An active inference model of car following: Advantages and applications

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets

F1/10: An Open-Source Autonomous Cyber-Physical Platform

In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible

Osteoporosis-related characteristics in care home residents in England: a retrospective cohort study.

BACKGROUND: The characteristics of care home populations, with respect to fracture risk factors, have not been well-defined. AIM: To describe osteoporosis-related characteristics among care home residents, including fracture risk factors, fracture rates, post-fracture outcomes, and osteoporosis treatment duration. DESIGN & SETTING: A descriptive cohort study of care home residents aged ≥60 years (n = 8366) and a matched cohort of non-care home residents (n = 16 143) in England from 2012 to 2019. Clinical Practice Research Datalink (CPRD) linked to Hospital Episode Statistics (HES) and Office for National Statistics (ONS) death data were used. METHOD: The characteristics were assessed using descriptive statistics. Fracture risk factors and fracture rates were described in both the care home and matched population. In the care home population, Kaplan-Meier curves were plotted to assess osteoporosis treatment duration. RESULTS: At index, fracture risk factors were more common in care home residents versus the matched cohort, including body mass index (BMI) <18.5 (12.2% versus 5.1%), history of falls (48.9% versus 30.7%), prior fracture (26.5% versus 10.8%), and prior hip fracture (17.1% versus 5.8%). Fracture rate was 43.5 (95% confidence interval [CI] = 39.7 to 47.5) in care home residents and 28.0 (95% CI = 26.3 to 29.9) per 1000 person-years in the matched cohort. Overall, osteoporosis treatment was initiated in 3.6% (n = 225/6265) of care home residents and 45.9% remained on treatment at 12 months. Among care home residents who experienced fracture, 21.9% (n = 72/329) received an osteoporosis diagnosis; 21.2% (n = 63/297) initiated osteoporosis treatment post-hip fracture. CONCLUSION: Care home residents had more fracture risk factors and higher fracture rates than the matched cohort; however, osteoporosis diagnosis, treatment rates, and treatment duration were low. There is an opportunity to improve osteoporosis management in this vulnerable population

Long-term monitoring of wildlife populations for protected area management in Southeast Asia

Long-term monitoring of biodiversity in protected areas (PAs) is critical to assess threats, link conservation action to species outcomes, and facilitate improved management. Yet, rigorous longitudinal monitoring within PAs is rare. In Southeast Asia (SEA), there is a paucity of long-term wildlife monitoring within PAs, and many threatened species lack population estimates from anywhere in their range, making global assessments difficult. Here, we present new abundance estimates and population trends for 11 species between 2010 and 2020, and spatial distributions for 7 species, based on long-term line transect distance sampling surveys in Keo Seima Wildlife Sanctuary in Cambodia. These represent the first robust population estimates for four threatened species from anywhere in their range and are among the first long-term wildlife population trend analyses from the entire SEA region. Our study revealed that arboreal primates and green peafowl (Pavo muticus) generally had either stable or increasing population trends, whereas ungulates and semiarboreal primates generally had declining trends. These results suggest that ground-based threats, such as snares and domestic dogs, are having serious negative effects on terrestrial species. These findings have important conservation implications for PAs across SEA that face similar threats yet lack reliable monitoring data

Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1

The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies

Abundance estimates for the endangered Green Peafowl Pavo muticus in Cambodia: identification of a globally important site for conservation

The catastrophic decline of the endangered Green peafowl Pavo muticus across its former range is well known, yet there are only a handful of reliable population estimates for this species from its remaining range, making global assessment challenging. We present the first rigorous population estimates for this species from Cambodia, and model the distribution and the relationships between this species and several environmental covariates from the Core Zone (187,900 ha) of Seima Protection Forest (SPF), eastern Cambodia. Using distance sampling the abundance of Green Peafowl in SPF in 2014 is estimated to be 541 (95% CI [252, 1160]). Density surface modelling was used to predict distribution and relative abundance within the study area, and there was some evidence that the species prefers areas of deciduous forest, non-forest, and to a lesser extent semi-evergreen forest. These results highlight the importance of the central and northern sections of SPF for this species. Furthermore, the analysis suggested that Green Peafowl abundance is higher in closer proximity to water, yet decreases in closer proximity to human settlement