Machine Learning on data with sPlot background subtraction
Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data.
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.
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural 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.
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.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The dynamics of a two-component Davydov-Scott (DS) soliton with a small mismatch of the initial location or velocity of the high-frequency (HF) component was investigated within the framework of the Zakharov-type system of two coupled equations for the HF and low-frequency (LF) fields. In this system, the HF field is described by the linear Schrödinger equation with the potential generated by the LF component varying in time and space. The LF component in this system is described by the Korteweg-de Vries equation with a term of quadratic influence of the HF field on the LF field. The frequency of the DS soliton`s component oscillation was found analytically using the balance equation. The perturbed DS soliton was shown to be stable. The analytical results were confirmed by numerical simulations.
The Handbook of CO₂ in Power Systems' objective is to include the state-of-the-art developments that occurred in power systems taking CO₂ emission into account. The book includes power systems operation modeling with CO₂ emissions considerations, CO₂ market mechanism modeling, CO₂ regulation policy modeling, carbon price forecasting, and carbon capture modeling. For each of the subjects, at least one article authored by a world specialist on the specific domain is included.