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algorithms python » algorithms within (Expand Search), algorithm within (Expand Search), algorithms often (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
python function » protein function (Expand Search)
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141
Average Traffic Speed under Different Strategies.
Published 2025“…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …”
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142
Collision rate for different strategies (%).
Published 2025“…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …”
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143
Relative distance from surrounding vehicles.
Published 2025“…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …”
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144
Zone division of On-ramp merging scenario.
Published 2025“…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …”
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145
GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios
Published 2025“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”
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146
Software: Order-flow and long-memory in a simulated financial market
Published 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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147
Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning
Published 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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148
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149
Data for "Saturation hysteresis during cyclic injections of immiscible fluids in porous media: an invasion percolation study"
Published 2025“…A pore-resolved interface tracking algorithm for simulating multiphase flow in arbitrarily structured porous media. …”
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150
G4SNVHunter workflow for identifying variants that affect G4 formation.
Published 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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151
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152
Recursive-Expansive Dynamics Frameworks Formalization with Negation Isolation and Inverse Zero Operators
Published 2025Subjects: “…Mathematical methods and special functions…”
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153
<b>Fig. 6 |</b> <b>Autonomous microrobot navigation upstream in a flow environment.</b>
Published 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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154
Framework of MAPPO.
Published 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
The average completion time of each method.
Published 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
The connection of physical space.
Published 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
End-to-end data transmission delay.
Published 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
Production workflow of stiffened H-beams.
Published 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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159
Collision risk warning.
Published 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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160
Framework of rMAPPO.
Published 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. …”