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1. Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

   https://doi.org/10.1038/s41534-022-00593-5  

   Bhusal, Narayan; Hong, Mingyuan; Miller, Ashe; Quiroz-Juárez, Mario A.; León-Montiel, Roberto de; You, Chenglong; Magaña-Loaiza, Omar S. (December 2022, npj Quantum Information)

   Mary Francina (Ed.)

   Abstract The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy. 

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2. Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version

   https://doi.org/10.24963/ijcai.2018/759  

   Vandal, Thomas; Kodra, Evan; Ganguly, Sangram; Michaelis, Andrew; Nemani, Ramakrishna; Ganguly, Auroop R (July 2018, International Joint Conferences on Artificial Intelligence Organization)

   The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios. 

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3. Bias Correction and Statistical Modeling of Variable Oceanic Forcing of Greenland Outlet Glaciers

   https://doi.org/10.1029/2023MS003610  

   Verjans, Vincent; Robel, Alexander; Thompson, Andrew F.; Seroussi, Helene (April 2023, Journal of Advances in Modeling Earth Systems)

   Abstract Variability in oceanic conditions directly impacts ice loss from marine outlet glaciers in Greenland, influencing the ice sheet mass balance. Oceanic conditions are available from Atmosphere‐Ocean Global Climate Model (AOGCM) output, but these models require extensive computational resources and lack the fine resolution needed to simulate ocean dynamics on the Greenland continental shelf and close to glacier marine termini. Here, we develop a statistical approach to generate ocean forcing for ice sheet model simulations, which incorporates natural spatiotemporal variability and anthropogenic changes. Starting from raw AOGCM ocean heat content, we apply: (a) a bias‐correction using ocean reanalysis, (b) an extrapolation accounting for on‐shelf ocean dynamics, and (c) stochastic time series models to generate realizations of natural variability. The bias‐correction reduces model errors by ∼25% when compared to independent in‐situ measurements. The bias‐corrected time series are subsequently extrapolated to fjord mouth locations using relations constrained from available high‐resolution regional ocean model results. The stochastic time series models reproduce the spatial correlation, characteristic timescales, and the amplitude of natural variability of bias‐corrected AOGCMs, but at negligible computational expense. We demonstrate the efficiency of this method by generating >6,000 time series of ocean forcing for >200 Greenland marine‐terminating glacier locations until 2100. As our method is computationally efficient and adaptable to any ocean model output and reanalysis product, it provides flexibility in exploring sensitivity to ocean conditions in Greenland ice sheet model simulations. We provide the output and workflow in an open‐source repository, and discuss advantages and future developments for our method. 

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4. Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets

   https://doi.org/10.1093/bioinformatics/btac212  

   Mu, Wancen; Sarkar, Hirak; Srivastava, Avi; Choi, Kwangbom; Patro, Rob; Love, Michael I (April 2022, Bioinformatics)

   Kendziorski, Christina (Ed.)

   Abstract Motivation Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial-, or time-dependent AI signals may be dampened or not detected. Results We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing (scRNA-seq) data, or other spatially- or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower RMSE of allelic ratio estimates than existing methods. In real data, airpart identified DAI patterns across cell states and could be used to define trends of AI signal over spatial or time axes. Availability The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. 

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5. Multidecadal Sea Level Prediction Using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data

   https://doi.org/10.1175/AIES-D-23-0089.1  

   Sinha, Saumya; Fasullo, John; Nerem, R Steven; Monteleoni, Claire (October 2024, Artificial Intelligence for the Earth Systems)

   Abstract Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm yr⁻¹) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2° spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions. Significance StatementLong-term projections are needed to help coastal communities adapt to sea level rise. Forecasting multidecadal sea level change is a complex problem. In this paper, we show the promise of machine learning in producing such forecasts 30 years in advance and over the global ocean. Continued improvements in prediction skills that build on this work will be vital in sea level rise adaptation efforts. 

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