Suchergebnisse - Theory of computation → Dynamic graph algorithms phrases Temporal constraint networks

Agile Controllability of Simple Temporal Networks with Uncertainty and Oracles

Autoren: Eder, Johann Posenato, Roberto Combi, Carlo et al.

Schlagwörter: 2012 ACM Subject Classification Computing methodologies → Temporal reasoning, Theory of computation → Dynamic graph algorithms phrases Temporal constraint networks, contingent durations, agile controllability

Dateibeschreibung: application/pdf

Relation: https://www.alexandria.unisg.ch/handle/20.500.14171/121953

Verfügbarkeit: https://doi.org/10.4230/LIPIcs.TIME.2024.1
https://www.alexandria.unisg.ch/handle/20.500.14171/121953
https://hdl.handle.net/20.500.14171/121953
https://doi.org/10.4230/LIPIcs.TIME.2024.4

A Primer on Deep Learning for Causal Inference.

Autoren: Koch, Bernard J. Sainburg, Tim Geraldo Bastías, Pablo et al.

Quelle: Sociological Methods & Research; May2025, Vol. 54 Issue 2, p397-447, 51p

Schlagwörter: ARTIFICIAL neural networks, TREATMENT effect heterogeneity, CAUSAL inference, DEEP learning, ALGORITHMS

Idea Density and Grammatical Complexity as Neurocognitive Markers.

Autoren: Iacono, Diego Feltis, Gloria C.

Quelle: Brain Sciences (2076-3425); Sep2025, Vol. 15 Issue 9, p1022, 33p

Schlagwörter: LINGUISTIC complexity, NEUROBIOLOGY, LINGUISTICS, COGNITIVE load, LANGUAGE & languages, DIAGNOSIS, COMPUTATIONAL linguistics

People: CHOMSKY, Noam, 1928-

A Data-Driven Framework for Flood Mitigation: Transformer-Based Damage Prediction and Reinforcement Learning for Reservoir Operations.

Autoren: Tofighi, Soheyla Gurbuz, Faruk Mantilla, Ricardo et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p3024, 37p

Schlagwörter: TRANSFORMER models, REINFORCEMENT learning, WATER storage, RISK assessment, HYDRAULIC models, EMPIRICAL research, FLOOD damage prevention

Geografische Kategorien: UNITED States

Unravelling emotions: exploring deep learning approaches for EEG-based emotion recognition with current challenges and future recommendations.

Autoren: Abgeena, Abgeena Garg, Shruti

Quelle: Cognitive Neurodynamics; 10/23/2025, Vol. 19 Issue 1, p1-32, 32p

Further N -Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI

Autoren: Edwards, Darren J.

Quelle: Frontiers in Computational Neuroscience; 2025, p1-53, 53p

Schlagwörter: LANGUAGE models, ARTIFICIAL intelligence, COGNITIVE bias, QUANTUM operators, COGNITIVE maps (Psychology)

A Convergence of Theories of Mind and Brain.

Autoren: Dubé, Chad Malmberg, Kenneth J.

Quelle: Computational Brain & Behavior; Dec2025, Vol. 8 Issue 4, p649-670, 22p

Digital Twin Applications in the Water Sector: A Review.

Autoren: Ghorbani Bam, Pooria Rezaei, Nader Roubanis, Alexander et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2957, 49p

Schlagwörter: DIGITAL twin, WATER management, REAL-time computing, PREDICTIVE control systems, RESOURCE allocation, RESOURCE management, ENVIRONMENTAL infrastructure

Machine Learning in Stream/River Water Temperature Modelling: a review and metrics for evaluation.

Autoren: Corona, Claudia Rebecca Hogue, Terri Sue

Quelle: Hydrology & Earth System Sciences Discussions; 8/26/2024, p1-38, 38p

Schlagwörter: MACHINE learning, ARTIFICIAL intelligence, WATER temperature, STREAM function, COMPUTER simulation

Combining recurrent neural networks with variational mode decomposition and multifractals to predict rainfall time series.

Autoren: Zhou, Hai Schertzer, Daniel Tchiguirinskaia, Ioulia

Quelle: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 18, p4437-4455, 19p

Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction.

Autoren: De León Pérez, David Salazar-Galán, Sergio Francés, Félix

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2932, 28p

Schlagwörter: HYDROLOGICAL forecasting, MACHINE learning, FORECASTING, MEASUREMENT uncertainty (Statistics), ARTIFICIAL intelligence, WATER management

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.

Autoren: Gkintoni, Evgenia Aroutzidis, Anthimos Antonopoulou, Hera et al.

Quelle: Brain Sciences (2076-3425); Mar2025, Vol. 15 Issue 3, p220, 74p

Schlagwörter: MACHINE learning, EMOTION recognition, COGNITIVE neuroscience, RECURRENT neural networks, DEEP learning

Technology × Therapy: Forms of Organizing Work With Telehealth Within an Academic Health Center.

Autoren: Shen, Gordon C. Shahani, Lokesh R.

Quelle: Journal of Applied Behavioral Science; Jun2025, Vol. 61 Issue 2, p280-313, 34p

Schlagwörter: ACADEMIC medical centers, PEOPLE with mental illness, COVID-19 pandemic, MEDICAL personnel, PSYCHIATRIC hospitals

Neural populations in the language network differ in the size of their temporal receptive windows.

Autoren: Regev, Tamar I. Casto, Colton Hosseini, Eghbal A. et al.

Quelle: Nature Human Behaviour; Oct2024, Vol. 8 Issue 10, p1924-1942, 19p

A Semi-Automated Framework for Flood Ontology Construction with an Application in Risk Communication.

Autoren: Li, Shenglin Erickson, Caleb Zajac, Michal et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 19, p2801, 31p

Schlagwörter: FLOODS, RISK communication, ONTOLOGY, LANGUAGE models, COMMUNITY involvement, EMERGENCY communication systems, AUTOMATION software

Speech Enhancement: A Review of Different Deep Learning Methods.

Autoren: Yechuri, Sivaramakrishna Vanabathina, Sunny Dayal

Quelle: International Journal of Image & Graphics; May2025, Vol. 25 Issue 3, p1-31, 31p

A novel hybrid architecture for video frame prediction: combining convolutional LSTM and 3D CNN.

Autoren: Aravinda, C. V. Al-Shehari, Taher Alsadhan, Nasser A. et al.

Quelle: Journal of Real-Time Image Processing; Jan2025, Vol. 22 Issue 1, p1-18, 18p

Machine learning in stream and river water temperature modeling: a review and metrics for evaluation.

Autoren: Corona, Claudia Rebecca Hogue, Terri Sue

Quelle: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 12, p2521-2549, 29p

Two‐stream spatiotemporal networks for skeleton action recognition.

Autoren: Wang, Lei Zhang, Jianwei Yang, Shanmin et al.

Quelle: IET Image Processing (Wiley-Blackwell); 9/18/2023, Vol. 17 Issue 11, p3358-3370, 13p

Schlagwörter: HUMAN activity recognition, PATTERN recognition systems, HUMAN behavior

Sentiment-Driven Statistical Modelling of Stock Returns over Weekends.

Autoren: Kutz, Pablo Kowalski Makarov, Roman N.

Quelle: Computation; Aug2025, Vol. 13 Issue 8, p201, 36p

Schlagwörter: SENTIMENT analysis, NATURAL language processing, RATE of return on stocks, MACHINE learning, STATISTICAL models, STATISTICAL measurement, BUSINESS journalism

Firma/Körperschaft: APPLE Inc.