Диссертации, представленные на защиту и подготовленные в НИУ ВШЭ

The Large Hadron Collider (LHC), which was built by the European Organization for Nuclear Research (CERN), is the world’s largest collider. The LHCb experiment at the LHC focuses on studying heavy flavor physics, making precise measurements of CP violation, and investigating other effects within and beyond the Standard Model.The LHCb detector consists of several components, including an electromagnetic calorimeter (ECAL). Simulating the expected detector response is crucial for the physical analysis of the collected data and deriving physical results. Using theGeant4 package to simulate detector responses is computationally expensive and resource intensive, so there is a need to speed up this process. This research paper explores the potential of using generative adversarial networks (GANs) to accelerate the simulation process of calorimeter response in high-energy physics experiments. The thesis contribution consists of three essential parts. The model’s performance is highly sensitive to its architecture, so it was improved comparing to the previously published once. The second contribution is a method that helps the model to take particular properties of generated objects into account and improve its generation quality. This method requires increasing models capacity, so an other approach that allows to balance between training stability and expressivity is proposed. By proposing methods to enhance simulation speed and improve calorimeter response accuracy, this work holds significant implications for the advancement ofLHCb and other high-energy physics projects.

Array (Tensor) DBMSs strive to be the best systems for managing, processing, and even visualizing large multidimensional arrays (tensors). It is a young, fast-evolving, and inherently inter-disciplinary area: many core data types in diverse domains are naturally modeled by arrays (tensors). This dissertation presents fundamental theoretical, systems, and practical contributions to the area of Array (Tensor) DBMSs. Namely, we introduce two novel Research & Development directions: physical world simulations and tunable queries in Array (Tensor) DBMSs. We also propose a new formal Array (Tensor) DBMS data model, novel algorithms, approaches, architectural and implementation aspects for operating on multidimensional arrays (tensors), which can exceed the speed of state-of-the-art approaches and technologies by orders of magnitude. The significance of contributions is demonstrated across a wide variety of practical applications and real-world data. This dissertation is based on the results presented at premier international conferences in computer science: VLDB and SIGMOD.

The subject of this dissertation is learning in experimental games from both theoretical and empirical perspectives. The main objective of this work is to develop a generalized concept of strategy-based learning and to test its application, including empirical identification, in a class of simple experimental games. First, we discuss why there are so many learning models, what properties in a dynamic context are crucial, and what are the criteria for the "goodness" of these models. Classification of models of learners based on their crucial properties is presented.  Second, we explicitly test whether the models in the set are distinguishable given a set of models and the sampling constraint. Finally, the experiment supports the viability of the methodological study and an improvement in explanatory power with a new class of models. In a repeated game framework, we explore the response of human subjects to the uncertain behavior of the strategically sophisticated opponent. We build a strategic extension of the classical learning models and calibrate its practical application to our experimental data.

The dissertation is devoted to studying supply chain resilience. One of the effects related to supply chain resilience: the “ripple effect” is analyzed in the context of supply chains of perishable products. The interaction between ripple effect and bullwhip effect is investigated for the first time. The growing complexity of supply chains and the number of factors affecting supply chain resilience levels lead to more complicated system behavior during negative risk events. In the dissertation special focus is aimed at after disruption system stabilization process, the postponed redundancy effect, related to the supply chain restoration phase, was formulated. The research applies agent-based and discrete event simulation as the main method for analysis. It helps to examine both supply chain network and process specifics.