Number of stage implication towards multistage interconnection network reliability

The reliable operation of interconnection networks is main concern in system performance. Reliable operation in multistage interconnection networks depend on their topology, network configuration and number of stages in the system. Performance improvement and reliability increasing are two major attributes in multistage interconnection network topology. As the number of stage and system complexity increase the reliability performance becomes an important issues. In this paper we observe two topological of multistage interconnection network called the shuffle exchange network and gamma network to investigate the effect on number of stage in multistage interconnection network reliability. Three types of stages namely as basic stage, lesser stage and extra stage have been compared and the results shows that lesser stage provide highest reliability performance among all topological measured in this paper

Very high energy observations of the BL Lac objects 3C 66A and OJ 287

Using the Solar Tower Atmospheric Cherenkov Effect Experiment (STACEE), we have observed the BL Lac objects 3C 66A and OJ 287. These are members of the class of low-frequency-peaked BL Lac objects (LBLs) and are two of the three LBLs predicted by Costamante and Ghisellini to be potential sources of very high energy (>100 GeV) gamma-ray emission. The third candidate, BL Lacertae, has recently been detected by the MAGIC collaboration. Our observations have not produced detections; we calculate a 99% CL upper limit of flux from 3C 66A of 0.15 Crab flux units and from OJ 287 our limit is 0.52 Crab. These limits assume a Crab-like energy spectrum with an effective energy threshold of 185 GeV.Comment: 24 pages, 15 figures, Accepted for publication in Astroparticle Physic

Neutrino telescopes under the ocean: The case for ANTARES

Neutrino telescopes offer an alternative way to explore the Universe. Several projects are in operation or under construction. A detector under the ocean is very promising because of the very accurate angular resolution that it provides. The ANTARES project is intended to demonstrate the feasibilty of such a detector.Comment: Talk given at the Neutrino98 conference, Takayama, Japan, June 4-9, 199

Application of Neural Networks for Energy Reconstruction

The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy distribution and good energy resolution can be achieved. Significant improvement of the energy resolution and linearity is reached in comparison with other weighting methods for energy reconstruction.Comment: 18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments & Methods

Data-Driven Sparse Structure Selection for Deep Neural Networks

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy state-of-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter -- scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection.Comment: ECCV Camera ready versio