Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks
A search for the lepton-flavour violating decay D0 → e ±µ ∓ is made with a dataset corresponding to an integrated luminosity of 3.0 fb−1 of proton-proton collisions at centre-of-mass energies of 7 TeV and 8 TeV, collected by the LHCb experiment. Candidate D0 mesons are selected using the decay D∗+ → D0π + and the D0 → e ±µ ∓ branching fraction is measured using the decay mode D0 → K−π + as a normalisation channel. No significant excess of D0 → e ±µ ∓ candidates over the expected background is seen, and a limit is set on the branching fraction, B(D0 → e ±µ ∓) < 1.3×10−8 , at 90% confidence level. This is an order of magnitude lower than the previous limit and it further constrains the parameter space in some leptoquark models and in supersymmetric models with R-parity violation.
The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.
During LHC Run 1, the LHCb experiment recorded around 1011 collision events. This paper describes Event Index — an event search system. Its primary function is to quickly select subsets of events from a combination of conditions, such as the estimated decay channel or number of hits in a subdetector. Event Index is essentially Apache Lucene  optimized for read-only indexes distributed over independent shards on independent nodes.