dataesr/scientific-paragraphs-categorization huggingface dataset:
B. PERFORMANCE EVALUATION 1) OVERALL PERFORMANCE ON CUDAS For each saliency model, its performance is quantified by calculating CC (or SIM) between the predicted saliency map PM and the ground truth saliency map SM over all stimuli contained in the CUDAS database. Fig. 4  shows the ranking results of the models' performance in terms of CC and SIM, respectively. The baseline performance, as defined in  [39] , is used to gauge the effectiveness of a saliency model. The baseline assumes that the centre of an image is the most salient region, which can be simply modelled by stretching a symmetric Gaussian to fit the aspect ratio of the image. It can be seen from Fig. 4  that all deep learning-based models are above the baseline performance and half of traditional models are below the baseline performance; and that SAM-ResNet, MSI-Net and SalGAN are consistently ranked top 3 in both CC and SIM rankings.
2) IMPACT OF DISTORTION STRENGTH In the CUDAS database, pristine images are degraded with three distinctive distortion levels Q1, Q2 and Q3. We want to evaluate whether and to what extent the strength of distortion can affect the performance of saliency models. Fig. 6  shows the average performance in terms of CC (note SIM exhibits the same trend as CC, and therefore, is not shown here) of all models on different distortion levels including the pristine reference. It tends to indicate that the stronger the distortion (i. e. , the lower the image quality), the lower performance of the saliency models. To statistically verify the impact of distortion strength, we perform hypothesis testing by selecting the CC metric as the dependent variable and distortion level as the independent variable. The Mann-Whitney U test is performed, and the results show that the saliency models perform significantly better on higher quality images than on lower quality images.
IV. INSIGHTS ON DEEP LEARNING MODELLING The evidence above shows the potential of deep learningbased models in solving the problem of saliency prediction in the context of image distortion. Now, we provide further practicalities towards a deep learning-based solution for saliency modelling. the target data. To solve our specific problem of predicting saliency of distorted images, one way is to investigate whether fine-tuning the existing models on the new CUDAS database will give good results. To this end, we conduct experiments using the deep learning-based models (in Section III) that are fine-tunable, including ML-Net, SalGAN, DVA, EML-NET, SAM-VGG, SAM-ResNet, and MSI-Net. More specifically, these models were first loaded with the pre-trained parameters on SALICON dataset (note this is a conventional operation for all deep learning-based saliency models), then fine-tuned on the CUDAS dataset.
The hyperparameters in fine-tuning for each model were set based on the recommendations in the literature and our empirical evidence of achieving the lowest loss value in the validation set during fine-tuning. To obtain comprehensive results, k-fold cross-validation (k = 6) is applied for each model's finetuning trial, where the CUDAS dataset is divided into six non-overlapping sets of equal size. To prevent data leakage, we ensure that there is no shared scene content between sets: (1) first, the 60 source images (see Fig. 1 ) are divided into six sets of 10 images each (i. e. , each set contains one of the columns of the 10 × 6 grid gallery as illustrated in Fig. 1 ); (2) second, within each set, its 10 source images and their corresponding 90 distorted images (10 scenes × 3 distortion types × 3 distortion levels) form the samples.
At each run of the k-fold cross-validation, one set is kept as a test set, one as a validation set, and the remaining four sets altogether are used as the training set. By doing so, the model is evaluated on the test set of unseen samples. It should be noted that each run of the k-fold cross-validation is independently conducted, without parameter sharing between runs. We report the average performance of k=6 times test results. Fig. 10  illustrates the performance (measured by CC and SIM) of saliency models before and after fine-tuning on the CUDAS database. It can be seen from Fig. 10  that fine-tuning deep learning-based models on the CUDAS dataset can significantly boost (i. e, p < 0. 05 by Wilcoxon signed-rank test) their performance. This implies that when predicting saliency of distorted images, fine-tuning models on the ground truth saliency data of distorted images is essential and that training models on the pristine image saliency data only will not provide optimal solution.
This means collecting more eyetracking databases in the context of image distortion will facilitate research in this area. Now, we have produced a new leaderboard on the CUDAS database, showing the ability of saliency models in handling distorted images. On the other hand, the leaderboard of MIT benchmark  [50]  shows the ability of saliency models in handling pristine images. It is valuable to cross compare the two leaderboards to guide the selection of models for specific applications. To this end, we calculate the Kendall rank correlation coefficient (KRCC) between two different performance leaderboard rankings. Note since the variant of EML-NET implemented by the MIT Benchmark is different from the variant (provided in the original publication) applied in our study, EML-NET is excluded from this comparison.
2) THE USE OF DIFFERENT BACKBONES The choice of backbone formation for deep learning-based models determines the effectiveness of the image feature extraction for saliency prediction. There are two different types of backbone formations used in the selected deep learning-based saliency models, being single-stream encoder and two-stream encoder. EML-NET uses a twostream encoder consisting of two deep backbones; while other models adopt a single-stream CNN-based backbone. We compare the use of these two different backbone formations in predicting saliency of distorted images. Table  4  shows the performance of single-stream encoder versus twostream encoder on the CUDAS database. Hypothesis testing (i. e. , Mann-Whitney U test) results show that saliency models based on a two-stream encoder achieve significantly better (p < 0.
V. DISCUSSION It should be noted that in this paper we focus on the analysis of saliency in the context of image distortion and the perceptual relevance of computational saliency models. For our specific application, CC and SIM are the most appropriate saliency evaluation metrics, as elaborated in Section III-A. 2). However, the CUDAS database can also be used as a standalone saliency benchmark, evaluating saliency models for various applications using different saliency evaluation metrics. For example, CUDAS can be used to benchmark models for salient object detection in a noisy environment, where AUC, KL-Div, and IG (centerbias as the baseline map  [45] ) are appropriate metrics for detection applications as they penalise target detection failures  [46]. To facilitate benchmarking, we hereby provide the results of evaluating state-of-the-art saliency models (i.
e. models used in Table  2 ) based on widely used saliency metrics including CC (Correlation Coefficient), SIM (Similarity), AUC-Judd (Area under ROC Curve-Judd)  [51] , AUC-Borji (Area under ROC Curve-Borji)  [52] , KL-Div (Kullback-Leibler divergence)  [45] , NSS (Normalized Scanpath Saliency)  [53] , and IG (Information Gain)  [54]. Table  5  lists the model performance results on the CUDAS database, where divergent rankings across metrics are evident. This further supports the earlier finding in the literature, ''specific tasks and applications may also call for a different choice of metrics''  [46]. Also, it should be noted that in this paper we use the same set of stimuli of the previously published image quality assessment database CUID  [40] , where the distortion simulation is limited to three different types.
Software-based automated scoring systems for the rapid detection of dicentric chromosomes have been developed and successfully applied in various studies (e. g. [96] ). Recent advances in using imaging flow cytometry to identify dicentric chromosomes have demonstrated the feasibility, however, there is still much room for improvement  [97]. Also automated robotically based high-throughput platform (RABiT, Rapid automated Biodosimetry Tool) has been designed to enhance the capacity of dicentric chromosome analysis  [98].
We present a near-real-time global gridded daily CO 2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO 2 emissions at a 0. 1° × 0. 1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO 2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO 2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO 2 emissions have rebounded and decreased.
Timely, fine-grained gridded carbon emission data sets are particularly important for global climate change research  3, 4. Often, fine-grained data are challenging to visualize over the globe, and clear visualization tools are also needed. Current global gridded CO 2 emissions datasets contain the Open Source Data Inventory for Anthropogenic CO 2 (ODIAC), the Community Emissions Data System (CEDS), and the Emission Database for Global Atmospheric Research (EDGAR)  [5] [6] [7] [8]. However, most of the datasets mentioned above have a data lag of at least.
one year. They do not present sub-monthly temporal changes associated with weather, climate seasonality, economic activity, and market shocks on energy production and trade. In a previous study, we described GRACED, a near-real-time Global Gridded Daily CO 2 Emissions Dataset, for 2019 and 2020 for the first time, which can be updated at a 0. 1° × 0. 1° spatial resolution and 1-day temporal resolution  9. In this study, we present GRACED for 2021, including gridded daily fossil fuel CO 2 emissions on the basis of multi-source datasets: the near-real-time global daily national dataset of sectoral CO 2 emission due to fossil fuel consumption and cement production (Carbon Monitor), Emissions Database for Global Atmospheric Research (EDGAR) and spatiotemporal patterns of nitrogen dioxide (NO 2 ) retrieved from satellites.
GRACED provides gridded CO 2 emissions in the following seven sectors: industry sector (incl. cement process), power sector, residential consumption sector, ground transport sector, international shipping sector, international aviation sector, and domestic aviation sector. By comparing with other global gridded CO 2 emission datasets, we further analyze spatiotemporal and sectoral uncertainties in CO 2 emissions over the period 2019-2021 to test the reliability of the estimated results. This research presents the latest analysis of near-real-time high-resolution gridded fossil CO 2 emissions for the year 2021. One of the advantages of our dataset is that it provides worldwide near-real-time monitoring of CO 2 emissions with different fine spatial scales at the sub-national level, such as cities, thus enhancing our comprehension of spatial and temporal changes in CO 2 emissions and anthropogenic activities.
With the new extension of GRACED time series, we present crucial daily-level input to analyze CO 2 emission changes in the post-COVID era, which will ultimately facilitate and aid in designing more localized and adaptive management policies for the purpose of climate change mitigation in the post-COVID era. Compared with our previous work, the new research on data quality verification of GRACED was first presented. The gridded uncertainty of the GRACED dataset is quantified for the first time. GRACED exhibited a grid-level two-sigma uncertainty value of ±23. 1%, ±19. 9%, and ±19. 9%, corresponding to 2019, 2020, and 2021. The primary source of GRACED uncertainty is Carbon Monitor data. Also, comparison with other gridded CO 2 emission datasets revealed a relatively low uncertainty of GRACED dataset.
Methods In this study, the framework diagram is shown in Fig. 1. It is a top-down approach that uses geographical patterns supplied by EDGAR, as well as a sub-national proxy according to TROPOMI NO 2 retrievals, in order to distribute Carbon Monitor daily emissions at country-level to our finer grid. input datasets. Carbon monitor national-level emissions inventory. Estimated GRACED emissions are based on the near-real-time daily CO 2 emissions from fossil fuel combustion and cement production provided by Carbon Monitor since January 1, 2019 (data accessible at https://carbonmonitor. org/)  3, 4, 10. Carbon Monitor provides emissions at a national and sectoral level, drawing on near-real-time activity data and inventories for the reference year of 2019  3. Since January 1, 2019, Carbon Monitor has calculated daily national CO 2 emissions in five sectors (electricity, industrial production, ground transportation, home consumption, and domestic aviation) as well as daily international aviation and shipping emissions.
We collect data from Carbon Monitor dataset, including emissions for the nations, groups of countries, or regions listed below: China, the United States (US), India, the United Kingdom (UK), Italy, Germany, France, the rest of the European Union, Russia, Japan, Brazil, and the rest of the globe. Carbon Monitor's input activity data covers about more than 70% of global industry and power emissions, 85% of ground transport emissions, and 100% of residential consumption and international bunker emissions, respectively. Carbon Monitor also provides the emissions as an aggregate for the rest of the world where data for some sectors (mainly electricity generation) is not directly available. National and worldwide daily CO 2 emissions with detailed information in six sectors and major nations were eventually computed by using these high temporal resolution data.
EDGAR v5. 0_FT2019 data. In GRACED, emissions are spatially allocated using EDGARv5. 0_FT2019 gridded spatial activity data underlying CO 2 emissions defined for sub-sectors in Intergovernmental Panel on Climate Change (IPCC) and novel geospatial proxies  7, 8. The European Union Emissions Database for Global Atmospheric Research (EDGAR) is an open-source platform developed and maintained by the European Environment Agency (EEA) and is widely used as the default for emission estimates in inventory systems. EDGAR supports the monitoring of climate policy implementations, and in particular the Paris Agreement and significantly contributes to the quantification of IPCC national inventory guidelines and to the evaluation of the GHG budgets at various levels (http://verify. lsce. ipsl. fr/)  8.
EDGAR emissions data are split into sub-sectors specified by IPCC methodology and geospatial data/spatial proxy data like point source and line source location with the resolution of 0. 1° × 0. 1°  7, 8. Driven by the rapid developments in scientific knowledge on the generation process of GHG emissions as well as the accessibility of more recent information, the EDGARv5. 0_FT2019 dataset now includes new spatial proxies to allocate the emissions related to population based upon the Global Human Settlements Layer (GHSL) product  8, 11. We used the version EDGARv5. 0_FT2019, which includes new spatial proxies to allocate population-related emissions based upon the Global Human Settlements Layer product  8. EDGAR v5. 0_FT2019 includes spatial activity data for all fossil CO 2 sources, including fossil fuel combustion, metal (ferrous and non-ferrous) production processes, non-metallic mineral processes (such as cement production), agricultural liming and solvents use, urea production  8.
For U R u y , , as this research went through a pre-processing process for the TROPOMI NO 2 data to calculate R u,y , we stated that the uncertainty of R u,y will be a constant value throughout the gridded dataset based on the result of Goldberg, et al. 21 , and van Geffen, et al. 22. Cooper, et al. 23  stated that the uncertainty of NO 2 are less than 5%, van Geffen, et al. 22  stated that the uncertainty originating from the slant column density (SCD) method in TROPOMI NO 2 is 10%, and Goldberg, et al. 21 mentioned that TROPOMI product have a low bias of 20% in urban areas. = * * = * * = U U U U 5 10 20 10(%) (12) R C ooper Geffen G oldberg u y , 3 3 Where U U U , ,.
Cooper Geffen Goldberg denote the uncertainty mentioned in Cooper, et al. 23  , van Geffen, et al. 22  and Goldberg, et al. 21. The uncertainty of each sector was calculated based on the equations mentioned previously. As shown in Fig. 6 , the smallest uncertainty in 2021 was obtained from the power sector (±13. 5%), where the majority of this uncertainty originates from the uncertainty of Carbon Monitor's power sector data (±10%)  3. The average value of uncertainty in grids for industry, residential consumption, and ground transport, other three sectors included in s 1 , were ±33. 4%, ±42. 0%, and ±15. 5%, respectively. On the other hand, average gridded uncertainty in international shipping sector, international aviation sector, and domestic aviation sector had a smaller uncertainty of ±16.
The gridded sectoral uncertainty of 2021 displays additional information about the distribution of the uncertainties (Fig. 8 ). International aviation, power, and domestic aviation sector exhibited a relatively constant uncertainty in every grid due to even distribution in the corresponding sectors' EDGAR spatial proxy uncertainty. However, the ground transport and residential consumption sector displayed a relatively high uncertainty in parts of Africa. This issue originates from low emission values of EDGAR data grids, causing higher spatial proxy uncertainty. Validation against other datasets. We also examined the distribution of emission in a grid-wise perspective for major emission datasets, GCP-GridFED  24  , ODIAC  25  and EDGAR  26  , and compared it with GRACED (Fig. 9 ). As GRACED employs extensive use of the EDGAR dataset, utilizing it as the main spatial proxy during the calculation procedure, the distribution of emission is very similar between GRACED and EDGAR throughout every emission range.
This also originates from the total emission of Carbon Monitor's small difference from EDGAR's total emission. GRACED's distribution is displayed in accordance with GCP in the middle range (8-16)  where the majority of the grids are included. The variance was only observed in the two extremes. The similarity in emission distribution and the number of non-zero emission grids were observed in GRACED, EDGAR and GCP-GRIDFED. However, ODIAC showed huge variance with the other three datasets in every range of emission. ODIAC showed a higher number of grids than the other datasets in every emission range. This huge variance could be originating from the calculation method of ODIAC which employs nighttime light data  27. The nightlight-based approach of ODIAC can cause higher calculation values in the dataset compared to emission inventories for certain cities, due to a poor correlation between anthropogenic activities and nightlight intensity  28.
A sectoral comparison between GRACED and data from International Energy Agency (IEA)  29  was also performed to validate the emission values of GRACED (Table  3 ). GRACED's sectoral emission showed consistency with IEA's emission, where in majority of the sectors, differences in emission were less than 10%. However, there exists a notable difference between the two datasets in residential consumption sector. This difference can be originating from the fact that IEA's building sector account both direct and indirect emissions. The chart below (Table  4 ) shows information about other published gridded CO 2 emission datasets. The majority of these datasets, including GRACED, have a spatial resolution of 0. 1° × 0. 1°. GRACED's spatial coverage also showed an equal level of other datasets. As mentioned previously, GRACED is the only dataset to provide near-real-time emission data in a temporal resolution of 1-day.
Due to the relatively recent creation Fig. 8  Uncertainty of sectoral CO 2 emission in 2021 for the industry, international aviation, domestic aviation, power, international shipping, residential consumption, ground transport and total sector. dataset of GRACED, the time coverage is shorter than other datasets. This issue will gradually be solved in the future as data accumulates. In order to further validate GRACED's data, uncertainty values of GRACED were compared with other datasets. We conducted the comparison between the uncertainty value of GRACED, and the published uncertainty value of EDGAR emission inventory. We used the latest inventory uncertainty value that EDGAR published for comparison. GRACED's overall uncertainty of ±21. 0% is higher than EDGAR's uncertainty value of ±7. 1%  30.
Considering additional uncertainties originating from spatial proxies of TROPOMI and EDGAR harnessed to distribute national-level emission, it is reasonable that the average grid-level uncertainty of GRACED is higher than certain national or global level emission inventories. As GRACED uses a top-down spatially gridding approach, errors and uncertainty in sectoral emission from Carbon Monitor, the national-level emission inventory, were evenly distributed in each grid. Therefore, we also compared the uncertainty of GCP-GridFED, another gridded carbon emission dataset that published its uncertainty results, with the uncertainty of GRACED. The uncertainty value for GCP-GridFED was of 2018, as the latest uncertainty results of GCP-GridFED released by GCP team is of 2018. The GCP-GridFED showed a consistent uncertainty in the past six decades, ranging from ±23.
Data Records The GRACED2021 products  18  are now available at https://doi. org/10. 6084/m9. figshare. 21427437. v1. We have also provided an example of Python code to help users produce, read in and plot emissions for any grid in the dataset (https://github. com/xinyudou820/GRACED2021). The emission data are stored in netCDF files per sector with the unit of kg carbon per hour (kgC/h) for each grid. GRACED2021 provided gridded CO 2 emissions with the spatial resolution of 0. 1 degree and the temporal resolution of daily. For each sector, all daily files of the same month are merged into monthly *. zip files. After merging, there are 96 files of GRACED2021 provided at Figshare. Among them, a total of 84 files are sectoral gridded CO 2 emissions and a total of 12 files are total gridded CO 2 emissions.
Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the socalled enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC.