Detecting change in stochastic sound sequences. by Benjamin Skerritt-Davis, Mounya Elhilali

“…Behavioral data indicate listeners collect statistical estimates to process incoming sounds, and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics. Further analysis of individual subjects' behavior indicates an important role of perceptual constraints in listeners' ability to track these sensory statistics with high fidelity. …”
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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity by Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema, Alexander Szorkovszky, Anis Yazidi, Pedro G. Lind

“…Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs’ ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. …”
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Inferring flow energy, space scales, and timescales: freely drifting vs. fixed-point observations by A. L. S. Ponte, L. C. Astfalck, L. C. Astfalck, M. D. Rayson, A. P. Zulberti, N. L. Jones

“…In particular, we show that our method correctly identifies whether drifters are preferentially sampling spatial vs. temporal variability. Pending further developments, this method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small-scale (e.g., <span class="inline-formula">&lt;100</span> km) ocean variability.…”
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DEVELOPMENT OF A METHOD FOR THE INTERACTIVE CONSTRUCTION OF EXPLANATIONS IN INTELLIGENT INFORMATION SYSTEMS BASED ON THE PROBABILISTIC APPROACH by Serhii Chalyi, Volodymyr Leshchynskyi

“…Methods: methods of data analysis, methods of system analysis, methods of constructing explanations, models of knowledge representation. …”
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Research on deep learning model for stock prediction by integrating frequency domain and time series features by Wenjie Sun, Jianhua Mei, Shengrui Liu, Chunhong Yuan, Jiaxuan Zhao

“…By fusing information from both domains, the deep neural network significantly improves prediction accuracy and reliability. While temporal feature analysis is common, frequency-domain features, derived via spectral analysis (e.g., Fourier Transform), can reveal latent periodicities and seasonality patterns in price movements. …”
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Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning by Junyeop Lee, Insung Ham, Yongmin Kim, Hanseok Ko

“…To address these challenges, we introduce a masking-based reconstruction approach within a contrastive learning context, aiming to enhance the model’s ability to learn discriminative temporal features. …”
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Object detection in motion management scenarios based on deep learning. by Baocheng Pei, Yanan Sun, Yebiao Fu, Ting Ren

“…However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. …”
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. …”
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Time Variability Correction of CMIP6 Climate Change Projections by Y. Shao, C. H. Bishop, S. Hobeichi, N. Nishant, G. Abramowitz, S. Sherwood

“…From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. …”
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Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution by Vivaldi Rinaldi, Masoud Ghandehari

“…Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. …”
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Adaptive Vision-Based Gait Environment Classification for Soft Ankle Exoskeleton by Gayoung Yang, Jeong Heo, Brian Byunghyun Kang

“…The results of this study indicate that advanced spatio-temporal feature analysis and environment classification improve the exoskeleton’s gait assistance, improving adaptability in complex environments for stroke patients.…”
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DEICTIC MITIGATION VS COMMISSIVE ACTS IN POLITICAL INTERVIEW (BASED ON THE MODERN EUROPEAN DISCOURSE OF AID TO UKRAINE) by Nataliia K. Kravchenko, Sviatoslav I. Vylinskyi, Oleksandr I. Yudenko

“…To achieve the purpose and objectives set in the article, the following research methods are used: the method of speech acts analysis and the method of inferencing the shields-based conventional implicatures, supplemented by elements of componential analysis. …”
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Efficient fault diagnosis in rolling bearings lightweight hybrid model by Peng Yang, Bozheng Zhang, Jianda Zhao

“…This enhancement further strengthens the model’s ability to capture temporal features, thereby improving diagnostic accuracy. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…Moreover, the analysis of the brain region segmentation revealed that the left temporal lobe exhibited higher discriminative power in detecting MDD, highlighting its important role in the neurobiology of depression. …”
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Comparative Transcriptome and Metabolome Profiling Revealed Molecular Cascade Events During the Enzymatic Browning of Potato Tubers After Cutting by Li Wang, Jianwei Shan, Jitao Liu, Kang An, Kun Yang, Chengchen Li, Xiaobo Li, Xingyao Xiong

“…Significant redox-related pathways occurred earlier in BS tubers compared to the BR tubers. Further analysis revealed that the BS tubers had a stronger phenolic synthesis ability compared to the BR tubers. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Machine learning models such as random forest, support vector regression, and extreme learning machine (ELM) extend statistical approaches but still lack the ability to establish temporal dependencies due to their static input-output mapping relationships. …”
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Marqueurs du point de départ spatial et temporel antéposés : une comparaison de depuis, dès et à partir de by Véronique Lagae

“…The main purpose of this paper is to refine the analysis of French adverbials expressing a temporal or a spatial starting point. …”
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Leopard seal song patterns have similar predictability to nursery rhymes by Lucinda E. H. Chambers, John R. Buck, Tracey L. Rogers

“…Future studies examining animal mating song would benefit from incorporating entropy analysis to question if information is conveyed through temporal structure alongside other acoustic variables.…”
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Modeling, Specification and Construction of PLC-programs by E. V. Kuzmin, V. A. Sokolov

“…The proposed programming approach provides an ability of a correctness analysis of PLC-programs using the model checking method. …”
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Human discrimination and modeling of high-frequency complex tones shed light on the neural codes for pitch. by Daniel R Guest, Andrew J Oxenham

“…However, accurate pitch perception remains possible even when spectrally resolved harmonics are presented at frequencies beyond the putative limits of neural phase locking, and it is unclear whether residual temporal information, or a coarser rate-place code, underlies this ability. …”
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