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Stability testing of stock returns connections 1

6th International Conference on network analysis, NET 2016

作者： Voronina, M.A. Koldanov, P.A. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Moscow Russia

ISBN: (数字)9783319568294

ISBN: (纸本)9783319568287

The problem of stability of connections of stock returns over time is considered. This problem is formulated as a multiple testing problem of homogeneity of covariance matrices. A statistical procedure based on Box’s M-test and Bonferroni correction is proposed. This procedure is applied to French and German stock markets. © Springer International Publishing AG 2017.

关键词： Financial markets

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hse-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images

arXiv

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arXiv 2022年

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

In this paper, we present the results of the hse-NN team in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline convolutional neural network. As a result, our team took the first place in the learning from synthetic data challenge and the third place in the multi-task learning *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Facial expression and attributes recognition based on multi-task learning of lightweight neural networks

arXiv

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arXiv 2021年

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

In this paper, the multi-task learning of lightweight convolutional neural networks is studied for face identification and classification of facial attributes (age, gender, ethnicity) trained on cropped faces without margins. The necessity to fine-tune these networks to predict facial expressions is highlighted. Several models are presented based on MobileNet, EfficientNet and RexNet architectures. It was experimentally demonstrated that they lead to near state-of-the-art results in age, gender and race recognition on the UTKFace dataset and emotion classification on the AffectNet dataset. Moreover, it is shown that the usage of the trained models as feature extractors of facial regions in video frames leads to 4.5% higher accuracy than the previously known state-of-the-art single models for the AFEW and the VGAF datasets from the EmotiW challenges. The models and source code are publicly available at https://***/hse-asavchenko/*** Codes 68T10 Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Recommending Restaurants based on Classification of Photos from the Gallery of Mobile Device

Recommending Restaurants based on Classification of Photos f...

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International Symposium on Intelligent Systems and Informatics (SISY)

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

ISBN: (纸本)9781665489898

The restaurant recommender systems are important for travelers, but may suffer from the “cold start” problem for new users. In this paper, it is assumed that photos of food in a gallery of mobile device of a user reflect his or her gastronomic preferences. The novel pipeline for modeling of the user's food preferences is introduced. At first, scene recognition neural network is used to extract photos of the restaurants from a gallery. It is proposed to train the multi-task convolutional network to predict the presence of food in an input image and the attributes of the restaurant. The recognized types of cuisine are summarized in a profile of gastronomic preferences of the user. The restaurants in a given city are recommended based on these preferences and additional attributes of a restaurant such as cumulative rating. Experimental study for the Yelp datasets is provided. Several neural network architectures are compared, among which EfficientNet models demonstrated the best performance.

关键词： Performance evaluation Protocols Neural networks Urban areas Pipelines Predictive models Prediction algorithms

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Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices

arXiv

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arXiv 2022年

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

In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. As a result, our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior analysis in-the-wild (ABAW) Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.15-0.2 higher performance measures for validation sets in uni-task Expression Classification, Valence-Arousal Estimation and Expression Classification. Due to simplicity, our approach may be considered as a new baseline for all four *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Finding Weakly Correlated Nodes in Random Variable networks

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Operations Research Forum 2024年第4期5卷 1-14页

作者： Koldanov, Petr Koldanov, Alexander Semenov, Dmitry Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova street Nizhny Novgorod 603093 Russian Federation

The issue of identifying sets of weakly correlated stocks is explored. Four distinct methods for constructing these sets are compared: the traditional approach using Pearson correlation, the traditional approach using Kendall correlation, and multiple hypothesis testing methods, which apply both Pearson and Kendall correlations. To derive specific findings, we analyze daily returns of a selection of stocks listed on the Frankfurt (FWB), London (LSE), and Paris (Euronext Paris) stock exchanges. Our results reveal a significant difference between the identified sets of weakly correlated stocks in Pearson and Kendall correlation networks. Notably, this difference is more substantial in the statistically significant sets of weakly correlated stocks derived from multiple hypothesis testing methods than in those obtained through traditional procedures. We recommend for the use of multiple hypothesis testing methods based on Kendall correlation for analyzing market data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Family-wise error rate (FWER) Kendall correlation Multiple hypotheses testing procedure Pearson correlation Random variable network Statistical significance Stock returns Weakly correlated stocks

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Device-Specific Facial Descriptors: Winning a Lottery with a SuperNet 27

Device-Specific Facial Descriptors: Winning a Lottery with a...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Savchenko, Andrey Maslov, Dmitry Makarov, Ilya Sber AI Lab Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia Moscow Russia ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia

ISBN: (纸本)9781643685489

We address the challenge of devising neural network architectures to extract facial descriptors across diverse mobile and edge *** neural architecture search, we introduce a novel framework that selects optimal subnetworks from a SuperNet using an evolutionary *** a surrogate gradient boosting classifier to avoid direct accuracy estimation of subnetworks on validation sets, our approach swiftly delivers the most efficient and accurate models tailored to specific devices within *** versatility through an Android demo app, our framework excels in tasks like face recognition and emotion understanding across various devices, achieving real-time processing and superior accuracy compared to existing mobile models. © 2024 The Authors.

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Heuristic for site-dependent truck and trailer routing problem with soft and hard time windows and split deliveries 1st

Heuristic for site-dependent truck and trailer routing probl...

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1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015

作者： Batsyn, Mikhail Ponomarenko, Alexander Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova street Niznhy Novgorod Russia

ISBN: (纸本)9783319279251

In this paper we develop an iterative insertion heuristic for a site-dependent truck and trailer routing problem with soft and hard time windows and split deliveries. In the considered problem a truck can leave its trailer for unloading or parking, make a truck-subtour to serve truck-customers, and return back to take the trailer. This can be done several times in one route. In our heuristic every route is constructed by sequentially inserting customers to it in the way similar to Solomon’s (1987) approach developed for simple vehicle routes. Our contributions include: heuristic insertion procedure for complex truck and trailer routes with transshipment locations;efficient randomized mechanisms for choosing the first customer for insertion, for making time window violations, and for making split-deliveries;an improvement procedure shifting deliveries in a route to earlier time;an efficient approach dealing with site-dependency feature based on the transportation problem in case of arbitrary intersecting vehicle sets and a fast vehicle assignment procedure in case of nested vehicle sets. © Springer International Publishing Switzerland 2015.

关键词： Light trailers

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Step down and step up statistical procedures for stock selection with Sharp ratio 1st

Step down and step up statistical procedures for stock selec...

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1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015

作者： Koldanov, A.P. Kalyagin, V.A. Pardalos, P.M. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Rodionova 136 Nizhny Novgorod603155 Russia

ISBN: (纸本)9783319279251

Stock selection by Sharp ratio is considered in the framework of multiple statistical hypotheses testing theory. The main attention is paid to comparison of Holm step down and Hochberg step up procedures for different loss functions. Comparison is made on the basis of conditional risk as a function of selection threshold. This approach allows to discover that properties of procedures depend not only on relationship between test statistics, but also depend on dispersion of Sharp ratios. Difference in error rate between two procedures is increasing when the concentration of Sharp ratios is increasing. When Sharp ratios do not have a concentration points there is no significant difference in quality of both procedures. © Springer International Publishing Switzerland 2015.

关键词： Statistical tests

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Tabu search for fleet size and mix vehicle routing problem with hard and soft time windows 7th

Tabu search for fleet size and mix vehicle routing problem w...

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7th International Conference on network analysis, NET 2017

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

ISBN: (纸本)9783319962467

The paper presents a tabu search heuristic for the Fleet Size and Mix Vehicle Routing Problem (FSMVRP) with hard and soft time windows. The objective function minimizes the sum of travel costs, fixed vehicle costs, and penalties for soft time window violations. The algorithm is based on the tabu search with several neighborhoods. The main contribution of the paper is the efficient algorithm for a real-life vehicle routing problem. To the best of our knowledge, there are no papers devoted to the FSMVRP problem with soft time windows, while in real-life problems, this is a usual case. We investigate the performance of the proposed heuristic on the classical Solomon instances with additional constraints. We also compare our approach without soft time windows and heterogeneous fleet of vehicles with the recently published results on the VRP problem with hard time windows. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Vehicle routing

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