An Open Source Pattern Recognition Toolbox for MATLAB

Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of different approaches to a given task. A toolbox with a unified framework across multiple pattern recognition techniques enables algorithm developers the ability to rapidly evaluate different choices prior to deployment. MATLAB is a widely used environment for algorithm development and prototyping, and although several MATLAB toolboxes for pattern recognition are currently available these are either incomplete, expensive, or restrictively licensed. In this work we describe a MATLAB toolbox for pattern recognition and machine learning known as the PRT (Pattern Recognition Toolbox), licensed under the permissive MIT license. The PRT includes many popular techniques for data preprocessing, supervised learning, clustering, regression and feature selection, as well as a methodology for combining these components using a simple, uniform syntax. The resulting algorithms can be evaluated using cross-validation and a variety of scoring metrics to ensure robust performance when the algorithm is deployed. This paper presents an overview of the PRT as well as an example of usage on Fisher's Iris dataset

The Influence of Bureau Scores, Customized Scores and Judgmental Review on the Bank Underwriting Decision-Making Process

In recent years commercial banks have moved toward automated forms of underwriting. This study employs unique bank loan-level data from a scoring lender to determine whether automated underwriting exhibits a potential ‘‘disparate impact’’ across income strata. The findings indicate that strict application of this custom scoring model leads to higher denial rates for low- to moderate-income borrowers when compared with both a naý¨ve judgmental system and a bureau scoring approach. These results suggest that financial regulators should focus more resources on the evaluation and study of customized scoring models.

Local and global spontaneous calcium events regulate neurite outgrowth and onset of GABAergic phenotype during neural precursor differentiation

Neural stem cells can generate in vitro progenitors of the three main cell lineages found in the CNS. The signaling pathways underlying the acquisition of differentiated phenotypes in these cells are poorly understood. Here we tested the hypothesis that Ca2+ signaling controls differentiation of neural precursors. We found low-frequency global and local Ca2+ transients occurring predominantly during early stages of differentiation. Spontaneous Ca2+ signals in individual precursors were not synchronized with Ca2+ transients in surrounding cells. Experimentally induced changes in the frequency of local Ca2+signals and global Ca2+ rises correlated positively with neurite outgrowth and the onset of GABAergic neurotransmitter phenotype, respectively. NMDA receptor activity was critical for alterations in neuronal morphology but not for the timing of the acquisition of the neurotransmitter phenotype. Thus, spontaneous Ca2+ signals are an intrinsic property of differentiating neurosphere-derived precursors. Their frequency may specify neuronal morphology and acquisition of neurotransmitter phenotype

Non-Markovian Stochastic Resonance

The phenomenological linear response theory of non-Markovian Stochastic Resonance (SR) is put forward for stationary two-state renewal processes. In terms of a derivation of a non-Markov regression theorem we evaluate the characteristic SR-quantifiers; i.e. the spectral power amplification (SPA) and the signal-to-noise ratio (SNR), respectively. In clear contrast to Markovian SR, a characteristic benchmark of genuine non-Markovian SR is its distinctive dependence of the SPA and SNR on small (adiabatic) driving frequencies; particularly, the adiabatic SNR becomes strongly suppressed over its Markovian counterpart. This non-Markovian SR theory is elucidated for a fractal gating dynamics of a potassium ion channel possessing an infinite variance of closed sojourn times.Comment: 4 pages, 1 figur