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1. Uniform Mordell–Lang Plus Bogomolov

   https://doi.org/10.1093/imrn/rnad167  

   Ge, Tangli (July 2023, International Mathematics Research Notices)

   Abstract In this paper, we prove a uniform version of Poonen’s “Mordell-Lang Plus Bogomolov” theorem [ 12], based on Vojta’s method. Our main contribution is to generalize Rèmond’s work on the large points in order to allow an extra $$\epsilon $$-neighborhood in the canonical height topology. The part on small points follows from [ 8]. 

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2. Stress-Plus-X (SPX) Graph Layout

   https://doi.org/10.1007/978-3-030-35802-0_23  

   Devkota, S; Ahmed, R; De Luca, F; Isaacs, K; Kobourov, S (January 2020, 27th International Symposium on Graph Drawing and Network Visualization (GD))

   Stress, edge crossings, and crossing angles play an important role in the quality and readability of graph drawings. Most standard graph drawing algorithms optimize one of these criteria which may lead to layouts that are deficient in other criteria. We introduce an optimization framework, Stress-Plus-X (SPX), that simultaneously optimizes stress together with several other criteria: edge crossings, minimum cross- ing angle, and upwardness (for directed acyclic graphs). SPX achieves results that are close to the state-of-the-art algorithms that optimize these metrics individually. SPX is flexible and extensible and can optimize a subset or all of these criteria simultaneously. Our experimental analysis shows that our joint optimization approach is successful in drawing graphs with good performance across readability criteria. 

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3. Max-Plus Synchronization in Decentralized Trading Systems

   https://doi.org/10.1109/CDC49753.2023.10383918  

   Riess, Hans; Munger, Michael; Zavlanos, Michael M (December 2023, IEEE)
4. The End-to-End Provenance Project

   https://doi.org/10.1016/j.patter.2020.100016  

   Ellison, Aaron M.; Boose, Emery R.; Lerner, Barbara S.; Fong, Elizabeth; Seltzer, Margo (May 2020, Patterns)
5. End-to-end programmable computing systems

   https://doi.org/10.1038/s44172-023-00127-7  

   Xiao, Yao; Ma, Guixiang; Ahmed, Nesreen_K; Capotă, Mihai; Willke, Theodore_L; Nazarian, Shahin; Bogdan, Paul (November 2023, Communications Engineering)

   Abstract Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose aunified, end-to-end, programmable graph representation learning(PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision. 

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