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31 results on '"Muravev, Anton"'

3. Neural Architecture Search by Estimation of Network Structure Distributions

Author

Muravev, Anton, Raitoharju, Jenni, and Gabbouj, Moncef

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their limits. Manual design of network architectures from scratch relies heavily on trial and error, while using existing pretrained models can introduce redundancies or vulnerabilities. Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist of identical repeating blocks. While such approach allows for faster search, it does so at the cost of expressivity. We instead propose an alternative probabilistic representation of a whole neural network structure under the assumption of independence between layer types. Our matrix of probabilities is equivalent to the population of models, but allows for discovery of structural irregularities, while being simple to interpret and analyze. We construct an architecture search algorithm, inspired by the estimation of distribution algorithms, to take advantage of this representation. The probability matrix is tuned towards generating high-performance models by repeatedly sampling the architectures and evaluating the corresponding networks, while gradually increasing the model depth. Our algorithm is shown to discover non-regular models which cannot be expressed via blocks, but are competitive both in accuracy and computational cost, while not utilizing complex dataflows or advanced training techniques, as well as remaining conceptually simple and highly extensible., Comment: 16 pages, 4 figures, 3 tables

Published
2019
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4. Altering the structures of 3D supramolecular assemblies from melamine and cyanuric acid derivatives in water.

Author

Orekhov, Nikita, Bukhtiiarova, Nina, Brushevich, Zlata A., Muravev, Anton A., Nadav, Elad, Tsarfati, Yael, Kossoy, Anna, Feldman, Isai, Zelenina, Anastasia, Rubekina, Anna A., Semenov, Sergey N., and Skorb, Ekaterina V.

Subjects
CYANURIC acid, ACID derivatives, METHYL groups, MOLECULAR dynamics, ELECTRON microscopy
Abstract

Herein, we obtained two supramolecular assemblies with layered structures from melamine, N-methylmelamine, and hexynyl-cyanuric acid in water. By combination of X-ray diffraction, electron microscopy, and molecular dynamics studies, we found that introducing one methyl group in melamine alters the arrangement of the layers in these structures. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Periodic Self-Assembly of Poly(ethyleneimine)–poly(4-styrenesulfonate) Complex Coacervate Membranes

Author

Kukhtenko, Ekaterina V., primary, Lavrentev, Filipp V., additional, Shilovskikh, Vladimir V., additional, Zyrianova, Polina I., additional, Koltsov, Semyon I., additional, Ivanov, Artemii S., additional, Novikov, Alexander S., additional, Muravev, Anton A., additional, Nikolaev, Konstantin G., additional, Andreeva, Daria V., additional, and Skorb, Ekaterina V., additional

Published
2022
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10. Piezo-Responsive Hydrogen-Bonded Frameworks Based on Vanillin-Barbiturate Conjugates

Author

Nebalueva, Anna S., primary, Timralieva, Alexandra A., additional, Sadovnichii, Roman V., additional, Novikov, Alexander S., additional, Zhukov, Mikhail V., additional, Aglikov, Aleksandr S., additional, Muravev, Anton A., additional, Sviridova, Tatiana V., additional, Boyarskiy, Vadim P., additional, Kholkin, Andrei L., additional, and Skorb, Ekaterina V., additional

Published
2022
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13. Thiacalixarenes with Sulfur Functionalities at Lower Rim: Heavy Metal Ion Binding in Solution and 2D-Confined Space

Author

Muravev, Anton, primary, Yakupov, Ayrat, additional, Gerasimova, Tatiana, additional, Islamov, Daut, additional, Lazarenko, Vladimir, additional, Shokurov, Alexander, additional, Ovsyannikov, Alexander, additional, Dorovatovskii, Pavel, additional, Zubavichus, Yan, additional, Naumkin, Alexander, additional, Selektor, Sofiya, additional, Solovieva, Svetlana, additional, and Antipin, Igor, additional

Published
2022
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14. Periodic Self-Assembly of Poly(ethyleneimine)–poly(4-styrenesulfonate) Complex Coacervate Membranes.

Author

Kukhtenko, Ekaterina V., Lavrentev, Filipp V., Shilovskikh, Vladimir V., Zyrianova, Polina I., Koltsov, Semyon I., Ivanov, Artemii S., Novikov, Alexander S., Muravev, Anton A., Nikolaev, Konstantin G., Andreeva, Daria V., and Skorb, Ekaterina V.

Subjects
ENERGY storage, COACERVATION, PERMEABILITY, POLYELECTROLYTES, CHRONOAMPEROMETRY, POLYETHYLENEIMINE
Abstract

Coacervation is a self-assembly strategy based on the complexation of polyelectrolytes, which is utilized in biomedicine and agriculture, as well as automotive and textile industries. In this paper, we developed a new approach to the on-demand periodic formation of polyelectrolyte complexes through a Liesegang-type hierarchical organization. Adjustment of reaction conditions allows us to assemble materials with a tunable spatiotemporal geometry and establish materials' production cycles with a regulated periodicity. The proposed methodology allows the membrane to self-assemble when striving to reach balance and self-heal after exposure to external stimuli, such as potential difference and high pH. Using chronopotentiometry, K+ ion permeability behavior of the PEI–PSS coacervate membranes was demonstrated. The periodically self-assembled polyelectrolyte nanomembranes could further be integrated into novel energy storage devices and intelligent biocompatible membranes for bionics, soft nanorobotics, biosensing, and biocomputing. [ABSTRACT FROM AUTHOR]

Published
2023
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16. Switching Ion Binding Selectivity of Thiacalix[4]arene Monocrowns at Liquid–Liquid and 2D-Confined Interfaces

Author

Muravev, Anton, primary, Yakupov, Ayrat, additional, Gerasimova, Tatiana, additional, Nugmanov, Ramil, additional, Trushina, Ekaterina, additional, Babaeva, Olga, additional, Nizameeva, Guliya, additional, Syakaev, Viktor, additional, Katsyuba, Sergey, additional, Selektor, Sofiya, additional, Solovieva, Svetlana, additional, and Antipin, Igor, additional

Published
2021
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18. Thermally Stable Nitrothiacalixarene Chromophores: Conformational Study and Aggregation Behavior

Author

Muravev, Anton, primary, Gerasimova, Tatiana, additional, Fayzullin, Robert, additional, Babaeva, Olga, additional, Rizvanov, Ildar, additional, Khamatgalimov, Ayrat, additional, Kadirov, Marsil, additional, Katsyuba, Sergey, additional, Litvinov, Igor, additional, Latypov, Shamil, additional, Solovieva, Svetlana, additional, and Antipin, Igor, additional

Published
2020
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24. Efficient Vector Quantization for Fast Approximate Nearest Neighbor Search

Author

Muravev, Anton, Signaalinkäsittelyn laitos - Department of Signal Processing, Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering, Tampere University of Technology, Gabbouj, Moncef, Iosifidis, Alexandros, and Ozan, Ezgi Can

Subjects
Master's Degree Programme in Information Technology
Abstract

Increasing sizes of databases and data stores mean that the traditional tasks, such as locating a nearest neighbor for a given data point, become too complex for classical solutions to handle. Exact solutions have been shown to scale poorly with dimensionality of the data. Approximate nearest neighbor search (ANN) is a practical compromise between accuracy and performance; it is widely applicable and is a subject of much research. Amongst a number of ANN approaches suggested in the recent years, the ones based on vector quantization stand out, achieving state-of-the-art results. Product quantization (PQ) decomposes vectors into subspaces for separate processing, allowing for fast lookup-based distance calculations. Additive quantization (AQ) drops most of PQ constraints, currently providing the best search accuracy on image descriptor datasets, but at a higher computational cost. This thesis work aims to reduce the complexity of AQ by changing a single most expensive step in the process – that of vector encoding. Both the outstanding search performance and high costs of AQ come from its generality, therefore by imposing some novel external constraints it is possible to achieve a better compromise: reduce complexity while retaining the accuracy advantage over other ANN methods. We propose a new encoding method for AQ – pyramid encoding. It requires significantly less calculations compared to the original “beam search” encoding, at the cost of an increased greediness of the optimization procedure. As its performance depends heavily on the initialization, the problem of choosing a starting point is also discussed. The results achieved by applying the proposed method are compared with the current state-of-the-art on two widely used benchmark datasets – GIST1M and SIFT1M, both generated from a real-world image data and therefore closely modeling practical applications. AQ with pyramid encoding, in addition to its computational benefits, is shown to achieve similar or better search performance than competing methods. However, its current advantages seem to be limited to data of a certain internal structure. Further analysis of this drawback provides us with the directions of possible future work.

Published
2016

28. Crystallization Control of Anionic Thiacalixarenes on Silicon Surface Coated with Cationic Poly(ethyleneimine)

Author

Botnar, Anna A., Novikov, Oleg P., Korepanov, Oleg A., Muraveva, Ekaterina A., Kozodaev, Dmitry A., Novikov, Alexander S., Nosonovsky, Michael, Skorb, Ekaterina V., and Muravev, Anton A.

Abstract

Surface modification of solid substrates with organic molecules and polyelectrolytes is a promising strategy toward advanced soft materials due to the control of molecular arrangement and supramolecular organization; however, understanding the nature of interactions within the assembly is challenging. Here a facile approach to the control of the architecture of calixarene macrocycles on soft surfaces is presented through the interplay of weak interactions involving a solid silicon substrate, a cationic polyelectrolyte layer, and anionic sulfonatothiacalix[4]arene (STCA). Topological analysis of atomic force microscopy (AFM) images of STCA on silicon, as well as silicon wafers modified with neutral polyethylenimine (PEI) and cationic PEI-H+, indicates different surface morphology and assembly behavior of STCA on such substrates. Drop-casting a calixarene solution onto silicon induces the formation of chaotically oriented needle crystals. When there is globular PEI, a nucleation point for the STCA crystals is formed on the polyelectrolyte surface, which grows into rosette structures. In contrast, protonated PEI with a chain-like structure alters the self-organization of STCA on silicon surfaces, leading to a dense uniform fiber-like network. Density functional theory modeling of the system components' self-assembly reveals thermodynamically favorable face-to-face antiparallel aggregation of STCA monomers and contribution of H-bonding into PEI(PEI-H+)–STCA and Si–STCA association.

Published
2024
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