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1. Multi-field inflation from single-field models

   https://doi.org/10.1088/1475-7516/2021/08/047  

   Bojowald, Martin; Brahma, Suddhasattwa; Crowe, Sean; Ding, Ding; McCracken, Joseph (August 2021, Journal of Cosmology and Astroparticle Physics)
2. Conformal field theories in a magnetic field

   https://doi.org/10.1103/PhysRevResearch.6.043093  

   Boyack, Rufus; Delacrétaz, Luca; Dupuis, Éric; Witczak-Krempa, William (November 2024, Physical Review Research)

   We study the properties of 2+1d conformal field theories (CFTs) in a background magnetic field. Using generalized particle-vortex duality, we argue that in many cases of interest the theory becomes gapped, which allows us to make a number of predictions for the magnetic response, background monopole operators, and more. Explicit calculations at large $N$for Wilson-Fisher and Gross-Neveu CFTs support our claim, and yield the spectrum of background (defect) monopole operators. Finally, we point out that other possibilities exist: certain CFTs can become metallic in a magnetic field. Such a scenario occurs, for example, with a Dirac fermion coupled to a Chern-Simons gauge field, where a non-Fermi liquid is argued to emerge. Published by the American Physical Society2024 

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3. Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning

   Carmona, Rene; Lauriere, Mathieu (October 2019, ArXivorg)

   We develop a general reinforcement learning framework for mean field control (MFC) problems. Such problems arise for instance as the limit of collaborative multi-agent control problems when the number of agents is very large. The asymptotic problem can be phrased as the optimal control of a non-linear dynamics. This can also be viewed as a Markov decision process (MDP) but the key difference with the usual RL setup is that the dynamics and the reward now depend on the state's probability distribution itself. Alternatively, it can be recast as a MDP on the Wasserstein space of measures. In this work, we introduce generic model-free algorithms based on the state-action value function at the mean field level and we prove convergence for a prototypical Q-learning method. We then implement an actor-critic method and report numerical results on two archetypal problems: a finite space model motivated by a cyber security application and a continuous space model motivated by an application to swarm motion. 

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4. Strong-field- versus weak-field-ionization pump-probe spectroscopy

   https://doi.org/10.1103/PhysRevA.98.053416  

   Horton, Spencer L.; Liu, Yusong; Chakraborty, Pratip; Marquetand, Philipp; Rozgonyi, Tamás; Matsika, Spiridoula; Weinacht, Thomas (November 2018, Physical Review A)
5. ELVIS: A Correlated Light-Field and Digital Holographic Microscope for Field and Laboratory Investigations – Field Demonstration

   https://doi.org/10.1017/S1551929520001133  

   Kim, Taewoo; Serabyn, Eugene; Liewer, Kurt; Oborny, Nathan; Wallace, J. Kent; Rider, Stephanie; Bedrossian, Manuel; Lindensmith, Christian; Nadeau, Jay (July 2020, Microscopy Today)

   Following the previous article, here we describe the first field demonstration of the ELVIS system, performed at Newport Beach, CA. We examined ocean water to detect microorganisms using the combined holographic and light-field fluorescence microscope and successfully detected both eukaryotes and prokaryotes. The shared field of view provided simultaneous bright-field (amplitude), phase, and fluorescence information from both chlorophyll autofluorescence and acridine orange staining. The entire process was performed in a nearly autonomous manner using a specifically designed sample processing unit (SPU) and custom acquisition software. We also discuss improvements to the system made after the field test that will make it more broadly useful to other types of fluorophores and samples. 

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