Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model by Xingcun Fan, Guangming Xiang, Wenbin Liao, Luchi Xiao, Siwei He, Na Luo, Hongzhong Lu, Xuefeng Yan

“…Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict regulatory correlations. …”
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Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing by Shuai Zhao, Shao-Qun Lin, Dao-Yuan Tan, Hong-Hu Zhu, Zhen-Yu Yin, Jian-Hua Yin

“…To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness. …”
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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos. by Shagun Sharma, Kalpna Guleria, Ayush Dogra, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…Initially, for developing this model, the collection of the MRI scans of non-tumour and glioma tumours has been done, which are further pre-processed by performing data balancing and image resizing. The configuration and development of the pre-trained MobileNetV2 base model have been performed, which is then applied to the federated learning(FL) framework. …”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…However, in many astrophysical applications it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. …”
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Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

“…Through utilizing the data acquisition capability of the distribution terminal unit (DTU) of the distribution line, the protection settings are reasonably set to collect fault flashover information without affecting FA functions, and analyze the signal waveform characteristics to extract fault waveform characteristics for equipment defect identification; The distribution network defect perception model is established based on the waveform matching algorithm, the identification of fault waveform is trained and learned, and the analytic hierarchy process (AHP) algorithm is used to quantify the risk assessment. …”
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Adaptive learning on mobile network traffic data by Zhen Liu, Nathalie Japkowicz, Ruoyu Wang, Deyu Tang

“…The concept drift detection method relies on the data distribution instead of the classification error rate. …”
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Quality medical data management within an open AI architecture – cancer patients case by Mirjana Ivanovic, Serge Autexier, Miltiadis Kokkonidis, Johannes Rust

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RecompGPT: Generative Pre-Trained Transformers-Assisted Interactive Human Gaze Pattern Learning and Distribution Modeling for Scene Recomposition by Wang Shang, Nassiriah Binti Shaari, Nur Sauri Bin Yahaya, Liu Hao

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Socio-demographic distribution and clinical profile of exotropia in India: Electronic medical records-driven big data analytics report by Sampada Kulkarni, Anthony Vipin Das, Ramesh Kekunnaya, Virender Sachdeva, Vivek Warkad

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A study on improved random forest-based anomaly detection of regional tariff data under distributed photovoltaic access by Shujun Ji, Kai Liu, Bo Ling, Jiadong Li, Jinteng Wang, Xun Ma

“…To ensure the reliability of the measurement and calculation results for electric energy and electricity charges, it is necessary to accurately determine the anomalous data in the regional electricity pricing datasets and consequently propose an anomaly detection method for regional electricity price data based on an improved random forest algorithm under distributed photovoltaic access. …”
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MACROSTRUCTURAL DISTRIBUTION OF THE SPECIFIC TRAINING TOOLS FOR CLASSIC MOUNTAIN RUNNING IN A COMBINED MODEL OF PREPARATION FOR "MAINLY UPHILL" AND “UP AND DOWNHILL” VARIANTS by K. Kisyov

“…In researches it has been observed the distribution of the specific training tools, that coincide or are very close in their biomechanics and bioenergetics to the racing activity. …”
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Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications by Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker, Raquel A. F. Mini

“…In our approach, edge clients perform real-time learning with continuous streaming local data. At the edge clients, we model intra-service behaviors and inter-service dependencies in multi-source distributed data based on a Span Causal Graph (SCG) representation and train a model through a combination of Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. …”
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Anomaly Detection for Suspension Systems Based on the Gaussian Distribution of Hyperspheres by Ping WANG, Zi MEI, Zhiqiang LONG

“…Meanwhile, the problem of the balance of suspension gap data increases the dif ficulty of anomaly detection. …”
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Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data by Hoon Jeong Lee, Arlene Chiu, Yida Lin, Sreyas Chintapalli, Serene Kamal, Eric Ji, Susanna M. Thon

“…Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. …”
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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

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Simulating of Changes in Water Distribution Uniformity Coefficient in Classic Stationary Sprinkler Irrigation Using Data-Mining Models by Fariborz Ahmadzadeh-Kaleybar, Shahram Shahmohammadi Kalalagh, Sina Fard Moradinia

“…The purpose of this research is to use support vector machine (SVM) and gene expression programming (GEP) models to simulate the coefficient of water distribution uniformity in the farm-conditions of Malekan plain in the northwest of Iran, which is in the catchment area of the Urmia lake is experiencing severe water stress.Field tests were carried out on seven farms equipped with a classic stationary sprinkler irrigation system with a movable sprinkler (Komet 162, 163) with variables of sprinkler intervals on laterals and manifolds, operating pressure and wind speed, and distribution uniformity coefficient data were obtained. …”
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A siamese neural network model for phase identification in distribution networks by Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara

“…Distribution system operators (DSOs) often lack high-quality data on low-voltage distribution networks (LVDNs), including the topology and the phase connection of residential customers. …”
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Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks by Andrés Tello, Huy Truong, Alexander Lazovik, Victoria Degeler

“…Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. …”
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A segment anything model-based geological remote sensing interpretation method with a distributed data-parallel deep learning framework by Xiaohui Huang, Ao Long, Wei Han, Yunliang Chen, Geyong Min, Dongmei Yan

“…Inspired by expert interpretation practices, which involve first delineating boundaries and then identifying semantics, we leverage the vision foundation model and propose a distributed interpretation framework including distributed training and inference phases based on data parallelism in distributed architectures. …”
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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

“…First, using an anomaly detection technique based on an autoencoder model, we select a large sample of objects (∼19,000) whose Spectral Energy Distribution is not well reconstructed by the model after training it on a well-behaved star sample. …”
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