SE-HCL: Schema Enhanced Hybrid Curriculum Learning for Multi-Turn Text-to-SQL by Yiyun Zhang, Sheng'an Zhou, Gengsheng Huang

“…Concretely, We measure the difficulty of the data from both a structural and model perspective. In terms of data structure, we mainly consider the turns of the question and the complexity of the schema and SQL query. …”
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Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients by Guanqing Chen, A. James O’Malley

“…., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. …”
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Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements by Dario Di Domenico, Inna Forsiuk, Simon Müller-Cleve, Simone Tanzarella, Florencia Garro, Andrea Marinelli, Michele Canepa, Matteo Laffranchi, Michela Chiappalone, Chiara Bartolozzi, Lorenzo De Michieli, Nicolò Boccardo, Marianna Semprini

“…The data is organized following the Brain Imaging Data Structure standard format and have been validated to ensure its reliability. …”
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Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index by Ying Zhao, Dongli Hu, Dongxia Huang, You Liu, Zitong Yang, Lei Mao, Chao Liu, Fangfang Zhou

“…We integrate a back propagation neural network with the technique of look-up-table in LS to guarantee the monotonicity and boundedness of the predicted placement positions. We design a data structure called the self-regulating index in LS to tentatively store and duly update placement positions for eliminating potential placement conflicts. …”
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Feature Selection with Graph Mining Technology by Thosini Bamunu Mudiyanselage, Yanqing Zhang

“…Our aim in this research was to examine the dependencies between features and select the optimal feature set with respect to the original data structure. Another important factor in our proposed method is that it can perform even in the absence of class labels. …”
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Targeted maximum likelihood based estimation for longitudinal mediation analysis by Wang Zeyi, Laan Lars van der, Petersen Maya, Gerds Thomas, Kvist Kajsa, Laan Mark van der

“…To tackle causal and statistical challenges due to the complex longitudinal data structure with time-varying confounders, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. …”
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Synthetic data as external control arms in scarce single-arm clinical trials. by Severin Elvatun, Daan Knoors, Simon Brant, Christian Jonasson, Jan F Nygård

“…It reduces the input data cardinality pre-synthesis and reverses it post-synthesis to regain the original data structure. Finally, we test a selection of state-of-the-art generators against a suite of utility and privacy metrics. …”
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Processing Methods for Digital Image Data Based on the Geographic Information System by Xi Wang, Taizheng Chen, Dongwei Li, Shiqi Yu

“…Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of processing methods for digital image data, elaborated the development background, current status, and future challenges of the GIS technology, introduced the methods and principles of permutation matrix algorithm and subimage averaging method, constructed the processing model for digital image data based on GIS, analyzed the data structure and its database establishment for digital image, proposed the processing methods for digital image data based on GIS, performed the enhancement processing and calculation classification of digital image data, and finally conducted a case analysis and its result discussion. …”
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Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs by Xiaohuan Shan, Haihai Li, Chunjie Jia, Dong Li, Baoyan Song

“…Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. …”
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Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks by Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie, Yuan Liu, Qianyi Zhan

“…The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. Fraudulent nodes in the transaction network are referred to as anomalous nodes. …”
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Development of Vacuum Vessel Design and Analysis Module for CFETR Integration Design Platform by Chen Zhu, Minyou Ye, Xufeng Liu, Shenji Wang, Shifeng Mao, Zhongwei Wang, Yi Yu

“…In addition, data from the model and FE analysis could be easily exchanged among different modules by providing a unifying data structure to maintain the consistency of the global design. …”
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Analysis of ChatGPT-Generated Codes Across Multiple Programming Languages by Sally Almanasra, Khaled Suwais

“…The study addresses key questions related to the performance, code quality, and error management of AI-generated code by analyzing solutions for 300 data structure problems and 300 problems from the LeetCode platform. …”
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Cache of cryptographic key based on query popularity by Wei JIN, Fenghua LI, Ziyan ZHOU, Xiyang SUN, Yunchuan GUO

“…In the current HDFS (Hadoop Distributed File System) key management system, the encryption zone keys are all loaded into the memory during startup of key service.With the increase of the key resource, the occupied memory space also grows, bringing the bottleneck of memory space and key indexing.There are three challenges induced: how to organize cached data and efficiently handle queries with missed keys, how to adjust key resources in the cache, and how to accurately predict the use of keys.In order to achieve fine-grained and efficient caching and improve the efficiency of key use, key caching optimization was considered from three aspects: key index data structure, key replacement algorithm, and key prefetching strategy.An architecture of key cache replacement module was designed, and then a key replacement algorithm based on the query frequency was set.Specifically, from the perspective of heat computing and key replacement, the potential influencing factors affecting the popularity of key cache were analyzed which considered the file system and user of key management system.Besides, the basic model of key usage popularity was constructed.The hash table and minheap linked list was combined to maintain the heat of the key in use, and the elimination algorithm was set based on heat identification.The key in the cache was dynamically updated, and key usage was adjusted by the time controller, so as to realize key replacement according to the key heat.For key prefetching, key usage rules were obtained through log mining and periodical usage analyzing of key provisioning policies, which considered business processes and the time period dimension of user accessing.Experimental results show that the proposed key replacement algorithm can effectively improve the hit rate of cache queries, reduce memory usage, and ameliorate the impact of key file I/O interaction on query performance.…”
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Developing a hybrid model for comparative analysis of financial data clustering algorithms by Mojtaba Movahedi, Mahdi Homayounfar, Mehdi Fadaei, Mansour Soufi

“…Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. …”
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Simulation of discrete control systems with parallelism of behavior by D. I. Cheremisinov, L. D. Cheremisinova

“…The key concepts of the TLM model for simulating the descriptions of control algorithms in the PRALU language are defined: data structure, transactions, processes, and a barrier mechanism for synchronization of parallel processes. …”
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LINKS. A System for Historical Family Reconstruction in the Netherlands by Kees Mandemakers, Gerrit Bloothooft, Fons Laan, Joe Raad, Rick J. Mourits, Richard L. Zijdeman

“…We present the background and philosophy of this matching system and explain its data structure and functioning. Currently the core data of the LINKS system consists of indexed civil certificates. …”
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Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline by Zhengxiao Yang, Hao Zhou, Sudesh Srivastav, Jeffrey G. Shaffer, Kuukua E. Abraham, Samuel M. Naandam, Samuel Kakraba

“…Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks: post-mean, post-max, post-min, and pre-mean aggregation. …”
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Advanced GPU Techniques for Dynamic Remeshing and Self-Collision Handling in Real-Time Cloth Tearing by Jong-Hyun Kim, Jung Lee

“…In this paper, we propose a new data structure that enables GPU-friendly calculations. …”
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A novel approach to analyzing the evolution of SARS-CoV-2 based on visualization and clustering of large genetic data compactly represented in operative memory by A. Yu. Palyanov, N. V. Palyanova

“…Aligning all genomes versus the initial Wuhan-Hu-1 reference sequence allows each to be represented as a data structure containing lists of point mutations, deletions and insertions. …”
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Multichannel Enhanced Millimeter-Wave SAR Imaging via Low-Rank Tensor-Train Decomposition by Bangjie Zhang, Gang Xu, Xiang-Gen Xia, Jianlai Chen, Rui Zhou, Shuai Shao, Wei Hong

“…In addition, ket augmentation is introduced to exhibit the local data structure more clearly than the original tensor under TT decomposition. …”
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