A GIF++ Gamma Irradiation Facility at the SPS H4 Beam Line

The current document describes a proposal to implement a new gamma irradiation facility, combined with a high-energy particle beam in the SPS H4 beam line in hall EHN1. This new GIF++ facility is motivated by strong needs from the LHC and sLHC detector and accelerator communities for the tests of LHC components and systems

Hybrid Software Development Approaches in Practice: A European Perspective

Agile and traditional development approaches are used in combination in todays software development. To improve the understanding and to provide better guidance for selecting appropriate development approaches, it is important to analyze such combinations in practice. Results obtained from an online survey strongly confirm that hybrid development approaches are widely used in industry. Our results show that hybrid development approaches: (i) have become reality for nearly all companies; (ii) are applied to specific projects even in the presence of company-wide policies for process usage; (iii) are neither planned nor designed but emerge from the evolution of different work practices; and, (iv) are consistently used regardless of company size or industry secto

NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images

Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory. Spiking neural models are distinguished from classical neurons by being biological plausible and exhibiting the same dynamics as those observed in biological neurons. This paper proposes a Natural Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired Convolutional Spiking Neural Network (CSNN), for classifying objects extracted from natural images. A two-stage training algorithm is proposed using unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an improvement over the 2-layer neural networks previously applied to this dataset.Comment: 12 page