Maxim Borisyak

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  • (1 + ε)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

    Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.

    06/14/2019 ∙ by Maxim Borisyak, et al. ∙ 33 share

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  • Towards automation of data quality system for CERN CMS experiment

    Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies marginal cases: both of good an bad data, and use human expert decision to classify remaining "grey area" cases. This study uses collision data collected by the CMS experiment at LHC in 2010. We demonstrate that proposed workflow is able to automatically process at least 20% of samples without noticeable degradation of the result.

    09/25/2017 ∙ by Maxim Borisyak, et al. ∙ 0 share

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  • Numerical optimization for Artificial Retina Algorithm

    High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduction of the total computational time per event. Test results on simplified simulated model of LHCb VELO (VErtex LOcator) detector are presented. Also this approach is well-suited for implementation of paralleled computations as GPGPU which look very attractive in the context of upcoming detector upgrades.

    09/25/2017 ∙ by Maxim Borisyak, et al. ∙ 0 share

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  • Muon Trigger for Mobile Phones

    The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays. Upon interacting with Earth's atmosphere, these events produce extensive particle showers which can be detected by cameras on mobile phones. A typical shower contains minimally-ionizing particles such as muons. As these particles interact with CMOS image sensors, they may leave tracks of faintly-activated pixels that are sometimes hard to distinguish from random detector noise. Triggers that rely on the presence of very bright pixels within an image frame are not efficient in this case. We present a trigger algorithm based on Convolutional Neural Networks which selects images containing such tracks and are evaluated in a lazy manner: the response of each successive layer is computed only if activation of the current layer satisfies a continuation criterion. Usage of neural networks increases the sensitivity considerably comparable with image thresholding, while the lazy evaluation allows for execution of the trigger under the limited computational power of mobile phones.

    09/25/2017 ∙ by Maxim Borisyak, et al. ∙ 0 share

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  • A genetic algorithm for autonomous navigation in partially observable domain

    The problem of autonomous navigation is one of the basic problems for robotics. Although, in general, it may be challenging when an autonomous vehicle is placed into partially observable domain. In this paper we consider simplistic environment model and introduce a navigation algorithm based on Learning Classifier System.

    07/27/2015 ∙ by Maxim Borisyak, et al. ∙ 0 share

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  • Machine Learning on data with sPlot background subtraction

    Data analysis in high energy physics has to deal with data samples produced from different sources. One of the most widely used ways to unfold their contributions is the sPlot technique. It uses the results of a maximum likelihood fit to assign weights to events. Some weights produced by sPlot are by design negative. Negative weights make it difficult to apply machine learning methods. Loss function becomes unbounded and neural network training divergent. In this paper we propose a mathematically rigorous way to transform the weights obtained by sPlot into class probabilities conditioned on observables thus enabling to apply any machine learning algorithm out-of-the box.

    05/28/2019 ∙ by Maxim Borisyak, et al. ∙ 0 share

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