Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps by Pablo Cervenansky, Alvaro Martin, Gadiel Seroussi

“…This includes the important lossless compression case, which corresponds to an error bound of zero. Our schemes first encode the position of the gaps, using arithmetic coding combined with a Krichevsky-Trofimov probability assignment on a Markov model, and then encode the data values separately. …”
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Multilevel Monte Carlo methods for ensemble variational data assimilation by M. Destouches, M. Destouches, M. Destouches, P. Mycek, P. Mycek, S. Gürol, S. Gürol, A. T. Weaver, A. T. Weaver, S. Gratton, E. Simon

“…Numerical experiments with a quasi-geostrophic model demonstrate the potential of the approach, as MLMC yields more accurate background error covariances and reduced analysis error. The challenges involved in cycling a multilevel variational data assimilation system are identified and discussed.…”
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Bayesian deconvolution for computational cognitive modeling of fMRI data by Jonathon R. Howlett, Katia M. Harlé, Alan N. Simmons, Charles T. Taylor

“…A central goal of cognitive neuroscience is to make inferences about underlying cognitive processes from observable data. However, current fMRI analysis tools cannot directly estimate latent parameters in computational cognitive models from blood-oxygen-level-dependent (BOLD) signal. …”
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Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis by Ahmad Khusro, Saddam Husain, Mohammad Hashmi

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Enabling data‐driven collaborative and reproducible environmental synthesis science by Julien Brun, Nicholas J. Lyon, Angel Chen, Ingrid Slette, Gabriel De La Rosa, Jennifer E. Caselle, Frank W. Davis, Martha R. Downs

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Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule by Meiyuan Miao, Chen Ye, Zhiping Xu, Laiding Zhao, Jiafeng Yao

“…The scheme is analyzed in terms of the in-body channel. The analysis and discussion for the scheme in wireless body area networks (WBANs) are divided into three aspects: bit error rate (BER) performance, energy efficiency (EE), and spectrum efficiency (SE), and complexity. …”
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Compression of Marine Environmental Data Using Convolutional Attention Autoencoder by Xuehai Sun, Peiyu Wang, Yanxia Zhou, Kedi Wu, Limin Huang, Xuewen Ma

“…Ocean temperature data is fundamental to the study of ocean dynamics and climate change, and its efficient compression and storage are critical for large-scale data analysis and transmission. …”
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Dynamic growth tomato inflorescence modeling with elastic mechanics data by Siyao Liu, Subo Tian, Zhen Zhang, Lingfei Liu, Tianlai Li

“…Current crop modeling methods can not simulate crop dynamic growth containing physical characteristics data, while the study of physical production management such as pollination of tomato requires the elastic force and dynamic growth process analysis of the inflorescence. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…The most commonly used machine learning and deep learning methods are identified and their functional characteristics are examined in detail. Comparative analysis demonstrates that deep learning and hybrid approaches outperform traditional machine learning methods in terms of prediction accuracy, as measured by standard error metrics. …”
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Improved Ellipsoidal Intersection Method for 2D Data Fusion by Muhammed Mert Ulupinar, Gokhan Soysal

“…This article presents a comprehensive review and analysis of data fusion techniques for both known and unknown correlation cases. …”
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Fitting Time Series Models to Fisheries Data to Ascertain Age by Kathleen S. Kirch, Norou Diawara, Cynthia M. Jones

“…ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. …”
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Evaluating convergence between two data visualization literacy assessments by Erik Brockbank, Arnav Verma, Hannah Lloyd, Holly Huey, Lace Padilla, Judith E. Fan

“…However, further exploration of individual error patterns suggests that these assessments probe somewhat distinct components of data visualization literacy, and we do not find evidence that these components correspond to the categories that guided the design of either test (e.g., questions that require retrieving values rather than making comparisons). …”
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A nutritional biomarker score of the Mediterranean diet and incident type 2 diabetes: Integrated analysis of data from the MedLey randomised controlled trial and the EPIC-InterAct... by Jakub G Sobiecki, Fumiaki Imamura, Courtney R Davis, Stephen J Sharp, Albert Koulman, Jonathan M Hodgson, Marcela Guevara, Matthias B Schulze, Ju-Sheng Zheng, Claudia Agnoli, Catalina Bonet, Sandra M Colorado-Yohar, Guy Fagherazzi, Paul W Franks, Thomas E Gundersen, Franziska Jannasch, Rudolf Kaaks, Verena Katzke, Esther Molina-Montes, Peter M Nilsson, Domenico Palli, Salvatore Panico, Keren Papier, Olov Rolandsson, Carlotta Sacerdote, Anne Tjønneland, Tammy Y N Tong, Yvonne T van der Schouw, John Danesh, Adam S Butterworth, Elio Riboli, Karen J Murphy, Nicholas J Wareham, Nita G Forouhi

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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Abstract This work presents a novel methodology for elucidating the characteristics of aggregation‐induced emission (AIE) systems through the application of data science techniques. A new set of chemical fingerprints specifically tailored to the photophysics of AIE systems is developed. …”
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Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…Our experiments used diverse preprocessing, feature engineering, and hyperparameter optimization methods for traditional ML models and large language models (LLMs) as gender bias detectors, comparing these results to evaluate model performance. Error analysis further explored top-performing model strengths and limitations. …”
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Cyclical hybrid imputation technique for missing values in data sets by Kurban Kotan, Serdar Kırışoğlu

“…Because incorrect imputation of missing data increases the error in the modeling phase and reduces the prediction performance of the model. …”
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Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability by Gunadi Widi Nurcahyo, Akbari Wafridh, Yuhandri

“…Anomalies and data unavailability are significant challenges in conducting surveys, affecting the validity, reliability, and accuracy of analysis results. …”
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Small earthquake location via machine learning with insufficient data by Ji Zhang, Aitaro Kato, Huiyu Zhu, Jie Zhang

“…Traditional seismic location methods based on travel time and waveform analysis may be ineffective for such events, while traditional single-station location methods require high signal-to-noise ratio (SNR) data. …”
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Image-data-driven simulation of fluid dynamics (proposal and evaluation) by Nobuto NAKAMICHI, Younghwa CHO, Nobuyuki OSHIMA

“…Finally, we extended the diffusion filter for calculating the level-set to a three-dimensional binarized voxel-based data and found that it can also be applied to three-dimensional voxel-based data. …”
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Different pixel sizes of topographic data for prediction of soil salinity. by Shima Esmailpour, Ebrahim Mahmoudabadi, Mohammad Ghasemzadeh Ganjehie, Alireza Karimi

“…The model's effectiveness was evaluated based on their Root Mean Square Error (RMSE) and coefficient of determination (R2). …”
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