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https://arxiv.org/html/2407.20187v1
If there are more blue nodes than red nodes then we say blue is the majority
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/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/list/math/new
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A lacuna in the proof of the old Lemma 3
https://arxiv.org/list/cs.SY/new
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https://arxiv.org/html/2608.02942v1
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https://arxiv.org/html/2608.02851v1
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https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2508.20101v1
Volatility in Financial Networks
1 Introduction
2 Heterogeneous Spatiotemporal GARCH model
2.1 Model Assumptions
2.2 Prediction at Unobserved Locations
2.3 Estimation of Parameter Surface
2.4 Properties of the Estimator
3 Monte Carlo
https://arxiv.org/html/2607.28294v1
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https://arxiv.org/html/2607.27099v1
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https://arxiv.org/html/2407.20441v1
I Introduction
II System Model and Problem Formulation
III Main result
IV Proof of the main result
V Simulations
VI Conclusion and Future Work
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi
https://arxiv.org/html/2407.15077v2
2 Problem Definition
3 Method
3.1 Monotonic Improvement of B2MAPO scheme
3.2 B2MAPO Framework
3.2.1 Batch Sequence Generation Layer
3.2.2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4.1 Hierarchical DAG
https://arxiv.org/html/2407.04974v1
Observable Environments
I Introduction
II State of the art
II-A Settings
II-B State estimation in model-based learning
II-C State estimation in model-free RL
II-C1 Neural Network-based Approaches
II-C2 Non-neural-based Approach
II-D
https://arxiv.org/list/cs.CL/new
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https://arxiv.org/pdf/1811.12628
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A core calculus gives an effect-confinement lemma and six safety properties
https://arxiv.org/html/2407.17789v2
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/html/2609.03159v1
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https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility
1 Introduction
1.1 What is a distributed instrument?
1.1.1 Distributed Instruments and Sensor Networks
1.1.2 Distributed Instruments and Swarms
1.2 Contribution and Organization
2 State of the Art
https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2607.26327v3
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