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Precedence constrained generalized traveling salesman problem: Polyhedral study, formulations, and branch-and-cut algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期309卷 488-505页

作者： Khachai, Daniil Sadykov, Ruslan Battaia, Olga Khachay, Michael Kedge Business Sch 680 Cours Liberat Talence France Univ Bordeaux Inria Ctr 200 Ave Vieille Tour Talence France RAS Ivannikov Inst Syst Programming Moscow Russia

The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) is an extension of two well-known combinatorial optimization problems - the Generalized Traveling Salesman Problem (GTSP) and the Precedence Constrained Asymmetric Traveling Salesman Problem (PCATSP), whose path version is known as the Sequential Ordering Problem (SOP). Similarly to the classic GTSP, the goal of the PCGTSP, for a given input digraph and partition of its node set into clusters, is to find a minimum cost cyclic route (tour) visiting each cluster in a single node. In addition, as in the PCATSP, feasible tours are restricted to visit the clusters with respect to the given partial order. Unlike the GTSP and SOP, to the best of our knowledge, the PCGTSP still remain to be weakly studied both in terms of polyhedral theory and algo-rithms. In this paper, for the first time for the PCGTSP, we propose several families of valid inequalities, establish dimension of the PCGTS polytope and prove sufficient conditions ensuring that the extended Balas' pi- and sigma-inequalities become facet-inducing. Relying on these theoretical results and evolving the state-of-the-art algorithmic approaches for the PCATSP and SOP, we introduce a family of MILP-models (formulations) and several variants of the branch-and-cut algorithm for the PCGTSP. We prove their high performance in a competitive numerical evaluation against the public benchmark library PCGTSPLIB, a known adaptation of the classic SOPLIB to the problem in question. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Travelling salesman Integer programming Branch-and-cut algorithm Facet-inducing inequalities Polyhedral structure

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LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment 23

LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alig...

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Abulkhanov, Dmitry Sorokin, Nikita Nikolenko, Sergey Malykh, Valentin Huawei Noahs Ark Lab Moscow Russia RAS Ivannikov Inst Syst Programming Moscow Russia RAS Steklov Inst Math St Petersburg Dept St Petersburg Russia

ISBN: (纸本)9781450394086

Data collection and mining is a crucial bottleneck for cross-lingual information retrieval (CLIR). While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and LAPCA that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA.

关键词： cross-lingual IR multilingual IR Transformer-based architectures

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Stochastic Gradient Methods with Preconditioned Updates

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2024年第2期201卷 471-489页

作者： Sadiev, Abdurakhmon Beznosikov, Aleksandr Almansoori, Abdulla Jasem Kamzolov, Dmitry Tappenden, Rachael Takac, Martin Russian Acad Sci ISP RAS Ivannikov Inst Syst Programming Moscow Russia Moscow Inst Phys & Technol MIPT Moscow Russia Mohamed Bin Zayed Univ Artificial Intelligence MBZ Abu Dhabi U Arab Emirates Univ Canterbury Christchurch New Zealand

This work considers the non-convex finite-sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly scaled and/or ill-conditioned, and a primary goal of this work is to introduce methods that alleviate this issue. Thus, here we include a preconditioner based on Hutchinson's approach to approximating the diagonal of the Hessian and couple it with several gradient-based methods to give new 'scaled' algorithms: Scaled SARAH and Scaled L-SVRG. Theoretical complexity guarantees under smoothness assumptions are presented. We prove linear convergence when both smoothness and the PL-condition are assumed. Our adaptively scaled methods use approximate partial second-order curvature information and, therefore, can better mitigate the impact of badly scaled problems. This improved practical performance is demonstrated in the numerical experiments also presented in this work.

关键词： Optimization Non-convex optimization Stochastic optimization Scaled methods Variance reduction

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Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY

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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2024年 5卷 428-440页

作者： Pozdnyakov, Vitaliy Kovalenko, Aleksandr Makarov, Ilya Drobyshevskiy, Mikhail Lukyanov, Kirill AIRI Moscow 121170 Russia ISP RAS Res Ctr Trusted Artificial Intelligence Moscow 109004 Russia Russian Acad Sci Ivannikov Inst Syst Programming Moscow 109004 Russia Natl Res Univ Moscow Inst Phys & Technol Moscow 141700 Russia

Integrating machine learning into Automated Control systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for Fault Detection and Diagnosis (FDD) in ACS using the Tennessee Eastman Process dataset. By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods. Our results highlight the strong vulnerability of models to adversarial samples and the varying effectiveness of defense strategies. We also propose a new defense strategy based on combining adversarial training and data quantization. This research contributes several insights into securing machine learning within ACS, ensuring robust FDD in industrial processes.

关键词： Training Neural networks Industrial electronics Glass box Predictive models Fault detection Data models Adversarial attacks automated control systems (ACS) defense methods fault detection and diagnosis (FDD) Tennessee Eastman Process

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Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions 24

Enhancing Cloud Security through Efficient Polynomial Approx...

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24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Pulido-Gaytani, Bernardo Tchernykh, Andrei Babenko, Mikhail Cortes-Mendoza, Jorge M. Gonzalez-Velez, Horacio Avetisyan, Arutyun CICESE Res Ctr Ensenada Baja California Mexico North Caucasus Fed Univ Stavropol Russia Natl Coll Ireland Dublin Ireland RAS Inst Syst Programming Moscow Russia

ISBN: (纸本)9798350377521;9798350377514

Current security cloud practices can successfully protect stored data and data in transit, but they do not keep the same protection during data processing. The data value extraction requires decryption, creating critical exposure points. As a result, privacy-preserving techniques are emerging as a crucial consideration in cloud computing. The homomorphic processing of machine learning models in the cloud represents a central challenge. The activation function is fundamental in constructing a privacy-preserving Neural Network (NN) with Homomorphic Encryption (HE). Standard activation functions require operations not supported by HE, so it is necessary to find cryptographically compatible replacement functions to operate over encrypted data. Multiple approaches address the limitation of function compatibility with polynomial approximation. These functions should exhibit a trade-off between complexity and accuracy, limiting the efficiency of conventional approximation techniques. The current literature on polynomial approximation of NN activation functions still lacks a thorough review. In this paper, we comprehensively review the standard activation functions of modern NN models and current polynomial approximation approaches. We highlight fundamental features to consider in the activation function and the approximation technique to operate over encrypted data.

关键词： cloud security homomorphic encryption neural networks polynomial approximation privacy-preserving

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Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval 23

Sinkhorn Transformations for Single-Query Postprocessing in ...

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Yakovlev, Konstantin Polyakov, Gregory Alimova, Ilseyar Podolskiy, Alexander Bout, Andrey Nikolenko, Sergey Piontkovskaya, Irina Huawei Noahs Ark Lab Moscow Russia RAS Ivannikov Inst Syst Programming Moscow Russia Steklov Inst Math St Petersburg Dept St Petersburg Russia

ISBN: (纸本)9781450394086

A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usually presumes that an entire matrix of test samples is available as DSL input. This work introduces a new postprocessing approach based on Sinkhorn transformations that outperforms DSL. Further, we propose a new postprocessing setting that does not require access to multiple test queries. We show that our approach can significantly improve the results of state of the art models such as CLIP4Clip, BLIP, X-CLIP, and DRL, thus achieving a new state-of-the-art on several standard text-video retrieval datasets both with access to the entire test set and in the single-query setting.

关键词： video-text retrieval dual-softmax loss Sinkhorn algorithm

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A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system

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ENGINEERING OPTIMIZATION 2023年第6期55卷 981-997页

作者： Pena, David Tchernykh, Andrei Dorronsoro, Bernabe Ruiz, Patricia Univ Cadiz Dept Comp Sci Cadiz Spain CICESE Res Ctr Dept Comp Sci Ensenada Baja California Mexico South Ural State Univ Chelyabinsk Russia RAS Ivannikov Inst Syst Programming Moscow Russia

An efficient public transport system is essential for sustainable city development, as it directly affects people's welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The conflicting objectives are the minimization of fuel consumption and unsatisfied user demand, which are essential to make transit buses an attractive alternative for users, thus promoting environmentally friendly mobility. The problem was solved with two well-known metaheuristics, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and cellular genetic algorithm for multi-objective optimization (MOCell), and their performance was compared using several metrics. Their parameters were tuned with a thorough study, and several evolutionary operators designed for the problem were considered. The outcomes suggest that a solution using various types of buses can produce diverse dispatching strategies, reducing pollutant emissions and maintaining tolerable ridership losses.

关键词： Public transport sustainable cities greenhouse gas emission multi-objective optimization evolutionary algorithms

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AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes 33

AADMIP: Adversarial Attacks and Defenses Modeling in Industr...

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33rd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Pozdnyakov, Vitaliy Kovalenko, Aleksandr Makarov, Ilya Drobyshevskiy, Mikhail Lukyanov, Kirill Russian Acad Sci Ivannikov Inst Syst Programming Moscow Russia Natl Res Univ Moscow Inst Phys & Technol Moscow Russia AIRI Moscow Russia ISP RAS Res Ctr Trusted Artificial Intelligence Moscow Russia

ISBN: (纸本)9781956792041

The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and describe useful applications. Our demo video is available at: http://***/3by9zcj5.

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Numerical simulation of underexpanded supersonic jets impingement on an inclined flat plate

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THERMOPHYSICS AND AEROMECHANICS 2021年第4期28卷 479-486页

作者： Epikhin, A. S. Elizarova, T. G. Ivannikov Inst Syst Programming RAS Moscow Russia Bauman Moscow State Tech Univ Moscow Russia Keldysh Inst Appl Math RAS Moscow Russia

The paper presents the results of numerical simulation of complex shock-wave structures arising from an under-expanded jets impingement on an inclined flat plate. The plate deflection angles equal to 45 degrees, 60 degrees, and 90 degrees are investigated, which corresponds to different types of shock waves interference. The gas-dynamic characteristics are calculated using the OpenFOAM software package with the QGDFoam solver. This solver based on a system of regularized (quasi-gas dynamic) equations. The numerical simulation results of the flowfields and pressure distributions at the plate are compared against the results of the Kurganov-Tadmor scheme and with experimental data. The features of the applied numerical approach for simulation of complex shock-wave structures with triplet points, contact discontinuities, and low-entropy flows are identified.

关键词： numerical simulation underexpanded jet interaction shock-wave structures regularized (quasi-gas dynamic) equations QGDFoam OpenFOAM

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Adaptive Experiments for State Identification in Finite State Machines with Timeouts 1

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9th Conference on Machines, Computations and Universality (MCU)

作者： Tvardovskii, Aleksandr Yevtushenko, Nina Natl Res Tomsk State Univ Tomsk 634050 Russia RAS Ivannikov Inst Syst Programming Moscow 109004 Russia

ISBN: (数字)9783031135026

ISBN: (纸本)9783031135026;9783031135019

Homing and synchronizing sequences are used for the current state identification in finite state machines (FSMs). Adaptive homing and synchronizing sequences for which the next input depends on the outputs to the previous ones, exist more often and usually are shorter than the preset. Thus, a lot of attention is paid to the existence check, derivation complexity and length of shortest adaptive state identification sequences. In this paper, we adapt the notions of adaptive homing and synchronizing sequences for FSMs with timeouts which are widely used for solving verification and testing problems of components of telecommunication systems. Based on the corresponding FSM abstraction, the procedures for deriving adaptive homing and synchronizing sequences are proposed for FSMs with timeouts when such sequences exist.

关键词： Finite state machines Timeouts Homing sequence Synchronizing sequence

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