Controllable Neural Story Plot Generation via Reinforcement Learning

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201

Event Representations for Automated Story Generation with Deep Neural Nets

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.Comment: Submitted to AAAI'1

Continental-scale drivers of lake drainage in permafrost regions

Lakes are ubiquitous with high-latitude ecosystems, covering up to 60 percent of the land surface in some regions. Due to their influence on an array of key biogeophysical processes, the recent decline in lake area (via gradual and abrupt) observed across permafrost ecosystems may hold significant implications for shifting carbon and energy dynamics. Since lakes are often highly dynamic, understanding the main drivers of lake area change may ultimately enable the prediction of lake persistence in a warmer climate; key to anticipating future carbon-climate feedbacks from Arctic ecosystems. Here we conducted a data-driven analysis of >600k lakes across four continental-scale transects (Alaska, E Canada, W Siberia, E Siberia), combining remote sensing-derived lake shape parameters and spatial dynamics with other ecosystem datasets, such as ground temperatures, climate, elevation/geomorphology, and permafrost landscape parameters. We grouped our lake-change dataset into non-drained, partially and completely drained lakes (25-75 %, >75% loss) and used the RandomForest Feature Importance to calculate the relative importance of each parameter. Furthermore we predicted the probability of lake drainage under current environmental conditions and changing permafrost temperatures. Initial results suggest a strong importance of ground temperatures, lake shape, and local geomorphology on lake drainage. Spatially coarser datasets of permafrost and thermokarst properties did not reveal correlations with the result. Our drainage prediction results show distinct spatial patterns, which are matching regional lake drainage patterns. Our model estimated ground temperature as one of the main impact factors, with an increased drainage likelihood in permafrost regions from -5 to 0 °C. Going forward, we will further test for short term influences, such as extreme weather events and wildfire on widespread lake drainage. As this analysis is purely data-driven, a comparison or combination with physics-based models and predictions will help to better validate our analysis

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Technical ReportThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Executive SummaryThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Mannose binding lectin is required for alphavirus-induced arthritis/myositis

Mosquito-borne alphaviruses such as chikungunya virus and Ross River virus (RRV) are emerging pathogens capable of causing large-scale epidemics of virus-induced arthritis and myositis. The pathology of RRV-induced disease in both humans and mice is associated with induction of the host inflammatory response within the muscle and joints, and prior studies have demonstrated that the host complement system contributes to development of disease. In this study, we have used a mouse model of RRV-induced disease to identify and characterize which complement activation pathways mediate disease progression after infection, and we have identified the mannose binding lectin (MBL) pathway, but not the classical or alternative complement activation pathways, as essential for development of RRV-induced disease. MBL deposition was enhanced in RRV infected muscle tissue from wild type mice and RRV infected MBL deficient mice exhibited reduced disease, tissue damage, and complement deposition compared to wild-type mice. In contrast, mice deficient for key components of the classical or alternative complement activation pathways still developed severe RRV-induced disease. Further characterization of MBL deficient mice demonstrated that similar to C3(-/-) mice, viral replication and inflammatory cell recruitment were equivalent to wild type animals, suggesting that RRV-mediated induction of complement dependent immune pathology is largely MBL dependent. Consistent with these findings, human patients diagnosed with RRV disease had elevated serum MBL levels compared to healthy controls, and MBL levels in the serum and synovial fluid correlated with severity of disease. These findings demonstrate a role for MBL in promoting RRV-induced disease in both mice and humans and suggest that the MBL pathway of complement activation may be an effective target for therapeutic intervention for humans suffering from RRV-induced arthritis and myositis.This work was supported by NIH/NIAMS R01 AR 047190 awarded to MTH