dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2003; Liu and Weisberg, 2005; Liu et al. , 2006a; Mihanovićet al. , 2011; Jouini et al. , 2016; Liu et al. , 2016; Arnault et al. , 2021). The SOM is a competitive neural network based on unsupervised learning  (Kohonen, 1982; Vesanto et al. , 2000). It takes a high-dimensional input space and maps it onto a low dimensional grid, while conserving relationships between the inputs  (Liu et al. , 2006). The SOM is organized in two distinct layers  (Kohonen, 1982; Vesanto et al. , 2000)  (e. g. , Figure  2 ). The first (or input) layer takes a high-dimensional dataset to be clustered (called the input dataset). Here, the input dataset is composed of the satellite U and V, SST and Chl-a over the study region (see Figure  1 , orange box and section 2. 1). The second layer, which is the output layer, consists of a given number of nodes (neurons, units, clusters or patterns) topologically structured in a low-dimensional 2D grid (map)  (Kohonen, 1982; Vesanto et al.
, 2000; Jouini et al. , 2016). The number of output neurons is user-defined following the level of detail and degree of accuracy required in the analysis  (Richardson et al. , 2003; Jouini et al. , 2016). Each neuron in the output layer has a weight vector connected to a subset of the input dataset and to its neighbouring neurons by a neighbourhood function, which dictates the map topology  (Vesanto et al. , 2000; Richardson et al. , 2003; Jouini et al. , 2016; Liu et al. , 2016). Indeed, the SOM' neurons are connected in way that determines a topological (neighbourhood) relationship between them  (Jouini et al. , 2016). Close neurons correspond to similar weight vectors, while distant neurons relate to different weight vectors  (Jouini et al. , 2016). The SOM algorithm organizes the grid (map) by first comparing the input data to each neuron  (Liu et al.
, 2006a). The neuron which best matches the input vector (i. e. , with the smallest Euclidean distance) is the winner neuron (or Best Matching Unit, BMU) and the centre of neighbouring neurons  (Richardson et al. , 2003; Liu et al. , 2016; Liu et al. , 2006b). The SOM, then, updates the excited neighbouring neurons and their associated weights  (Richardson et al. , 2003). This iterative training process is called self-organizing  (Liu et al. , 2016). There are two types of self-organizing: a traditional sequential training and a batch training  (Liu et al. , 2006a). The sequential training provides the input samples successively to the SOM, while gradually adjusting the weight vectors. In the batch training, the SOM receives the whole input dataset at once and the final weight vectors become the weighted averages of the data vectors associated with each node  (Liu et al.
, 2006a). After the self-organizing process converges, the output weight vectors characterize the dominant patterns from the input data  (Liu et al. , 2016). Figure  2  sums up the architecture and functioning of the SOM algorithm described above. Here, we use the batch training approach (cf. section 2. 2. 2). We use the MATLAB SOM-Toolbox version 2. 0  (Vesanto et al. , 2000; Kohonen, 2014) , which is an efficient SOM implementation developed by the Laboratory of Information and Computer Science in the Helsinki University of Technology freely available at http://www. cis. hut. fi/projects/somtoolbox/. This toolbox was successfully used previously in oceanographic applications  (Jouini et al. , 2016; Liu et al. , 2016; Liu et al. , 2006a; Liu et al. , 2006b; Arnault et al. , 2021). The mapping quality of the SOM can be quantified through the average quantization error (QE) and topographic error (TE) which are returned by the toolbox  (Liu et al.
The normalization of variables gives them equal importance, which is essential FIGURE 2 | Summary diagram of the 3x4 SOM architecture and functioning described in section 2. 2. During the pre-processing, the input dataset which consist of 2-D daily satellite data (U(tj), V(t j ), SST (t j ), Chl-a(t j ) with j=1: n days) is concatenated into a row vector X (which makes up the input layer). X is composed of n=3479 arrays (one for each day of the observational record) each of p=4416 elements (1104 pixels *4 variables). Each SOM neuron (or pattern) gets assigned a weight vector ((w 1,1 … w 1,p)… (w 12,1 … w 12,p) ) that changes with the iterative training (second layer). When a BMU (wining neuron, dark blue circle) is found its weight vector and neighbouring neurons (light green shaded area) are updated.
g. ,  [3x3, 3x2] ) led to extracted patterns with too simplified surface processes and those with larger sizes (e. g. ,  [4x4, 4x5, 5x5] ) resulted in highly detailed and numerous patterns, making it problematic to narrow down the main variability modes. Note that the number of observations that contributed to the construction of each neuron was also considered in choosing the "optimum" map size (not shown). The selection of a neighbourhood radius and number of training iterations were tested based on the QE and TE returned values, as commonly recommended  (Liu et al. , 2006a). It is also important to note that the pre-processing of the input dataset (here U, V, SST, Chl-a) includes an additional step before the normalizing of the data (Figure  2 ). We transform each re-gridded U (V, SST or Chl-a) image of the period Nov-Mar 1997-2020, into a single row vector by concatenating each row in the image.
The final input vector consists of 3479 arrays (one for each day of the observational record) each of 4416 elements (1104 pixels *4 variables). We then remove non ocean pixels, following  Richardson et al. (2003)  and normalize data with a range normalization for each variable (so all the variables are between 0 and 1). Note that the possible data gaps in SST and Chl-a data due to cloud coverage, are dealt with by choosing products that are gap (cloud) free as already mentioned. Indeed, although the SOM algorithm can robustly handle data gaps  (Liu et al. , 2006b) , using datasets with large number of gaps can lead to less realistic SOM outputs. Hence, for the Chl-a data, which is most affected by cloud coverage, we chose the GlobColour product which has L4 daily gap-free composites over the Ocean Colour -Climate Change Initiative (OC-CCI) product whose daily composites are L3 with higher number of missing data.
Unlike other SOM oceanographic applications where the SOM was applied on a single parameter (e. g. ,  Richardson et al. , 2003)  or on a set of physical parameters only (e. g. ,  Arnault et al. , 2021) , our analysis combines ocean physical and biological parameters and links together their frequency of occurrence thus highlighting the biophysical interactions (e. g. , P1 is 10. 38% for Chl-a, SST and currents). As a next step, integrating into the SOM other variables such as winds to account for atmospheric effects and study links with the biological response. The available satellite wind data present some limitations in terms of spatio-temporal coverage/ resolution and missing data near the coasts which can impact the SOM clustering. However, new/future satellite missions (e. g. , CyGNSS and the next generation of Metop satellites) offer higher resolution and better-quality wind data.
At the same time, it should be noted that the mechanisms mediating the interactions, interaction strengths and even the interaction directions vary in different contexts, while the results presented here show only their aggregate outcomes. Further, a better understanding of local-scale mechanisms potentially cascading to planetary-scale feedbacks is needed for prioritizing management actions. 6] [37] [38] [39] [40]  ). These datasets can be used in local-scale models to validate our interaction strengths while retaining global consistency, which would reduce the uncertainty in comparing results between different local contexts. However, this endeavour would require case-specific knowledge and sophisticated models. Therefore, while we have validated our results with comparisons to ref. 9  and various case studies (Supplementary Section 6), we leave extensive local-scale validation for future work.
To consider differences between the non-region-specific and region-specific answers that could lead to lost or skewed information if ignored during aggregation, the following steps were performed for each of the interactions: (1) For each expert, all estimates (best, lower, upper, CI) were standardized to 100% CI with linear extrapolation  67, 79  on the basis of the CI they provided. This standardization was performed to enable fitting a PERT distribution. (2) Non-region-specific best, lower and upper values were aggregated with an unweighted median. (3) A PERT distribution was drawn with the 'mc2d' R package  78  for the non-region-specific aggregated values. (4) A PERT distribution was drawn for each non-region and region-specific set of estimates (lower, upper, best). (  5 ) Each non-region-specific distribution was compared to the non-region-specific aggregated distribution with the Kullback-Leibler divergence metric within the 'LaplacesDemon' R package  80.
Acknowledgements This study was funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 819202 ),  Aalto University School of Engineering Doctoral Programme , Maa-ja vesitekniikan tuki ry and the  Academy of Finland -funded project  TREFORM  (grant no. 339834 ). M. P. was supported by the  Erling Persson Family Foundation. B. I. S. is supported by a  Royal Commission for the Exhibition of 1851 Research Fellowship. L. W. -E, A. T. , I. F, L. S. A. and T. D. acknowledge financial support from the  European 1225 Research Council  through the ' Earth Resilience in the Anthropocene ' project (no. ERC-2016-1226 ADG 743080 ). K. L. N. was supported by  Australian Research Council  funding ( DE210100606 ). A. U. -L.
was supported by  María de Maeztu  excellence accreditation 2018-2022 (ref. MDM-2017-0714 ), funded by  MCIN/AEI/10. 13039/501100011033 /and by the  Basque Government  through the  BERC 2022-2025  programme. J. J. was supported by the  NASA GISS Climate Impacts Group  and the  Open Philanthropy Project. Z. S. was supported by  Chinese Universities Scientific Fund  ( 2022TC098 ). L. L. is supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  and  European Commission ERDF Ramón y Cajal  grant ( RYC-2016-20269 ) and  Programa Propio  from the  Universidad Politécnica de Madrid. S. J. L. is the recipient of an  Australian Research Council   Future Fellowship  (project no. FT200100381 ) funded by the  Australian Government. We would like to thank members of the  Water and Development group  for testing an earlier version of the elicitation web application and  A.
Demonstration on mock data To test our assumption that we can proceed using only proper motions, we apply our method to mock data with known properties. We base this on a dynamical model constructed using the AGAMA software package  (Vasiliev 2019 ). This model consists of two disc components and a stellar halo, and is designed to be in equilibrium within a given axisymmetric gravitational potential (the main model from McMillan 2017 ). We sample 40 000 000 particles from the model which, since the model is axisymmetric, we can place at any position angle in the Galaxy we are interested in. The samples we construct from this model, which come from 10 • ranges in between 130 • and 230 • , contain between 2800 000 and 3200 000 particles when we apply the constraint that they also feature only stars with | b | < 10 •.
We populate this model with 10 million particles in the halo, 5 million particles in the disc, and 2. 25 million particles in the bulge, then evolve it using the N -body code RAMSES  (Teyssier 2002 ). The perturbation is modelled as a nearly impulsive interaction of a point mass which intersects the Galactic plane at R ≈ 18 kpc travelling with a velocity of ∼330 km s -1 , and a Galactic azimuth φ = 0. 4  The disc crossing occurs at a simulation time T ≈ 100 Myr , and after T = 150 Myr the mass of the perturber be gins to e xponentially decrease, such that by the time of its second disc crossing it has a mass of ∼10 -6 times its original mass. This simplifies the encounter and allows us (like Bland-Hawthorn & Tepper-Garc ía 2021 ) to focus on the effect of a single impact. MNRAS 516, 4988-5002 (2022) Bland-Hawthorn & Tepper-Garc ía ( 2021 ), following the analytical models of  Binney & Sch önrich ( 2018 ) , showed that the impact of a perturber like this is able to produce a phase spiral in the Solar neighbourhood that is qualitatively similar to that found in Gaia data, and that it will have produced similar phase spirals across the disc (mainly differing in their phase).
INTRODUCTION Semantic segmentation can be formulated as the task of labeling all pixels in an image with semantic classes. Most state-of-the-art semantic segmentation models are based on the encoder-decoder architecture or its variants which can also be unified into two parts, namely encoder and decoder. Specifically, the encoder extract information from the original input, and the decoder integrate previously extracted information and recover semantic information from it. In recent years, researchers have been committed to exploring different technologies to learn a more general representation, such as using very large dataset  (Sun et al. , 2017; Deng et al. , 2009)  to train a model, or designing more complex model  (Simonyan and Zisserman, 2014; He et al. , 2016; Dosovitskiy et al. , 2020).
Multi-branch. Learning different information through multiple parallel data streams has been proved to have more advantages for representation and generalization. Specifically, some existing works deployed multi-branch structure in the feature extraction stage  (Wang et al. , 2020; Tao et al. , 2020; Takikawa et al. , 2019) , and other works deployed multi-branch to the prediction stage  (Huang et al. , 2017; Fu et al. , 2019). HRNet  (Wang et al. , 2020)  repeatedly exchanged the information across different resolutions by a series of parallel feature extraction streams in the encoding process to maintain highresolution representations. Based on HRNet, (Tao et al. , 2020) proposed a hierarchical multi-scale attention approach in which each data stream learned a certain image scale so that the model can consider the information of multiple input image scales when predicting.
EXPERIMENTAL RESULTS In this section, we conduct experiments using Cityscapes dataset  (Cordts et al. , 2016)  and Freiburg Forest dataset  (Valada et al. , 2016). In the following, we first modify some classic image semantic segmentation algorithms to build their two-branch decoder counterpart, then we compare the proposed two-branch architecture with the original network. Finally we carry out a series of ablation experiments on Freiburg Forest dataset. Our models are trained using Pytorch  (Paszke et al. , 2019)  on one Nvidia Tesla P100 GPU with mixed precision settings.
Datasets Cityscapes. The Cityscapes dataset is a large-scale database for urban street scene parsing. It contains 5000 finely annotated images captured from 50 cities with 19 semantic object categories, in which 2875 images are used for training, 500 images and 1525 images are used for validation and testing separately. All images are provided with the resolution of 2048 × 1024. We followed  (Valada et al. , 2019)  and report results on the reduced 11 class label set. Freiburg Forest. The Freiburg Forest dataset is an unstructured forested environments dataset. It contains 6 segmentation classes: sky, trail, grass, vegetation, obstacle and void. The dataset contains over 15000 images and 325 images with pixel level handannotated ground truth map. We follow  (Valada et al. , 2019)  and use the same train and test splits provided by the dataset.
Implementation Details In order to comprehensively test our proposed method, we deploy our proposed two-branch decoder on three classic baseline networks, namely, SegNet  (Badrinarayanan et al. , 2017) , DeeplabV3+  (Chen et al. , 2018b)  and HRNet  (Wang et al. , 2020). Twobranch SegNet is shown in Figure  5. We divide the output of the encoder into two groups, one of which is input to the original data stream, in our two-branch implementation the original data stream means the upper branch of the decoder, and another is input to the additional data stream, the lower branch of the decoder. Next, we follow residual-liked module to fuse the two outputs, meanwhile deploy the BECLoss and cross-entropy loss during the training. More concretely, we supervise the learning process of the two branches through L BEC1 and L BEC2 , and the combination of two outputs are guided by the auxiliary L CE.
We follow the same way to implement the counterpart of DeeplabV3+ and HRNet. Note that we only take the backbone in the original model as an encoder, and the rest as the decoder. In practice, we use Res50  (He et al. , 2016) , Vgg16  (Simonyan and Zisserman, 2014)  and HRNet-W18  (Wang et al. , 2020)  as backbones. We initialize different encoders with the model pre-trained on ImageNet, this is totally the same as the original implementations  (Badrinarayanan et al. , 2017; Chen et al. , 2018b; Wang et al. , 2020). In order to speed up the convergence and reduce the interference of the initial learning rate setting, we employ a cyclical exponent learning rate policy  (Smith, 2017)  where the min lr and max lr are set to 1e -5 and  And for both dataset we use the same mini batch size as 8. In addition, as we compare the original models with their two-branch encoder counterpart, so we perform the same settings for each comparison pair to ensure fairness.
Results on Cityscapes Dataset Table  1  summarizes the results of our two-branch decoder with different baselines. We can see that our approach significantly improves the mean IoU Specifically, our approach improves the mean IoU of original encoder-decoder frameworks namely Seg-Net, Deeplabv3+ and HRNet by 4. 81, 2. 14 and 1. 56 respectively. Moreover, thanks to the reasonableness of proposed two-branch structure, our decoder is more efficient then the original decoder. In particular, our two-branch implementation of SegNet (SegNetT) greatly reduces the number of parameters while significantly improving the performance. DeepLabv3+T and HRNet only slightly increases the parameters (0. 9M) or keep the number of parameters while improving the performance of the model. Our results also reflect that the original decoder does not make full use of the information extracted by the encoder.
Results On Freiburg Forest Dataset We carry out experiments on the Freiburg Forest dataset to further evaluate the effectiveness of our method. Quantitative results of Freiburg Forest are shown in Table  3. The baselines (Seg-Net, DeepLabv3+, HRNet) yield mean IoU 69. 99%, 77. 48% and 78. 29%. Our two-branch counterpart boost the performance to 81. 79%, 82. 73% and 83%. We can see that our methods outperforms their baselines with notable advantage, especially for the class of "obstacle", which is hardest to segment because of its serious class imbalance, and "obstacle" class consists of various types of objects which are diffi- DeepLabv3+T (ours) 93. 95 91. 99 (+1. 09)97. 62 ( +0. 33)82. 31 ( +1. 84)54. 85 ( +0. 12)92. 55 ( +0. 63)62. 69 ( +6. 13)94. 17 ( +1. 12) 77. 87 (+6. 09) 82. 4 (+3.
5) 76. HRNet-T (ours) 94. 83 (+0. 52)92. 68 ( +0. 62)97. 98 ( +0. 31) 84. 3 (+1. 99) 56. 28 (+1. 34)93. 06 ( +0. 47)67. 17 ( +3. 86)94. 83 ( +0. 49) 79. 98 (+3. 61) 84. 35 (+2. 08)77. 09 ( +1. 69) cult to be unified into the same class. Furthermore, this dataset only contains large-scale targets such as tree, trail, etc. Therefore, it is difficult to optimize the segmentation accuracy of these classes through multiscale learning. Benefiting from the efficient information integration capabilities of our proposed twobranch decoder paradigm, our method shows impressive advantages on the above issues. Several examples are shown in Figure  7.
BECLoss and Boundary All two-branch variants are implemented by replacing the decoder of the original network with our proposed two-branch decoder, and through our well-designed BECLoss to explicitly supervise the learning process of the model, the two branches can learn complementary information. In addition, we introduce boundary information into BECLoss to improve the inherent sensitivity of our models to boundary pixels. To verify the validity of our method, we conduct a group of ablations to analyze the influence of various factors within our method. We report the results over the segmentation baseline SegNet on Cityscapes and Freiburg Forest dataset in Table  4. As shown in Table  4 , two-branch decoder improve the performance remarkably. Compared with the baseline SegNet, employing two-branch decoder yields a result of 78.
54% mean IoU on Cityscapes dataset and 78. 9% mean IoU on Freiburg Forest dataset, which brings 2. 72% and 8. 91% improvement. In addition, when we gradually replaced the cross-entropy loss CELoss of loss1 and loss2 with the BECLoss we designed, the performance further improves to 79. 5% and 81. 43%. Furthermore, we notice that when we use only one BECLoss, the re-  sult very slightly exceeds the result of using two BE-CLoss, as shown in the third row and the fifth row, the result from 79. 54% goes to 79. 5% on Cityscapes dataset. We further introduce boundary information to BECLoss, performance increased to 80. 64%. Results show that our proposed two-branch decoder and boundary enhanced BECLoss bring great benefit to scene parsing.
The comparative experiment shows that two-branch decoder paradigm and BECLoss can significantly improve the performance of the original encoder-decoder model consistently on challenge outdoor scenes, i. e. Cityscapes and Freiburg Forest datasets. It is worth mentioning that although we added an additional branch to decoder, it did not increase the number of parameters significantly. In addition, In the inference process, even if we delete a branch, we can still get performance far beyond the original counterpart. As a perspective for future work, we would like to design a more universal and general branch which can be applied in our two-branch decoder paradigm.
We use an extremely simple branch, as shown in Figure2(b), retraining on the Cityscapes dataset, and we named the trained model 'ED'. Moreover, we test the output results of each branch separately on the trained two-branch decoder model. Specifically, we take Seg-Net as an example. In the inference process, we only keep the upper branch of the model in Figure5, and the output result obtained corresponds to 'O * '. When we only keep the lower branch, the result corresponds to 'D * '. 'O&D' goes to the result of original twobranch model. The results of the main branch in our trained two-branch model are 80. 49%, 82. 35% and 83. 87%, which is significantly exceed the counterparts of original encoder-decoder models (75. 82%, 80. 31% and 82. 34%) while the number of parameters used dropped remarkably.
Sequencing and other biological data are now more frequently available and at a lower price. Mutual tools and strategies are needed to analyze the huge amount of heterogeneous data generated by several research teams and devices. Bioinformatics represents a growing field in the scientific community globally. This multidisciplinary field provides a great amount of tools and methods that can be used to conduct scientific studies in a more strategic way. Coordinated actions and collaborations are needed to find more innovative and accurate methods for a better understanding of real-life data. A wide variety of organizations are contributing to KaruBioNet in Guadeloupe (French West Indies), a Caribbean archipelago. The purpose of this group is to foster collaboration and mutual aid among people from different disciplines using a 'one health' approach, for a better comprehension and surveillance of humans, plants or animals' health and diseases.