بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
flow function » from function (توسيع البحث), low functional (توسيع البحث), loss function (توسيع البحث)
algorithm spc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithm seu (توسيع البحث)
spc function » _ function (توسيع البحث), a function (توسيع البحث), i function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
flow function » from function (توسيع البحث), low functional (توسيع البحث), loss function (توسيع البحث)
algorithm spc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithm seu (توسيع البحث)
spc function » _ function (توسيع البحث), a function (توسيع البحث), i function (توسيع البحث)
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Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection
منشور في 2025"…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…"
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144
Software: Order-flow and long-memory in a simulated financial market
منشور في 2025"…Key scripts apply custom metaorder generation algorithms to the empirical data to estimate and compare the $\alpha$ and $\gamma$ exponents.…"
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145
Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning
منشور في 2024"…This dissertation extends and applies an adjoint-based machine learning method, the deep learning PDE augmentation method (DPM), for closed-loop active control on both laminar and turbulent flows. The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function, which we construct using algorithmic differentiation applied to the flow solver. …"
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146
Data for "Saturation hysteresis during cyclic injections of immiscible fluids in porous media: an invasion percolation study"
منشور في 2025"…A pore-resolved interface tracking algorithm for simulating multiphase flow in arbitrarily structured porous media. …"
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147
G4SNVHunter workflow for identifying variants that affect G4 formation.
منشور في 2025"…<b>(B)</b> Function-level schematic of the G4SNVHunter workflow, showing the relationships between key modules and their data flow. …"
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148
Recursive-Expansive Dynamics Frameworks Formalization with Negation Isolation and Inverse Zero Operators
منشور في 2025الموضوعات: "…Mathematical methods and special functions…"
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149
<b>Fig. 6 |</b> <b>Autonomous microrobot navigation upstream in a flow environment.</b>
منشور في 2025"…In stronger flow, initial difficulties lead to more negative rewards, but the algorithm shows significant improvement by 400,000 steps. …"
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150
Framework of MAPPO.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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151
The average completion time of each method.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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152
The connection of physical space.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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153
End-to-end data transmission delay.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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154
Production workflow of stiffened H-beams.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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155
Collision risk warning.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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156
Framework of rMAPPO.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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157
Data_and_model_files.
منشور في 2025"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …"
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158
Data Sheet 1_A machine-learning approach for pancreatic neoplasia classification based on plasma extracellular vesicles.pdf
منشور في 2025"…Multiple studies explore how EVs size, surface biomarkers or content can determine their unique role and function in the recipient cell’s gene expression, metabolism and behavior affecting cancer development. …"
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159
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IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems
منشور في 2025"…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…"