Changes In Regional Snowfall In Central North America (1961-2017): Mountain Versus Plains

Long-term snowfall change offers insight for understanding climate change, managing water resources, and assessing climate model performance, especially at regional scales where topography plays an important role in shaping regional climate and water availability. In this study, we examined the changes of annual snowfall using observations from 1961 to 2017 in central North America, a region with high contrast in topographic complexities. There is a general, yet distinct difference in the snowfall trends demarcated approximately along the 105° W meridian. To its east, which is dominated by plains, snowfall had increased overall, except in a limited area south of 42° N, where snowfall decreased slightly. To the west of 105° W, which is dominated by the Rocky Mountains, there was a wide spread of decreasing trend, with only two pockets of area at an elevation of \u3e2000 m exhibiting increasing snowfall trends. Multiple linear regression analysis showed that, in addition to the average annual snowfall, snowfall trends significantly correlated with elevation in the mountain region and with average snow season temperature in the plains region, suggesting different mechanisms potentially shaping snowfall trends in the two regions

ActiveRMAP: Radiance Field for Active Mapping And Planning

A high-quality 3D reconstruction of a scene from a collection of 2D images can be achieved through offline/online mapping methods. In this paper, we explore active mapping from the perspective of implicit representations, which have recently produced compelling results in a variety of applications. One of the most popular implicit representations - Neural Radiance Field (NeRF), first demonstrated photorealistic rendering results using multi-layer perceptrons, with promising offline 3D reconstruction as a by-product of the radiance field. More recently, researchers also applied this implicit representation for online reconstruction and localization (i.e. implicit SLAM systems). However, the study on using implicit representation for active vision tasks is still very limited. In this paper, we are particularly interested in applying the neural radiance field for active mapping and planning problems, which are closely coupled tasks in an active system. We, for the first time, present an RGB-only active vision framework using radiance field representation for active 3D reconstruction and planning in an online manner. Specifically, we formulate this joint task as an iterative dual-stage optimization problem, where we alternatively optimize for the radiance field representation and path planning. Experimental results suggest that the proposed method achieves competitive results compared to other offline methods and outperforms active reconstruction methods using NeRFs.Comment: Under revie

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Abstract Background Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. Methods In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart. Results The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments. Conclusion Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weight

An investigation into working behavior characteristics of parabolic CFST arches applying structural stressing state theory

This paper conducts the experimental and simulative analysis of stressing state characteristics for parabolic concretefilled steel tubular (CFST) arches undergoing vertical loads. The measured stain data is firstly modeled as the generalized strain energy density (GSED) to describe structural stressing state mode. Then, the normalized GSED sum Ej,norm at each load Fj derives the Ej,norm-Fj curve reflecting the stressing state characteristics of CFST arches. Furthermore, the Mann-Kendall criterion is adopted to detect the stressing state change of the CFST arch during its load-bearing process, leading to the revelation of a vital stressing state leap characteristic according to the natural law from quantitative change to qualitative change of a system. The revealed qualitative leap characteristic updates the existing definition of the CFST arch’s failure load. Finally, the accurate formula is derived to predict the failure/ultimate loads of CFST arches. Besides, a method of numerical shape function is proposed to expand the limited strain data for further analysis of the stressing state submodes. The GSED-based analysis of structural stressing state opens a new way to recognize the unseen working behavior characteristics of arch structures and the updated failure load could contribute to the improvement on the structural design codes

Precise Temporal Action Localization by Evolving Temporal Proposals

Locating actions in long untrimmed videos has been a challenging problem in video content analysis. The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action. Imitating the human perception procedure with observations and refinements, we propose a novel three-phase action localization framework. Our framework is embedded with an Actionness Network to generate initial proposals through frame-wise similarity grouping, and then a Refinement Network to conduct boundary adjustment on these proposals. Finally, the refined proposals are sent to a Localization Network for further fine-grained location regression. The whole process can be deemed as multi-stage refinement using a novel non-local pyramid feature under various temporal granularities. We evaluate our framework on THUMOS14 benchmark and obtain a significant improvement over the state-of-the-arts approaches. Specifically, the performance gain is remarkable under precise localization with high IoU thresholds. Our proposed framework achieves mAP@IoU=0.5 of 34.2%

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data