Implementation Action Plan for organic food and farming research

The Implementation Action Plan completes TP Organics’ trilogy of key documents of the Research Vision to 2025 (Niggli et al 2008) and the Strategic Research Agenda (Schmid et al 2009). The Implementation Action Plan addresses important areas for a successful implementation of the Strategic Research Agenda. It explores the strength of Europe’s organic sector on the world stage with about one quarter of the world’s organic agricultural land in 2008 and accounting for more than half of the global organic market. The aims and objectives of organic farming reflect a broad range of societal demands on the multiple roles of agriculture and food production of not only producing commodities but also ecosystem services. These are important for Europe’s economic success, the resilience of its farms and prosperity in its rural areas. The organic sector is a leading market for quality and authenticity: values at the heart of European food culture. Innovation is important across the EU economy, and no less so within the organic sector. The Implementation Action Plan devotes its third chapter to considering how innovation can be stimulated through organic food and farming research and, crucially, translated into changes in business and agricultural practice. TP Organics argues for a broad understanding of innovation that includes technology, know-how and social/organisational innovations. Accordingly, innovation can involve different actors throughout the food sector. Many examples illustrate innovations in the organic sector includign and beyond technology. The various restrictions imposed by organic standards have driven change and turned organic farms and food businesses into creative living laboratories for smart and green innovations and the sector will continue to generate new examples. The research topics proposed by TP Organics in the Strategic Research Agenda can drive innovation in areas as wide ranging as production practices for crops, technologies for livestock, food processing, quality management, on-farm renewable energy or insights into the effects of consumption of organic products on disease and wellbeing and life style of citizens. Importantly, many approaches developed within the sector are relevant and useful beyond the specific sector. The fourth chapter addresses knowledge management in organic agriculture, focusing on the further development of participatory research methods. Participatory (or trans-disciplinary) models recognise the worth and importance of different forms of knowledge and reduced boundaries between the generators and the users of knowledge, while respecting and benefitting from transparent division of tasks. The emphasis on joint creation and exchange of knowledge makes them valuable as part of a knowledge management toolkit as they have the capacity to enhance the translation of research outcomes into practical changes and lead to real-world progress. The Implementation Action Plan argues for the wider application of participatory methods in publicly-funded research and also proposes some criteria for evaluating participatory research, such as the involvement and satisfaction of stakeholders as well as real improvements in sustainability and delivery of public goods/services. European agriculture faces specific challenges but at the same time Europe has a unique potential for the development of agro-ecology based solutions that must be supported through well focused research. TP Organics believes that the most effective approaches in agriculture and food research will be systems-based, multi- and trans-disciplinary, and that in the development of research priorities, the interconnections between biodiversity, dietary diversity, functional diversity and health must be taken into account. Chapter five of the action plan identifies six themes which could be used to organise research and innovation activities in agriculture under Europe’s 8th Framework Programme on Research Cooperation: • Eco-functional intensification – A new area of agricultural research which aims to harness beneficial activities of the ecosystem to increase productivity in agriculture. • The economics of high output / low input farming Developing reliable economic and environmental assessments of new recycling, renewable-based and efficiency-boosting technologies for agriculture. • Health care schemes for livestock Shifting from therapeutics to livestock health care schemes based on good husbandry and disease prevention. • Resilience and “sustainagility” Dealing with a more rapidly changing environment by focusing on ‘adaptive capacity’ to help build resilience of farmers, farms and production methods. • From farm diversity to food diversity and health and wellbeing of citizens Building on existing initiatives to reconnect consumers and producers, use a ‘whole food chain’ approach to improve availability of natural and authentic foods. • Creating centres of innovation in farming communities A network of centres in Europe applying and developing trans-disciplinary and participatory scientific approaches to support innovation among farmers and SMEs and improving research capacities across Europe

Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented

Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization

Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and fuzzy. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, according to its dimensions. The ENG model analysis shows that the net evolves as a scale-free graph, and justifies in a deeply physical sense- the term gas here used.Comment: 16 pages, 8 figure

Multi-channel Encoder for Neural Machine Translation

Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More specifically, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine (NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6.52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English- French task, our single shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep models.Comment: Accepted by AAAI-201

Musical instrument mapping design with Echo State Networks

Echo State Networks (ESNs), a form of recurrent neural network developed in the field of Reservoir Computing, show significant potential for use as a tool in the design of mappings for digital musical instruments. They have, however, seldom been used in this area, so this paper explores their possible applications. This project contributes a new open source library, which was developed to allow ESNs to run in the Pure Data dataflow environment. Several use cases were explored, focusing on addressing current issues in mapping research. ESNs were found to work successfully in scenarios of pattern classification, multiparametric control, explorative mapping and the design of nonlinearities and uncontrol. 'Un-trained' behaviours are proposed, as augmentations to the conventional reservoir system that allow the player to introduce potentially interesting non-linearities and uncontrol into the reservoir. Interactive evolution style controls are proposed as strategies to help design these behaviours, which are otherwise dependent on arbitrary values and coarse global controls. A study on sound classification showed that ESNs could reliably differentiate between two drum sounds, and also generalise to other similar input. Following evaluation of the use cases, heuristics are proposed to aid the use of ESNs in computer music scenarios