In this paper, a micropower interface IC for a capacitive 3-axis micro-accelerometer implemented in a 0.25 -μm CMOS process is presented. The fully-integrated sensor interface consists of a sensor front-end that converts the acceleration signal into the digital domain, a decimator, a frequency reference, a clock generator for the front-end, a voltage and current reference, the required referenc...

Missing values occur in studies of various disciplines such as social sciences, medicine, and economics. The missing mechanism in these studies should be investigated more carefully. In this article, some models, proposed in the literature on longitudinal data with dropout are reviewed and compared. In an applied example it is shown that the selection model of Hausman and Wise (1979, Econometri...

Dropout in randomised controlled trials is common and threatens the validity of results, as completers may differ from people who drop out. Differing dropout rates between treatment arms is sometimes called differential dropout or attrition. Although differential dropout can bias results, it does not always do so. Similarly, equal dropout may or may not lead to biased results. Depending on the ...

The shunt-LDO regulator is a new regulator concept which combines a shunt and a Low Drop-Out (LDO) regulator. Designed as an improved shunt regulator to match the needs of serially powered detector systems, it can also be used as a pure LDO regulator for general application in powering schemes requiring linear regulation. The flexibility of the design makes the shunt-LDO regulator a good candid...

We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that ...

Recurrent Neural Networks are very powerful computational tools that are capable of learning many tasks across different domains. However, it is prone to overfitting and can be very difficult to regularize. Inspired by Recurrent Dropout [1] and Skip-connections [2], we describe a new and simple regularization scheme: Stochastic Dropout. It resembles the structure of recurrent dropout, but offer...

Deep Neural Networks often require good regularizers to generalize well. Dropout is one such regularizer that is widely used among Deep Learning practitioners. Recent work has shown that Dropout can also be viewed as performing Approximate Bayesian Inference over the network parameters. In this work, we generalize this notion and introduce a rich family of regularizers which we call Generalized...

Dropout of sport is an issue in sport and public health domains. The aim of this study was to identify the potential dropout reasons of school athletes and to examine if their perception of dropout was affected by the previous dropout experience. There were 50 subjects who were divided into two groups based on their previous dropout experience (Dropout Group=22, No Dropout Group=28). They fille...

Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager et al. We focus on linear classification where a convex proxy to the misclass...

This paper presents the design and postlayout simulation results of a capacitor-less low dropout (LDO) regulator fully integrated in low-cost standard 180 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology which regulates output voltage at 1.2 V from 3.3 to 1.3 battery over −40 120 °C temperature range. To meet with constraints system-on-chip (SoC) battery-operated devices, ultralow p...