dataesr/scientific-paragraphs-categorization huggingface dataset:
D. G. and L. M. S. gratefully acknowledge financial support from the  CRT foundation  under Grant No. 2018. 2323 " Gaseous or rocky? Unveiling the nature of small worlds ". C. M. acknowledges the support from the  SNSF  under grant  200021_204847  "Plan-etsInTime". S. G. S. acknowledges support from  FCT  through FCT contract nr. CEECIND/00826/2018  and  POPH/FSE  (EC). A. C. C. and T. W. acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant number  ST/R003203/1. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants  ESP2016-80435 MDM-2017-0737   Unidad de Excelencia Maria de Maeztu-Centro de Astrobiología (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through  FCT. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  SCORE No 851555 ). J. H. supported by the  Swiss National Science Foundation (SNSF)  through the  Ambizione  grant # PZ00P2_180098. P. E. C. is funded by the  Austrian Science Fund (FWF) Erwin Schroedinger Fellowship , program  J4595-N. B. -O. D. acknowledges support from the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  MB22. 00046  and from the  Swiss National Science Foundation  ( PP00P2-190080 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES ; grant agreement No  724427 ).
It has also been carried out in the frame of the  National Centre  for Competence in Research PlanetS supported by the  Swiss National Science Foundation (SNSF). D. E. acknowledges financial support from the  Swiss National Science Foundation  for project  200021_200726. M. G. and V. V. G. are F. R. S. -FNRS Senior Research Associates. S. H. gratefully acknowledges  CNES  funding through the grant  837319. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme  Investissements d'Avenir  supervised by the  Agence Nationale  pour la Recherche. L. Bo. , G. Br. , V. Na. , I. Pa. , G. Pi. , R. Ra. , G. Sc. , V. Si. , and  T. Zi. acknowledge support from  CHEOPS ASI-INAF  agreement n.
Furthermore, this scheme provides faster access to resources, thereby solving bottleneck issues in the network and providing key escrow resilience in identity-based authentication protocols. This scheme's performance is its major drawback because it results in higher computational and communication costs. In  [20] , an online internet voting protocol scheme based on dual signature was put forth, enabling the voters to cast their ballot by simply scanning their thumbprint. Following the designed approach, once a voter has been successfully authenticated from the database, he can acquire a poll. When a voter submits a vote, the system performs these three operations (1) generates a dual signature (2) authenticates server envelope and (3) voting server envelope. The system sends an authentication server envelope concatenated with the voting server envelope to the authentication server using the transmission link.
This work proposes an end-to-end user-centric sampling method aimed at selecting the images from an image collection that are able to maximize the information perceived by a given user. As main contributions, we first introduce novel metrics that assess the amount of perceived information retained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volume spanned by the set in the corresponding latent space, we show how to take into account the user's preferences in such a volume calculation to build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimum set of images that maximize the information perceived by a given user. Experiments using the coco dataset show the ability of the proposed approach to accurately integrate user preference while keeping a reasonable diversity in the sampled image set.
Introduction Image sampling based on user perception has recently increased interest given the unstoppable increase of stored datasets. For example, let's think of the large-scale image collections associated with Instagram and other social media accounts, online retailers, or your own personal photograph gallery saved on the cloud. Storing such large-scale datasets has become a big burden, from an economical as well as sustainability perspective  [1]. Traditional image data sampling methods can be understood as a way to alleviate such burden, by retaining only pictures from a dataset with the ideal goal of providing a summary (in the sense of a brief description) of the database. The challenging aspect is, however, to understand which is the best set of images to select (not to delete), especially given the user's preferences.
A subset given out from this sampling scheme does not completely represent the whole dataset instead, it gives the user's preferences at that point of time. Hence, this sampling tool mainly asks for how much data to sample and what semantic information the user needs to retain. The user is finally left with the impression that no pixel information was lost during the sampling. In order to define such an acceptable sample, the semantic information in the images is more significant than the pixel information. Unlike the conventional similarity searchbased sampling approaches, we prefer to use a sampling strategy that preserves the user's perceived information while maintaining image diversity. This is because the way a user perceives an image or an item can be a powerful sign of his likes and dislikes (and decisions), and thus, the amount of perceived information is proven to be a relevant measure of the user's preferences which essentially is a semantic information.
However, the representations must be able to model a certain amount of very precise data and easily deal with the newly added items to the data collection. The wide range of features available when considering the feature space of a large-scale image database stretches the dimensionality leading to computational troubles and also bringing down the overall prediction accuracy. Selecting an optimal image subset of preferred features from such a large image collection still exists as an NP-hard problem. To address this, the perceived information (PI)-based sampling introduced in  [2]  is extended and strengthened by adopting image embeddings and incorporating a user preference model. This leads to the sampling algorithm proposed in this work, named Reinforced Image Collection Sampling (RICS).
With respect to classical video summarization schemes  [3] [4] [5] [6] [7] , which usually preserves the partial information of the original sources to produce a data outline, our work implements the personalized sampling experience even when the original source dataset is completely lost or unavailable. The methods such as  [8]  combine an attention mechanism to identify the important parts of the video and are further trained unsurprisingly. Image grouping methods  [9] [10] [11]  with image captioning are also in line with combining self-attention mechanism with contrastive feature construction to effectively summarize common information from each image group while capturing discriminative information between them. There exists a variety of minimum redundancy maximum relevance (MRMR)-based schemes  [12] [13] [14] [15] , cosine similarity approaches  [16] [17] [18]  and multi-class feature selection approaches for feature selection strategy  [19]  designed and implemented for row-wise feature selection, whereas our feature vectors are arranged as columns.
How this arrangement happens is explained in Sect. 3. There are also works based on determinantal point processes (DPPs)  [20] -based sampling schemes that perform well for cases below the rank of the feature matrix and thus it is useful for the smallscale datasets. We often come across various large image collections, for example, from social media accounts, blogs, stock photographs, etc. This work aims to sample such a vast image collection and deliver a customized visual search engine by maximizing the presence of user-preferred images while minimizing the inter-image redundancies present in the sampled data collection.
Contributions The Reinforced Image Collection Sampling (RICS) is novel when considering the following key contributions. • We extend the PI-based sampling scheme introduced in  [2]  to deliver a visual search engine by appropriately plugging a ResNeXt weakly supervised learning (WSL)based image embedding to it. • In addition, this work proposes a novel user multi-preference modeling for the image collection sampling to ensure personalized image collection sampling experience for each user. The remainder of this paper is organized as follows. Section 2 presents the data collection sampling problem including motivation. The detailed description on each subportion of the work, i. e. , the image embedding and user multi-preference modeling, are sketched, respectively, in Sects. 3 and 4. The experiments and the evaluation strategies along with quantitative and qualitative results are provided in Sect.
Collation of user preference model and dataset features Image embedding has been intensively studied in the literature for various tasks  [24] [25] [26]. In this work, we use the weakly supervised learning (WSL) of the ResNeXt model  [27, 28] , which is an evolved version of the widely adopted ResNet model  [29, 30]  in which a so-called cardinality dimension is added. Moreover, the weakly supervised learning (WSL) version from the Facebook AI Research (FAIR) group became established as it was trained on 3. 5 billion public Instagram pictures in order to predict around 8000 hashtags sourced by the users. The mini version of ResNeXt-WSL (32x8d) is chosen for the proposed work since it is still accurate enough on the public benchmarks with less memory requirement and computational reliability.
ResNeXt also has its clear influence here as a powerful pre-trained network to extract embedding vectors that can accurately describe any kind of picture in an abstract latent feature space on account of the in-depth pre-training done on ImageNet dataset  [31]  using a wide range of about 940 million public images and 1. 5k hashtags sourced by the users. Thus, ResNeXt-WSL becomes the well-suited representation learner here for the proposed work. An illustration of our representation learning section is illustrated in Fig. 2. We focus on the recently released WSL model [28], i. e. , the simplified version that was released in the PyTorch hub (bottleneck width = 8) which uses a 32 × 8d architecture. Leaving aside the in-depth architecture, we are interested in the final section of the average pooling layer in the ResNeXt which combines all the filters into a single 2048-dimensional vector.
This vector was then meant to predict the 1000 classes of the ImageNet dataset  [32]  after the softmax layer. Since the class predictions are not a point of interest for the proposed work, we proceed with the 2048-dimensional feature array from the average pool layer as the embedding features for our image collection. The pooling in turn is significant in reducing the dimensionality of the feature maps  [29]   Fig. 2  The representation learning section of the proposed work the dataset's high-level information. In this work, each item of a data collection is represented with a feature vector. All the feature vectors corresponding to each item of the data collection are aggregated in a matrix B. Therefore, a row of the matrix corresponds to one feature describing each item of the data collection.
This can be justified in view of the fact that the PI metric reflects the volume spanned by the covariance matrix of the features, i. e. , information in the latent space. The non-relying nature on the source dataset size is one special characteristic of this covariance matrix which makes it compatible with sampling tasks. Moreover, the properties of the PI  [2]  match absolutely with the objectives of the image collection sampling scheme. Considering PI as a quality metric in the proposed scheme, we first model the feature description of every image in the dataset. We assume the existence of a kernel function that maps every item in X to an L-dimensional feature space, as the function f in Eq. (  2 ). Thus, all the N feature vectors representing each image in the collection are now gathered as the columns of a matrix B ∈ R L×N.
This PI sampling scheme  (6)  max  3  The different supercategories of coco dataset embedded on t-SNE with image embedding and user preference modeling is hence called Reinforced Image Collection Sampling (RICS) and is briefed in Algorithm 1 and Algorithm 2. ω u π u (ω(X )) s. t. |ω(X )| ≤ M (7) max Y⊆X L 2 log 2 (2π e|UB Y B T Y U|) s. t. |Y| ≤ M Fig.
Experimental results and analysis This section gives an idea about how the implementation and visualization are carried out followed by a detailed evaluation of the quantitative and qualitative results. The software implementation was carried out using Pycharm version 3. 8 on a single PC with CPU specifications as Intel i7-8665U CPU (1. 90GHz, 2112 MHz with 4 Core(s), 8 Logical Processor(s)), along with 32 GB RAM and 64-bit operating system. The Microsoft Common Objects in COntext (COCO) dataset  [35]  is used to evaluate the performance of the proposed sampling scheme. The details of this dataset are delivered in the next subsection.
Common Objects in COntext (COCO) dataset Common Objects in COntext (COCO)  [35, 36]  is a large-scale object detection, segmentation and captioning dataset, widely used as a benchmark for many machine learning tasks. We chose the Detection 2017 dataset (validation fold) for evaluating the proposed data collection sampling. This dataset consists of 5,000 realistic images with different shapes and resolutions from diverse contexts with multiple annotations on each image. As it covers several disorganized scenes with various backgrounds, overlapping objects, etc. , it is possible to train models on objects and people in realistic settings. Moreover, there are few data loaders and libraries for COCO already implemented in Python and PyTorch. The 5000 pictures are annotated and classified into 80 categories grouped in 12 super categories.
User's preference modeling We first evaluate the ability of the algorithm proposed in Sect. 4 to capture the user's preference. For that, we assume that a user explicitly gives its preferred categories. Thanks to our proposed algorithm, we transfer his preference into the latent space by building the matrix U. The popularity of each item i can then be calculate as   3  shows the organization of the whole dataset and the associated supercategories. For a visual comparison of this estimated popularity and the true category, we use the t-Distributed Stochastic Neighbor Embedding (t-SNE) representation  [37]. t-SNE is used to represent high-dimensional datasets or data points in reduced (e. g. , 2 or 3) dimensional space. It enables the interpretability of the data in the lower dimension. It is implemented by applying a nonlinear dimensionality reduction technique where the focus is on keeping very similar data points close together in lower-dimensional space.
Thus, it can be adopted as a visualization method for high-dimensional datasets such as coco with 5000 images. Note that for images that contain more than one supercategory inside, the t-SNE plots a point corresponding to the first supercategory appearing in an image in the coco dataset. Figure  4  compares, in the t-SNE representation, the estimated popularity and the actual user's category for different examples of user's preferences. We can see, for example, that the calculated values of popularity undoubtedly emphasize the category animal (with high values to the features of category animal). In other words, the images belonging to the category animal are more popular. Similarly, 3 popular categories are demonstrated in row 2 of Fig. 4  when 3 preferences, i. e. , animal, furniture and food are submitted.
Ub ⊤ i b i U. Figure In addition to this, the proposed user multi-preference model was tested by comparing the PI-based sampling driven by this proposed user model and a random sampling driven by an Oracle user model. In this oracle model, we assume that the true category is known and the popularity of the user-preferred category (for example, animal) is set to 1 and the popularity of the other categories is set to 0. 1. From the comparison illustrated in Fig. 5 , we can first see that the proposed sampling method indeed gives more importance to the preferred category. However, compared to the random sampling, the diversity in the dataset is preserved.
e animal, RICS (in row 1) has very well acknowledged the user preferences compared to random sampling (row 2). The number of red points is higher for RICS than random sampling for the sampling size M = 500, 1000 and 1500 as illustrated in columns 1-3. Similar conclusions can be drawn for the case of multiple preferences. We then use the PI metric introduced in  [2]  and also presented in Eq. (  5 ) to evaluate the sampling quality. The higher the PI, the better the sampling scheme. The PI metric analysis of different image collection sampling methods for different categories of user preferences (using the coco dataset) is shown in Table  1. The PI values obtained while sampling with sample sizes 500, 1000 and 1500 for different user categories are shown. The baseline method used here to compare is the random sampling scheme.
RICS due to its consideration for user preference has a higher performance outperforming the random sampling method. In addition, the PI values exhibit a drastic increment than random sampling method due to the right choice of the trade-off parameter. Both the methods uphold features diversity, while RICS wisely retains user-preferred samples making it more popular. The achieved sampled subsets with high PI are marked as bold in the table. Analyzing the observed outcomes in detail, it Fig. 4  Columns 1-2 represent, respectively, the user-preferred category in the coco dataset, the t-SNE visualization for the popularity values in accordance with the user preference. While row 1 illustrates single user preference, i. e. , category animal colored in red, Row 2 displays the popularity for 3 user preferences, i.
Conclusions and future scope This work presented an extension of the PI metric-based scheme to large-scale image collection sampling by strengthening it with an image embedding and user multi-preference modeling. The novelty of this approach lies in the modeling of a mighty mite user multi-preference modeling scheme to ensure personalized sampling experience for each user in accordance with the user's preferences at that point of time. The work also realized an image embedding scheme and tested it with the coco dataset. The excellence of the work is demonstrated by both the quantitative and qualitative evaluation. The extensive t-SNE-based visual evaluation and KNN-based neighbors plots prove to be significant in visually establishing the superiority of the proposed work. In the future, this can be extended with an alternative sampling approach and extensions can be done to realize user models from a bigger description (for example, a paragraph) of what the user prefers.
Conclusions The methodology proposed shows the first results of a new bioinspired procedure for the optimization of aerodynamic profiles, which demonstrate results similar to the NACA-23021 and RAF-34 profiles. Further implementations of this preliminary work will include the extension to 3D and to higher Reynolds numbers, through the link of adequate CFD softwares, such as OpenFoam  [13] , to take into consideration more complex behaviors  [2, 3] , and to explore other applications  [8].
Human WAT contains four major cell classes To define the cellular landscape of human WAT, we retrieved scSeq, snSeq and spatial transcriptomic (STx) results from ten published reports comprising samples from subcutaneous, visceral, and perivascular WAT  [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. We combined this with unpublished data from four additional cohorts (Massier et al. #1-4), resulting in a total of 17 datasets across studies and depots (Supplementary Table  1 ). As displayed in Fig. 1a , together these included 401,320 quality-filtered cells/nuclei (hereafter referred to as objects) obtained from 103 samples of 83 donors spanning over a broad range in age (22-77 years) and body mass index (BMI, 17-55 kg/m 2 ). To create a meta-map, we first defined cell clusters by processing all datasets individually using Seurat v4.
1  16  (Supplementary Fig. 1a  and Supplementary Data 1). Based on these results, we calculated a Jaccard index comparing cell cluster marker genes between studies. We found that all data, except the one from Hildreth et al. 11  , largely overlapped (Fig. 1a ). This demonstrates that: (i) most cell types were present across the different cohorts/depots and (ii) snSeq, scSeq, and STx on average capture similar trends. Because STx is a spotbased approach that requires specific tools for data deconvolution  17  , we analyzed the Bäckdahl et al. 7  study separately. For the remaining snSeq/scSeq datasets, we performed a high-level topological analysis, which revealed that clusters identified in the individual studies separated into four major cell classes: adipocytes, fibroblast and adipogenic progenitors (FAPs), vascular and immune cells (Fig.
1b ). To validate our findings, we applied CellTypist  18  to the immune cells which confirmed our annotations for this class (Supplementary Fig. 1b ). Our comprehensive classification allowed us to estimate the cellular composition of WAT (Fig. 1c, d ). For this, we first used snSeq data, as this method, in contrast to scSeq, captures fat cells. After quantifying the number of adipocytes in each dataset, we next compared the proportions of the other cell classes between methods. We found that FAPs constituted the largest class of ~40% of the total cell population according to both snSeq and scSeq. This was followed by adipocytes and immune cells, which were present in a 1:1 ratio and together constituted ~40% of the cells. For adipocytes, this is in line with what has previously been reported in the literature  19.
About 15% of the objects were vascular cells, but the recovery of this cell class was strongly influenced by methods where the proportion was approximately three times higher with snSeq compared to scSeq. This could be due to pre-selection bias introduced by enzymatic digestion as has been shown before for endothelial cells in mouse kidney  20. Comparisons across depots showed that there were no significant differences in cell class abundance. As expected, scSeq studies where cells had been enriched for either FAPs (CD45 -)  13  or immune cells (CD45 + )  14  prior to sequencing displayed markedly higher proportions of these cell types compared to the rest of the datasets, supporting the validity of our classification (Supplementary Fig. 1a ). Of note, for perivascular WAT, snSeq data were only available from one cohort implying that the generalizability of the results from this depot needs to be further validated.