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2. Learning to See: Applying Inverse Recurrent Inference Machines to See through Refractive Scattering

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   Kouroshnia, Arvin; Nguyen, Kenny; Ni, Chunchong; SaraerToosi, Ali; Broderick, Avery E (May 2025, The Astrophysical Journal)

   Abstract The Event Horizon Telescope (EHT) has produced horizon-resolving images of Sagittarius A* (Sgr A*). Scattering in the turbulent plasma of the interstellar medium distorts the appearance of Sgr A* on scales only marginally smaller than the fiducial resolution of EHT. The scattering process both diffractively blurs and adds stochastic refractive substructures that limits the practical angular resolution of EHT images of Sgr A*. We explore the ability of a novel recurrent neural network machine learning framework to mitigate these scattering effects, after training on sample data that are agnostic to general relativistic magnetohydrodynamics (GRMHD). We demonstrate that if instrumental limitations are negligible, it is possible to nearly completely mitigate interstellar scattering at a wavelength of 1.3 mm. We validate and quantify the fidelity of this scattering mitigation scheme with physically relevant GRMHD simulations. We find that we can accurately reconstruct resolved structures at the scale of 3μas, well below the nominal instrumental resolution of EHT, 24μas. 

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3. Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension

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4. Electrons see the guiding light

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   Miao, Bo; Shrock, Jaron; Milchberg, Howard (August 2023, Physics Today)

   To accelerate electrons to multi-GeV energies with lasers, keep the bright light tight. 

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5. Can NeRFs See without Cameras?

   Amballa, Chaitanya; Basu, Sattwik; Wei, Yu-Lin; Yang, Zhijian; Ergezer, Mehmet; Choudhury, Romit_Roy (September 2025, 39th Conference on Neural Information Processing Systems (NeurIPS 2025))

   Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how optical rays bring color information from a 3D object to the camera pixels. Radio frequency (RF) or audio signals can also be viewed as a vehicle for delivering information about the environment to a sensor. However, unlike camera pixels, an RF/audio sensor receives a mixture of signals that contain many environmental reflections (also called “multipath”). Is it still possible to infer the environment using such multipath signals? We show that with redesign, NeRFs can be taught to learn from multipath signals, and thereby “see” the environment. As a grounding application, we aim to infer the indoor floorplan of a home from sparse WiFi measurements made at multiple locations inside the home. Although a difficult inverse problem, our implicitly learnt floorplans look promising, and enables forward applications, such as indoor signal prediction and basic ray tracing. 

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