Calibrationless Parallel MRI using Model based Deep Learning (C-MODL). (arXiv:1911.12443v1 [cs.LG])

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We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (SLR) methods that self learn linear annihilation filters from the same subject. It pre-learns non-linear annihilation relations in the Fourier domain from exemplar data. The pre-learning strategy significantly reduces the…

Modelling publication bias and p-hacking. (arXiv:1911.12445v1 [stat.ME])

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Publication bias and p-hacking are two well-known phenomena which strongly affect the scientific literature and cause severe problems in meta-analysis studies. Due to these phenomena, the assumptions are seriously violated and the results of the meta-analysis studies cannot be trusted. While publication bias is almost perfectly captured by the model of Hedges, p-hacking is much…

QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning. (arXiv:1911.12446v1 [cs.LG])

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Machine Learning algorithms based on Brain-inspired Hyperdimensional (HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy-efficiency in different machine learning tasks, such as classification, semi-supervised learning and clustering. A weakness of existing HD computing-based ML algorithms is the fact that they have to…

Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions. (arXiv:1911.12463v1 [cs.LG])

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This letter introduces an abstract learning problem called the “set embedding”: The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy set embedding examples.

Analysis of Hydrological and Suspended Sediment Events from Mad River Wastershed using Multivariate Time Series Clustering. (arXiv:1911.12466v1 [cs.LG])

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Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, helps to characterize the dynamics and flux of such…

Maximum likelihood estimators for scaled mutation rates in an equilibrium mutation-drift model. (arXiv:1911.12494v1 [q-bio.PE])

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The stationary sampling distribution of a neutral decoupled Moran or Wright-Fisher diffusion with neutral mutations is known to first order for a general rate matrix with small but otherwise unconstrained mutation rates. Using this distribution as a starting point we derive results for maximum likelihood estimates of scaled mutation rates from site frequency data under…

The Form of a Half-baked Creative Idea: Empirical Explorations into the Structure of Ill-defined Mental Representations. (arXiv:1911.12549v1 [q-bio.NC])

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Creative thought is conventionally believed to involve searching memory and generating multiple independent candidate ideas followed by selection and refinement of the most promising. Honing theory, which grew out of the quantum approach to describing how concepts interact, posits that what appears to be discrete, separate ideas are actually different projections of the same underlying…

Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints. (arXiv:1911.12656v1 [q-bio.NC])

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Optimality principles have been useful in explaining many aspects of biological systems. In the context of neural encoding in sensory areas, optimality is naturally formulated in a Bayesian setting, as neural tuning which minimizes mean decoding error. Many works optimize Fisher information, which approximates the Minimum Mean Square Error (MMSE) of the optimal decoder for…

Revisiting the coupling between accessibility and population growth. (arXiv:1911.12684v1 [physics.soc-ph])

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The coupling between population growth and transport accessibility has been an elusive problem for more than 60 years now. Due to the lack of theoretical foundations, most of the studies that considered how the evolution of transportation networks impacts the population growth are based on regression analysis in order to identify relevant variables. The recent…

Reliability and comparability of human brain structural covariance networks. (arXiv:1911.12755v1 [q-bio.NC])

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Structural covariance analysis is a promising and increasingly used structural Magnetic Resonance Imaging (MRI) analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. However, to our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects as well…