dataesr/scientific-paragraphs-categorization huggingface dataset:
Our goal was to develop a dataset that enables a thorough investigation of the extent to which increased image resolution can reduce the rounding error problem, and create in the future a new product for monitoring tropical forest dynamics in a region experiencing rapid forest change. Combining these 2 different image sources allowed us to first investigate whether high-resolution optical data are sufficient on its own to reduce forest cover classification errors, or whether SAR, with its relative insensitivity to atmospheric conditions and greater sensitivity to vegetational structural aspects such as height  [16] , provides additional information needed to most effectively minimize error. The answer to this latter question is not certain, and therefore of broad interest, as other dual-sensor land cover mapping studies (e.
g. , for crop types) have shown that SAR often does little to improve classification accuracy relative to multi-spectral optical data (e. g. ,  [54, 55] ). To examine the degree to which our new, high-resolution maps improved on previous products, we compared the accuracy of our maps with several forest cover maps drawn from existing products. We further evaluated how forest cover estimates derived from our maps compare to those developed from other products, assessing differences at the level of individual countries, and between different topographic regions (highland versus lowland), to more fully examine how rounding errors propagate into inventory uncertainties. We also estimated how fractional cover varies with the decreasing resolution, to more fully understand the connection between resolution and rounding errors.
Sentinel-1 SAR imagery Based on prior work demonstrating the applicability of SAR products for land-cover mapping  [15, 16, 20] , we chose the 10-m resolution dual-polarization Ground Range Detected (GRD) scene (VV + VH) aboard Sentinel-1 to map tropical forests from 1 June 2019 to 30 November 2019 in the GEE platform. We selected the SAR data to address potential overestimation resulting from confusion with herbaceous vegetation, as well as potential underestimation due to optical satellite observations omitting deciduous or semi-deciduous characteristics  [16]. Briefly, the Sentinel-1 satellites acquire C-band SAR imagery independent of time of day or weather  [53]. Sentinel-1 A and B were launched in April 2014 and April 2016, respectively. Their repeat cycle is 12 days for a single satellite or 6 days for a dual-satellite constellation.
Sentinel-1 SAR data products provide single polarization (VV or HH) for Wave mode, and dual (VV + VH or HH + HV) or single (HH or VV) polarization for SM, IW, and EW modes. The GEE platform contains all of the GRD scene data from October 2014 to the present  [52]. Each scene has one of 3 resolutions (10, 25, or 40 m), 4 band combinations (VV, HH, VV + VH, and HH + HV), and 3 instrument modes (SM, IW, and EW). These data were pre-processed with the Sentinel-1 Toolbox for thermal noise removal, radiometric calibration, and terrain correction.
Mapping approach To undertake our assessment, and to improve fine-scale mapping of forest cover, we developed a machine-learning approach that applied the Random Forests (RF) model  [61]  to Planet and Sentinel-1 imagery within the GEE platform. The workflow involved collecting satellite imagery and existing land cover products, constructing labeled samples, developing an RF classifier, and implementing the machine learning algorithms into Downloaded from https://spj. science. org on  December 12, 2023  which labeled samples were separated into training, testing, and product comparison datasets (Fig. 2 ). We defined trees as all vegetation over 5 m in height, labeled as forest pixels. To investigate the performance of the method, we compared the Planet/Sentinel-1 forest cover with existing forest cover products of various resolutions (i.
After loading the second biannual Planet red, green, blue, and NIR bands (i. e. , the time window is June to November) and the Sentinel-1 VV/ VH bands from 1 June 2019 to 30 November 2019 in the Earth Engine Data Catalog, we leveraged the GEE mosaic function to produce spatially continuous SAR imagery and resampled it using bilinear interpolation to match the spatial resolution of Planet imagery. The NDVI band was calculated as follows  [67] : where red and NIR represent Planet red and NIR bands, respectively. The training (Fig. 3A ) and testing (Fig. 3B ) samples in the previous section were matched with the input imagery based on the same geolocations to obtain a set of predictors that were randomly segmented into training and testing datasets. Then, the RF model parameters were set to 100 trees with others remaining at their default values.
Next, the RF models were trained and tested repeatedly until the accuracy stabilized (see the next section). The trained model was then used to   create predictions within 2 × 2° grid cells, which were used to reduce the amount of data per serving, followed by several post-processing steps (mosaic, mask, project, etc. ), resulting in final classification maps for the SEA region that constitute the Planet/Sentinel-1 forest cover product for 2019. To evaluate the degree to which Sentinel-1 added extra information that helped the classifier distinguish between forest and non-forest, we trained 2 versions of the model: one using only the Planet variables, and the other including both Planet and Sentinel-1 variables.
To create forest cover maps from the 3 land cover datasets (1, 2, and 4), we converted all forest-related and non-forest classes into 2 separate classes to produce binary maps. It can be challenging to select images with the same time reference to reduce errors in our mapping process. However, we have made efforts to choose images that are as closely aligned as possible with the time of our mapping, to minimize any discrepancies. In the sample-based evaluation stage, we compared the performance of our maps with that of existing forest cover products using the 3,000 product comparison samples (Fig. 3C ) at ±95% confidence intervals  [70]. To compare the variability in forest inventory estimates arising from each product, we calculated from each set of maps the total forest cover for each country in the SEA region, as well the total forested area in lowland and highland regions.
Acknowledgments Funding: This study was supported by the  National Natural Science Foundation of China  (grant no. 42071022 ), the startup fund provided by the  Southern University of Science and Technology  (grant no. 29/Y01296122 ), and the  China Postdoctoral Science Foundation  (grant no. 2022M711472 ). J. W. was supported by the  Hung Ying Physical Science Research Fund   2021-22  and the  Innovation and Technology Fund  (funding support to  State Key Laboratories in Hong Kong of Agrobiotechnology)  of the  HKSAR, China. Author contributions: Z. Z. designed the research; F. Y. performed the analysis and wrote the draft; L. D. E. contributed to the development of the map and model assessment methods. All authors contributed to the interpretation of the results and the writing of the paper.
Data Availability Forest area statistics of FAO FRA at the country scale were collected from https://www. fao. org/forest-resources-assessment/ fra-2020/country-reports/. Planet imagery, Sentinel-1 SAR imagery, and PALSAR FNF forest cover product are available on the GEE platform: https://developers. google. com/earth-engine/datasets/catalog/projects_planet-nicfi_assets_basemaps_asia, https://developers. google. com/earth-engine/datasets/catalog/.
To identify novel solutions to improve rice yield under rising temperatures, molecular components of thermotolerance must be better understood. Alternative splicing (AS) is a major post-transcriptional mechanism impacting plant tolerance against stresses, including heat stress (HS). AS is largely regulated by splicing factors (SFs) and recent studies have shown their involvement in temperature response. However, little is known about the splicing networks between SFs and AS transcripts in the HS response. To expand this knowledge, we constructed a co-expression network based on a publicly available RNA-seq dataset that explored rice basal thermotolerance over a time-course. Our analyses suggest that the HSdependent control of the abundance of specific transcripts coding for SFs might explain the widespread, coordinated, complex, and delicate AS regulation of critical genes during a plant's inherent response to extreme temperatures.
, 2017; Calixto et al. , 2018; Vitoriano and Calixto, 2021). Our knowledge of the true scale and function of SFs involved in heat-induced AS are limited and need to be addressed  (Rosenkranz et al. , 2022). Genome-wide transcriptome data can offer vast amounts of valuable information, and a systemsoriented, network-based analysis of this data could be used to help decipher the molecular mechanisms behind stress responses  (Raza, 2020; Winck et al. , 2021). Co-expression networks, for example, are powerful systems biology tools that use expression datasets to predict candidate regulators, their targets and other important elements from biological systems  (Coneva et al. , 2014; Aghamirzaie et al. , 2016; Zhang et al. , 2022). Here, we generated a co-expression regulatory network from a publicly available RNAseq dataset of the rice basal or inherent thermotolerance response  (Luo et al.
Method Differential AS analysis with RNA-seq data In our work, we used the SRP190858 RNA-seq dataset  (Luo et al. , 2019 ), which we have described previously  (Vitoriano and Calixto, 2021). Briefly, this dataset contained data from the stem and leaves of Nipponbare rice plants grown for 2 weeks at 28 °C and subjected to 45 °C HS over 2 days. Three biological replicates were harvested at eight time points during the HS, namely at 0 h (28 °C), 1 h after going 45 °C, 3 h, 6 h, 12 h, 24 h, 36 h and 48 h. RNA-seq raw files were decompressed through fastq-dump (Bioconda) and trimmed with Trimmomatic (v0. 36)  (Bolger et al. , 2014)  in pairedread mode (Supplementary Figure  S1 ). Salmon in paired-read mode  (Patro et al. , 2017)  coupled with the rice Nipponbare reference transcriptome  (Kawahara et al.
Normalisation, transcripts clustering and splicing network construction Z-scores were calculated using expression data of DTU transcripts in TPM and used for clustering analysis. For this, we subtracted each transcript's mean expression over all the time points to each expression data at a given time and divided the result by the standard deviation of this transcript's expression over all the time points. We used WGCNA (v1. 70. 3)  (Langfelder and Horvath, 2008)  and the Z-scores of DTU transcripts to classify them into co-expressed clusters in response to HS. The transcript expression similarities for hclust clustering were optimised with the power parameter set as 17 following the elbow heuristic method on a scale-free property measure for our clustering 0. 7  (Khanin and Wit, 2006). The obtained dendrogram was cut with the dynamicTreeCut package  (Langfelder et al.
, 2008) , with the deepSplit parameter set as 4-in order to have the most homogeneous clusters as well as the least transcripts in the outlier cluster (cluster 0), which was removed in further analysis. For each SF/RBP-coding transcript of interest, correspondent TPM values were extracted and Z-scores were calculated. The identification of protein-coding transcripts was carried out using TranSuite, with default parameters (v0. 2. 3)  (Entizne et al. , 2020). We then calculated the Pearson correlations between cluster means (centroid) and SFs/RBPs normalised expression. This allowed us to create a bipartite graph visualised as a co-expression network in Cytoscape  (Shannon et al. , 2003). A minimum correlation threshold of 0. 924293 (in absolute value) was chosen.
In silico functional analysis We explored the following databases: Phytozome for gene-related nucleotide sequences  (Goodstein et al. , 2012) , RGAP  (Kawahara et al. , 2013)  and Oryzabase  (Kurata and Yamazaki, 2006)  for gene functional reports, PLAZA for precomputed phylogenetic trees  (Van Bel et al. , 2022)  and InterPro for the biological function of protein domains  (Blum et al. , 2021). Schematic diagrams of gene structures were made with the help of the Exon-Intron Graphic Maker program [http:// wormweb. org/exonintron (accessed on 4th October 2022)]. Gene Ontology statistical overrepresentation test was carried out with Panther version 16. 0 [http://pantherdb. org/(accessed on 26th October 2022)] with the binomial test and cut-off p-value < 0. 5. Subcellular localization prediction was carried out with LOCALIZER 1.
Construction of a HS response splicingrelated network To construct a rice HS splicing network, we used the RNA-seq time-course data published by  Luo et al. (2019)   (SRA dataset SRP190858). This dataset was chosen because it generated the highest number of DAS genes upon HS (1,633) when compared to other available datasets (the second highest had 678 genes)  (Vitoriano and Calixto, 2021). Additionally, it has one of the highest sampling regimes of rice plants undergoing HS (0 h, 1 h, 3 h, 6 h, 12 h, 24 h, 36 h, and 48 h), which will increase the quality of our network. The experimental design of this RNA-seq dataset consisted of rice plants grown at 28 °C for 2 weeks and later exposed to 45 °C for 48 h. We previously investigated heat-responsive genes and transcripts in this dataset and found 3,140 DTU transcripts (Supplementary File S2) from 1,633 rice genes  (Vitoriano and Calixto, 2021).
To use the expression data of these DTU transcripts in the construction of our network, we grouped these transcripts into 19 different clusters using the WGCNA method (Supplementary File S2, Supplementary Figure  S2 ). To identify the potential regulatory elements in our network, we selected a list of putative protein-coding transcripts of SFs and RBPs that were significantly differentially expressed (DE), DAS or both DE + DAS in the heat  (Vitoriano and Calixto, 2021). From this list, we only kept transcripts coding for a functional protein with a nuclear localisation signal. As a result, we obtained 186 protein-coding SF/ RBP transcripts, from 106 genes, with 48 SF/RBP genes having more than one protein-coding transcript (Supplementary File S3). Next, we correlated the cluster means of DTU transcript Z-scores with those of the Z-scores of individual protein-coding SF/RBP transcripts.
Discussion We have constructed a co-expression network based on transcriptomic data of rice leaves undergoing HS over 2 days. This network involves two kinds of nodes: protein-coding transcripts expressed from heat-sensitive genes that are also putative AS regulators (SF/RBPs) and clusters of transcripts regulated by AS (DTU transcript modules). An edge represents a strong correlation between these two kinds of nodes and we explored this correlation in terms of AS regulation. The advantages of our approach over a simpler gene-specific one are 1) AS regulation affects the proportion of transcripts expressed from a FIGURE 3 Heat-induced AS of (A) LOC_Os07g37800, (B) OsABCD1 (LOC_Os01g11946), (C) LOC_Os03g24520 and (D) OsGATA17 (LOC_Os02g05510). 5′ and 3′ UTRs are open boxes; introns are represented with thin lines; coding sequences are shown as dark boxes, except for domain-encoding exons, which are coloured (Pfam accession ID in brackets).
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multigranularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately.
Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins. The source code can be found in https://github. com/Zongwei97/HIDANet/.
[8] ,  [32]  propose the stochastic framework to analyze the uncertainty during human labeling and model the distribution of the saliency output. Different from previous works,  [33] ,  [34]  attempt to address RGB-D SOD from the 3D point of view with a 3D convolutional neural network. The recent  [35]  leverages the depth cues to mimicks multi-view images and then fuse them to form the final output. On the other side, multi-stream models  [9] -  [16] ,  [36]  have achieved leading performances in RGB-D SOD. These models adopt two parallel encoders on different modalities, and the features are fused through different strategies. Several works  [9] ,  [37] ,  [38]  firstly enhance the depth features before fusing with RGB features. It is worth noting that a portion of the depth maps in existing saliency datasets are not of satisfactory quality.
It is worth mentioning the success of skip connections can be mainly attributed to aggregation between the semantic features provided by the contracting path and fine-grained features from the expansion path. From a new perspective, we consider the encoder-decoder features as multi-modal features, and a unified cross-fusion scheme is applied to boost the performance. Attention for Feature Enhancement. Attention methods such as transformer  [49] , CBAM  [18] , SEnet  [19] , DA  [50] , and ECA  [21]  have demonstrated their success in other vision tasks. A number of RGB-D saliency models also equip attention modules to extract attentive features from different modalities. VST  [51]  and TriTrans  [14]  adopt transformer  [49]  for saliency detection. [17] ,  [52] ,  [53]  apply the SE module to compute modality-specific attention for feature calibration.
Multi Granularities Fig. 2. The overall architecture of our HiDAnet with U-Net-like design. It consists of granularity-based attention (GBA Section III-A), cross dual-attention module (CDA Section III-B), and efficient multi-input fusion (EMI Section III-C). RFB is the receptive field block from  [57]  for accurate object detection. White blocks denote the network backbone. Our granularity-based attention strengthens the discriminatory power of RGB and depth features separately. Our cross dual-attention module takes advantage of cross-domain cues to attentively realize multi-modal and multi-level fusion in a coarse-to-fine manner. Our efficient fusion scheme effectively models the shared information from each modality. The shared features are further improved with the skip connections for final saliency map generation.
III. METHOD Fig. 2  presents the overall framework of our proposed HiDAnet. Note that the Otsu masks are generated from the depth map during the pre-processing. Firstly, RGB and depth maps are fed into two parallel encoders for feature extraction. For each individual encoder (RGB/Depth), we propose a granularity-based module (GBA) with the help of input Otsu masks to enhance the discriminatory power, e. g. , foreground and background. This module is naturally embedded into different levels of the encoder to correlate with the network hierarchies. With the enhanced features, we propose a unified fusion mechanism (CDA) for multi-modal and multi-level fusion. It enables a cross-domain interaction with both channel and spatial attention to learn the informative shared features in a coarse-to-fine manner.
A. Benchmark Datasets To verify the effectiveness of our approach, we firstly train with the conventional training dataset following the protocol presented in  [9] ,  [10] ,  [13] ,  [14] ,  [17]  with 2,195 samples: 1,485 samples from the NJU2K-train  [63]  and 700 samples from the NLPR-train  [64]. For testing, experiments are conducted on five classical benchmark RGB-D datasets. DES  [65]  : includes 135 images of indoor scenes captured by a Kinect camera. NLPR-test  [64] : contains 300 natural images captured by a Kinect under different illumination conditions. NJU2K-test  [63] : contains 500 stereo image pairs from different sources such as the Internet, 3D movies, and photographs taken by a Fuji W3 stereo camera, where several depth maps are estimated through an optical flow method  [66].
STERE  [67] : includes 1,000 stereoscopic images downloaded from the Internet where the depth map is estimated using the SIFT flow method  [68]. SIP  [39] : contains 929 images with humans in the scene, and images are acquired by a mobile device. We further evaluate our model on a newly published dataset COME15K  [12]  where the depth is estimated through a modified optical flow algorithm  [69]. In this case, our model is trained with provided 8,025 training samples and tested on the "Difficult" set with 3,000 images.
B. Experimental Settings Our model is implemented based on Pytorch and trained with a V100 GPU. Our backbone is initialized with the pretrained weights obtained from ImageNet. For the depth stream, we modify the first convolution to start from one channel. The input RGB-D resolution is fixed to 352×352. We choose the Adam algorithm as our optimizer. We initialize the learning rate to be 1e-4 which is further divided by 10 every 60 epochs. The total training time takes around 6 hours for 100 epochs. During training, we adopt random flipping, rotating, and border clipping for data augmentation. During inference, the prediction maps from the shared branch are the final outputs (middle branch of Fig. 2 ). We evaluate our performance with four generallyrecognized metrics: F-measure is a region-based similarity metric that takes into account both Precision (P) and Recall (R).
C. Comparison with SOTA RGB-D models Quantitative Comparison: We provide in Figure  7  an overview of the average performance on conventional benchmark datasets, i. e. , DES  [65] , NLPR  [64] , NJU2K  [63] , STERE  [67] , and SIP  [39]. The detailed quantitative performances can be found in Tab. I. We also present in Tab. II the quantitative comparison on the newly published challenging COME15K  [12]  dataset. All saliency maps are directly provided by authors or computed by authorized codes. Under the consideration of a fair comparison, we conduct experiments with different backbones such as VGG16  [80] , ResNet50  [20] , and Res2Net50  [81]. It can be seen that our HiDAnet with each backbone achieves comparable and superior performance compared to the SOTA models with the same backbone.
Specifically, our HiDANet with VGG16 backbones achieves significantly better performance on NLPR and SIP datasets, while being very competitive on the model size with 269 MB and around 6 FPS. Our HiDAnet with ResNet50 backbones further sets new SOTA records on DES, NLPR, and NJU2K datasets with 523 MB and around 12 FPS. We also follow the SOTA SPNet and replace our backbone with Res2Net50. It can be seen that our method performs favorably compared to SPNet with only 525 MB compared to that of SPNet with 702 MB. Our FPS is around 11. We also exhibit in Fig. 9  the PR curves with several latest published models to further demonstrate the superior performance of our model. Finally, in addition to the difference in the backbone, we observe that existing works adopt different architectures, i. e.