Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process

When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e., low precision), a lack of relevant document retrieval (i.e., low recall), acceptable retrieval time, and minimum storage space. This paper proposed a novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The proposed IRS includes three main stages; the first stage (i.e., the advanced documents indexing method) is preprocessing, which consists of two steps: dataset documents reading and advanced documents indexing method (ADIM), resulting in a set of two tables. The second stage is the query searching algorithm to produce a set of words or keywords and the related documents retrieving. The third stage (i.e., the searching algorithm) consists of two steps. The modified genetic algorithm (MGA) proposed new fitness functions using a cross-point operator with dynamic length chromosomes with the adaptive function of the culture algorithm (CA). The proposed system ranks the most relevant documents to the user query by adding a simple parameter (∝) to the fitness function to guarantee the convergence solution, retrieving the most relevant user’s document by integrating MGA with the CA algorithm to achieve the best accuracy. This system was simulated using a free dataset called WebKb containing Worldwide Webpages of computer science departments at multiple universities. The dataset is composed of 8280 HTML-programed semi-structured documents. Experimental results and evaluation measurements showed 100% average precision with 98.5236% average recall for 50 test queries, while the average response time was 00.46.74.78 milliseconds with 18.8 MB memory space for document indexing. The proposed work outperforms all the literature, comparatively, representing a remarkable leap in the studied field.

/pdf/an-efficient-information-retrieval-system-using-evolutionary-35165dpd.pdf

An Efficient Information Retrieval System Using Evolutionary Algorithms

The field of deep learning has witnessed significant progress in recent times, particularly in areas such as computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications, enhancing industrial productivity and facilitating social development. However, it extremely suffers from the unstable training process and stringent requirements of computational resources. With the increasing demands on the adaption of computational capacity, though numerous studies have explored the efficient training field to a certain extent, a comprehensive summarization/guideline on those general acceleration techniques of training large-scale deep learning models is still much anticipated. In this survey, we present a detailed review of the general techniques for training acceleration. We consider the fundamental update formulation and split its basic components into five main perspectives: (1) “data-centric”: including dataset regularization, data sampling, and data-centric curriculum learning techniques, which can significantly reduce the computational complexity of the data samples; (2) “model-centric”, including acceleration of basic modules, compression training, model initialization and model-centric curriculum learning techniques, which focus on accelerating the training via reducing the calculations on parameters and providing better initialization; (3) “optimization-centric”, including the selection of learning rate, the employment of large batchsize, the designs of efficient objectives, and model average techniques, which pay attention to the training policy and improving the generality for the large-scale models; (4) “budgeted training”, including some distinctive acceleration methods on source-constrained situations, e.g. for limitation on the total iterations; (5) “system-centric”, including some efficient distributed frameworks and open-source libraries which provide adequate hardware support for the implementation of above mentioned acceleration algorithms. By presenting this comprehensive taxonomy, our survey presents a comprehensive review to understand the general mechanisms within each component and their joint interaction. Meanwhile, we further provide a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms. Overall, we hope that this survey will serve as a valuable guideline for general efficient training. 

On Efficient Training of Large-Scale Deep Learning Models

Grouped spatial autoregressive model

The driving mechanisms of industrial air pollution spatial correlation networks: A case study of 168 Chinese cities

Distributed one-step upgraded estimation for non-uniformly and non-randomly distributed data

Customer churn is one of the most important concerns for large companies. Currently, massive data are often encountered in customer churn analysis, which bring new challenges for model computation. To cope with these concerns, sub‐sampling methods are often used to accomplish data analysis tasks of large scale. To cover more informative samples in one sampling round, classic sub‐sampling methods need to compute non‐uniform sampling probabilities for all data points. However, this method creates a huge computational burden for data sets of large scale and therefore, is not applicable in practice. In this study, we propose a sequential one‐step (SOS) estimation method based on repeated sub‐sampling data sets. In the SOS method, data points need to be sampled only with uniform probabilities, and the sampling step is conducted repeatedly. In each sampling step, a new estimate is computed via one‐step updating based on the newly sampled data points. This leads to a sequence of estimates, of which the final SOS estimate is their average. We theoretically show that both the bias and the standard error of the SOS estimator can decrease with increasing sub‐sampling sizes or sub‐sampling times. The finite sample SOS performances are assessed through simulations. Finally, we apply this SOS method to analyse a real large‐scale customer churn data set in a securities company. The results show that the SOS method has good interpretability and prediction power in this real application.

Sequential one‐step estimator by sub‐sampling for customer churn analysis with massive data sets

Text mining has recently attracted a great deal of attention with the accumulation of text documents in all fields. In this article, we focus on the use of textual information to explain continuous...

Sequential Text-Term Selection in Vector Space Models

Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose an improved Naive Bayes method for text classification. It is analytically simple and free of subjective judgements on the correct and incorrect labels. By specifying the generating mechanism of incorrect labels, we optimize the corresponding log-likelihood function iteratively by using an EM algorithm. Our simulation and experiment results show that the improved Naive Bayes method greatly improves the performances of the Naive Bayes method with mislabeled data.

pdf/improved-naive-bayes-with-mislabeled-data-3uxwwjlq.pdf

Improved Naive Bayes with Mislabeled Data

Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. By transfer learning sufficiency (denoted as the null hypothesis), we mean that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.

pdf/testing-sufficiency-for-transfer-learning-2uwd5ume.pdf

Testing Sufficiency for Transfer Learning

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