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Freshness: newest result fetched 25 d ago · median age of these results 25 d · 1 of 30 carry a fetch time
https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2607.24970v2
one-to-one random markets, IA’s expected average rank is asymptotically logn\log n, just like DA’s
https://arxiv.org/html/2403.16413v3
org perpetual non-exclusive license
arXiv:2403
https://arxiv.org/html/2608.03153v1
If large entry-level gaps and substantial selection bias were to show up anywhere, this economy is a plausible place to expect them
https://arxiv.org/html/2608.02854v1
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https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/html/2608.03486v1
At the same time, the recent theoretical work of Kolesár and Plagborg-Møller (2025) has demonstrated that statistical identification strategies developed under linearity may fail when the structural relationship between the reduced-form innovations and shocks is nonlinear, even if the shocks themselves satisfy
https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.03452v1
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https://arxiv.org/html/2607.28294v1
At the boundary κ=1\kappa=1, where 𝔼[xi]=∞\mathbb{E}[x_{i}]=\infty, the rate is slower, namely T/logT\sqrt{T/\log T}
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/2402.03300v3
2 ArXiv Papers Seem Ineffective in Improving Mathematical Reasoning
5
https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.28323v1
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https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.02817v1
3 Log-linear pricing and closed-form pass-through
A
https://arxiv.org/pdf/2608.01934
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2303.12712
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https://arxiv.org/list/cs.NA/new
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New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.04007
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https://arxiv.org/pdf/2608.02694
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