This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for probl...

Background: Human luteinizing hormone (hLH) stimulates steroid biosynthesis of the ovary, triggers ovulation and prepares androgen production of testicular Leydig cells. LH belongs to the family of glycoprotein hormones that are heterodimers consisting of a common α-subunit and specific β-subunit. hLH is necessary for clinical and infertility treatment. Recombinant DNA technology provides a use...

Probabilistic graphical models are tools that are used to represent the probability distribution of a vector of random variables X = (X1, . . . , XN ). In this paper we introduce functions f(x1, . . . , xN) defined over the given vector. These functions also are random variables. The main result of the paper is an algorithm for finding the expected value and other moments for some classes of f(...

In this paper we consider the problem of distributed throughput maximization of networks with multi-channel ALOHA medium access protocol. In the multi-channel ALOHA protocol, each user tries to randomly access a channel using a probability vector defining the access probability to the various channels. First, we characterize the Nash Equilibrium Points (NEPs) of the network when users solve the...

Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researches have been performed to criticality determine the structure and function of repetitiv...

In this paper, we propose a new partition-based filter for removing mixed high probability impulse and Gaussian noises by extraction of the signals. The proposed filter sorts the elements of sample vector and extracts the signals around the median from sorted signals. The extraction vector is classified into one of M partitions, and a particular filtering operation is then activated. The output...

A.1. Weighted Majority in Discrete MDPs For notation simplicity, for each state s 2 S , we represent the policy ⇡(·|s) as a discrete probability vector ⇡s 2 (A). We also represent dt as a S-dimension probability vector from S-d simplex, consisting of dt (s), 8s 2 S . For each s, we use Qt (s) to denote the A-dimension vector consisting of the state-action cost-to-go Qt (s, a) for all a 2 A. Wit...

A novel method for edge detection in vector images is proposed that does not require any prior knowledge of the imaged scenes. In the derivation, it is assumed that the observed vector images are realizations of spatially quasistationary processes, and that the vector observations are generated by parametric probability distribution functions of known form whose parameters are in general unknow...

Several methods of watermark detection involve computing a v ector from some input media, computing the normalized correlation between that vector and a predeened watermark vector, and comparing the result against a threshold. We show that, if the probability density function of vectors that arise from random, unwatermarked media is a zero-mean, spherical Gaussian, then the probability that suc...

A new sharp upper bound for the probability of error of the likelihood ratio test is given for the detection in white Gaussian noise of any random vector whose norm is greater than, or equal to, a given value and whose probability of presence is less than, or equal to, one half. Also, a new test for the detection of such vectors is described. This test does not depend on the distribution of the...