Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019

The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening potential patients and early detection of cases. This could also save the lives of elderly patients who have a higher mortality rate from this disease.

Question classification in question answering based on real-world web data sets

To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.

Reconstruction of incomplete satellite SST data sets based on EOF method

As for the satellite remote sensing data obtained by the visible and infrared bands myers,on, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thin clouds difficult to be detected would cause the data of the inversion products to be abnormal. Alvera et a1.（2005） proposed a method for the reconstruction of missing data based on an Empirical Orthogonal Functions （EOF） decomposition, but his method couldn＇t process these images presenting extreme cloud coverage（more than 95%）, and required a long time for recon- struction. Besides, the abnormal data in the images had a great effect on the reconstruction result. Therefore, this paper tries to improve the study result. It has reconstructed missing data sets by twice applying EOF decomposition method. Firstly, the abnormity time has been detected by analyzing the temporal modes of EOF decomposition, and the abnormal data have been eliminated. Secondly, the data sets, excluding the abnormal data, are analyzed by using EOF decomposition, and then the temporal modes undergo a filtering process so as to enhance the ability of reconstruct- ing the images which are of no or just a little data, by using EOF. At last, this method has been applied to a large data set, i.e. 43 Sea Surface Temperature （SST） satellite images of the Changjiang River （Yangtze River） estuary and its adjacent areas, and the total reconstruction root mean square error （RMSE） is 0.82℃. And it has been proved that this improved EOF reconstruction method is robust for reconstructing satellite missing data and unreliable data.

Influence of image data set noise on classification with a convolutional network

To evaluate the influence of data set noise, the network in network(NIN) model is introduced and the negative effects of different types and proportions of noise on deep convolutional models are studied. Different types and proportions of data noise are added to two reference data sets, Cifar-10 and Cifar-100. Then, this data containing noise is used to train deep convolutional models and classify the validation data set. The experimental results show that the noise in the data set has obvious adverse effects on deep convolutional network classification models. The adverse effects of random noise are small, but the cross-category noise among categories can significantly reduce the recognition ability of the model. Therefore, a solution is proposed to improve the quality of the data sets that are mixed into a single noise category. The model trained with a data set containing noise is used to evaluate the current training data and reclassify the categories of the anomalies to form a new data set. Repeating the above steps can greatly reduce the noise ratio, so the influence of cross-category noise can be effectively avoided.

Evolution algorithm for water storage forecasting response to climate change with little data sets:the Wolonghu Wetland,China

An attempt of applying a novel genetic programming(GP) technique,a new member of evolution algorithms,has been made to predict the water storage of Wolonghu wetland response to the climate change in northeastern part of China with little data set.Fourteen years(1993-2006) of annual water storage and climatic data set of the wetland were taken for model training and testing.The results of simulations and predictions illustrated a good fit between calculated water storage and observed values(MAPE=9.47,r=0.99).By comparison,a multilayer perceptron(MLP)(a popular artificial neural network model) method and a grey model(GM) with the same data set were applied for performances estimation.It was found that GP technique had better performances than the other two methods both in the simulation step and predicting phase and the results were analyzed and discussed.The case study confirmed that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under condition of little data set.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Scaling up Kernel Grower Clustering Method for Large Data Sets via Core-sets

核栽培者是聚类最近 Camastra 和 Verri 建议的方法的一个新奇的核。它证明为各种各样的数据的好性能关于流行聚类的算法有利地设定并且比较。然而，方法的主要缺点是在处理大数据集合的弱可伸缩能力，它极大地限制它的应用程序。在这份报纸，我们用核心集合建议一个可伸缩起来的核栽培者方法，它是比为聚类的大数据的原来的方法显著地快的。同时，它能处理很大的数据集合。象合成数据集合一样的基准数据集合的数字实验显示出建议方法的效率。方法也被用于真实图象分割说明它的性能。

Threshold Selection Study on Fisher Discriminant Analysis Used in Exon Prediction for Unbalanced Data Sets

In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same size if the number of the data is big enough. But for some situations the data are not sufficient or not equal, the threshold used in FDA may have important influence on prediction results. This paper presents a study on the selection of the threshold. The eigen value of each exon/intron sequence is computed using the Z-curve method with 69 variables. The experiments results suggest that the size and the standard deviation of the data sets and the threshold are the three key elements to be taken into consideration to improve the prediction results.

Contrasting Vertical Structure of Recent Arctic Warming in Different Data Sets

Arctic region is experiencing strong warming and related changes in the state of sea ice, permafrost, tundra, marine environment and terrestrial ecosystems. These changes are found in any climatological data set comprising the Arctic region. This study compares the temperature trends in several surface, satellite and reanalysis data sets. We demonstrate large differences in the 1979-2002 temperature trends. Data sets disagree on the magnitude of the trends as well as on their seasonal, zonal and vertical pattern. It was found that the surface temperature trends are stronger than the trends in the tropospheric temperature for each latitude band north of 50?N for each month except for the months during the ice-melting season. These results emphasize that the conclusions of climate studies drawn on the basis of a single data set analysis should be treated with caution as they may be affected by the artificial biases in data.

Top-k probabilistic prevalent co-location mining in spatially uncertain data sets

A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions： 1） the concept of the top-k prob- abilistic prevalent co-locations based on a possible world model is defined; 2） a framework for discovering the top- k probabilistic prevalent co-locations is set up; 3） a matrix method is proposed to improve the computation of the preva- lence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for ex- act solutions; 4） a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5） an ap- proximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.

Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic systems with aggravating nutrient imbalance

The bioaccumulation of mercury(Hg)in aquatic ecosystem poses a potential health risk to human being and aquatic organism.Bioaccumulations by plankton represent a crucial process of Hg transfer from water to aquatic food chain.However,the current understanding of major factors affecting Hg accumulation by plankton is inadequate.In this study,a data set of 89 aquatic ecosystems worldwide,including inland water,nearshore water and open sea,was established.Key factors influencing plankton Hg bioaccumulation(i.e.,plankton species,cell sizes and biomasses)were discussed.The results indicated that total Hg(THg)and methylmercury(MeHg)concentrations in plankton in inland waters were significantly higher than those in nearshore waters and open seas.Bioaccumulation factors for the logarithm of THg and MeHg of phytoplankton were 2.4–6.0 and 2.6–6.7 L/kg,respectively,in all aquatic ecosystems.They could be further biomagnified by a factor of 2.1–15.1 and 5.3–28.2 from phytoplankton to zooplankton.Higher MeHg concentrations were observed with the increases of cell size for both phyto-and zooplankton.A contrasting trend was observed between the plankton biomasses and BAF_(MeHg),with a positive relationship for zooplankton and a negative relationship for phytoplankton.Plankton physiologic traits impose constraints on the rates of nutrients and contaminants obtaining process from water.Nowadays,many aquatic ecosystems are facing rapid shifts in nutrient compositions.We suggested that these potential influences on the growth and composition of plankton should be incorporated in future aquatic Hg modeling and ecological risk assessments.

Data Set and Evaluation of Automated Construction of Financial Knowledge Graph

With the technological development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, the research on knowledge graph has been carried out in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph(FR2 KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference(CCKS2020). FR2 KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2 KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.

Constructing Isosurfaces from 3D Data Sets Taking Account of Depth Sorting of Polyhedra

Creating and rendering intermediate geometric primitives is one of the approaches to visualize data sets in 3D space. Some algorithms have been developed to construct isosurface from uniformly distributed 3D data sets. These algorithms assume that the function value varies linearly along edges of each cell. But to irregular 3D data sets, this assumption is inapplicable. Moreover, the depth sorting of cells is more complicated for irregular data sets, which is indispensable for generating isosurface images or semitransparent isosurface images, if Z-buffer method is not adopted.In this paper, isosurface models based on the assumption that the function value has nonlinear distribution within a tetrahedroll are proposed. The depth sorting algorithm and data structures are developed for the irregular data sets in which cells may be subdivided into tetrahedra. The implementation issues of this algorithm are discussed and experimental results are shown to illustrate potentials of this technique.

AOL4PS:A Large-scale Data Set for Personalized Search

Personalized search is a promising way to improve the quality of Websearch,and it has attracted much attention from both academic and industrial communities.Much of the current related research is based on commercial search engine data,which can not be released publicly for such reasons as privacy protection and information security.This leads to a serious lack of accessible public data sets in this field.The few publicly available data sets have not become widely used in academia because of the complexity of the processing process required to study personalized search methods.The lack of data sets together with the difficulties of data processing has brought obstacles to fair comparison and evaluation of personalized search models.In this paper,we constructed a large-scale data set AOL4 PS to evaluate personalized search methods,collected and processed from AOL query logs.We present the complete and detailed data processing and construction process.Specifically,to address the challenges of processing time and storage space demands brought by massive data volumes,we optimized the process of data set construction and proposed an improved BM25 algorithm.Experiments are performed on AOL4 PS with some classic and state-of-the-art personalized search methods,and the experiment results demonstrate that AOL4 PS can measure the effect of personalized search models.

A dataset of scientific literature on floods,1990-2017

With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has been the subject of numerous scientific publications.On January 1,2018,we conducted literature data collection and processing on flood research and categorized the retrieved paper records into Whole SCI Dataset(WS)and High-Citation SCI Dataset(HCS).These data sets can serve as basic data for bibliometric analysis to identify the status of global flood research during 1990-2017.Our study shows that while the Chinese Academy of Sciences was the most productive institution during this period,the United States was the most productive country.Besides,our keyword analysis reveals the potential popular issues and future trends of flood research.

An RDF Data Set Quality Assessment Mechanism for Decentralized Systems

With the rapid growth of the linked data on the Web,the quality assessment of the RDF data set becomes particularly important,especially for the quality and accessibility of the linked data.In most cases,RDF data sets are shared online,leading to a high maintenance cost for the quality assessment.This also potentially pollutes Internet data.Recently blockchain technology has shown the potential in many applications.Using the blockchain storage quality assessment results can reduce the centralization of the authority,and the quality assessment results have characteristics such as non-tampering.To this end,we propose an RDF data quality assessment model in a decentralized environment,pointing out a new dimension of RDF data quality.We use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment results.We have implemented a system DCQA to test and verify the feasibility of the quality assessment model.The proposed method can provide users with better cost-effective results when knowledge is independently protected.

An Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data

In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.

一个基于现实世界的大型Web参照数据集——UK2006 Datasets的初步研究

文章介绍了WEBSPAM-UK2006数据集,一个大型的基于现实世界的,人工评判过一些垃圾行为的web数据集合,详细的对数据集的构成进行了分析,对数据集采用Python进行了初步的预处理,为以后在反垃圾网页行为方面的算法和判定研究提供了非常有意的经验和参考。

Oil-gas reservoir in the Mesozoic strata in the Chaoshan depression,northern South China Sea:a new insight from long off set seismic data

The Chaoshan depression,a Mesozoic basin in the Dongsha sea area,northern South China Sea,is characterized by well-preserved Mesozoic strata,being good conditions for oil-gas preservation,promising good prospects for oil-gas exploration.However,breakthrough in oil-gas exploration in the Mesozoic strata has not been achieved due to less seismic surveys.New long-off set seismic data were processed that acquired with dense grid with single source and single cable.In addition,the data were processed with 3D imaging method and fi ner processing was performed to highlight the target strata.Combining the new imaging result and other geological information,we conducted integrated interpretation and proposed an exploratory well A-1-1 for potential hydrocarbon.The result provides a reliable basis for achieving breakthroughs in oil and gas exploration in the Mesozoic strata in the northern South China Sea.

A Generalized Rough Set Approach to Attribute Generalization in Data Mining

This paper presents a generalized method for updating approximations of a concept incrementally, which can be used as an effective tool to deal with dynamic attribute generalization. By combining this method and the LERS inductive learning algorithm, it also introduces a generalized quasi incremental algorithm for learning classification rules from data bases.