Optimisation of the first principle code Octopus for massive parallel architectures: application to light harvesting complexes

150 p.

Spin squeezing, macrorealism and the heisenberg uncertainty principle

169 p.

A prior for steepness in stock-recruitment relationships, based on an evolutionary persistence principle

Priors are existing information or beliefs that are needed in Bayesian analysis. Informative priors are important in obtaining the Bayesian posterior distributions for estimated parameters in stock assessment. In the case of the steepness parameter (h), the need for an informative prior is particularly important because it determines the stock-recruitment relationships in the model. However, specifications of the priors for the h parameter are often subjective. We used a simple population model to derive h priors based on life history considerations. The model was based on the evolutionary principle that persistence of any species, given its life history (i.e., natural mortality rate) and its exposure to recruitment variability, requires a minimum recruitment compensation that enables the species to rebound consistently from low critical abundances (Nc). Using the model, we derived the prior probability distributions of the h parameter for fish species that have a range of natural mortality, recruitment variabilities, and Nt values.

Adopt sustainable development principle in aquaculture

The inadequate planning and inefficient management of coastal aquaculture has resulted into serious socioeconomic consequences. These are the displacement of rural communities which traditionally depended on mangroves due to large-scale mangrove conversion for shrimp and fish farming, land subsidence caused by excessive pumping of groundwater for use in aquaculture, financial losses due to disease outbreaks, and public health consequences due to red tide. In order to maximize the socioeconomic benefits of coastal aquaculture the adoption of the principles of sustainable development is recommended. Sustainable development is the management and conservation of natural resource base and the orientation of technological and institutional change in such a manner to ensure the attainment and continued satisfaction of human needs for present and future generations.

Predictive coding and the slowness principle: an information-theoretic approach.

Understanding the guiding principles of sensory coding strategies is a main goal in computational neuroscience. Among others, the principles of predictive coding and slowness appear to capture aspects of sensory processing. Predictive coding postulates that sensory systems are adapted to the structure of their input signals such that information about future inputs is encoded. Slow feature analysis (SFA) is a method for extracting slowly varying components from quickly varying input signals, thereby learning temporally invariant features. Here, we use the information bottleneck method to state an information-theoretic objective function for temporally local predictive coding. We then show that the linear case of SFA can be interpreted as a variant of predictive coding that maximizes the mutual information between the current output of the system and the input signal in the next time step. This demonstrates that the slowness principle and predictive coding are intimately related.