An explicit dynamics GPU structural solver for thin shell finite elements

With the availability of user oriented software tools, dedicated architectures, such as the parallel computing platform and programming model CUDA (Compute Unified Device Architecture) released by NVIDIA, one of the main producers of graphics cards, and of improved, highly performing GPU (Graphics Processing Unit) boards, GPGPU (General Purpose programming on GPU) is attracting increasing interest in the engineering community, for the development of analysis tools suitable to be used in validation/ verification and virtual reality applications. For their inherent explicit and decoupled structure, explicit dynamics finite element formulations appear to be particularly attractive for implementations on hybrid CPU/GPU or pure GPU architectures. The issue of an optimized, double-precision finite element GPU implementation of an explicit dynamics finite element solver for elastic shell problems in small strains and large displacements and rotations, using unstructured meshes, is here addressed. The conceptual difference between a GPU implementation directly adapted from a standard CPU approach and a new optimized formulation, specifically conceived for GPUs, is discussed and comparatively assessed. It is shown that a speedup factor of about 5 can be achieved by an optimized algorithm reformulation and careful memory management. A speedup of more than 40 is achieved with respect of state-of-the art commercial codes running on CPU, obtaining real-time simulations in some cases, on commodity hardware. When a last generation GPU board is used, it is shown that a problem with more than 16 millions degrees of freedom can be solved in just few hours of computing time, opening the way to virtualization approaches for real large scale engineering problems

Improving Low-Resource Question Answering using Active Learning in Multiple Stages

Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain. We also investigate Active Learning for question answering in different stages, overall reducing the annotation effort of humans. For this purpose, we consider target domains in realistic settings, with an extremely low amount of annotated samples but with many unlabeled documents, which we assume can be obtained with little effort. Additionally, we assume sufficient amount of labeled data from the source domain is available. We perform extensive experiments to find the best setup for incorporating domain experts. Our findings show that our novel approach, where humans are incorporated as early as possible in the process, boosts performance in the low-resource, domain-specific setting, allowing for low-labeling-effort question answering systems in new, specialized domains. They further demonstrate how human annotation affects the performance of QA depending on the stage it is performed.Comment: 16 pages, 8 figure

Implementing the circular economy paradigm in the agri-food supply chain: The role of food waste prevention technologies

Food systems are plagued by the grand sustainability challenge of food waste, which represents a urging issue from economic, environmental and social point of view. dThe Circular Economy paradigm can open up different actions which are framed within the so-called Food Waste Hierarchy (FWH). In these regards, scholars recommend to leverage on those practices that are able to prevent the generation of surplus food, preserving a higher share of the sustainable value. For these pre-harvest and post-harvest practices that go under the name of prevention or reuse strategies in different FWH, technology plays a crucial role. Through a set of 34 semi-structured interviews with technology providers as well as with companies in the agri-food supply chain, the present work investigates extensively the range of the available technologies and the detailed objectives of such technologies for food loss and waste prevention (i.e., forecasting, monitoring, grouping, shelf life extension, product quality and value upgrading). Moreover, different forms of collaboration enable to reach these objectives in different ways. Collaboration with technology providers can be based on continuous technical assistance and consulting for data elaboration and data analysis as well as on full data sharing and co-design, allowing to achieve a different impact on food loss and waste prevention. Finally, our study reveals that the adoption of different technological options can represent the engine to establish vertical collaborations between the adopter of the technology and another stage in the agri-food supply chain, in order to fight food waste and loss with a coordinated supply chain effort

SIVEQ: an Integrated System for the Valorization of Surplus Food

Food waste is one of the key challenges of the agri-food sector: one third of the global food production is wasted yearly, while paradoxically 815 million people do not have access to sufficient and nutritious food. Food waste represents an economic loss for the agri-food supply chain and the whole society and significantly contributes to the GHG emissions. In Italy up to 5.1 million tons of food is wasted: nearly half of it is generated by agri-food supply chain actors. Retailers contribute to the 14% of the overall food waste produced and the main cause relies on products reaching the expiration date. Over the last years retailers have increasingly taken action in order to recover surplus food, encouraged by positive changes in the regulatory environment and the increasing relevance of Corporate Social Responsibility policies adopted by companies. Food donations have been increasing, but in many cases the surplus food redistribution process to food-aid organizations is still occasional and not formalized, leaving space for efficiency improvement. Surplus food close to expiration date, if not properly and timely handled, inevitably turns into waste. In this paper we introduce SIVEQ: a systematic solution which relies on novel technologies such as IoT and big data analytics to tackle this issue. Our system represents an added value to all actors involved, not only for NPOs who collect and redistribute surplus food