dataesr/scientific-paragraphs-categorization huggingface dataset:
Lately, Light Detection And Ranging (LiDAR) technology has proven to be uniquely positioned to provide highly accurate sample measurements of vertical height of structures (measure correlated to the delay in the reception of the echoes of the transmitted pulse) and along with information on the materials' reflective property (considering the intensity of the reflected signal). However, it is still limited by the high running costs. Hence, the complementarity of optical/SAR/LiDAR measures can lead to a more comprehensive description of a surveyed scene if considering these data jointly. The differences among these three modalities can be seen at a glance by looking at Figure  1 , in which a composition of the three acquisitions is presented. The importance of fusing different modalities was already pointed out in many early works  [1] ,  [2]  such as for the recognition of man-made objects by fusing LiDAR data and thermal images  [3]  or for scene interpretation  [4]  and image classification  [5]  when jointly considering optical and SAR images.
Each contest addressed different aspects of data fusion within the context of remote sensing applications. The contests proposed so far are briefly introduced in the following. The focus of the 2006 Contest was on the fusion of images with different spatial and spectral characteristics  [26]  (see Sec. III-A for details on this application). Six simulated Pleiades images were provided by the French National Space Agency (CNES). Each data set included a very high spatial resolution monochromatic image (0. 80 m resolution) and its corresponding multi-spectral image (3. 2 m resolution). A high spatial resolution multi-spectral image was available as ground reference, which was used by the organizing committee for evaluation but not distributed to the participants. In 2007, the Contest theme was urban mapping using SAR and optical data, and 9 ERS amplitude data sets and 2 Landsat multi-spectral images were made available  [27].
The task was to obtain a classification map as accurate as possible with respect to the unknown (to the participants) ground reference, depicting land cover and land use patterns for the urban area under study. The 2008 Contest was dedicated to the classification of very high spatial resolution (1. 3 m) hyperspectral imagery  [28]. The task was again to obtain a classification map as accurate as possible with respect to the unreleased ground reference. The data set was collected by the Reflective Optics System Imaging Spectrometer (ROSIS-03) optical sensor with 115 bands covering the 0. 43-0. 86 µm spectral range. Each set of results was tested and ranked a first time using the Kappa coefficient. The best five results were used to perform decision fusion with majority voting. Then, re-ranking was carried out after evaluating the level of improvement with respect to the fusion results.
In 2009-2010, the aim of the Contest was to perform change detection using multi-temporal and multi-modal data  [29]. Two pairs of data sets were available over Gloucester, UK, before and after a flood event. The data set contained SPOT and ERS images (before and after the disaster). The optical and SAR images were provided by CNES. Similar to previous years' Contests, the ground truth used to assess the results was not provided to the participants. A set of WorldView-2 multi-angular images was provided by DigitalGlobe for the 2011 Contest  [30] ,  [31]. This unique set was composed of five Ortho Ready Standard multi-angular acquisitions, including both 16 bit panchromatic and multispectral 8-band images. The data were collected over Rio de Janeiro (Brazil) in January 2010 within a three minute time frame with satellite elevation angles of 44.
7 • , 56. 0 • , and 81. 4 • in the forward direction, and 59. 8 • and 44. 6 • in the backward direction. Since there were a large variety of possible applications, each participant was allowed to decide a research topic to work on, exploring the most creative use of optical multi-angular information. At the end of the Contest, each participant was required to submit a paper describing in detail the problem addressed, the method used, and the final result generated. The 2012 Contest was designed to investigate the potential of multi-modal/multi-temporal fusion of very high spatial resolution imagery in various remote sensing applications  [16]. Three different types of data sets (optical, SAR, and LiDAR) over downtown San Francisco were made available by Digi-talGlobe, Astrium Services, and the United States Geological Survey (USGS).
The former was issued to promote innovation in classification algorithms, and to provide objective and fair performance comparisons among state-of-the-art algorithms. For this task, users were asked to submit a classification map of the data using the training samples generated by the DFTC via photo-interpretation. The validation set was kept unknown to the participants and used for the quantitative evaluation. The best paper challenge had the objective of promoting novel synergistic use of hyperspectral and LiDAR data. The deliverable was a 4-page manuscript that addressed the problem, methodology, and results. Participants were encouraged to consider various open problems on multi-sensor data fusion, and to use the provided data set to demonstrate novel and effective approaches to solve these problems.
The full scale validation employs indexes that do not require the availability of a reference image since they evaluate the relationships, such as the spectral coherence, among the original images and the pansharpened product  [50] ,  [51]. In this case the evaluation is done at the native scale of the images but clearly the results depend on the definition of such indexes. We leverage the results of the DF Contest issued in 2006  [26]  for bringing about a discussion on the performances of different pansharpening algorithms. In this contest, the participants were asked to perform pansharpening on a set of simulated images from the Pleiades sensor and a spatially downsampled image acquired by QuickBird. Each data set included VHR panchromatic image and its corresponding multispectral image.
A high spatial resolution multispectral image was available as ground reference, which was used by the organizing committee for evaluation but not distributed to the participants. This reference image was simulated in the Pleiades data set and it was the original multispectral image in the QuickBird one. The results of the algorithms submitted by the different research groups were compared with a standardized evaluation procedure, including both visual and quantitative analysis. The former aimed at comparing the results in terms of general appearance of the images as well as by means of a local analysis focusing on the rendering of objects of interest such as linear features, punctual objects, surfaces, edges of buildings, roads, or bridges. The quantitative evaluation was performed using quality indexes for measuring the similarity of the fused results with respect to the reference image.
In order to cope with these latter sources of radiometric variations and detect the relevant changes, the multivariate alteration detection (MAD) technique with iterative reweighted (IR-MAD) scheme  [61] ,  [62]  was proposed. When considering data acquired by different modalities, capabilities in providing a fast response can greatly improve. However, using different data belonging to sources that might be significantly different can be a severe issue to handle. Comparison between modalities can be meaningless if not done appropriately, differences in acquisitions can become prohibitive for the generation of consistent results. In 2009-2010, the contest was issued to address the task of CD using multi-temporal and multi-modal data  [29]. See Figure  4  for the dataset used in the contest.
The two pairs of data sets made available to the participants were acquired before and after a flood event. The class "change" was the area flooded by the river and the class "no change" was the ground that had not been concerned by the flooding. The optical and SAR images were provided by CNES. The participants were allowed to use supervised or an unsupervised method with all the data, the optical data only, or the SAR data only. A variety of supervised and unsupervised approaches were proposed by the participants. Interestingly, a simple unsupervised change detection method resulted in similar classification accuracies compared with supervised approaches. As expected, the approaches that utilized both SAR and optical data outperformed other approaches, although the contribution of SAR data alone was minimal to the overall change detection accuracy (due to the high discrimination capability of the optical data for this task).
C. Classification Various past contests have focused on the fusion of data in order to provide superior classification accuracy (compared to considering the single modalities only) for remote sensing applications. Previous contests have provided other multimodality fusion scenarios -both in terms of sensors and challenges (e. g. use of optical imagery, LiDAR data, SAR data etc. ) for various image classification scenarios  [27] ,  [28]. We take the most recent one -the 2013 contest involving multisensor (hyperspectral and LiDAR) for urban classification, as an example to highlight emerging trends. This contest saw a very wide range of submissions -utilizing hyperspectral only, or using hyperspectral fused with LiDAR in the original measurement domain or in feature spaces resulting from spatial and other related features extracted from the dataset.
Submissions that provided high classification performance often utilized LiDAR data in conjunction with the hyperspectral image, particularly to alleviate confusions in areas where the spectral information was not well-posed to provide a good solution (e. g. , classes that had similar material compositions but different elevation profiles), and vice-versa. Another focus area of emerging and promising contributions to the range of submissions, involved post-processing of classification results to mitigate salt-and-pepper errors in classification. We note that this classification contest was designed to pose some unique challenges -specifically, the training mask and test masks were spatially disjointed, and had substantial variability. Some classes existed under a cloud shadow in the testing masks, testing algorithms while other were submitted for their capability to adapt to such variations.
Most submissions did not fare well under cloud shadows, but submissions where contestants utilized spatial contextual information fared much better in general, even under cloud shadows. The winning algorithm was based on spectral unmixing, and utilized abundance maps derived from hyperspectral imagery as features, in conjunction with raw hyperspectral and LiDAR data, using Markov Random Fields and ensemble classification. As a general trend, we have seen a great degree of variability between classification performance of various methods submitted for data fusion and classification -be they feature level fusion or decision level fusion. It is difficult to identify any one method that performs well in general -to a great degree, this depends on the underlying problem and the nature of the datasets.
The emerging paradigms of deep learning provide an approach to systematically and hierarchically learn the underlying structure in datasets via deep neural networks  [76] ,  [77]. In recent years, deep hierarchical neural models have been proposed to learn a feature hierarchy -from input images to the back-end classifier. Typically in such architectures, image patches are convolved with filters, responses repeatedly subsampled, and refiltered -when passed through sufficient layers of convolution, subsampling (and nonlinear mapping through activation functions), it is expected and observed with real data that the resulting feed forward network is very effective for image analysis. Although deep learning has been successfully applied to many computer vision applications, its utility for single and multi-sensor remote sensing data has been very limited, although the potential benefits to multi-sensor data fusion are enormous.
Features were extracted by one or more modalities and subsequently fused in order to compose a new enriched set of characteristics. Demonstrations of fusion on a single modality are are given for example, when combining spectral with spatial features. In this case, in order to properly perform such fusion, the differences between the modes should be taken into account in order to be able to properly exploit them. For example, in the context of classification with LiDAR and optical images, if one wants to use both sources as input to a classifier, then registration problems should be solved (e. g. , by rasterizing the LiDAR data to the same spatial resolution of the optical image). • Decision level. Fusion of decisions occurs at the highest semantic level. Among the contests, we recall that the one of 2008  [28]  was based on such DF paradigm (i.
Moreover, it is only through the joint consideration of multisensoral data that it is possible to observe some phenomena (e. g. , for the retrieval of biophysical parameters which cannot be sensed by using the acquisitions of a single sensor or single modality  [83] ). Likewise, this more complete description of the observed world can make certain operations possible. In classification the discrimination between several classes might only be possible if multimodal data is considered. For instance, LiDAR gives information on the elevation of the objects in a scene, while a multispectral sensor captures the spectral properties of the materials on their surfaces. Clearly, land cover types differing in both of these characteristics could not be discriminated by considering only one of these modalities.
So this last aspect opens some questions on the motivation of the fusion, since considering a fusion of different modes further increases the complexity of the system and the computational burden. So the use of different modes should be supported by its actual need. In order to address this last aspect, a priori information on the application and a knowledge of the characteristics of the different modalities should be considered in advance. • Despite the clear benefits that data fusion can bring, it can lead to some important challenges. Data acquired from different sources might come in completely different formats. For example, imaging sensors provide data over a lattice, whereas LiDAR generates a set of sparse and non-uniformly spaced acquisitions. In addition, pixels in optical images and data in LiDAR are multivariate real values whilst radar images have complex values.
V. PERSPECTIVES From the current status resumed in the previous section, here we will account for still open challenges and new perspectives of DF in remote sensing. • The number of new satellites that are planned to be launched in the near future is constantly increasing, and companies such as Planet Labs and SkyBox are building Earth observation constellations of hundreds of satellites. In addition to this increasing trend, satellite platforms are getting more and more diversified in terms of characteristics. For example, the recently launched WorldView-3 by DigitalGlobe includes 29 bands in the VNIR-SWIR region of the spectra, ranging from 30 cm to 30 m resolution. These two aspects are leading to the generation of data acquired by a plethora of different sensors, that will consequently produce an increasing need of DF analysis in order to fully exploit such data.
g. ,  [2] ) but it has not taken off extensively in the remote sensing research community, even if, coupling the two information sources has been proven to be successful, being a standard technology in many EO visualization systems such as Google Earth. Perhaps a reason for the lack of established DF techniques for fusing GIS layers and remote sensing data can be attributed to the inherently different features of the two sources. In many cases, GIS layers can not be fused straightforwardly with remote sensing data, since they come in vectorial format, they might contain descriptive data (i. e. , geo-localized textual information), they deal natively with semantic objects instead of pixels and coregistration with remote sensing images can be a severe issue. However, GIS data are largely available thanks to local information systems held by municipalities or by worldwide databases such as OpenStreetMap.
• Another fundamental challenge is related to the validation of the results. This is an perennial problem for tasks in which there is no reference available (e. g. , pansharpening)  [82] ,  [90]. However, the availability of commonly recognized validation paradigms is essential for evaluating newly proposed algorithms in a quantitative way. VI. CONCLUSION By reviewing the outcomes of the contests issued by the DFTC, it is possible to remark their main contributions such as i) fostering the methodological development on the topics defined by each contest; ii) making datasets available to the community -sometimes such datasets were valuable because it might be unusual in real operational scenarios to have so many data acquired from different sensors with such high spatial resolution, such as for the contests 2012 (VHR SAR, VHR optical from different sensors and LiDAR) and 2011 (images VHR multiangular) and iii) encouraging the emergence of new applications or research directions based the data of the contests.
Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Due to the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile.
, 2013)  or antibiotic resistance mechanisms  (Schmieder et al. , 2012). In this paper we focus on the problem of supervised taxonomic binning, where we wish to assign each read in a metagenomics sample to a node of a pre-defined taxonomy. Two main computational strategies have been proposed for that purpose: (i) alignment-based approaches, where the read is searched against a reference sequence database with sequence alignment tools like BLAST  (Huson et al. , 2007)  or short read mapping tools (e. g. , BWA,  Li et al. , 2009) , and (ii) compositional approaches, where a machine learning model such as a naive Bayes (NB) classifier  (Wang et al. , 2007; Parks et al. , 2011)  or a support vector machine  (SVM, McHardy et al. , 2007; Patil et al. , 2012)  is trained to label the read based on the set of k-mers it contains.
,K w j x , where w x is the standard inner product between vectors. To train the linear model, we start from a training set of sequences x 1 ,. , x n ∈ R 4 k with known taxonomic labels c 1 ,. , c n ∈ {1,. , K}. A NB classifier, for example, is a linear model where the weights are estimated from the k-mer count distributions on each class. Another class of linear models popular in machine learning, which include SVM, are the discriminative approaches that learn the weights by solving an optimization problem which aims to separate the training data of each class from each other. More precisely, to optimize the weight w j of the j-th class, one typically assigns a binary label y i to each training example (y i = 1 if c i = j, or y i = -1 otherwise) and solves an optimization problem of the form min w 1 n n i=1 (y i , w x i ) + λ w 2 , (1) where (y, t) is a loss function quantifying how "good" the prediction t is if the true label is y, and λ ≥ 0 is a regularization parameter to tune, helpful to prevent overfitting in high dimension.
To solve (1) efficiently when n, k and K take large values, we use a dedicated implementation of stochastic gradient descent (SGD  Bottou, 1998)  available in the Vowpal Wabbit software  (VW Langford et al. , 2007; Agarwal et al. , 2014). In short, SGD exploits the fact that the objective function in (1) is an average of n terms, one for each training example, to approximate the gradient at each step using a single, randomly chosen term. Although SGD requires more steps to converge to the solution than standard gradient descent, each step is n times faster and the method is overall faster and more scalable. In addition, although the dimension p = 4 k of the data is large, VW exploits the fact that each training example is sparse, leading to efficient memory storage and fast updates at each SGD step.
Data We simulate metagenomics samples by generating reads from three different reference databases, which we refer to below as the mini, the small and the large databases. The mini reference database contains 356 complete genome sequences covering 51 bacterial species, listed in Table  1. We use this database to train and extensively vary the parameters of the different models. To measure the performance of the different models, we generate new fragments from 52 genomes not present in the reference database, but originating from one of the 51 species 1. The small and large databases are meant to represent more realistic situations, involving a larger number of candidate bacterial species and a larger number of reference genomes. To define the reference and validation databases that will respectively be used to build and evaluate the predictive models, we first downloaded the 5201 complete bacterial and archeal genomes available in the NCBI RefSeq database as of July 2014  (Pruitt et al.
, 2012) , by means of a functionality embedded in the Fragment Classification Package (FCP)  (Parks et al. , 2011). We then filtered these sequences according to a criterion proposed in  Parks et al. (2011) : we only kept genomes that belong to genera represented by at least 3 species. We also removed genomes represented by less than 10 6 nucleotides in order to filter draft genome sequences, plasmids, phages, contigs and other short sequences. The 2961 remaining sequences originate from 774 species, among which 193 are represented by at least 2 strains. We split the sequences of these 193 species into two parts. We randomly pick one strain within each of these 193 species to define a validation database, that will be used to estimate classification performance, through the sampling of genomic fragments or the simulation of sequencing reads.
Proof of concept on the mini database In this section we present a study on the mini dataset, aiming to evaluate the impact of increasing the number of fragments used to train the model as well as the length of the k-mers considered. For that purpose, we learn several classification models based on fragments of length L = 200 or L = 400 sampled from the 356 reference genomes in the mini reference database, represented by k-mers of size in {4, 6, 8, 10, 12}. The number of fragments used to learn the models is gradually increased by drawing several "batches" of fragments in order to cover, on average, each nucleotide of the reference genomes a pre-defined number of times c. We vary the coverage c between 0. 1 to its maximal value, equal to the length of the fragments considered. This leads to learning models from around n = 2.