INTRODUCTION In addition to measurement errors, individual anatomical conditions could be made responsible for unexpected prediction errors in the determination of the correct intraocular lens power for cataract surgery. Obviously, such anatomical conditions might be relevant for both eyes. The purpose of this study was to evaluate whether the postoperative refractive error of the first eye has...

The dorsal anterior cingulate cortex (dACC) has been implicated in a variety of cognitive control functions, among them the monitoring of conflict, error, and volatility, error anticipation, reward learning, and reward prediction errors. In this work, we used a Bayesian ideal observer model, which predicts trial-by-trial probabilistic expectation of stop trials and response errors in the stop-s...

Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the network twice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock price which was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, the network is determined again f...

As a key part of the brain's reward system, midbrain dopamine neurons are thought to generate signals that reflect errors in the prediction of reward. However, recent evidence suggests that "upstream" brain areas may make important contributions to the generation of prediction error signals. To address this issue, we recorded neural activity in midbrain reticular formation (MRNm) while rats per...

In this Letter, we investigate the correlation rate of a random sequence data set which is collected by RFID (Radio Frequency IDentification) readers in an indoor location. Using a passive RFID tag introduces reading error, which causes a loss of original data. From the question of how sensing errors of RFID readers affect the location prediction algorithm used for context awareness services at...

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning. We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward. We found that these neurons encoded the difference between th...

â  the prediction of maximum temperatures as one of the most important climatic parameters due to climate change, global warming and the recent drought will provide definitely more opportunity for planning and the provision of necessary arrangements for the planners. maximum temperatures are much important in management of natural and water resources, agriculture, development of pests and disea...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...

Motion compensated prediction in conventional video coders is based on the encoder reconstruction of the previous frame where motion is estimated so as to minimize the encoder prediction error. Such design paradigms optimize the prediction at the encoder and ignore the effects of packet loss. In this paper, we propose a new prediction framework, which uses the expected decoder reconstruction of...

Optimal prediction is a computational method for systems that cannot be properly resolved, in which the unresolved variables are viewed as random. This paper presents a first analysis of optimal prediction as a numerical method. We prove the convergence of the scheme for a class of equations of Schrödinger type and derive error bounds for the mean error between the optimal prediction solution a...