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https://arxiv.org/html/2608.02800v1
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https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
https://arxiv.org/html/2412.12213v2
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https://arxiv.org/list/cs.DS/new
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New submissions (showing 7 of 7 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
https://arxiv.org/pdf/2005.13905
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https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function
https://arxiv.org/html/2407.06328v2
In such dynamic game settings, if agents have limited capabilities for observing the game’s state, they must estimate it and use this estimate to infer expected payoffs and select one of available strategies
https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/pdf/2609.18842
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https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)
https://arxiv.org/src/2006.02759
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https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/pdf/2006.02602
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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/pdf/2407.00125
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/src/2006.02085
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https://arxiv.org/list/cs.CG/new
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New submissions (showing 1 of 1 entries)
[1] arXiv:2608