Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap with arXiv:1912.0313

Fishing, reproductive volume and regulation: population dynamics and exploitation of the eastern Baltic cod

The relative importance of exploitation rate and environmental variability in generating fluctuations of harvested populations is a key issue in academic ecology as well as population management. We studied how the eastern Baltic cod (Gadus morhua) is affected by fishing and environmental variation by using a newly developed single species state-space model. Survey data and auxiliary environmental data were used to estimate the model parameters. The model was then used to predict future development of the eastern Baltic cod under different fishing mortalities and abiotic conditions. Abiotic condition was represented by an index: reproductive volume which is the volume of water suitable (in terms of salinity and oxygen content) for the successful development of the early life stages of Baltic cod. The model included direct density dependence, fishing, and a lagged effect of reproductive volume. Our analysis showed that fishing rate is approximately three times more important than reproductive volume in explaining the population dynamics. Furthermore, our model suggests either under- or over-compensatory dynamics depending on the reproductive volume and long term catch levels. It follows that fishing can either reduce or increase temporal oscillations of the cod stock depending on whether the dynamics is over- or undercompensatory, respectively. The sustainable level of fishing rate is however dependent on reproductive volume. Our model predicts a dual role of fishing rate, stabilizing when reproductive volume is high and destabilizing when it is low. Exploitation rate may therefore increase or decrease the risk of the population of cod dropping below a given biomass reference point depending on the environmental conditions, which has practical implications for fisheries management