Actions speak louder than words: designing transdisciplinary approaches to enact solutions

Sustainability science uses a transdisciplinary research process in which academic and non-academic partners collaborate to identify a common problem and co-produce knowledge to develop more sustainable solutions. Sustainability scientists have advanced the theory and practice of facilitating collaborative efforts such that the knowledge created is usable. There has been less emphasis, however, on the last step of the transdisciplinary process: enacting solutions. We analyzed a case study of a transdisciplinary research effort in which co-produced policy simulation information shaped the creation of a new policy mechanism. More specifically, by studying the development of a mechanism for conserving vernal pool ecosystems, we found that four factors helped overcome common challenges to acting upon new information: creating a culture of learning, co-producing policy simulations that acted as boundary objects, integrating research into solution development, and employing an adaptive management approach. With an increased focus on these four factors that enable action, we can better develop the same level of nuanced theoretical concepts currently characterizing the earlier phases of transdisciplinary research, and the practical advice for deliberately designing these efforts

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Adaptive Neural Models of Queuing and Timing in Fluent Action

Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a fine scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over-shoot the amounts needed for the precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals controls muscles. But a recent model shows how the lateral cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine system design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.National Institute of Mental Health (R01 DC02852

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Temporal structure is skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefronatal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables such as time-to-contact. At a finer scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over- shoot the amounts needed for precise acts. Each context of action may require a different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive patterns of analog signals. From some parts of the cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine design to serve the lowest and highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between leveels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.National Institute of Mental Health (R01 DC02582

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Store Working Memory Networks for Storage and Recall of Arbitrary Temporal Sequences

Neural network models of working memory, called Sustained Temporal Order REcurrent (STORE) models, are described. They encode the invariant temporal order of sequential events in short term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items is invariant in the sense that, relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored.Air Force Office of Scientific Research (90-0128, F49620-92-J-0225); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309); British Petroleum (89A-1204); Advanced Research Projects Agency (90-0083, N00014-92-J-4015); National Science Foundation (IRI-90-00539

Co-management: A Synthesis of the Lessons Learned from the DFID Fisheries Management Science Programme

For the last eleven years, the UK Department for International Development (DfID) have been funding research projects to support the sustainable management of fisheries resources (both inland and marine) in developing countries through the Fisheries Management Science Programme (FMSP). A number of these projects that have been commissioned in this time have examined fisheries co-management. While these projects have, for the most part, been implemented separately, the FMSP has provided an opportunity to synthesise and draw together some of the information generated by these projects. We feel that there is value in distilling some of the important lessons and describing some of the useful tools and examples and making these available through a single, accessible resource. The wealth of information generated means that it is impossible to cover everything in detail but it is hoped that this synthesis will at least provide an overview of the co-management process together with some useful information relating to implementing co-management in a developing country context and links to the more detailed re-sources available, in particular on information systems for co-managed fisheries, participatory fish stock assessment (ParFish) and adaptive learning that have, in particular, been drawn upon for this synthesis. This synthesis is aimed at anyone interested in fisheries management in a developing country context

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible