Measurement of the Charm-Mixing Parameter yCP
A measurement of the charm-mixing parameter yCP using D0 → KþK−, D0 → πþπ−, and D0 → K−πþ decays is reported. The D0 mesons are required to originate from semimuonic decays of B− and B0 mesons. These decays are partially reconstructed in a data set of proton-proton collisions at center-of-mass energies of 7 and 8 TeV collected with the LHCb experiment and corresponding to an integrated luminosity of 3 fb−1. The yCP parameter is measured to be ð0.57 0.13ðstatÞ 0.09ðsystÞÞ%, in agreement with, and as precise as, the current world-average value.
We present a model for freight train time prediction based on station network analysis and specific feature engineering. We discuss the first pipeline to improve the freight flight duration prediction in Russia. While every freight company use only reference book made by RZD (Russian Railways) based on railroad distances with accuracy measured in days, we argue that one could predict the flight duration with error less than twenty hours while decreasing error to twelve hours for certain type of freight trains.
Reconstruction and identification in calorimeters of modern High Energy Physics experiments is a complicated task. Solutions are usually driven by a priori knowledge about expected properties of reconstructed objects. Such an approach is also used to distinguish single photons in the electromagnetic calorimeter of the LHCb detector on LHC from overlapping photons produced from high momentum pi0 decays. We studied an alternative solution based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. Constructing such a discriminator from “first principles” allowed improve separation performance from 80% to 93%, that means reducing primary photons fake rate by factor of two. In presentation we discuss different approaches to the problem, architecture of the classifier, its optimization, and compare performance of the ML approach with classical one.
The production of W and Z bosons in association with jets is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 ± 0.02 fb−1 . The W boson is identified using its decay to a muon and a neutrino, while the Z boson is identified through its decay to a muon pair. Total cross-sections are measured and combined into charge ratios, asymmetries, and ratios of W+jet and Z+jet production cross-sections. Differential measurements are also performed as a function of both boson and jet kinematic variables. All results are in agreement with Standard Model predictions.
In this paper, we study the problem of predicting collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, in which authors play the role of nodes, and weighted edges connecting two authors are formed by storing either a number or quality metric of research papers co-authored by these authors. Our task is then formulated as regression machine learning model based on network features constructed using network embedding. We evaluate our edge embeddings on large AMiner co-authorship network for (un)weighted node2vec network embeddings and also on the dataset containing temporal information on National Research University Higher School of Economics (HSE) over twenty five years of research articles indexed in Russian Science Citation Index and Scopus for predicting the quality of future research publications measures in terms of quartiles corresponding to published journals indexed in Scopus. We showed that our model of network edge representation has better performance for stated regression task on both, AMiner and HSE co-authorship networks.
The law of accelerating returns can be viewed as a concept that describes acceleration of technological progress. The idea is that tools are used for developing more advanced tools that are applied for creating even more advanced tools etc. A similar idea has been implemented in algorithms for advancing artificial intelligence. In this paper, the results of applying these algorithms in games are discussed. Nevertheless, real life tasks seem more complicated. The game theoretic approach can be applied for transition from theoretical and unrealistic games to more complex and practical tasks. Applications of the game theoretic approach to advance artificial intelligence in solving tasks in the credit industry are proposed.
This volume contains the papers presented at the session "Data Science" within the V International Conference on Information Technology and Nanotechnology (ITNT-2019). The conference was held in Samara, Russia, during May 21-24, 2019 (itnt-conf.org). The conference is a forum for leading researchers from all over the world aimed to discuss the latest advances in the basic and applied research in the field of Information Technology and Nanotechnology. It is also aimed to attract young people to advanced scientific research and share the latest trends in training and research programs for future ITNT specialists . In addition to the session "Data Science", ITNT-2019 also included three other sessions: "Computer Optics and Nanophotonics", "Image Processing and Earth Remote Sensing" and "Mathematical Modeling of Physico-Technical Processes and Systems". The whole forum brought together more than 450 scientists from United Kindom, Japan, Switzerland, Iran, Poland, Bulgaria, Finland, China, Kazakhstan and Russia, as well as representatives of global high-tech corporations, developers of modern electronics – Huawei, Nvidia, Intel, and Azimuth Photonics, and more than 60 cities in the world. 436 talks enabled discussion on a wide range of topics. The topics of the session "Data Science" were grouped into the following key directions: Data Mining (Big data, Systems and platforms, Methods); Machine Learning (Neural networks, Statistical methods, Feature-based classification, Applications); Security, Cryptography (Cryptosystems design and analysis, Mathematical and algorithmic aspects, Efficient implementations of algorithms, Network security); High Performance Computing (Parallel programming models and languages, Highperformance implementations, Complex systems simulation).
The Fifth HCT Information Technology Trends (ITT 2018) is a major international research conference for the presentation of innovative ideas, approaches, technologies, research findings and outcomes, best practices and case studies, national and international projects, institutional standards and policies on Emerging Technologies for Artificial Intelligence. ITT 2018 will provide an outstanding forum for researchers, practitioners, students, policy makers, and users to exchange ideas, techniques and tools, raise awareness and share experiences related to all practical and theoretical aspects of Emerging Technologies for Artificial Intelligence, so as to develop solutions related to communications, computer science and engineering, control systems as well as interdisciplinary research and applications.
A full amplitude analysis of Λ 0 b → J/ψ pπ− decays is performed with a data sample acquired with the LHCb detector from 7 and 8 TeV pp collisions, corresponding to an integrated luminosity of 3 fb−1 . A significantly better description of the data is achieved when, in addition to the previously observed nucleon excitations N → pπ−, either the Pc(4380)+ and Pc(4450)+ → J/ψ p states, previously observed in Λ 0 b → J/ψ pK− decays, or the Zc(4200)− → J/ψ π− state, previously reported in B0 → J/ψ K+π − decays, or all three, are included in the amplitude models. The data support a model containing all three exotic states, with a significance of more than three standard deviations. Within uncertainties, the data are consistent with the Pc(4380)+ and Pc(4450)+ production rates expected from their previous observation taking account of Cabibbo suppression.