Representation of the Consumer Interest in the Federal Government

We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices

The Influence of the Socratic Tradition on Cambridge Practice and Its Implication on Chinese Higher Education

This paper presents the use of polyelectrolyte-decorated amyloid fibrils as gate electrolyte in electrochromic electrochemical transistors. Conducting polymer alkoxysulfonate poly(3,4-ethylenedioxythiophene) (PEDOT-S) and luminescent conjugate polymer poly(thiophene acetic acid) (PTAA) are utilized to decorate insulin amyloid fibrils for gating lateral poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) electrochemical transistors. In this comparative work, four gate electrolytes are explored, including the polyelectrolytes and their amyloid-fibril complexes. The discrimination of transistor behaviors with different gate electrolytes is understood in terms of an electrochemical mechanism. The combination of luminescent polymers, biomolecules and electrochromic transistors enables multi functions in a single device, for example, the color modulation in monochrome electrochromic display, as well as biological sensing/labeling.Funding Agencies|"OPEN" project at the Center of Organic Electronics (COE) at Linkoping University, Sweden||Strategic Research Foundation SSF||</p

Local Approximations, Real Interpolation and Machine Learning

We suggest a novel classification algorithm that is based on local approximations and explain its connections with Artificial Neural Networks (ANNs) and Nearest Neighbour classifiers. We illustrate it on the datasets MNIST and EMNIST of images of handwritten digits. We use the dataset MNIST to find parameters of our algorithm and apply it with these parameters to the challenging EMNIST dataset. It is demonstrated that the algorithm misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow artificial neural networks (ANNs with few hidden layers) that both have more than 1.3% of errorsComment: arXiv admin note: substantial text overlap with arXiv:2204.1314

Improving image contrast and material discrimination with nonlinear response in bimodal atomic force microscopy

Atomic force microscopy has recently been extented to bimodal operation, where increased image contrast is achieved through excitation and measurement of two cantilever eigen-modes. This enhanced material contrast is advantageous in analysis of complex heterogeneous materials with phase separation on the micro or nanometre scale. Here we show that much greater image contrast results from analysis of nonlinear response to the bimodal drive, at harmonics and mixing frequencies. The amplitude and phase of up to 17 frequencies are simultaneously measured in a single scan. Using a machine-learning algorithm we demonstrate almost threefold improvement in the ability to separate material components of a polymer blend when including this nonlinear response. Beyond the statistical analysis performed here, analysis of nonlinear response could be used to obtain quantitative material properties at high speeds and with enhanced resolution.Funding Agencies|Knut and Alice Wallenberg Foundation; Vetenskapsradet</p