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2018 School-Seminar on Optimization Problems and their Applications, OPTA-SCL 2018

作者： Bychkov, Ilya Batsyn, Mikhail Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova Nizhniy Novgorod603093 Russia

The Vehicle Routing Problem (VRP) is one of the most popular combinatorial optimization problems which is closely related to the real-life optimization challenges. Being developed for more than 60 years the problem has been considered in many different formulations. In real-life goods distribution such constraints as fleet size and mix, site-dependency constraints, hard and soft time windows, vehicle capacity constraints are very important. In this paper we consider Capacitated Vehicle Routing Problem with hard Time Windows. We propose a hybrid heuristic algorithm which contains elements of ant colony optimization strategy and tabu search technique. Our algorithm shows good performance and results for the well-known Solomon dataset. Copyright © by the paper's authors.

关键词： Ant colony optimization

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Fast Heuristic for Vehicle Routing Problem on Trees 19th

Fast Heuristic for Vehicle Routing Problem on Trees

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19th International Conference on Mathematical Optimization Theory and Operations Research,MOTOR 2020

作者： Utkina, Irina Bukanova, Olga Batsyn, Mikhail V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova Street Niznhy Novgorod Russia

ISBN: (纸本)9783030586560

In this paper we propose an efficient heuristic for the Vehicle Routing Problem on Trees (TVRP). An initial solution is constructed with a greedy algorithm based on the Depth-First Search (DFS) approach. To optimize initial solutions obtained by our DFS heuristic, Ruin-and-Recreate (RR) method is then applied. For diversification purposes a randomization mechanism is added to the construction of initial solutions and DFS+RR algorithm is executed multiple times until the best found solution stops changing. The results of our approach are compared with the solutions obtained by the exact model of Chandran & Raghavan (2008). The computational experiments show that the suggested heuristic is fast and finds solutions which differ from optimal ones less than by 1% in average. © 2020, Springer Nature Switzerland AG.

关键词： Heuristic methods

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Gender domain adaptation for automatic speech recognition

Gender domain adaptation for automatic speech recognition

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Artem Sokolov Andrey V. Savchenko HSE University Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (数字)9781728180533

ISBN: (纸本)9781728180540

This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male and female subsets, respectively, if the layers in the encoder and decoder are not frozen, and the tuning is started from the last checkpoints. Moreover, we adapted our base model on the complete L2 Arctic dataset of accented speech and finetuned it for particular speakers and male and female genders separately. The models trained on the gender subsets obtained 1-2% lower WER when compared to the model tuned on the whole L2 Arctic dataset. Finally, it was experimentally confirmed that the concatenation of the pretrained voice embeddings (x-vector) and embeddings from a conventional encoder cannot significantly improve the speech recognition accuracy.

关键词： Training Adaptation models Filter banks Acoustics Decoding Arctic Tuning

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LARGE RAW EMOTIONAL DATASET WITH AGGREGATION MECHANISM

arXiv

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arXiv 2022年

作者： Kondratenko, Vladimir Sokolov, Artem Karpov, Nikolay Kutuzov, Oleg Savushkin, Nikita Minkin, Fyodor Sber Russia Nvidia Armenia HSE University Laboratory of Algorithms and Technologies for Network Analysis Russia

We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russian speech and their transcripts. Therefore it is the biggest open bi-modal data collection for SER task nowadays. It is annotated using a crowd-sourcing platform and includes two subsets: acted and real-life. Acted subset has a more balanced class distribution than the unbalanced real-life part consisting of audio podcasts. So the first one is suitable for model pre-training, and the second is elaborated for fine-tuning purposes, model approbation, and validation. This paper describes pre-processing routine, annotation, and experiment with a baseline model to demonstrate some actual metrics which could be obtained with the Dusha data *** Codes 62-07 Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Using modular decomposition technique to solve the maximum clique problem 7th

Using modular decomposition technique to solve the maximum c...

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7th International Conference on network analysis, NET 2017

作者： Utkina, Irina Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova Str. Nizhny Novgorod Russia

ISBN: (纸本)9783319962467

In this article, we use the modular decomposition technique to solve the weighted maximum clique problem exactly. Proposed algorithm takes the modular decomposition tree, constructed in linear time, and finds maximum weighted clique via recursive tree search (DFS).Wewant to showthat modular decomposition reduces calculation time. However, not all graphs have modules, so in the article there are algorithms to construct them. The results showcomparison of proposed solutionwith Ostergard’s algorithm on DIMACS benchmarks and on generated graphs. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Trees (mathematics)

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Comparison of statistical procedures for Gaussian graphical model selection 7th

Comparison of statistical procedures for Gaussian graphical ...

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7th International Conference on network analysis, NET 2017

作者： Grechikhin, Ivan S. Kalyagin, Valery A. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova St Nizhny Novgorod603093 Russia

ISBN: (纸本)9783319962467

Graphical models are used in a variety of problems to uncover hidden structures. There is an important number of different identification procedures to recover graphical model from observations. In this paper, undirected Gaussian graphical models are considered. Some Gaussian graphical model identification statistical procedures are compared using different measures, such as Type I and Type II errors, ROC AUC. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Gaussian distribution

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Fuzzy analysis and Deep Convolution Neural networks in Still-to-video Recognition

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Optical Memory and Neural networks (Information Optics) 2018年第1期27卷 23-31页

作者： Savchenko, A.V. Belova, N.S. Savchenko, L.V. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia National Research University Higher School of Economics Moscow Russia Nizhny Novgorod State Linguistic University Nizhny Novgorod Russia

We discuss the video classification problem with the matching of feature vectors extracted using deep convolutional neural networks from each frame. We propose the novel recognition method based on representation of each frame as a sequence of fuzzy sets of reference classes whose degrees of membership are defined based on asymptotic distribution of the Kullback–Leibler information divergence and its relation with the maximum likelihood method. In order to increase the classification accuracy, we perform the fuzzy intersection (product triangular norms) of these sets. Experimental study with YTF (YouTube Faces) and IJB-A (IARPA Janus Benchmark A) video datasets and VGGFace, ResFace and LightCNN descriptors shows that the proposed approach allows us to increase the accuracy of recognition by 2–6% comparing with the known classification methods. © 2018, Allerton Press, Inc.

关键词： Image recognition

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Faster Exploration of Some Temporal Graphs 1

Faster Exploration of Some Temporal Graphs

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1st Symposium on Algorithmic Foundations of Dynamic networks, SAND 2022

作者： Adamson, Duncan Gusev, Vladimir V. Malyshev, Dmitriy Zamaraev, Viktor Department of Computer Science Reykjavik University Iceland Materials Innovation Factory University of Liverpool United Kingdom Laboratory of Algorithms and Technologies for Network Analysis HSE University Russia Nizhny Novgorod Russia Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772242

A temporal graph G = (G1, G2,..., GT ) is a graph represented by a sequence of T graphs over a common set of vertices, such that at the ith time step only the edge set Ei is active. The temporal graph exploration problem asks for a shortest temporal walk on some temporal graph visiting every vertex. We show that temporal graphs with n vertices can be explored in O(kn1.5 log n) days if the underlying graph has treewidth k and in O(n1.75 log n) days if the underlying graph is planar. Furthermore, we show that any temporal graph whose underlying graph is a cycle with k chords can be explored in at most 6kn days. Finally, we demonstrate that there are temporal realisations of sub cubic planar graphs that cannot be explored faster than in Ω(n log n) days. All these improve best known results in the literature. © Duncan Adamson, Vladimir V. Gusev, Dmitriy Malyshev, and Viktor Zamaraev;licensed under Creative Commons License CC-BY 4.0

关键词： Graphic methods

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Iterative local search heuristic for truck and trailer routing problem 1

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6th International Conference on network analysis, NET 2016

作者： Grechikhin, Ivan S. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis 136 Rodionova St Nizhny Novgorod603093 Russia

ISBN: (数字)9783319568294

ISBN: (纸本)9783319568287

Vehicle Routing Problem is a well-known problem in logistics and transportation. There is a big variety of VRP problems in the literature, as they arise in many real-life situations. It is a NP-hard combinatorial optimization problem and finding an exact optimal solution is practically impossible in real-life formulations. There is an important subclass of VRP, which is called Truck and Trailer Routing Problem. For this class of problems, every vehicle contains truck and, possibly, trailer parts. In this work, Site-Dependent Truck and Trailer Routing Problem with Hard and Soft TimeWindows and Split Deliveries are *** develop an Iterative Local Search heuristic for solving this problem. The heuristic is based on the local search approach and also allows infeasible solutions. A greedy heuristic is applied to construct an initial solution. © Springer International Publishing AG 2017.

关键词： Light trailers

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Multi-Class Classification Using Quantum Kernel Methods

Multi-Class Classification Using Quantum Kernel Methods

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Mokhles, Mostafa Makarov, Ilya National Research University Higher School of Economics Faculty of Computer Science Moscow Russia Moscow Russia HSE University Laboratory of Algorithms and Technologies for Networks Analysis Nizhny Novgorod Russia

ISBN: (纸本)9781665464291

Quantum machine learning has recently attracted attention in various research fields. One of the most promising areas are kernel methods in quantum computers as they leverage the quantum computers advantage over classical kernels. The embedding of data in Hilbert space of quantum computers is called Quantum Embedding Kernels (QEKs). Many previous researches have explored the idea of using quantum embedding kernels for binary classification problems, demonstrating the advantage of quantum computing. This research is concerned with using these methods for multi-class classification problem and benchmark the results against well-known datasets such as IRIS and MNIST. © 2023 IEEE.

关键词： Embeddings

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