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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Xu, Wenjie Adachi, Masaki Jones, Colin N. Osborne, Michael A. Automatic Control Laboratory EPFL Switzerland Urban Energy Systems Laboratory Empa Switzerland Machine Learning Research Group University of Oxford United Kingdom Toyota Motor Corporation Japan

Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels). Experts' labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted. We introduce the first principled approach that provides two key guarantees. (1) Handover guarantee: similar to a no-regret property, we establish a sublinear bound on the cumulative number of experts' binary labels. Initially, multiple labels per query are needed, but the number of expert labels required asymptotically converges to zero, saving both expert effort and computation time. (2) No-harm guarantee with data-driven trust level adjustment: our adaptive trust level ensures that the convergence rate will not be worse than the one without using advice, even if the advice from experts is adversarial. Unlike existing methods that employ a user-defined function that hand-tunes the trust level adjustment, our approach enables data-driven adjustments. Real-world applications empirically demonstrate that our method not only outperforms existing baselines, but also maintains robustness despite varying labelling accuracy, in tasks of battery design with human experts. © 2024 Neural information processing systems foundation. All rights reserved.

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Principled Bayesian optimisation in collaboration with human experts 24

Principled Bayesian optimisation in collaboration with human...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Wenjie Xu Masaki Adachi Colin N. Jones Michael A. Osborne Automatic Control Laboratory EPFL and Urban Energy Systems Laboratory Empa Machine Learning Research Group University of Oxford and Toyota Motor Corporation Automatic Control Laboratory EPFL Machine Learning Research Group University of Oxford

ISBN: (纸本)9798331314385

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Synthesis and Characterization of Biochar Obtained from Microwave-Assisted Copyrolysis of Torrefied Sawdust and Polystyrene

ACS Sustainable Resource Management

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ACS Sustainable Resource Management 2024年第9期1卷 2074-2085页

作者： Ramesh Potnuri Chinta Sankar Rao Control Systems and Machine Learning Research Laboratory Department of Chemical Engineering National Institute of Technology Karnataka Surathkal Mangalore Karnataka 575025 India

This study focuses on copyrolyzing pretreated sawdust and polystyrene utilizing microwave-assisted pyrolysis (MAP) with equal mixing to synthesize and characterize biochar. Graphite was used as a susceptor to facilitate precise pyrolysis temperature control. Potassium hydroxide (KOH) powder serves as a catalyst, influencing the char yields and properties. Torrefied raw sawdust at various temperatures (125–175 °C) enhances biochar yields (24–29 wt %). The feedstocks sawdust and polystyrene are characterized by elemental, proximate, and TGA examinations. Furthermore, comprehensive surface, crystallographic, FTIR, and SEM-EDX analyses are performed on microwave copyrolyzed biochar. The developments in BET surface area during copyrolysis show changes concerning pretreatment temperatures: 125 °C (5.6 m2/g) < 150 °C (6.8 m2/g) < 175 °C (8.6 m2/g). Functional groups connected to the alcohols’ O–H bend and C–O stretching vibrations are detected in the biochar samples through FTIR analysis. Sharp peaks with 2θ values between 33.2° and 36.2° appear in the XRD scan of biochar, indicating the presence of crystalline components in the sample. The EDX results demonstrated that the components of biochar included Mg, C, O, and Ca, indicating that it could have plenty of advantageous applications. The study highlights the obstruction of sawdust char’s porous structures by polystyrene, hindering volatile emissions and leading to increased heating rates. These findings underscore the unique contributions of this method to biochar production.

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Information Security control Engineering for an Industrial Company with '1C' Software

Information Security Control Engineering for an Industrial C...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Zalozhnev, Alexey Yu. Chistov, Dmitry V. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Active Systems Laboratory Moscow Russia Financial University Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350327816

The issues related to information security control engineering for an industrial company with '1C' software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given. © 2023 IEEE.

关键词： industrial company information security information security control security engineering software software system

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Investor Risk Profile Determination Model 16

Investor Risk Profile Determination Model

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

ISBN: (纸本)9798350337907

An assessment of the investor's risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market. © 2023 IEEE.

关键词： Convolution

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Principled Bayesian Optimisation in Collaboration with Human Experts

arXiv

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arXiv 2024年

Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels). Experts’ labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted. We introduce the first principled approach that provides two key guarantees. (1) Handover guarantee: similar to a no-regret property, we establish a sublinear bound on the cumulative number of experts’ binary labels. Initially, multiple labels per query are needed, but the number of expert labels required asymptotically converges to zero, saving both expert effort and computation time. (2) No-harm guarantee with data-driven trust level adjustment: our adaptive trust level ensures that the convergence rate will not be worse than the one without using advice, even if the advice from experts is adversarial. Unlike existing methods that employ a user-defined function that hand-tunes the trust level adjustment, our approach enables data-driven adjustments. Real-world applications empirically demonstrate that our method not only outperforms existing baselines, but also maintains robustness despite varying labelling accuracy, in tasks of battery design with human experts. © 2024, CC BY-NC-ND.

关键词： Metadata

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Rao, Akash K Uttrani, Shashank Shah, Darshil Menon, Vishnu K Bhavsar, Arnav Roy Chowdhury, Shubhajit Negi, Ramsingh Dutt, Varun Manipal Academy of Higher Education India Indian Institute of Technology Applied Cognitive Science Laboratory Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Cognitive Control and Machine Learning Group The Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization India

ISBN: (纸本)9781665410205

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS. © 2024 IEEE.

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

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. © 2023 IEEE.

关键词： Speech recognition

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUDatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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