particulate matters commonly found dispersed in the atmosphere are composed of a large variety of substances. dustfall is the usual index of particles in the size range greater than 10 m. two semi automated so2 sampling units were installed in tehran university campus (from 21 january 1973 to 22 dec. 1974) and gheitarieh area (from 23 august to 23 oct. 1974) hydrogen peroxide method was used fo...

Future technologies below 90nm will present transistors so small that they will be heavily influenced by electromagnetic noise and SEU induced errors. This way, together with process variability, design as known today is likely to change. Since many soft errors might appear at the same time, a different design approach must be taken. The use of stochastic computation operators as an inherently ...

In this paper the problem of European option valuation in a Levy process setting is analysed. In our model the underlying asset follows a geometric Levy process. The jump part of the log-price process, which is a linear combination of Poisson processes, describes upward and downward jumps in price. The proposed pricing method is based on stochastic analysis and the theory of fuzzy sets. We assu...

The concept of geometric-arithmetic indices was introduced in the chemical graph theory. These indices are defined by the following general formula:     ( ) 2 ( ) uv E G u v u v Q Q Q Q GA G , where Qu is some quantity that in a unique manner can be associated with the vertex u of graph G. In this paper the exact formula for two types of geometric-arithmetic index of Vphenylenic nanotube ar...

Sample path Large Deviation Principles (LDP) of the Freidlin-Wentzell type are derived for a class of diffusions which govern the price dynamics in common stochastic volatility models from Mathematical Finance. LDP are obtained by relaxing the non-degeneracy requirement on the diffusion matrix in the standard theory of Freidlin and Wentzell. As an application, a sample path LDP is proved for th...

Randomized algorithms that base iteration-level decisions on samples from some pool are ubiquitous in machine learning and optimization. Examples include stochastic gradient descent and randomized coordinate descent. This paper makes progress at theoretically evaluating the difference in performance between sampling withand without-replacement in such algorithms. Focusing on least means squares...

It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging. The stochastic gradient descent algorithm becomes unstable in the presence of noisy gradient updates resulting from arithmetic with limited numeric precision. One of the wellaccepted solutions facilitating the training of low precision fixed p...

The popular Dynamic Markov Compression Algorithm (DMC) [8] is a member of the family of data compression algorithms that combine an on-line stochastic data model with an arithmetic coder. DMC's distinguishing feature is an elegant but ad hoc modeling technique that provides state-of-the-art compression performance and matchless conceptual simplicity. In practice, however, the cost of DMC's simp...

Randomized algorithms that base iteration-level decisions on samples from some pool are ubiquitous in machine learning and optimization. Examples include stochastic gradient descent and randomized coordinate descent. This paper makes progress at theoretically evaluating the difference in performance between sampling withand without-replacement in such algorithms. Focusing on least means squares...

In this paper, an interior point algorithm for linear programs is adapted for solving multistage stochastic linear programs. The algorithm is based on Monteiro and Adler’s path-following algorithm for deterministic linear programs. In practice, the complexity of the algorithm is linear with respect to the size of the sample space. The algorithm starts from a feasible solution of the problem and...