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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 1100-1107页

作者： Bai, Yingshuang Huang, Zhaohui Sun, Chen Zhang, Yujie Cui, Tao Atungsiri, Samuel Sony China Res Lab Wireless Network Res Dept Beijing 100027 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Sony Europe BV Lund Kista Branch NL-2132 LS Hoofddorp Netherlands Sony Europe BV Weybridge KT13 0XW England

Artificial intelligence (AI) has been widely applied across various industries, including wireless communication. AI has been a topic of extensive discussion within the 3rd Generation Partnership Project (3GPP), particularly in the context of the physical layer. It involves applications such as beam management, positioning accuracy enhancement, and channel state information (CSI) feedback improvement. Evaluation results from various companies indicate significant gains in beam management and positioning through AI integration. While AI can replace traditional mechanisms to enhance performance, it also introduces new overheads. For example, the introduction of AI models necessitates addressing model life cycle management (LCM) issues, such as model identification, activation/deactivation, monitoring, updating/finetuning, selection, switching. These operations result in a significant amount of overhead. In this paper, we present the progress of model LCM in 3GPP, and also propose a tree-structured model to reduce the overhead associated with LCM operations. This model can be used in scenarios where multiple models serve the same functionality by merging similar structures, thereby saving storage space, and making the processes of model switching, expansion, and deletion more effective. We also conduct simulations to demonstrate that our approach maintains stable AI model performance while simplifying the model structure.

关键词： Artificial intelligence 3rd generation partnership project life cycle management tree-structured model 3rd generation partnership project life cycle management tree-structured model

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An Optimal tree-structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

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IEEE ACCESS 2021年 9卷 21843-21858页

作者： Zhou, Anan Yi, Benshun Liu, Yusheng Luo, Laigan Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China

To reduce recovery cost of repairing multiple failed nodes, many repair schemes have been proposed for erasure codes based distributed storage systems. However, most of the existing researches ignore the network topology of storage devices. Motivated by such considerations, we combine delay repair schemes with network topology and propose a tree-structured model based on fountain codes with large value of {n,k,r}to improve the repair efficiency. More precisely, with the consideration of network topology, a new target named data recovery cost is defined to measure the efficiency of coded fragment download and source file reconstruction, and then the optimal recovery threshold is derived to minimize the average data recovery cost of general tree-structured model. Moreover, we analyze and compare the average data recovery cost of general tree-structure with different systematic parameters. To further improve the data transmission efficiency, an optimal tree-structured scheme based on improved tabu search algorithm (ITSA-ORT) is proposed. Compared with other algorithms, the ITSA-ORT scheme uses Prim algorithm to generate the initial solution and then uses special method to obtain the corresponding neighborhood structure. The experimental results show that the proposed scheme can find a globally optimal solution and obtain lower cost of data recovery. In addition, the ITSA-ORT scheme has lower computational complexity than the optimal tree-structured scheme based on particle swarm optimization algorithm (PSO-ORT) and the optimal tree-structured scheme based on firefly algorithm (FA-ORT).

关键词： Centralized communication model fountain codes delay repair network topology tree-structured model optimization algorithms

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Construction of a survival tree for dependent censoring

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2021年第1期31卷 63-78页

作者： Shimokawa, Asanao Miyaoka, Etsuo Tokyo Univ Sci Dept Math Tokyo 1628601 Japan

In this study, we examined the problem of constructing a model for time-to-event data considering dependent censoring. Our goal was to construct a set of subgroups of covariate space, wherein each element had the same failure model considering the dependency of failure and censoring times. As such, a model was constructed based on the parametric form from the identifiability problem of censoring. We used the copula to represent the dependency between failure and censoring times. Under the assumption of parametric models for failure and censoring times and a copula function, which have unknown parameters, we proposed a method for constructing the tree-structured model through the test statistics. We subsequently evaluated the performance of the splitting rule and tree obtained using the proposed method and compared it with the general method that assumes independent censoring through simulation studies. We also present the analysis results for AIDS clinical trial research to show the utility of the method.

关键词： CART copula informative censoring parametric modeling tree-structured model

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Dynamic multi-resolution spatial models

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ENVIRONMENTAL AND ECOLOGICAL STATISTICS 2007年第1期14卷 5-25页

作者： Johannesson, Gardar Cressie, Noel Huang, Hsin-Cheng Lawrence Livermore Natl Lab Livermore CA 94550 USA Ohio State Univ Dept Stat Columbus OH 43210 USA Acad Sinica Inst Stat Sci Taipei 115 Taiwan

Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution of stratospheric ozone. Remote-sense data are typically spatial, temporal, and massive. Existing prediction methods such as kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented, and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite data is missing.

关键词： Bayesian models change-of-resolution Kalman filter spatio-temporal prediction total column ozone tree-structured model

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Unsupervised Color Quantization with the Growing Neural Forest 16th

Unsupervised Color Quantization with the Growing Neural Fore...

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14th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Jose Palomo, Esteban Benito-Picazo, Jesus Lopez-Rubio, Ezequiel Dominguez, Enrique Univ Malaga Dept Comp Languages & Comp Sci Bulevar Louis Pasteur 35 E-29071 Malaga Spain Univ Yachay Tech Sch Math Sci & Informat Technol Hacienda San Jose S-N Urcuqui Ecuador

ISBN: (纸本)9783319591476;9783319591469

Image processing has become a very common application for artificial intelligence-based algorithms. More precisely, color quantization has become an important issue when it comes to supply efficient transmission and storage for digital images, which consists of color indexing for minimal perceptual distortion image compression. Artificial Neural Networks have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work we present a novel color quantization approach based on the Growing Neural Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of trees is learnt instead of a general graph. Its suitability for color quantization processes is supported by experimental results obtained, where the GNF outperforms other self-organizing models such as the GNG, GHSOM and SOM. As future work, more datasets and competitive models will be taken into account.

关键词： Color quantization Self-organization tree-structured model Unsupervised learning

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Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit

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Pediatric Pulmonology 2024年第5期59卷 1256-1265页

作者： Lin, Siang-Rong Wu, Jeng-Hung Liu, Yun-Chung Chiu, Pei-Hsin Chang, Tu-Hsuan Wu, En-Ting Chou, Chia-Ching Chang, Luan-Yin Lai, Fei-Pei Institute of Applied Mechanics National Taiwan University Taipei City Taiwan Department of Pediatrics National Taiwan University Hospital Taipei City Taiwan Department of Pediatrics Chi-Mei Medical Center Tainan City Taiwan Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan Department of Electrical Engineering National Taiwan University Taipei City Taiwan

Objectives: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making. Study Design: Retrospective cohort study conducted at a single tertiary hospital. Patients: This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia. Methodology: Two prediction models were developed using tree-structured machine learning algorithms. The primary outcomes were ICU mortality and 24-h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set. Results: A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24-h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69–0.91) and 0.92 (95% CI, 0.86–0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO₂), or higher partial pressure of carbon dioxide (PCO2) were observed. Conclusions: This study demonstrated that the machine learning models for predicting ICU mortality and 24-h ICU mortality in children with pneumonia have the potential to support decision-making, especially in resource-limited settings. © 2024 Wiley Periodicals LLC.

关键词： children clinical indices dynamic mortality prediction tree-structured model

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Sample-Optimal and Efficient Learning of tree Ising models 2021

Sample-Optimal and Efficient Learning of Tree Ising Models

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53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC)

作者： Daskalakis, Constantinos Pan, Qinxuan MIT EECS Cambridge MA 02139 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450380539

We show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ! from an optimal O(n ln n/epsilon(2)) samples, where O(center dot) hides an absolute constant which, importantly, does not depend on the model being learned-neither its tree nor the magnitude of its edge strengths, on which we place no assumptions. Our guarantees hold, in fact, for the celebrated Chow-Liu algorithm [1968], using the plug-in estimator for estimating mutual information. While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is epsilon-close to the true one in total variation distance, a guarantee called "proper learning." Our guarantees do not follow from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including the very recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-suboptimal and/or time-inefficient algorithms, unless further assumptions are placed on the model, such as bounds on the "strengths" of the model's edges. While we establish guarantees for a widely known and simple algorithm, the analysis that this algorithm succeeds and is sample-optimal is quite complex, requiring a hierarchical classification of the edges into layers with different reconstruction guarantees, depending on their strength, combined with delicate uses of the subadditivity of the squared Hellinger distance over graphical models to control the error accumulation.

关键词： tree-structured model Bayesian Network Ising model Markov Random Field Chow-Liu Algorithm Subadditivity Hellinger Distance Estimation Sample Complexity

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Extended Abstract: A Color Quantization Approach based on the Growing Neural Forest

Extended Abstract: A Color Quantization Approach based on th...

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3rd IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Palomo, Esteban J. Lopez-Rubio, Ezequiel Univ Yachay Tech Sch Math Sci & Informat Technol Hacienda San Jose S-N Urcuqui Ecuador Univ Malaga Dept Comp Languages & Comp Sci Bulevar Louis Pasteur35 Malaga Spain

ISBN: (纸本)9781509051052

In this work, a novel color quantization approach based on the Growing Neural Forest (GNF) is proposed. The GNF is a recently proposed improvement of the Growing Neural Gas (GNG), where a set of trees is learned instead of a general graph. Thus, this model is suitable for color quantization purposes. Experimental results confirm the good performance of the GNF for color quantization tasks.

关键词： Color quantization self-organization tree-structured model unsupervised learning

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MSP: Learned Query Performance Prediction Using MetaInfo and Structure of Plans 6th

MSP: Learned Query Performance Prediction Using MetaInfo and...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Liu, Honghao Peng, Zhiyong Zhang, Zhe Jiang, Huan Peng, Yuwei Wuhan Univ Wuhan 430000 Peoples R China Huawei Xian 710000 Peoples R China

ISBN: (纸本)9783031252006;9783031252013

Query performance prediction is important and challenging in database management systems. The traditional cost-based methods perform poorly predicting query performance due to inaccurate cost estimates. In recent years, research shows that learning-based query performance prediction without actual execution has outperformed traditional models. However, existing learning-based models still have limitations in feature encoding and model design. To address these limitations, we propose a method of query performance prediction based on the binary tree-structured model fully expressing the impact between plan tree nodes. We also present an efficient metadata encoding method, taking into account the data type and value distribution of the columns, which we call metaInfo. This encoding method can support various complex SQL queries on changing data. The experiments are conducted on real-world datasets, and the experimental results show that our approach outperforms the state-of-the-art method.

关键词： Query performance prediction Feature encoding tree-structured model

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A Fast Implementation of DPM-based Facial Landmark Localization

A Fast Implementation of DPM-based Facial Landmark Localizat...

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International Conference on Mechatronics and Information Technology (ICMIT 2013)

作者： Cao, Keming Li, Yanlong Chen, Jiansheng Yang, Chang Wang, Meng Shao, Huan Su, Guangda Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9783037859414

Face detection, pose estimation and facial landmark localization are three fundamental problems in pattern recognition. These three tasks have high request of algorithm efficiency and accuracy. Zhu and Ramanan proposed a model based on mixture of tree structures [1] to solve the three tasks simultaneously and it obtains state-of-the-art result. However, the efficiency of their,algorithm is relatively low. Our improved algorithm combines Viola Jones detector and tree-structured model and achieves a speed-up of tens of times even hundreds of times of original algorithm on ordinary laptop according to images of different sizes.

关键词： face detection pose estimation facial landmark localization tree-structured model

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