This paper develops a novel passive stochastic gradient algorithm. In approximation, the algorithm does not have control over location where noisy gradients of cost function are evaluated. Classical algorithms use kernel that approximates Dirac delta to weigh based on how far they evaluated from desired point. this we construct multi-kernel The performs substantially better in high dimensional ...

The clock synchronization problem is to determine the time difference Delta between two spatially separated clocks. When message delivery times between the two clocks are uncertain, O(2(2n)) classical messages must be exchanged between the clocks to determine n digits of Delta. On the other hand, as we show, there exists a quantum algorithm to obtain n digits of Delta while communicating only O...

Visual P300 responses were recorded by using checkerboard-type stimuli. (1) High amplitude P300-delta responses were visible even in single trial ERPs. (2) An algorithm for efficient selection of P300 single trials (based on evaluation of delta responses) is introduced. (3) The 'universal' character of the P300-delta response demonstrated in this report may open new avenues for the understandin...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

this paper proposes an efficient tracking method for velocity control of an electro-hydraulic servo system (ehss) in the presence of flow nonlinearities and internal friction. the tracking method controller is a kind of feedback error learning structure. in the proposed method, the feedback error learning (fel) algorithm is used to control the velocity. there is no need to compute the system ja...

We present efficient differentially private algorithms for learning unions of polygons in the plane (which are not necessarily convex). Our $(\alpha,\beta)$--probably approximately correct and $(\varepsilon,\delta)$--differentially using a sample size $\tilde{O}\left(\frac{1}{\alpha\varepsilon}k\log d\right)$, where domain is $[d]\times[d]$ $k$ number edges union polygons. obtained by designing...