A Dynamical System for Prioritizing and Coordinating Motivations

We develop a dynamical systems approach to prioritizing multiple tasks in the context of a mobile robot. We take navigation as our prototypical task, and use vector field planners derived from navigation functions to encode control policies that achieve each individual task. We associate a scalar quantity with each task, representing its current importance to the robot; this value evolves in time as the robot achieves tasks. In our framework, the robot uses as its control input a convex combination of the individual task vector fields. The weights of the convex combination evolve dynamically according to a decision model adapted from the bio-inspired literature on swarm decision making, using the task values as an input. We study a simple case with two navigation tasks and derive conditions under which a stable limit cycle can be proven to emerge. While owing along the limit cycle, the robot periodically navigates to each of the two goal locations; moreover, numerical study suggests that the basin of attraction is quite large so that significant perturbations are recovered with a reliable return to the desired task coordination pattern. For more information: Kod*lab and http://www.paulreverdy.com/2018/05/11/motivation-dynamics-simulations

Motivation dynamics for autonomous composition of navigation tasks

We physically demonstrate a reactive sensorimotor architecture for mobile robots whose behaviors are generated by motivation dynamics. Motivation dynamics uses a continuous dynamical system to reactively compose low-level control vector fields using valuation functions which capture the potentially competing influences of external stimuli relative to the system\u27s own internal state. We show that motivation dynamics 1) naturally accommodates external stimuli through standard signal processing tools, and 2) can effectively encode a repetitive higher-level task by composing several low-level controllers to achieve a limit cycle in which the robot repeatedly navigates towards two alternatively valuable goal locations in a commensurately alternating order. We show that these behaviors are robust to perturbations including imperfect models of robot kinematics, sensor noise, and disturbances resulting from the need to traverse difficult terrain. We argue that motivation dynamics can provide a useful alternative to controllers based on hybrid automata in situations where the control operates at a low level close to the physical hardware. For more information: Kod*la

The Acquisition and Analysis of Electroencephalogram Data for the Classification of Benign Partial Epilepsy of Childhood with Centrotemporal Spikes

In this thesis, I will expand upon each step in the process of acquiring and analyzing electroencephalogram (EEG) for the classification of benign childhood epilepsy with centrotemporal spikes. Despite huge advancements in the field of health informatics—natural language processing, machine learning, predictive modeling—there are significant barriers to the access of clinical data. These barriers include information blocking, privacy policy concerns, and a lack of stakeholder support. We will see that these roadblocks are all responsible for stunting biomedical research in some way, including my own experiences in acquiring the data for the second chapter of this thesis. This second chapter expands upon just one possible advancement that can be achieved when researchers attain clinical data (in this case, EEG data). BECTS is a type of epilepsy that only displays epileptiform activity on night-time EEGs. We hypothesize that a brain affected by BECTS is also developmentally different during the daytime, and based on this assumption, our analysis aims to uncover these electrodynamic distinctions. After course-graining raw EEG segments, we extracted sample entropy, recurrence rate, laminarity, and determinism using recurrence quantitative analysis. Our results displayed two major findings. First, awake BECTS and control patients can be classified with no overlap using all of these features. Second, BECTS patients show differences in sleep state RQA values from centrotemporal and non-centrotemporal regions. We cannot confirm if these differences display epileptiform activity, however, because we do not have controls for sleep studies. With proper development and implementation, this research has the potential to become a clinical decision support tool and decrease the need for inconvenient sleep studies

“I simply didn't think, ok?” some reflections on the quality of scientific research

In this paper, I explore the elusive yet crucial issue of the quality of research, taking the renowned theoretical physicist Richard Feynman as a narrative expedient. The story follows Feynman along two main episodes that mark the transition from curiosity-oriented science to big technoscientific enterprise:  the Manhattan Project and the  Space Shuttle Challenger disaster. Along the way, I examine the relationship between quality and truth, fitness for purpose and integrity, considering their relevance and limitations. I conclude by reflecting on quality and reflexivity in current times

Designing policy robustness: outputs and processes

Faced with growing policy complexity and environmental uncertainty, policymakers are increasingly concerned with ensuring that policy processes retain functionality amidst shock and uncertainty. In this paper, we seek to address the ways in which robustness \u2013 or the capability of policies to maintain functionality and effectiveness in policy goal attainment \u2013 can be designed into policies, institutions or systems. We suggest that robust policy designs can be characterized by diversity, modularity and redundancy, whereas robust policy design processes require the presence of polycentric decisional process, political capacity and technical capacity. In identifying these design elements of policy robustness, we argue that robustness is a property that can be designed to ensure that policies continue to deliver, over time, its intended functions, purposes and objectives, even under negative circumstances

PICES Press, Vol. 15, No. 2, July 2007

Contents [Individual sections are downloadable from the official URL link listed below]: PICES Science in 2007 (pdf, 0.1 Mb) 2007 Wooster Award (pdf, 0.1 Mb) FUTURE - A milestone reached but our task is not done (pdf, < 0.1 Mb) International symposium on "Reproductive and Recruitment Processes of Exploited Marine Fish Stocks" (pdf, 0.1 Mb) Recent results of the micronekton sampling inter-calibration experiment (pdf, 0.1 Mb) 2007 PICES workshop on "Measuring and monitoring primary productivity in the North Pacific" (pdf, 0.1 Mb) 2007 Harmful Algal Bloom Section annual workshop events (pdf, 0.1 Mb) A global approach for recovery and sustainability of marine resources in Large Marine Ecosystems (pdf, 0.3 Mb) Highlights of the PICES Sixteenth Annual Meeting (pdf, 0.4 Mb) Ocean acidification of the North Pacific Ocean (pdf, 0.3 Mb) Workshop on NE Pacific Coastal Ecosystems (2008 Call for Salmon Survival Forecasts) (pdf, 0.1 Mb) The state of the western North Pacific in the first half of 2007 (pdf, 0.4 Mb) PICES Calendar (pdf, 0.4 Mb) The Bering Sea: Current status and recent events (pdf, 0.3 Mb) PICES Interns (pdf, 0.3 Mb) Recent trends in waters of the subarctic NE Pacific (pdf, 0.3 Mb) Election results at PICES (pdf, 0.2 Mb) A new PICES award for monitoring and data management activities (pdf, < 0.1 Mb

Analysis and control of agreement and disagreement opinion cascades

We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality, which depend only on network structure, are shown to be key to characterizing the nonlinear behavior of agreement and disagreement opinion formation and cascades. Our results are relevant for the analysis and control of opinion cascades in real-world networks, including biological, social and artificial networks, and for the design of opinion-forming behaviors in robotic swarms. We illustrate an application of our model to a multi-robot task-allocation problem and discuss extensions and future directions opened by our modeling framework

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently—or in the future—could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning an d strategies