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1. Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

   https://doi.org/10.1029/2022JG006876  

   Cawse‐Nicholson, K. ; Raiho, A. M. ; Thompson, D. R. ; Hulley, G. C. ; Miller, C. E. ; Miner, K. R. ; Poulter, B. ; Schimel, D. ; Schneider, F. D. ; Townsend, P. A. ; et al ( August 2022 , Journal of Geophysical Research: Biogeosciences)

   High‐resolution space‐based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal‐to‐noise ratio. Different applications drive divergent instrument designs, so optimization for wide‐reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications‐agnostic, data‐driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high‐dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal‐to‐noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher‐level algorithms, products, applications, or disciplines.

    

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2. WISH: wavefront imaging sensor with high resolution

   https://doi.org/10.1038/s41377-019-0154-x  

   Wu, Yicheng ; Sharma, Manoj Kumar ; Veeraraghavan, Ashok ( May 2019 , Light: Science & Applications)

   Wavefront sensing is the simultaneous measurement of the amplitude and phase of an incoming optical field. Traditional wavefront sensors such as Shack-Hartmann wavefront sensor (SHWFS) suffer from a fundamental tradeoff between spatial resolution and phase estimation and consequently can only achieve a resolution of a few thousand pixels. To break this tradeoff, we present a novel computational-imaging-based technique, namely, the Wavefront Imaging Sensor with High resolution (WISH). We replace the microlens array in SHWFS with a spatial light modulator (SLM) and use a computational phase-retrieval algorithm to recover the incident wavefront. This wavefront sensor can measure highly varying optical fields at more than 10-megapixel resolution with the fine phase estimation. To the best of our knowledge, this resolution is an order of magnitude higher than the current noninterferometric wavefront sensors. To demonstrate the capability of WISH, we present three applications, which cover a wide range of spatial scales. First, we produce the diffraction-limited reconstruction for long-distance imaging by combining WISH with a large-aperture, low-quality Fresnel lens. Second, we show the recovery of high-resolution images of objects that are obscured by scattering. Third, we show that WISH can be used as a microscope without an objective lens. Our study suggests that the designing principle of WISH, which combines optical modulators and computational algorithms to sense high-resolution optical fields, enables improved capabilities in many existing applications while revealing entirely new, hitherto unexplored application areas.

    

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3. Conformal Stretch Sensors for High Resolution Motion Sensing and Control

   https://doi.org/10.1002/mame.201800520  

   Lin, Lancy ; Chu, Michael ; Park, Sun‐Jun ; Zakashansky, Julia Alice ; Khine, Michelle ( January 2019 , Macromolecular Materials and Engineering)

   While motion sensors such as accelerometers are commonplace now, highly sensitive conformal sensors that can quantify fine motion with high resolution remain to be realized. Current strain sensors exhibit a trade‐off between dynamic range and strain sensitivity; these sensors therefore are not suitable for applications that require both large strain and excellent signal resolution, such as gesture control for virtual and augmented reality or feedback or prosthetics. Shape‐memory polymers coupled with thin films on stretchable elastomeric support substrates have recently emerged as fabrication platforms that improve strain sensor characteristics. Herein, skin‐mountable strain sensors are introduced, with large dynamic ranges (over 350%), higher gauge factor (GF) with linear responses to strain (from 0–150%), smaller footprint sizes, and less hysteresis compared to other sensors. It is demonstrated that these sensors are able to resolve down to 5° resolution of the proximal interphalangeal joint, which would enable gesture control for gaming and other motion sensing and control applications. Because these sensors have a small form factor and are low‐cost and disposable, such technology can find various electronic applications.

    

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4. Learning frequency‐aware convolutional neural network for spatio‐temporal super‐resolution water surface waves

   https://doi.org/10.1002/cav.2116  

   Peng, Chen ; Tu, Zaili ; Qiu, Sheng ; Li, Chen ; Wang, Changbo ; Qin, Hong ( August 2022 , Computer Animation and Virtual Worlds)

   As a usual component in virtual scenes, water surface plays an important role in various graphical applications, including special effects, video games, and virtual reality. Although recent years have witnessed significant progress based on Navier–Stokes equations and simplified water models, large‐scale water surface waves with high‐frequency visual details remain computationally expensive for interactive applications. This article proposes a novel frequency‐aware neural network to synthesize consistent and detailed water surface waves from low‐resolution input. At its core, our approach leverage the wavelet transformation theory over space, frequency and direction, and incremental supervision to decompose the 4D amplitude function into multiple smaller subproblems. Specifically, we first customize four subnetworks and corresponding loss functions for super‐resolution of spatial resolution, temporal evolution, wave direction subdivision, and wave number, respectively. Then, to enforce the upsampling along each dimension orthogonal to each other, we introduce a cooperative training scheme to fine‐tune and integrate the proposed subnetworks with carefully designed training dataset. Our method can visually enhance high‐resolution spatial details, temporal coherence, interactions with complex boundaries, and various wave patterns with flexible control along multiple dimensions. Through extensive experiments, our method arrives at 13 speedup for 32 upsampling of various simulation scenarios. We also validate the effectiveness and robustness of our method to produce realistic water surface waves toward artistic innovation.

    

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5. Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics

   https://doi.org/10.1093/nar/gkad1121  

   Li, Zilong ; Schlick, Tamar ( November 2023 , Nucleic Acids Research)

   The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXA and Fbn2 mouse genes ranging in size from 50 to 100 kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.

    

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