The influence of temperature on the development of Baltic Sea sprat (Sprattus sprattus) eggs and yolk sac larvae

In spring 2004 and 2005 we performed two sets of experiments with Baltic sprat (Sprattus sprattus balticus Schneider) eggs and larvae from the Bornholm Basin simulating ten different temperature scenarios. The goal of the present study was to analyse and parameterise temperature effects on the duration of developmental stages, on the timing of important ontogenetic transitions, growth during the yolk sac phase as well as on the survival success of eggs and early larval stages. Egg development and hatching showed exponential temperature dependence. No hatching was observed above 14.7°C and hatching success was significantly reduced below 3.4°C. Time to eye pigmentation, as a proxy for mouth gape opening, decreased with increasing temperatures from 17 days post hatch at 3.4°C to 7 days at 13°C whereas the larval yolk sac phase was shortened from 20 to 10 days at 3.8 and 10°C respectively. Maximum survival duration of non-fed larvae was 25 days at 6.8°C. Comparing the experimental results of Baltic sprat with existing information on sprat from the English Channel and North Sea differences were detected in egg development rate, thermal adaptation and in yolk sac depletion rate (YSDR). Sprat eggs from the English Channel showed significantly faster development and the potential to develop at temperatures higher than 14.7°C. North Sea sprat larvae were found to have a lower YSDR compared to larvae from the Baltic Sea. In light of the predictions for global warming, Baltic sprat stocks could experience improved conditions for egg development and surviva

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

Intelligent Interfaces to Empower People with Disabilities

Severe motion impairments can result from non-progressive disorders, such as cerebral palsy, or degenerative neurological diseases, such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), or muscular dystrophy (MD). They can be due to traumatic brain injuries, for example, due to a traffic accident, or to brainste

Weakly Supervised Localization and Learning with Generic Knowledge

ISSN:0920-5691ISSN:1573-140