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1. Edge Distribution of Thinned Real Eigenvalues in the Real Ginibre Ensemble

   https://doi.org/10.1007/s00023-022-01182-0  

   Baik, Jinho; Bothner, Thomas (April 2022, Annales Henri Poincaré)

   Abstract This paper is concerned with the explicit computation of the limiting distribution function of the largest real eigenvalue in the real Ginibre ensemble when each real eigenvalue has been removed independently with constant likelihood. We show that the recently discovered integrable structures in [2] generalize from the real Ginibre ensemble to its thinned equivalent. Concretely, we express the aforementioned limiting distribution function as a convex combination of two simple Fredholm determinants and connect the same function to the inverse scattering theory of the Zakharov–Shabat system. As corollaries, we provide a Zakharov–Shabat evaluation of the ensemble’s real eigenvalue generating function and obtain precise control over the limiting distribution function’s tails. The latter part includes the explicit computation of the usually difficult constant factors. 

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2. Machine learning the real discriminant locus

   https://doi.org/10.1016/j.jsc.2022.08.001  

   Bernal, Edgar A.; Hauenstein, Jonathan D.; Mehta, Dhagash; Regan, Margaret H.; Tang, Tingting (March 2023, Journal of Symbolic Computation)
3. Leximin Allocations in the Real World

   https://doi.org/10.1145/3274641  

   Kurokawa, David; Procaccia, Ariel D.; Shah, Nisarg (November 2018, ACM Transactions on Economics and Computation)
4. Real-Time LaCAM for Real-Time MAPF

   https://doi.org/10.1609/socs.v18i1.35993  

   Liang, Runzhe; Veerapaneni, Rishi; Harabor, Daniel; Li, Jiaoyang; Likhachev, Maxim (July 2025, Proceedings of the International Symposium on Combinatorial Search)

   The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner a finite amount of time before executing and replanning, is more practical for real-world multi-agent systems. Several methods utilize real-time planning schemes but none are provably complete, which leads to livelock or deadlock. Our main contribution is Real-Time LaCAM, the first Real-Time MAPF method with provable completeness guarantees. We do this by leveraging LaCAM in an incremental fashion. Our results show how we can iteratively plan for congested environments with a cutoff time of milliseconds while still maintaining the same success rate as full-horizon LaCAM. We also show how it can be used with a single-step learned MAPF policy. 

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5. Understanding the idiosyncrasies of real persistent memory

   https://doi.org/10.14778/3436905.3436921  

   Gugnani, Shashank; Kashyap, Arjun; Lu, Xiaoyi (December 2020, Proceedings of the VLDB Endowment)

   null (Ed.)

   High capacity persistent memory (PMEM) is finally commercially available in the form of Intel's Optane DC Persistent Memory Module (DCPMM). Researchers have raced to evaluate and understand the performance of DCPMM itself as well as systems and applications designed to leverage PMEM resulting from over a decade of research. Early evaluations of DCPMM show that its behavior is more nuanced and idiosyncratic than previously thought. Several assumptions made about its performance that guided the design of PMEM-enabled systems have been shown to be incorrect. Unfortunately, several peculiar performance characteristics of DCPMM are related to the memory technology (3D-XPoint) used and its internal architecture. It is expected that other technologies (such as STT-RAM, memristor, ReRAM, NVDIMM), with highly variable characteristics, will be commercially shipped as PMEM in the near future. Current evaluation studies fail to understand and categorize the idiosyncratic behavior of PMEM; i.e., how do the peculiarities of DCPMM related to other classes of PMEM. Clearly, there is a need for a study which can guide the design of systems and is agnostic to PMEM technology and internal architecture. In this paper, we first list and categorize the idiosyncratic behavior of PMEM by performing targeted experiments with our proposed PMIdioBench benchmark suite on a real DCPMM platform. Next, we conduct detailed studies to guide the design of storage systems, considering generic PMEM characteristics. The first study guides data placement on NUMA systems with PMEM while the second study guides the design of lock-free data structures, for both eADR- and ADR-enabled PMEM systems. Our results are often counter-intuitive and highlight the challenges of system design with PMEM. 

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