For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a ...variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
Plain Language Summary
Feedbacks associated with Arctic warming are consequential for both the region and the strongly coupled global climate system. To assess the variability of the impacts of global warming and associated feedbacks in model‐based predictions, we quantified the sensitivity of the Arctic climate state to nine uncertain variables parameterizing the U.S. Department of Energy's global climate model known as the Energy Exascale Earth System Model (E3SM). Because the computational cost of repeatedly running high‐resolution configurations of E3SM was prohibitive, we used an ultra‐low resolution (ULR) configuration as a physics‐based surrogate for sensitivity analysis. Our first ever global sensitivity study of version 1 of the E3SM identified that the atmospheric parameters in E3SM's cloud physics model had the most impact on the atmosphere, sea ice, and ocean quantities of interest. This result demonstrates the importance of fully coupled climate analyses, which are necessary to identify such cross‐component influences. While we constructed confidence intervals that quantify the error in our estimates of parameter sensitivity introduced by using a limited number of ULR E3SM model runs, future investigation is needed to quantify the impact of resolution on error.
Key Points
We perform the first global sensitivity analysis using the fully coupled ultra‐low resolution Energy Exascale Earth System Model
Uncertainty in cloud physics parameters is found to most greatly impact Arctic climate predictions
Our inferred quantity of interest parameter correlations uncover key physical feedbacks and can guide model tuning
► We present a study on font-family and font-size recognition in the framework of a priori approach. ► The font and size systems are based on GMMs and applied to Arabic word images at ultra low ...resolution. ► We show the benefit of font recognition by comparing two HMMs based word recognition systems.
In this paper, we propose a new font and size identification method for ultra-low resolution Arabic word images using a stochastic approach. The literature has proved the difficulty for Arabic text recognition systems to treat multi-font and multi-size word images. This is due to the variability induced by some font family, in addition to the inherent difficulties of Arabic writing including cursive representation, overlaps and ligatures. This research work proposes an efficient stochastic approach to tackle the problem of font and size recognition. Our method treats a word image with a fixed-length, overlapping sliding window. Each window is represented with a 102 features whose distribution is captured by Gaussian Mixture Models (GMMs). We present three systems: (1) a font recognition system, (2) a size recognition system and (3) a font and size recognition system. We demonstrate the importance of font identification before recognizing the word images with two multi-font Arabic OCRs (cascading and global). The cascading system is about 23% better than the global multi-font system in terms of word recognition rate on the Arabic Printed Text Image (APTI) database which is freely available to the scientific community.
Abstract
For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a ...variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
Indoor posture recognition is vital for monitoring/detecting exercises, activities of daily living, accidental falls, unusual behavior, etc. However, high-resolution image based systems have a high ...accuracy, they are considered as intrusive and most of the current state-of-the-art image classifiers (VGG, ImageNet, ResNext) are not applicable for ultra-low resolution (\lt 32 pixels in extent) image classification due to their downsizing feature extraction architecture. Thus, we propose a shallow LowNet model for classifying privacy preserved 16 \times 16 posture images with its feature preserving architecture, variable ReLU slopes, and a custom loss function. LowNet outperformed, with an Accuracy of 98.94% and F1-score of 79.86%, the existing models (LeNet, ResNet1, ResNet-2) which can run on our Ultra low-resolution Thermal Posture Image (UTPI38) dataset (offered here) with 38 classes (4374 samples) collected from 23 volunteers. More experimental results are discussed on the custom loss, and variable ReLU slopes which gave 8.2% performance increase. Thus, we conclude that LowNet is useful in a multiclass ultra-low-resolution thermal posture image classification task.
Low‐resolution diffraction data (resolution below 12 Å) from crystals of a filamentous six‐Ig fragment of titin, I65–I70, were used in ab initio phasing with the aim of calculating its lattice ...packing and molecular envelope. Filamentous molecules, characterized by marked anisometry and idiosyncratic crystal lattices, have not been addressed before using this methodology. In this study, low‐resolution phasing (19–122 Å) successfully identified the region of the unit cell occupied by the molecule. Phase extension to a higher resolution (12 Å) yielded regions of high density that corresponded either to the positions of individual Ig domains or to zones of dense intermolecular contacts, hindering the identification of individual domains and the interpretation of electron‐density maps in terms of a molecular model. This problem resulted from the acutely uneven packing of the molecules in the crystal and it was further accentuated by the presence of partially disordered regions in the molecule. Addition of low‐resolution reflections with phases computed ab initio to those obtained experimentally using MIRAS improved the initial electron‐density maps of the atomic model, demonstrating the generic utility of low‐resolution phases for the structure‐elucidation process, even when individual molecules cannot be resolved in the lattice.
This paper describes the organisation and results of the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 14th International Conference on ...Document Analysis and Recognition (ICDAR'2017), during November 10-15, 2017, Kyoto, Japan. This competition has used the freely available Arabic Printed Text Image (APTI) database. A first and second editions took place respectively in ICDAR'2011 and ICDAR'2013. In this edition, we propose four challenges. Six research groups are participating in the competition with thirteen systems. These systems are compared using the font, font-size, font and font-size, and character and word recognition rates. The systems were tested in a blind manner using the first 5000 images of APTI database set 6. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.
This paper describes the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 12th International Conference on Document Analysis and Recognition ...(ICDAR'2013), during August 25-28, 2013, Washington DC, United States of America. This competition has used the freely available Arabic Printed Text Image (APTI) database. A first edition took place in ICDAR'2011. In this edition, four groups with six systems are participating in the competition. The systems are compared using the recognition rates at character and word levels. The systems were tested in a blind manner using set 6 of APTI database. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.
