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Tsinghua Science and Technology 2013年第5期18卷 446-453页

作者： Hongjie Yu Deshuang Huang the department of Mathematics School of Science Anhui Science and Technology University machine learning and Systems Biology Laboratory Tongji University

Numerical characterizations of DNA sequence can facilitate analysis of similar sequences. To visualize and compare different DNA sequences in less space, a novel descriptors extraction approach was proposed for numerical characterizations and similarity analysis of sequences. Initially, a transformation method was introduced to represent each DNA sequence with dinucleotide physicochemical property matrix. Then, based on the approximate joint diagonalization theory, an eigenvalue vector was extracted from each DNA sequence,which could be considered as descriptor of the DNA sequence. Moreover, similarity analyses were performed by calculating the pair-wise distances among the obtained eigenvalue vectors. The results show that the proposed approach can capture more sequence information, and can jointly analyze the information contained in all involved multiple sequences, rather than separately, whose effectiveness was demonstrated intuitively by constructing a dendrogram for the 15 beta-globin gene sequences.

关键词： descriptors approximate joint diagonalization dendrogram physicochemical property similarity analysis

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

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Energy Geoscience

Energy Geoscience 2024年第2期5卷 225-231页

作者： Goutami Das Saumen Maiti machine learning and Geocomputing Laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log Pore pressure machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

The method of residual‑based bootstrap averaging of the forecast ensemble

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Financial Innovation 2023年第1期9卷 991-1002页

作者： Vera Ivanyuk department of Data Analysis and machine learning Financial University49 Leningradsky ProspektMoscowRussia 125993

This paper presents an optimization approach—residual-based bootstrap averaging(RBBA)—for different types of forecast *** traditional residual-mean-square-error-based ensemble forecast averaging approaches,the RBBA method attempts to find optimal forecast weights in an ensemble and allows for their combi-nation into the most effective additive *** the RBBA method,all the different types of forecasts obtain the optimal weights for ensemble residuals that are statisti-cally optimal in terms of the fitness function of the *** studies have been conducted to demonstrate why and how the RBBA method *** experi-mental results based on the real-world time series of contemporary stock exchanges show that the RBBA method can produce ensemble forecasts with good generalization ability.

关键词： Forecast ensembles Time series Artificial neural networks

Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(Information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

Regularity for minimizing sequences of some variational integrals

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Science China Mathematics 2023年第4期66卷 777-798页

作者： Hongya Gao Yanan Shan Wei Ren Hebei Key Laboratory of machine learning and Computational Intelligence College of Mathematics and Information ScienceHebei UniversityBaoding 071002China

This paper deals with regularity properties for minimizing sequences of some integral functionals related to the nonlinear elasticity *** some structural conditions,we derive that the minimizing sequence and the deriv... 详细信息

关键词： regularity minimizing sequence variational integral energy Ekeland variational principle

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University department of Mathematics and Statistics University of Ottawa Center for Statistics and machine learning Princeton University Industrial Artificial Intelligent Center Pazhou Laboratory

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

Few-shot learning for screening 2D Ga2CoS4-x supported single-atom catalysts for hydrogen production

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Journal of Energy Chemistry 2025年第1期100卷 665-673页

作者： Nabil Khossossi Poulumi Dey department of Mechanical Engineering (ME) Delft University of Technology

Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction（HER）,which faces challenges of slow kinetics and high *** electrocatalysts,particularly single-atom catalysts （SACs） on two-dimensional （2D） materials,are *** study presents a few-shot machine learning （ML） assisted high-throughput screening of 2D septuple-atomic-layer Ga2CoS4-xsupported SACs to predict HER catalytic ***,density functional theory （DFT）calculations showed that 2D Ga2CoS4is inactive for ***,defective Ga2CoS4-x（x=0–0.25）monolayers exhibit excellent HER activity due to surface sulfur vacancies （SVs）,with predicted overpotentials （0–60 mV） comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER *** demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D *** DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was *** intrinsic descriptor linking SAC atomic properties to HER overpotential was *** study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.

关键词： Hydrogen production Electrocatalyst 2D material Density functional theory machine learning Surface sulfur vacancy

Using Semantic Web Technologies to Improve the Extract Transform Load Model

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Computers, Materials & Continua 2021年第8期68卷 2711-2726页

作者： Amena Mahmoud Mahmoud Y.Shams O.M.Elzeki Nancy Awadallah Awad department of Computer Science Kafrelshiekh UniversityKafrelshiekhEgypt department of machine learning Kafrelsheikh UniversityKafrelshiekhEgypt department of Computer Science Mansoura UniversityMansouraEgypt department of Computer and Information Systems Sadat Academy for Management SciencesCairoEgypt

Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple *** studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured *** the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked ***,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and *** principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s *** results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.

关键词： Semantic web big data ETL model linked data geospatial data

Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala machine learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing machine learning Image segmentation

Neurogenesis dynamics in the olfactory bulb:deciphering circuitry organization, function, and adaptive plasticity

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Neural Regeneration Research 2025年第6期20卷 1565-1581页

作者： Moawiah M.Naffaa department of Psychology and Neuroscience Duke UniversityDurhamNCUSA department of Cell Biology Duke University School of MedicineDurhamNCUSA

Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased

关键词： network adaptability neurogenesis neuronal communication olfactory bulb olfactory learning olfactory memory synaptic plasticity