A Behavior Authoring Framework for Multi-Actor Simulations

There has been growing academic and industry interest in the behavioral animation of autonomous actors in virtual worlds. However, it remains a considerable challenge to author complicated interactions between multiple actors in a way that balances automation and control flexibility. In this paper, we propose a behavior authoring framework which provides the user with complete control over the domain of the system: the state space, action space and cost of executing actions. Actors are specialized using effect and cost modifiers, which modify existing action definitions, and constraints, which prune action choices in a state-dependent manner. Behaviors are used to define goals and objective functions for an actor. Actors having common or conflicting goals are grouped together to form a composite domain, and a multi-agent planner is used to generate complicated interactions between multiple actors. We demonstrate the effectiveness of our framework by authoring and generating a city simulation involving multiple pedestrians and vehicles that interact with one another to produce complex multi-actor behaviors

Parallelized Egocentric Fields for Autonomous Navigation

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations

Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms

Navigation and steering in complex dynamically changing environments is a challenging research problem, and a fundamental aspect of immersive virtual worlds. While there exist a wide variety of approaches for navigation and steering, there is no definitive solution for evaluating and analyzing steering algorithms. Evaluating a steering algorithm involves two major challenges: (a) characterizing and generating the space of possible scenarios that the algorithm must solve, and (b) defining evaluation criteria (metrics) and applying them to the solution. In this paper, we address both of these challenges. First, we characterize and analyze the complete space of steering scenarios that an agent may encounter in dynamic situations. Then, we propose the representative scenario space and a sampling method that can generate subsets of the representative space with good statistical properties. We also propose a new set of metrics and a statistically robust approach to determining the coverage and the quality of a steering algorithm in this space. We demonstrate the effectiveness of our approach on three state of the art techniques. Our results show that these methods can only solve 60% of the scenarios in the representative scenario space

APP21 transgenic rats develop age-dependent cognitive impairment and microglia accumulation within white matter tracts.

Background Most of the animal models commonly used for preclinical research into Alzheimer\u27s disease (AD) largely fail to address the pathophysiology, including the impact of known risk factors, of the widely diagnosed sporadic form of the disease. Here, we use a transgenic rat (APP21) that does not develop AD-like pathology spontaneously with age, but does develop pathology following vascular stress. To further the potential of this novel rat model as a much-needed pre-clinical animal model of sporadic AD, we characterize APP21 transgenic rats behaviorally and histologically up to 19 months of age. Methods The open field test was used as a measure of activity; and the Morris water maze was used to assess learning, memory, and strategy shift. Neuronal loss and microglia activation were also assessed throughout the brain. Results APP21 transgenic rats showed deficits in working memory from an early age, yet memory recall performance after 24 and 72 h was equal to that of wildtype rats and did not deteriorate with age. A deficit in strategy shift was observed at 19 months of age in APP21 transgenic rats compared to Fischer wildtype rats. Histologically, APP21 transgenic rats demonstrated accelerated white matter inflammation compared to wildtype rats, but interestingly no differences in neuron loss were observed. Conclusions The combined presence of white matter pathology and executive function deficits mirrored what is often found in patients with mild cognitive impairment or early dementia, and suggests that this rat model will be useful for translationally meaningful studies into the development and prevention of sporadic AD. The presence of widespread white matter inflammation as the only observed pathological correlate for cognitive deficits raises new questions as to the role of neuroinflammation in cognitive decline

Emissions of forest floor and mineral soil carbon, nitrogen and mercury pools and relationships with fire severity for the Pagami Creek Fire in the Boreal Forest of northern Minnesota

Forest fires cause large emissions of C (carbon), N (nitrogen) and Hg (mercury) to the atmosphere and thus have important implications for global warming (e.g. via CO2 and N2O emissions), anthropogenic fertilisation of natural ecosystems (e.g. via N deposition), and bioaccumulation of harmful metals in aquatic and terrestrial systems (e.g. via Hg deposition). Research indicates that fires are becoming more severe over much of North America, thus increasing element emissions during fire. However, there has been little research relating forest floor and mineral soil losses of C, N and Hg to on-the-ground indices of fire severity that enable scaling up those losses for larger-scale accounting of fire-level emissions. We investigated the relationships between forest floor and mineral soil elemental pools across a range of soil-level fire severities following the 2011 Pagami Creek wildfire in northern Minnesota, USA. We were able to statistically differentiate losses of forest floor C, N and Hg among a five-class soil-level fire severity classification system. Regression relationships using soil fire severity class were able to predict remaining forest floor C, N and Hg pools with 82–96% confidence. We correlated National Aeronautics and Space Administration Airborne Visible and Infrared Imaging Spectrometer-Classic imagery to ground-based plot-scale estimates of soil fire severity to upscale emissions of C, N and Hg to the fire level. We estimate that 468 000 Mg C, 11 000 Mg of N and over 122 g of Hg were emitted from the forest floor during the burning of the 28 310 ha upland area of the Pagami Creek fire

Product Characterization for Entrained Flow Coal/Biomass Co-Gasification

The U.S. Department of Energyâs National Energy Technology Laboratory (DOE NETL) is exploring affordable technologies and processes to convert domestic coal and biomass resources to high-quality liquid hydrocarbon fuels. This interest is primarily motivated by the need to increase energy security and reduce greenhouse gas emissions in the United States. Gasification technologies represent clean, flexible and efficient conversion pathways to utilize coal and biomass resources. Substantial experience and knowledge had been developed worldwide on gasification of either coal or biomass. However, reliable data on effects of blending various biomass fuels with coal during gasification process and resulting syngas composition are lacking. In this project, GE Global Research performed a complete characterization of the gas, liquid and solid products that result from the co-gasification of coal/biomass mixtures. This work was performed using a bench-scale gasifier (BSG) and a pilot-scale entrained flow gasifier (EFG). This project focused on comprehensive characterization of the products from gasifying coal/biomass mixtures in a high-temperature, high-pressure entrained flow gasifier. Results from this project provide guidance on appropriate gas clean-up systems and optimization of operating parameters needed to develop and commercialize gasification technologies. GEâs bench-scale test facility provided the bulk of high-fidelity quantitative data under temperature, heating rate, and residence time conditions closely matching those of commercial oxygen-blown entrained flow gasifiers. Energy and Environmental Research Center (EERC) pilot-scale test facility provided focused high temperature and pressure tests at entrained flow gasifier conditions. Accurate matching of syngas time-temperature history during cooling ensured that complex species interactions including homogeneous and heterogeneous processes such as particle nucleation, coagulation, surface condensation, and gas-phase reactions were properly reproduced and lead to representative syngas composition at the syngas cooler outlet. The experimental work leveraged other ongoing GE R&D efforts such as biomass gasification and dry feeding systems projects. Experimental data obtained under this project were used to provide guidance on the appropriate clean-up system(s) and operating parameters to coal and biomass combinations beyond those evaluated under this project

Grand Challenges in Global Health: Ethical, Social, and Cultural Issues Based on Key Informant Perspectives

The authors interviewed key informants from the developing world and the Grand Challenges investigators to explore their ethical, social, and cultural concerns about the program

Grand Challenges in Global Health: Community Engagement in Research in Developing Countries

The authors argue that there have been few systematic attempts to determine the effectiveness of community engagement in research

An HLA-G/SPAG9/STAT3 axis promotes brain metastases

Brain metastases (BM) are the most common brain neoplasm in adults. Current BM therapies still offer limited efficacy and reduced survival outcomes, emphasizing the need for a better understanding of the disease. Herein, we analyzed the transcriptional profile of brain metastasis initiating cells (BMICs) at two distinct stages of the brain metastatic cascade-the "premetastatic" or early stage when they first colonize the brain and the established macrometastatic stage. RNA sequencing was used to obtain the transcriptional profiles of premetastatic and macrometastatic (non-premetastatic) lung, breast, and melanoma BMICs. We identified that lung, breast, and melanoma premetastatic BMICs share a common transcriptomic signature that is distinct from their non-premetastatic counterparts. Importantly, we show that premetastatic BMICs exhibit increased expression of HLA-G, which we further demonstrate functions in an HLA-G/SPAG9/STAT3 axis to promote the establishment of brain metastatic lesions. Our findings suggest that unraveling the molecular landscape of premetastatic BMICs allows for the identification of clinically relevant targets that can possibly inform the development of preventive and/or more efficacious BM therapies