Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in terms of computing resources. Thus new approaches to event generation and simulation of detector responses are needed. In LHCb, the accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU time. An alternative approach is described here, when one generates high-level reconstructed observables using a generative neural network to bypass low level details. This network is trained to reproduce the particle species likelihood function values based on the track kinematic parameters and detector occupancy. The fast simulation is trained using real data samples collected by LHCb during run 2. We demonstrate that this approach provides high-fidelity results.
This article describes the issues of analysis and assessment of the human factor for predicting the violation committed by the locomotive driver when driving the electric rolling stock. An intelligent system overview for assessing the likelihood of a violation by a locomotive driver is given. Such a system can generate recommendations depending on previously committed violations. One of the tasks is to reduce the risk of locomotive safety devices malfunctions, which are part of the locomotive electrical equipment. The solution to the problem of predicting the occurrence of possible violations is solved using tools and machine learning algorithms. A model has been built that generates recommendations for the driver based on information about previously committed violations and several static characteristics of the locomotive driver.
The paper considers the problem of forecastingthe company's share price in the conditions of playing on the stock exchange using artificial neural networks. An artificial neural network of direct propagation is used. The method of reverse error propagation is selected for the training method. A wide range of experiments was conducted on a set of data that covers the summer period of stock market trading. This makes it possible to analyze and compare the effectiveness of various artificial neural network designs using various activation functions.Artificial neural networkshave been used in the last decade to solve problems of image classification, clustering/classification, function approximation function, prediction /forecasting / forecasting, optimization, contentaddressable memory and control.
This book constitutes the proceedings of the 25th International Symposium on Foundations of Intelligent Systems, ISMIS 2020, held in Graz, Austria, in October 2020. The conference was held virtually due to the COVID-19 pandemic.
The 35 full and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. Included is also one invited talk. The papers deal with topics such as natural language processing; deep learning and embeddings; digital signal processing; modelling and reasoning; and machine learning applications.
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being – to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.
This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.
The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.
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.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. We show our unsupervised learning pipeline may help segmentation models. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.
The 𝐵0𝑠⎯⎯⎯⎯⎯⎯⎯→𝜒𝑐2𝐾+𝐾−Bs0¯→χc2K+K− decay mode is observed and its branching fraction relative to the corresponding 𝜒𝑐1χc1decay mode, in a ±15MeV/𝑐2±15MeV/c2 window around the 𝜙ϕ mass, is found to be (𝐵0𝑠⎯⎯⎯⎯⎯⎯⎯→𝜒𝑐2𝐾+𝐾−)(𝐵0𝑠⎯⎯⎯⎯⎯⎯⎯→𝜒𝑐1𝐾+𝐾−)=(17.1±3.1±0.4±0.9)%,B(Bs0¯→χc2K+K−)B(Bs0¯→χc1K+K−)=(17.1±3.1±0.4±0.9)%, where the first uncertainty is statistical, the second systematic and the third due to the knowledge of the branching fractions of radiative 𝜒𝑐χc decays. The decay mode 𝐵0𝑠⎯⎯⎯⎯⎯⎯⎯→𝜒𝑐1𝐾+𝐾−Bs0¯→χc1K+K− allows the 𝐵0𝑠Bs0 mass to be measured as 𝑚(𝐵0𝑠)=5366.83±0.25±0.27MeV/𝑐2,m(Bs0)=5366.83±0.25±0.27MeV/c2,where the first uncertainty is statistical and the second systematic. A combination of this result with other LHCb determinations of the 𝐵0𝑠Bs0 mass is made.
Theoretical analysis in  suggested that adversarially trained generative models are naturally inclined to learn distribution with low support. In particular, this effect is caused by the limited capacity of the discriminator network. To verify this claim,  proposed a statistical test based on the birthday paradox that partially confirmed the analysis. In this paper, we continue this line of work and develop a parameter-free and straightforward method to estimate the support size of an arbitrary decoder-based generative model. Our approach considers the decoder network from a geometric viewpoint and evaluates the support size as the volume of the manifold containing the generative model samples. Additionally, we propose a method to measure non-uniformity of a generative model that can provide additional insight into the model’s behavior. We then apply these tools to perform a quantitative comparison of common generative models.
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.