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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Tsvirkun, Anatoliy Sunchalina, Anna Goroshnikova, Tatiana Sunchalin, Andrey Zholobova, Galina Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of the Development of Large-Scale Systems Moscow Russia Financial University under the Government of the Russian Federation Faculty of International Economic Relations Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods. © 2023 IEEE.

ISBN: (纸本)9798350337907

关键词： Neural networks

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition

A Generalized Subspace Distribution Adaptation Framework for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shaokai Li Peng Song Liang Ji Yun Jin Wenming Zheng School of Computer and Control Engineering Yantai University China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province School of Physics and Electronic Engineering Jiangsu Normal University China Key Laboratory of Child Development and Learning Science (Southeast University) Ministry of Education Research Center for Learning Science Southeast University China

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.

关键词： Measurement learning systems Emotion recognition Databases Transfer learning Signal processing algorithms Speech recognition

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A Structured Prediction Approach for Robot Imitation learning

arXiv

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arXiv 2023年

作者： Duan, Anqing Batzianoulis, Iason Camoriano, Raffaello Rosasco, Lorenzo Pucci, Daniele Billard, Aude Robotics and Machine Intelligence Laboratory The Hong Kong Polytechnic University Hong Kong Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Visual And Multimodal Applied Learning Laboratory Politecnico di Torino Turin Italy DIBRIS Università degli Studi di Genova Genoa Italy Laboratory for Computational and Statistical Learning IIT@MIT Istituto Italiano di Tecnologia Massachusetts Institute of Technology CambridgeMA United States Center Università di Genova Genoa Italy Dynamic Interaction Control research line Italian Institute of Technology Genoa Italy

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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GSLB: The Graph Structure learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Improving Generative Model-based Unfolding with Schrödinger Bridges

arXiv

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arXiv 2023年

作者： Diefenbacher, Sascha Liu, Guan-Horng Mikuni, Vinicius Nachman, Benjamin Nie, Weili Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Machine Learning Research Group NVIDIA Research United States

machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one based on discriminative models and one based on generative models. The main advantage of discriminative models is that they learn a small correction to a starting simulation while generative models scale better to regions of phase space with little data. We propose to use Schrödinger Bridges and diffusion models to create SBUnfold, an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of SBUnfold is that its generative model maps one set of events into another without having to go through a known probability density as is the case for normalizing flows and standard diffusion models. We show that SBUnfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset. © 2023, CC BY.

关键词： Phase space methods

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Meso-substituted cationic 3-and 4-N-Pyridylporphyrins and their Zn(II)derivatives for antibacterial photodynamic therapy

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Journal of Innovative Optical Health Sciences 2022年第1期15卷 1-16页

作者： Aram G.Gyulkhandanyan Marina H.Paronyan Anna G.Gyulkhandanyan Karapet R.Ghazaryan Marina V.Parkhats Boris M.Dzhagarov Maria V.Korchenova Ekaterina N.Lazareva Elena S.Tuchina Grigor V.Gyulkhandanyan Valery V.Tuchin H.Buniatyan Institute of Biochemistry National Academy of Sciences of Armenia 5/1 Paruir Sevak Street Yerevan 0014Armenia INSERM U1204 Universite d'Evry vald'Essonne-Universite Paris-Saclay 1 Ruedu Pere JarlanBatiment Maupertuis91025Evry-CourcouronnesFrance B.I.Stepanov Institute of Physics National Academy of Sciences of Belarus 68-2 Nezavisimosti AvenueMinsk 220072Belarus Science Medical Center Saratov State University 83 Astrakhanskaya StreetSaratov 410012Russia Laboratory of Laser Molecular Imaging and Machine Learning(LMIML)National Research Tomsk State University 36 Lenin Avenue Tomsk 634050Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Russian Academy of Sciences Saratov 410028Russia

Photodynamic inactivation of microorganisms known as antibacterial photodynamic therapy(APDT)is one of the most promising and innovative approaches for the destruction of pathogenic *** the photosensitizers(PSs),compounds based on cationic porphyrins/metalloporphyrins are most successfully used to inactivate *** of meso-substituted cationic pyridylporphyrins and metalloporphyrins with various peripheral groups in the third and fourth positions of the pyrrole ring have been synthesized in *** aim of this work was to determine and test the most e®ective cationic porphyrins and metalloporphyrins with high photoactivity against Gram negative and Gram positive *** was shown that the synthesized cationic pyridylporphyrins/metalloporphyrins exhibit a high degree of phototoxicity towards both types of bacteria,including the methicillinresistant *** *** complexes of porphyrins are more phototoxic than metal-free porphyrin *** e®ectiveness of these Zn–metalloporphyrins on bacteria is consistent with the level of singlet oxygen *** was found that the high antibacterial activity of the studied cationic porphyrins/metalloporphyrins depends on four factors:the presence in the porphyrin macrocycle of a positive charge(+4),a central metal atom(Zn2þÞand hydrophobic peripheral functional groups as well as high values of quantum yields of singlet *** results indicate that meso-substituted cationic pyridylporphyrins/metalloporphyrins cannd wider application in photoinactivation of bacteria than anionic or neutral PSs usually used in APDT.

关键词： Antibacterial photodynamic therapy cationic porphyrins/metalloporphyrins phototoxicity Zn-metalloporphyrins singlet oxygen quantum yield Gram negative and Gram positive bacteria S.aureus MRSA E.coli Salmonella typhimurium.

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Analysis of the research Status of Information Technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Active Wildfires Detection and Dynamic Escape Routes Planning for Humans Through Information Fusion between Drones and Satellites

Active Wildfires Detection and Dynamic Escape Routes Plannin...

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International Conference on Intelligent Transportation

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Müegyetem rkp. 3 H-1111 Budapest Hungary and Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest Hungary Faculty of Transportation Engineering and Vehicle Engineering Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest University of Technology and Economics (BME-KJK) Müegyetem rkp. 3 H-1111 Budapest Hungary Budapest Hungary

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction of wildfire areas and real-time dynamic escape route planning for people in distress. Firstly, the fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. Secondly, the road segmentation and the road condition assessment are performed by D-linkNet and NDVI values in the central area of the fire source by UAV. Finally, the dynamic optimal route planning for humans in real time is performed by the weighted A* algorithm in the road network with the dynamic fire spread model. Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.

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Morphological changes in rat lung tissue during inhalation of e-cigarette liquid aerosol

Morphological changes in rat lung tissue during inhalation o...

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Saratov Fall Meeting 2021: Optical Technologies for Biology and Medicine

作者： Bucharskaya, Alla Mudrak, Dmitry Yanina, Irina Navolokin, Nikita Polozhenkov, Alexandr Genina, Elina Kistenev, Yury Tuchinb, Valery Centre of Collective Use Saratov State Medical University 112B. Kazachya str. Saratov410012 Russia National Research Saratov State University Medical center 83 Astrakhanskaya str. Saratov410012 Russia National Research Tomsk State University Laboratory of Laser Molecular Imaging and Machine Learning 36 Lenin Ave. Tomsk634050 Russia National Research Saratov State University Department of Optics and Biophotonics 83 Astrakhanskaya str. Saratov410012 Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Ras 24 Rabochaya str. Saratov410028 Russia

ISBN: (纸本)9781510653658

For potential use as a diagnostic test, morphological changes in the lung tissue of rats after inhalation of e-cigarette liquid based on polypropylene glycol and glycerol were studied ex vivo and in vivo. Lungs of Wistar rats placed for 1 hour in ecigarette liquid were used in the ex vivo study. The in vivo study was performed on 12 mature male Wistar rats. The animals were divided into two groups - experimental and control, 6 rats in each. Rats of the experimental group were placed in aerosol administration chamber and exposed to an aerosol of nicotine-free liquid for electronic cigarettes. Rats of the control group were intact. The animals were removed from the experiment one hour after inhalation, lung tissue samples were examined ex vivo using optical coherence tomography (OCT) and lung tissue spectroscopy. After manipulations, lung samples were fixed in 10% formalin solution and embedded in paraffin. Then histological sections were made according to the conventional methods, followed by hematoxylin and eosin staining. Comparative analysis of B-scans ex vivo of lung tissue samples placed in e-cigarette liquid and control samples showed the presence of a luminescent effect on lung tissue in the studied solution, as alveoli are filled with the solution and scattering is reduced. The structure of the tissue differs from the control samples. B-scan in vivo studies are characterized by the presence of voids of rather large size. Histological examination of ex vivo and in vivo specimens noted morphological changes in lung tissue structure corresponding to the changes observed in OCT. © 2022 SPIE. All rights reserved.

关键词： Lung Tissues Liquids In vivo imaging Tissue optics Optical coherence tomography Scattering Aerosols Absorption Light scattering

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