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1. Progress in neuromorphic photonics

   https://doi.org/10.1515/nanoph-2016-0139  

   Ferreira de Lima, Thomas; Shastri, Bhavin J.; Tait, Alexander N.; Nahmias, Mitchell A.; Prucnal, Paul R. (January 2017, Nanophotonics)

   Abstract As society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications. 

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2. Ricochet Progress and Status

   https://doi.org/10.1007/s10909-023-02971-5  

   Augier, C.; Beaulieu, G.; Belov, V.; Berge, L.; Billard, J.; Bres, G.; Bret, J. -L.; Broniatowski, A.; Calvo, M.; Cazes, A.; et al (May 2023, Journal of Low Temperature Physics)
3. Ripplocations: A Progress Report

   https://doi.org/10.3389/fmats.2020.00146  

   Barsoum, Michel W. (May 2020, Frontiers in Materials)
4. Progress on Meson-Baryon Scattering

   https://doi.org/10.22323/1.396.0170  

   C. Morningstar; J. Bulava; A. Hanlon; B. Hörz; D. Mohler; A. Nicholson; S. Skinner; A. Walker-Loud (January 2021, 38th International Symposium on Lattice Field Theory, LATTICE2021 26th-30th July, 2021)

   Progress in computing various meson-baryon scattering amplitudes is presented on a single en- semble from the Coordinated Lattice Simulations (CLS) consortium with m π = 200 MeV and Nf = 2 + 1 dynamical fermions. The finite-volume Lüscher approach is employed to determine the lowest few partial waves from ground- and excited-state energies computed from correla- tion matrices rotated in a single pivot using a generalized eigenvector solution. This analysis requires evaluating matrices of correlation functions between single- and two-hadron interpolat- ing operators which are projected onto definite spatial momenta and finite-volume irreducible representations. The stochastic LapH method is used to estimate all needed quark propagators. Preliminary results are presented for I = 1/2, 3/2 N π amplitudes including the ∆(1232) resonance and the I = 0 S-wave amplitude with unit strangeness relevant for the Λ(1405). 

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5. Rigorous progress in coarse-graining

   Noid, W.G.; Szukalo, R.J.; Kidder, K.M.; Lesniewski, M.C. (May 2024, Annual review of physical chemistry)

   Low resolution coarse-grained (CG) models provide remarkable com- putational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to ma- chine learning methods. We then discuss recent approaches for si- multaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density- and temperature-dependence of these potentials. We also briefly dis- cuss exciting progress in modeling high resolution observables with low- resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understand- ing the limitations of prior CG models, but also for developing robust computational methods that resolve these limitations in practice. 

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