So far, we’ve mainly been talking about learning algorithms that model p(y|x; θ), the conditional distribution of y given x. For instance, logistic regression modeled p(y|x; θ) as hθ(x) = g(θ T x) where g is the sigmoid function. In these notes, we’ll talk about a different type of learning algorithm. Consider a classification problem in which we want to learn to distinguish between elephants (...

31 32 This study investigated the effects of angiotensin II (Ang II) on slow and rapid 33 baroreflex responses of barosensitive bulbospinal neurons in the rostral ventrolateral medulla 34 (RVLM) in urethane-anesthetized rabbits to determine whether the sympathetic baroreflex 35 modulation induced by application of Ang II into the RVLM can be explained by the total 36 action of Ang II on individ...

We tackle the problem of answer type prediction, applying a transition-based neural network parser to understand the syntactic structure of a query and infer the focus word or phrase that contains the correct type. To achive this, we first come up with a framework to combine a dataset of labeled question-answer pairs and a type inventory into a dataset to learn types. Second, we fit a sigmoid p...

Artificial neural networks (ANNs) are a widely used machine learning approach for estimating low-cycle fatigue parameters. ANN structure has its parameters such as hidden layers, neurons, activation functions, training and so forth, these have significant influence over the results. Three layer combinations, neurons ranging from 1 to 25, different functions like hyperbolic tangent sigmoid (tans...

Mathematical models of population growth have been constructed to provide an abstract of some significant aspect of true ecological situation. In this paper, we put some models where the parameters of the biological growth model systematically change over time. Verhust (1838) first proposed three-parameter model for the growth of single-species populations [1,5,9] that shows a logistic sigmoid ...

This paper shows that the performance of the Hoppeld network for solving optimization problems can be improved by using a new activation (output) function. The eeects of the activation function on the performance of the Hoppeld network are analyzed. It is shown that the sigmoid activation function in the Hoppeld network is sensitive to noise of neurons. The reason is that the sigmoid function i...

Previously, many different types of NTS barosensitive neurons were identified. However, the time course of NTS barosensitive neuronal activity (NA) in response to arterial pressure (AP) changes, and the relationship of NA-AP changes, have not yet been fully quantified. In this study, we made extracellular recordings of single NTS neurons firing in response to AP elevation induced by occlusion o...

* Corresponding author. E-mail addresses: [email protected] (M. Fang). 1537-5110/$ – see front matter a 2009 IAgrE doi:10.1016/j.biosystemseng.2009.12.008 Heavy metal stress in soils results in subtle changes in leaf chlorophyll concentration, which are related to crop growth and crop yield. Accurate estimation of the chlorophyll concentration of a crop under heavy metal stress is essential...

Microscopic hyperspectral imaging technology is a potential non-destructive and non-contact method for colour measurement of micrometre-sized textile fibres. However, specularity on the fibre surface can distort accurate information affect accuracy measurement. This paper proposed specular-constrained sparse approximation (SCSA) specular-diffuse reflection separation from images wool First, spe...

In this paper, we analyze a large published data set of cross sections of frontal sinuses of 3 to 11-year-olds (105 males and 87 females) from Central Europe to investigate several issues relating to frontal sinus ontogeny. Despite a large variation in every one year age cohort, we detect no asymmetry of the left average versus the right average frontal sinus lobe cross-sectional areas in the p...