Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder by Haoyang Chen, Na Li, Hangguan Shan, Eryun Liu, Zhiyu Xiang

“…Extensive evaluations on the nuScenes dataset demonstrate the effectiveness of MS-SLV, achieving a 12.37% reduction in average displacement error and a 7.67% reduction in final displacement error over state-of-the-art methods. …”
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Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…Results show under a 50% probability of upstream data loss, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula>-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. …”
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CRISPR.BOT an autonomous platform for streamlined genetic engineering and molecular biology applications by Fatmanur Erkek, Rana Kizilkaya, Seyma Baybara, Gamze Gulden, Berranur Sert, Muzeyyen Sena Yazicilar, Menekse Cagla Yilmaz, Sibel Pinar Odabas, Enes Bal, Gamze Yelgen, Tarik Teymur, Yasin Ay, Solen Dogdu, Nilufer Neslihan Tiryaki, Buse Baran, Beste Gelsin, Hasret Araz, Ilayda Cavrar, Cihan Tastan

“…Traditional workflows, laden with laborious and error-prone stages, incur prolonged timelines and escalating costs. …”
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A Trajectory Prediction Method for High-Speed and High-Maneuverability Glide Vehicle Based on Mid-Terminal Guidance Handover Point Identification by Ma Kangkang, Zhao Liangyu, Hu Xingzhi, Li Mingjie

“…Compared to directly utilizing a deep learning mo-del for prediction, the proposed prediction method demonstrates a reduction of 37.61% in average prediction error and 37.34% in maximum prediction error within a prediction time of 240 s.…”
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MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy by Entao Luo, Li Shi, Jiyan Liu, Zheng Wu, Guoyun Duan, Lingxuan Zeng, Tangsen Huang

“…Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. …”
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Using de novo assembly to identify structural variation of eight complex immune system gene regions. by Jia-Yuan Zhang, Hannah Roberts, David S C Flores, Antony J Cutler, Andrew C Brown, Justin P Whalley, Olga Mielczarek, David Buck, Helen Lockstone, Barbara Xella, Karen Oliver, Craig Corton, Emma Betteridge, Rachael Bashford-Rogers, Julian C Knight, John A Todd, Gavin Band

“…Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. …”
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A speech recognition method with enhanced transformer decoder by Hengbo Hu, Tong Niu, Zhenhua He

“…Experimental results on the Mandarin Aishell-1 dataset demonstrate that when the encoder is a Conformer, the enhanced decoder achieves a 16.1% reduction in character error rate compared to the Transformer decoder. …”
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Semi-Supervised Learning of Statistical Models for Natural Language Understanding by Deyu Zhou, Yulan He

“…In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.…”
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Evaluation of the CRISPR/Cas9 system as genome editing platform for the Mucopolysaccharidosis IV A using a strategy for induction of higher homologous recombination frequency by Diego Alejandro Suarez, Andrés Felipe Leal, Carlos Javier Alméciga Díaz

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Graph2Mat: universal graph to matrix conversion for electron density prediction by Pol Febrer, Peter Bjørn Jørgensen, Miguel Pruneda, Alberto García, Pablo Ordejón, Arghya Bhowmik

“…The novel prediction model also allows for two new and promising measures of uncertainty (total charge error and self-consistency error) that will facilitate its practical usage, e.g. within active learning workflows. …”
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Combined compression and encryption of linear wireless sensor network data using autoencoders by N. Shylashree, Sachin Kumar, Hong Min

“…The Encoder part of the trained Autoencoder, housed at the Base Station (BS), reduces the number of data samples at the encoded output. …”
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A Near-Infrared Imaging System for Robotic Venous Blood Collection by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu, Aihong Ji

“…The U-Net+ResNet18 neural network integrates the residual blocks from ResNet18 into the encoder of the U-Net to form a new neural network. …”
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A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems by Md Shahab Uddin, Ahsan Ahmed, Md Aktarujjaman, Mohammad Moniruzzaman, Mumtahina Ahmed, M. F. Mridha, Md. Jakir Hossen

“…Experimental results on real and synthetic healthcare datasets demonstrate that the proposed model outperforms traditional regressors, deep neural networks, and standalone RL agents across multiple evaluation metrics, including cost prediction error, diagnostic classification accuracy, cumulative reward, and average billing reduction. …”
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GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT Integration by Ting Liu, Yuan Liu

“…Additionally, two self-supervised tasks are designed for improved model accuracy and robustness. (3) Results: The fine-tuned model had comparatively optimal performance metrics with significant reductions in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). (4) Conclusions: Ultimately, the integration of this model into travel time prediction, based on two large-scale real-world trajectory datasets, demonstrates enhanced performance and computational efficiency.…”
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A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems by Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li, Peilin Zhong, Jie Zeng, Chenguang Wang

“…Experimental results on IEEE standard test systems and real-world power grids demonstrate the framework’s superiority as compared to state-of-the-art AI models, achieving a 48.1% reduction in prediction error, a 6.3% improvement in the classification F1 score, and a 52.1% decrease in inference time, offering a robust solution for modern power system monitoring and safety assessments.…”
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The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions by Stavros Pantelakos, Martha Nifora, Georgios Agrogiannis

“…Previous studies have attempted to extrapolate cost containment in conjunction with the implementation of digital pathology solutions mostly on the basis of operational cost savings or diagnostic error reduction. However, no study has attempted to link a wider spectrum of potential diagnostic tasks performed by artificial intelligence algorithms to financial figures. …”
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TransformerPayne: Enhancing Spectral Emulation Accuracy and Data Efficiency by Capturing Long-range Correlations by Tomasz Różański, Yuan-Sen Ting, Maja Jabłońska

“…The newly introduced TransformerPayne emulator outperformed all other tested models, achieving a mean absolute error (MAE) of approximately 0.15% when trained on the full grid. …”
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Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning by Xinyuan Zheng, Patrick Worhunsky, Qiong Liu, Xueqi Guo, Xiongchao Chen, Heng Sun, Jiazhen Zhang, Takuya Toyonaga, Adam P. Mecca, Ryan S. O’Dell, Christopher H. van Dyck, Gustavo A. Angarita, Kelly Cosgrove, Deepak D’Souza, David Matuskey, Irina Esterlis, Richard E. Carson, Rajiv Radhakrishnan, Chi Liu

“…Methods We implemented a convolution-based 3D encoder-decoder to predict [11C]UCB-J SV2A PET images. …”
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Urban Signalized Intersection Traffic State Prediction: A Spatial–Temporal Graph Model Integrating the Cell Transmission Model and Transformer by Anran Li, Zhenlin Xu, Wenhao Li, Yanyan Chen, Yuyan Pan

“…Validation using real traffic data from pNEUMA demonstrates that CeT significantly outperforms baseline models in two-phase signalized intersection scenarios, achieving reductions of 11.47% in Mean Absolute Error (MAE), 13.48% in Root Mean Square Error (RMSE), and an increase of 4.36% in Accuracy (ACC). …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…The study meticulously constructed and trained the TF-CNN-BiLSTM model using TensorFlow with the Adam optimizer and mean squared error loss function. The training process was optimized with learning rate reduction and early stopping callbacks to prevent overfitting. …”