate-friendly foods is a feasible strategy to mitigate it. This study aimed to investigate the associations between knowledge, attitude, perception towards climate change, and barriers to climate-friendly foods with dietary choices of university students in Klang Valley. A cross-sectional study was conducted among 303 Malaysian university students (71.9% Chinese) aged 18 to 30 years in Klang Valley, by using Google form to assess knowledge, attitude, perception towards climate change, barriers to climate-friendly food, and climate-friendly dietary choices. The average climate-friendly diet score (CFDS) was 0.36±2.21, with a significantly higher CFDS among females than males (p=0.012). The majority of them were having good knowledge (76.6%), a good attitude (66.3%), and a moderate level of perception (62.0%) towards climate change. About two-thirds of them reported social media as the main (63.0%) and preferred (63.7%) sources to receive information about climate change. Through multiple linear regression, barriers to climate-friendly food choices (β=-0.084; p<0.001) significantly contributed to climate-friendly dietary choices (F=4.215; p<0.001), whereby 14.9% of the variances were climate-friendly dietary choices of university students. Findings could be incorporated into dietary education to tackle barriers to climate-friendly foods among university students

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.Comment: 14 pages, 5 figure