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1. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

   https://doi.org/10.3390/rs11212492  

   Peng, Bo ; Meng, Zonglin ; Huang, Qunying ; Wang, Caixia ( November 2019 , Remote Sensing)

   null (Ed.)

   Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes). 

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2. Kymograph analysis with high temporal resolution reveals new features of neurofilament transport kinetics

   https://doi.org/10.1002/cm.21411  

   Fenn, J. Daniel ; Johnson, Christopher M. ; Peng, Juan ; Jung, Peter ; Brown, Anthony ( November 2017 , Cytoskeleton)

   We have used kymograph analysis combined with edge detection and an automated computational algorithm to analyze the axonal transport kinetics of neurofilament polymers in cultured neurons at 30 ms temporal resolution. We generated 301 kymographs from 136 movies and analyzed 726 filaments ranging from 0.6 to 42 µm in length, representing ∼37,000 distinct moving and pausing events. We found that the movement is even more intermittent than previously reported and that the filaments undergo frequent, often transient, reversals which suggest that they can engage simultaneously with both anterograde and retrograde motors. Average anterograde and retrograde bout velocities (0.9 and 1.2 µm s⁻¹, respectively) were faster than previously reported, with maximum sustained bout velocities of up to 6.6 and 7.8 µm s⁻¹, respectively. Average run lengths (∼1.1 µm) and run times (∼1.4 s) were in the range reported for molecular motor processivity in vitro, suggesting that the runs could represent the individual processive bouts of the neurofilament motors. Notably, we found no decrease in run velocity, run length or run time with increasing filament length, which suggests that either the drag on the moving filaments is negligible or that longer filaments recruit more motors.

    

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3. Restoration of the Tully–Fisher Relation by Statistical Rectification

   https://doi.org/10.3847/2041-8213/ad2856  

   Fu, Hai ( February 2024 , The Astrophysical Journal Letters)

   I employ the Lucy rectification algorithm to recover the inclination-corrected distribution of local disk galaxies in the plane of absolute magnitude (M_i) and Hivelocity width (W₂₀). By considering the inclination angle as a random variable with a known probability distribution, the novel approach eliminates one major source of uncertainty in studies of the Tully–Fisher relation: inclination angle estimation from axial ratio. Leveraging the statistical strength derived from the entire sample of 28,264 Hi-selected disk galaxies atz< 0.06 from the Arecibo Legacy Fast ALFA survey, I show that the restored distribution follows a sharp correlation that is approximately a power law between −16 >M_i> −22:$M_i=M_0-2.5\beta [\log (W_{20}/250\mathrm{km}/\mathrm{s})]$, withM₀= −19.77± 0.04 andβ= 4.39 ± 0.06. At the brighter end (M_i< −22), the slope of the correlation decreases toβ≈ 3.3, confirming previous results. Because the method accounts for measurement errors, the intrinsic dispersion of the correlation is directly measured:$\sigma (\log W_{20})\approx 0.06$dex between −17 >M_i> −23, whileσ(M_i) decreases from ∼0.8 in slow rotators to ∼0.4 in fast rotators. The statistical rectification method holds significant potential, especially in the studies of intermediate-to-high-redshift samples, where limited spatial resolution hinders precise measurements of inclination angles.

    

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4. Imaging through scattering media via spatial–temporal encoded pattern illumination

   https://doi.org/10.1364/PRJ.456156  

   Zhao, Xingchen ; Nie, Xiaoyu ; Yi, Zhenhuan ; Peng, Tao ; Scully, Marlan O. ( June 2022 , Photonics Research)

   Optical imaging through scattering media has long been a challenge. Many approaches have been developed for focusing light or imaging objects through scattering media, but usually, they are either invasive, limited to stationary or slow-moving media, or require high-resolution cameras and complex algorithms to retrieve the images. By utilizing spatial–temporal encoded patterns (STEPs), we introduce a technique for the computation of imaging that overcomes these restrictions. With a single-pixel photodetector, we demonstrate non-invasive imaging through scattering media. This technique is insensitive to the motion of the media. Furthermore, we demonstrate that our image reconstruction algorithm is much more efficient than correlation-based algorithms for single-pixel imaging, which may allow fast imaging for applications with limited computing resources.

    

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5. Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation

   https://doi.org/10.1002/esp.4743  

   Tinoco, Rafael O. ; San Juan, Jorge E. ; Mullarney, Julia C. ( January 2020 , Earth Surface Processes and Landforms)

   We present a critical analysis of experimental findings on vegetation–flow–sediment interactions obtained through both laboratory and field experiments on tidal and coastal environments. It is well established that aquatic vegetation provides a wide range of ecosystem services (e.g. protecting coastal communities from extreme events, reducing riverbank and coastal erosion, housing diverse ecosystems), and the effort to better understand such services has led to multiple approaches to reproduce the relevant physical processes through detailed laboratory experiments. State‐of‐the‐art measurement techniques allow researchers to measure velocity fields and sediment transport with high spatial and temporal resolution under well‐controlled flow conditions, yielding predictions for hydrodynamic and sediment transport scenarios that depend on simplified or bulk vegetation parameters. However, recent field studies have shown that some simplifications on the experimental setup (e.g. the use of rigid elements, a single diameter, a single element height, regular or staggered layout) can bias the outcome of the study, by either hiding or amplifying some of the relevant physical processes found in natural conditions. We discuss some observed cases of bias, including general practices that can lead to compromises associated with simplified assumptions. The analysis presented will identify potential pathways to move forward with laboratory and field measurements, which could better inform predictors to produce more robust, universal and accurate predictions on flow–vegetation–sediment interactions. © 2020 John Wiley & Sons, Ltd.

    

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