dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the best 𝑁 𝑏𝑒𝑠𝑡 simulations were selected for each individual component of 𝜒 𝐼𝐴  with 𝑋 𝑏𝑒𝑠𝑡 (𝑠) the set of the best 𝑁 𝑏𝑒𝑠𝑡 = 2 • 𝑛 𝑝𝑎𝑟 samples for dataset 𝑠, with 𝑛 𝑝𝑎𝑟 the number of parameters in the current subset. Parameters 𝑝 were ranked based on their maximum diaphony over all patient datasets 𝑠 and outputs 𝑞. For a regional LV parameter, the maximum diaphony of all six segments was used in this ranking. Step 2d: Parameter subset reduction Initially, parameters with the lowest diaphony were removed from the subset. A regional LV parameter removed from the subset was returned into the subset as a global LV parameter, while global LV parameters removed were fixed to their reference values. Using this reduction approach, regional LV parameters determining the same tissue property in different LV segments were grouped into one parameter group, which was considered to be removed from the subset.
Segmental normalized work 𝑊 𝑛𝑜𝑟𝑚,𝑖 was then calculated as 𝑊 𝑖 normalized by the summed 𝑊 𝑖 of all 18 LV segments: 𝑊 𝑛𝑜𝑟𝑚,𝑖 = 𝑊 𝑖 ∑ 𝑊 𝑗 18 𝑗=1 (14)     Hereafter, septal-to-LV lateral wall workload imbalance ∆𝑊 𝑛𝑜𝑟𝑚,𝐿𝑊-𝑆 was calculated as the difference in summed 𝑊 𝑛𝑜𝑟𝑚,𝑖 of the six LV posterolateral wall segments (𝐿𝑊) and summed 𝑊 𝑛𝑜𝑟𝑚,𝑖 of the six septal segments (𝑆): ∆𝑊 𝑛𝑜𝑟𝑚,𝐿𝑊-𝑆 = ∑ 𝑊 𝑛𝑜𝑟𝑚,𝑗 𝑗∈𝐿𝑊 -∑ 𝑊 𝑛𝑜𝑟𝑚,𝑗 𝑗∈𝑆 (15)   Statistical tests were performed using SPSS Statistics 24 (IBM, Chicago, IL, USA). To test the null hypotheses, the nonparametric Kruskal-Wallis test was performed with post-hoc Bonferroni correction to calculate p-values.
Availability of data and materials The datasets generated and/or analyzed during the current study are available in the [NAME will be provided if manuscript is considered for publication] repository, [LINK will be added if manuscript is considered for publication]. TK and NvO conceived the study. TK performed all simulations and wrote the first version of the manuscript. AB, FK, BvK, AT, MC, GB, AvS, and KV obtained the clinical data. NvO, TvL, RM, WH, TD, and JL helped with analysis and interpretation of the data. All co-authors critically read the paper and approved it.
, and the strain-and strain rate-based components per segment 𝜒 𝜀 𝑖 2  and 𝜒 𝜀̇𝑖 2. Diaphony 𝑑 was then calculated for all parameters 𝑝 and for all sixteen outputs 𝑞 per patient dataset 𝑠: 2 , meaning 𝜒 𝑉 𝐸𝐷 2 , 𝜒 𝐸𝐹 2 , 𝜒 𝑡 𝑐𝑦𝑐𝑙𝑒 2 𝑑 𝑝,𝑞 (𝑠) = | 1 𝑁 𝑏𝑒𝑠𝑡 ∑ 𝑥 𝑝 ∈𝑋 𝑏𝑒𝑠𝑡 (𝑠) 𝑒 𝑖2𝜋𝑥 𝑝 |,.
Introduction Remote sensing satellites are dedicated to collect image data from the Earth's surface. However, because of some constraints on the signal-to-noise ratio (SNR) for the sensor hardware, we cannot get high spatial and spectral resolutions in a unique acquisition. Thus, satellites, such as, IKONOS, GaoFen, QuickBird, and WorldView-3, usually capture images containing several spectral bands, called multispectral (MS) images, together with panchromatic (PAN) images having high spatial resolution, i. e. , containing many image details. Hence, the fusion of these kinds of data is often required to get very high spatio-spectral resolution products. Pansharpening is the fusion of a PAN image and an MS image to obtain the final outcome with the same spatial resolution as the PAN image and the same spectral resolution as the MS image.
As a newly developed category to solve pansharpening, deep learning requires physical support at a higher level. The structure design is of critical importance since it is closely related to the performance gain of the model. By building a convolutional neural network (CNN) with a certain structure and functional units, (e. g. , deep residual network  [25] , multiscale and multidepth network  [26] ), the DL method can reproduce the nonlinear relationship between MS images, PAN images, and ideal fusion images through the training on satellite datasets. The groundbreaking attempt was made by Masi et al. [27] , in 2016, with a three-layer CNN designed specifically for pansharpening, achieving promising results. Inspired by PNN, many researchers developed various structures relied upon CNNs. Among them, the residual module in ResNet  [28]  is widely used for pansharpening  [25, 4, 29].
Extracting Feature Maps As mentioned above, the traditional MRA methods extract details calculating the difference between the PAN image and the low-pass filtered PAN image. Thus, the final result depends on the adopted pre-defined filters which may mechanically discard some desired information. Thanks to the use of a convolutional layer, a set of parameters can be learned and dynamically adjusted to thoroughly explore the specific details and select expected features. Besides, to make the model adapt to the different datasets and to heradicate the misfit problem caused by the fixed filters, we extract end-to-end high-frequency information by learning the mapping H(•) in  (5). In  [39] , Zhang et al. used ResNet blocks as basic structure for feature extraction. However, to retain more information from the original images and to reduce the computational burden, we only adopted one ResNet block to form the PAN branch in TDNet.
Conv(3×3，64) Conv(3×3，64) Conv(3×3，64) Conv(3×3，8) ReLU ResNet Block 1 R P    W H c W H    R D 64    W H R DF ResNet Block Conv(3×3，64) MaxPooling Conv(3×3，8) PD D  R 2h2w64 D D  R 2 h2wc Figure  4 : An overview of the PAN branch, see the bottom of Fig. 3. DF is a feature map containing detailed information using 64 channels. D is a feature map with the same number of channels as GT. The output of the MaxPooling is denoted as PD D , and the feature map with detailed information with reduced size is denoted as D D. Please, note that the number of convolution kernels is related to an exemplary fusion case involving a 8-bands dataset. As shown in Fig. 2 (b), the extracted details from the PAN image are obtained by the PAN branch depicted in Fig. 3. The difference with the traditional MRA approaches is that the MS image is upsampled twice, i.
Spatial Attention Module for Detail Injection Recalling the original MRA formula (2) and the MRA-inspired formula (5), we can remark that the detail image D multiplied by G in (  2 ) is equivalent to the spatial attention. Since the injection coefficient G is generally dependent on MS and P, it motivates us to design spatial attention involving these two components. Specifically, we concatenate MS and D together to carry out the convolution operation as shown in Fig. 2 (b), aiming to learn a weight matrix W ∈ R H×W ×c containing the sufficient features of the MS and the P images. The proposed injection strategy is to multiply the learned feature D obtained by the PAN branch and the weight matrix W, then adding it to the MS generated by PixelShuffle to yield the MRAB output.
Datasets Three different datasets captured by three different sensors (i. e. , WorldView-3, GaoFen-2 and the QuickBird) are considered in this paper. The WorldView-3 works in the visible and near-infrared spectrum range, which provide eight MS-band (coastal, blue, green, yellow, red, red edge, near-infrared 1, and near-infrared 2) and a PAN channel with a spatial resolution of 1. 2 m and 0. 3 m, respectively. The radiometric resolution of WorldView-3 is 11 bits. Both GaoFen-2 and QuickBird provide four MS-band (red, green, blue, and near-infrared) and a PAN channel. For GaoFen-2, the spatial resolution is about 3. 2 m for the MS bands and 0. 8 m for the PAN channel, and the radiometric resolution is 10 bits. For QuickBird, the spatial resolution is about 2. 44 m for the MS bands and 0. 61 m for the PAN channel, and the radiometric resolution is 11 bits.
Dataset Simulation In this work, we mainly conduct the network training on WorldView-3 datasets, which are available to download on the website *. In particular, there are no GT images used as reference. Thus, the original LRMS and PAN images are simultaneously blurred and downsampled according to Wald's protocol  [41]. The original LRMS image is used as reference image, i. e. , the GT image. We simulate the WorldView-3 dataset with 12,580 samples (also called patch pairs), each sample including PAN (with size 64 × 64), LRMS (with size 16 × 16 × 8), and GT (with size 64 × 64 × 8) patches. For the 12,580 samples, we divided them into 8806/2516/1258 (70%/20%/10%) as for the training dataset, validation dataset, and testing dataset, respectively. The simulation process for the datasets is the same as in  [5].
Training Platform and Parameter Setting The proposed network is coded with Python 3. 8. 0 and Pytorch 1. 7. 0, and is trained with NVIDIA GPU GeForce GTX 3080. We use Adam optimizer, in which the betas and weight decay are set as (0. 9, 0. 999) and 0, respectively, to minimize the loss function (  6 ) by 300 epochs, and the batch size is set as 32. To achieve better performance, we set the initial learning rate as 0. 01, then dynamically adjusting it to 0. 001 after 220 epochs. About the hyper-parameter γ in (6), more discussions can be found in Sect. 4. 8. 2.
Reduced Resolution Assessment The reduced resolution assessment measures the similarity between the fused image and the ideal reference image (the original MS image). The similarity can be determined by the calculation of several evaluation indexes. For the reduced resolution experiments, the spectral angle mapper (SAM)  [48] , the dimensionless global error in synthesis (ERGAS)  [49] , the spatial correlation coefficient (SCC)  [50] , and the Q2 n (Q8 for 8-band datasets and Q4 for 4-band    datasets)  [51]  are employed as evaluation indexes. The ideal values for SAM and ERGAS are 0, whereas 1 for Q2 n and SCC. As mentioned in Sect. 4. 2, we have 1258 testing samples from WorldView-3 images. We first compare the proposed TDNet with the five state-of-the-art CNN-based pansharpening approaches on the 1258 samples.
The Effect of MRAB To explore whether the MRAB contributes to the final result, we remove the MRAB from TDNet running the training with the same data and parameters. Tab. 4 presents the average outcomes and the corresponding standard deviations for the TDNet with and without MRAB (w/o MRAB). It can be observed that the fusion results w/o MRAB have inferior performance on all the metrics compared with the original TDNet. This indicates that the MRAB can help the network in learning more details and features. Figure  12 : TDNet with MSCB (top row) and without MSCB (bottom row, also called SSCB). Note that the numbers in color boxes mean the number of convolution kernels. in Fig. 12 , to explore whether such MSCB can favor the fusion task, we change the original multi-scale convolution kernels (i.
Evaluation on 4-band Datasets The datasets used in the above experiments are all data with 8 spectral bands acquired by the same sensor (i. e. , WorldView-3). In this section, we will focus on assessing performance on 4-band datasets acquired by the GaoFen-2 and the QuickBird sensors. The dataset simulation for the two sensors is the same as that of the 8-band WorldView-3 datasets mentioned in Sect. Moreover, we simulated 48 testing samples with spatial size 256 × 256 to assess the performance for all the compared approaches. More details about the generation of these test cases can be found in  [5]. In Figs. 13  and 14 , we show the performance comparing all the five DL-based approaches *. Since it is not easy to distinguish the differences having a look at the 8-bits RGB images, we present the absolute error maps (AEMs).
4. 2. For the GaoFen-2 dataset, we downloaded the data acquired over Beijing from the website * and we simulated 21,607 training samples (PAN size, 64 × 64). Besides, 81 testing samples (PAN size, 256 × 256) acquired over the city of Guangzhou are used for comparison purposes. About the QuickBird test case, a large dataset acquired over the city of Indianapolis is exploited to simulate 20,685 training samples (PAN size, 64 × 64).
Average assessment of the compared approaches for 81 GaoFen-2 testing samples and 48 QuickBird testing samples. (Bold: Guangzhou datasets (GaoFen-2) PNN 1. 6599 ± 0. 3606 1. 5707 ± 0. 3243 0. 9274 ± 0. 0202 0. 9281 ± 0. 0206 DiCNN1 1. 4948 ± 0. 3814 1. 3203 ± 0. 3543 0. 9445 ± 0. 0211 0. 9458 ± 0. 0222 PanNet 1. 3954 ± 0. 3261 1. 2239 ± 0. 2828 0. 9468 ± 0. 0222 0. 9558 ± 0. 0123 DMDNet 1. 2968 ± 0. 3156 1. 1281 ± 0. 2669 0. 9529 ± 0. 0218 0. 9644 ± 0. 0100 FusionNet 1. 1795 ± 0. 2714 1. 0023 ± 0. 2271 0. 9627 ± 0. 0167 0. 9710 ± 0. 0074 TDNet 1. 0926 ± 0. 2645 0. 9303 ± 0. 2267 0. 9695 ± 0. 0131 0. 9750 ±0. 0132 Indianapolis datasets (QuickBird) PNN 5. 7993 ± 0. 9474 5. 5712 ± 0. 4584 0. 8572 ± 0. 1481 0. 9023 ± 0. 0489 DiCNN1 5. 3071 ± 0. 9957 5. 2310 ± 0. 5411 0. 8821 ± 0. 1431 0. 9224 ± 0.
http://openremotesensing. net/kb/codes/pansharpening/ * Note that the given source code in Open Remote Sensing does not contain the trained models for WorldView-2 and WorldView-3, thus we re-implemented the network with default parameters in Python using Tensorflow for fair comparisons. * Code link: https://xueyangfu. github. io/ * DiCNN1 has been implemented by ourselves with default parameters. * BDPN has been implemented by ourselves with default parameters. * DMDNet has been implemented.
An Example Before describing the pseudocode of EG-PQE , we explain how it solves the PQE problem of Example 1. That is, we consider taking clause C 1 out of ∃X[F (X, Y )] where F = C 1 ∧ • • • ∧ C 4 , C 1 = x 3 ∨ x 4 , C 2 = y 1 ∨x 3 , C 3 = y 1 ∨ x 4 , C 4 = y 2 ∨x 4 and Y = {y 1 , y 2 } and X = {x 3 , x 4 }. EG-PQE iteratively generates a full assignment #» y to Y and checks if (C 1 ) y is redundant in ∃X[F y ] (i. e. , if C 1 is redundant in ∃X[F ] in subspace #» y ). Note that if (F \ {C 1 }) y implies (C 1 ) y , then (C 1 ) y is trivially redundant in ∃X[F y ]. To avoid such subspaces, EG-PQE generates #» y by searching for an assignment ( #» y , #» x ) satisfying the formula (F \{C 1 })∧C 1. (Here #» y and #» x are full assignments to Y and X respectively. ) If such ( #» y , #» x ) exists, it satisfies F \ {C 1 } and falsifies C 1 thus proving that (F \ {C 1 }) y does not imply (C 1 ) y.
Main Idea The pseudocode of EG-PQE + is shown in Fig. 2. It is different from that of EG-PQE only in line 11 marked with an asterisk. The motivation for this change is as follows. Line 11 describes proving redundancy of C for the case where C y is not implied by (F \ {C}) y and F y is satisfiable. Then EG-PQE simply uses a satisfying assignment as a proof of redundancy of C in subspace #» y. This proof is unnecessarily strong because it proves that every clause of F (including C) is redundant in ∃X[F ] in subspace #» y. Such a strong proof is hard to generalize to other subspaces. The idea of EG-PQE + is to generate a proof for a much weaker proposition namely a proof of redundancy of C (and only C). Intuitively, such a proof should be easier to generalize. So, EG-PQE + calls a procedure PrvClsRed generating such a proof.
Systematic, routine, and comprehensive evaluation of Earth System Models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The PCMDI Metrics Package (PMP) is an open-source Python software package that provides "quick-look" objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large-to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including extremes.
The PMP results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. An important priority of the PMP is to document evaluation statistics for all Historical and AMIP simulations submitted to recent phases of CMIP, providing version-controlled information for all data sets and software packages being used. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. In this paper, we present an overview of the PMP including its history to date, capabilities, recent updates, and future direction.
Introduction Earth System Models (ESMs) are key tools for projecting climate change and conducting research to enhance our understanding of the Earth system. Enhancing the reliability of models is therefore important, yet evaluating ESMs is a complex endeavor, given the vast range of climate characteristics across space and time scales. A necessary step to evaluate the performance of ESMs is quantifying their consistency with available observations. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) has worked closely with the World Climate Research Programme's (WCRP) Working Group on Coupled Models (WGCM) and Working Group on Numerical Experimentation (WGNE) to design and support Model Intercomparison Projects (MIPs)  (Potter et al. , 2011). This effort began with the Atmospheric Model Intercomparison Project (AMIP;  Gates, 1992; Gates et al.
To respond to the need, PCMDI has developed the PCMDI Metrics Package (PMP), to quantitatively synthesize results from the archive of CMIP simulations via performance metrics that help characterize the overall agreement between models and observations  (Gleckler et al. , 2016). In this paper, we describe the latest update of the PMP and its focus on providing a diverse suite of summary statistics that can be used to construct "quick-look" summaries of ESM performance from simulations made publicly available to the research community, notably CMIP. For our purposes, "performance metrics" are typically (but not exclusively) well-established statistical measures that https://doi. org/10. 5194/egusphere-2023-2720 Preprint. Discussion started: 24 November 2023 c Author(s) 2023. CC BY 4. 0 License.
quantify the consistency between observed and simulated characteristics. One goal of the PMP is to further diversify the suite of high-level performance tests that help characterize the simulated climate. The results provided by the PMP are frequently used to address two overarching and recurring questions: 1) What are the relative strengths and weaknesses between different models? and 2) How are models improving with further development? Addressing the second question is often referred to as "benchmarking" and this motivates an important emphasis of the effort described in this paper-striving to advance the documentation of all data and results of the PMP in an open and ultimately reproducible manner. The rest of the paper is organized as follows. In section 2, we provide a technical description of the PMP and its accompanying reference datasets.
Software package and data description The PMP is a Python-based open-source software framework (https://github. com/PCMDI/pcmdi_metrics) designed to objectively gauge the consistency between ESMs and available observations via well-established statistics. The PMP has been mainly used for the evaluation of CMIP-class models. A subset of CMIP experiments are particularly well suited to comparing models with observations. The experiments of particular interest include those involving prescribed sea surface temperature (SST) in accordance with the AMIP protocol, as well as coupled model simulations labeled as "Historical" that are driven by varying natural and anthropogenic forcings. Some of the metrics applicable to these experiments may also be relevant to others (e. g. , multi-century coupled control runs called "PiControl" and idealized "4xCO2" simulations that are designed for estimating climate sensitivity).
The PMP has been applied to multiple generations of CMIP in a quasi-operational fashion as new simulations are made available, new analysis methods are incorporated, or new observational data become accessible. Shortly after simulations from the most recent phase of the CMIP (i. e. , CMIP6) became accessible, PMP quick-look summaries were provided on the PCMDI's website (https://pcmdi. llnl. gov/metrics/), offering a resource to scientists involved in CMIP or others interested in the evaluation of ESMs. To facilitate this, in PCMDI the PMP is technically linked to the Earth System Grid Federation (ESGF) that is a primary CMIP data delivery infrastructure  (Williams et al. , 2016). The PMP is designed to readily work with model output that has been processed using the Climate Model Output Rewriter (CMOR; https://cmor.
llnl. gov/), which is a software library developed to prepare model output as CF-compliant  (Hassell et al. , 2017; Eaton et al. , 2022, http ://cfconventions. org/) netCDF files. The CMOR is used by most modeling groups contributing to CMIP, ensuring all model output adheres to the CMIP data structures that themselves are based on the CF conventions. It is possible to use the PMP on model output that has not been prepared by CMOR, but this usually requires additional work, e. g. , mapping the data to meet the community standards. For reference datasets, the PMP uses observational products processed to be compliant with the Observations for Model Intercomparison Projects (obs4MIPs; https://pcmdi. github. io/obs4MIPs/). The obs4MIPs effort was https://doi. org/10. 5194/egusphere-2023-2720 Preprint.
Discussion started: 24 November 2023 c Author(s) 2023. CC BY 4. 0 License. initiated circa 2010  (Gleckler et al. , 2011)  to advance the use of the observations in model evaluation and research. Substantial progress has been made in establishing obs4MIPs data standards that technically align with CMIP model output (e. g. ,  Teixeira et al. , 2014; Ferraro et al. , 2015) , with the data products published on the ESGF  (Waliser et al. , 2020). Obs4MIPs-compliant data were prepared with CMOR, and the data directly available via obs4MIPs are used as PMP reference datasets. The PMP leverages other Python-based open-source libraries. A primary fundamental tool used in the latest PMP version is the Python package, Xarray Climate Data Analysis Tools (xCDAT;  Vo et al. , 2023;  https://xcdat. readthedocs.
io). The xCDAT is developed to provide a more efficient, robust, and streamlined user experience in climate data analysis when using xarray (https://docs. xarray. dev/). Portions of the PMP rely on the precursor of the xCDAT, a Python library called Community Data Analysis Tools (CDAT,  Williams et al. , 2009; Williams, 2014; Doutriaux et al. , 2019) , which has been fundamental since the early development stages of the PMP. The xarray software provides much of the functionality of CDAT (e. g. , I/O, indexing, and subsetting). However, it lacks some key climate domain features that have been frequently used by scientists and exploited by the PMP (e. g. , regridding, utilization of spatial/temporal bounds for computational operations) which motivated the development of the xCDAT. Completing the transition from CDAT to xCDAT is a technical priority for the next version of PMP.
Some visualization capabilities described in Section 3 are made available through the PMP. Users can also further explore the model data comparisons using their preferred visualization methods or incorporate the results into their own studies from the summary statistics from the PMP. Noting the above, the scope of the PMP is fairly targeted. It is not intended to be "all-purpose", e. g. by incorporating the vast range of diagnostics used in model evaluation. To help advance open and reproducible science, the PMP has been maintained with an open-source policy with accompanying metadata for data reproducibility and reusability. The PMP code is distributed and released with version control. Online documentation (http://pcmdi. github. io/pcmdi_metrics/), including user demo Jupyter Notebooks, and a database of pre-calculated PMP statistics for all AMIP and Historical simulations in the CMIP archive are also available online.