dataesr/scientific-paragraphs-categorization huggingface dataset:
To achieve this, we developed a Matlab program that computes the radiation pattern of any 3D arrays by importing the pattern of a single element. The characterisation of the global resulting field is carried out by calculating and displaying • Directivity • Polarisation chart • Polarisation ellipse 𝐄𝐄 "#"$% θ, φ = * 𝐄𝐄 + (θ +-, φ +-) / 0 1 𝑒𝑒 3-4𝒓𝒓1𝐮𝐮 7 The radiation pattern of any array is computed using the following equation: The second array consists of 5 rings of 20 elements each, and these are arranged so that the spacing between elements increases as the angle increases. There is no element at θ= 0°. The characterisation of the fields is carried out thanks to the polarisation chart which displays the trajectory of the field vectors and the polarisation ellipticity. The polarisation ellipticity is calculated using the E fields as: Directivity patterns.
Unlike most works in document classification, our approaches use only partial information (titles and abstracts) of documents in order to predict relevant topics for representing their full content. Since the content of documents is not fully exploited, using large datasets for building the classifiers could be useful for capturing more information. Forthis reason, we used classifiers built from large collections of previously annotated documents. This is a very challenging task, which has motivated the recent launch of BioASQ: an international challenge on large-scale biomedical semantic indexing and question answering 1   [6]. The rest of the paper is organized as follows. First, related work concerning biomedical document indexing and, more generally, multi-label classification isreviewedin Section 2.
Related work The identification of relevant topics from documents in order to des scribe their content is a very important task widely addressed in the literature. In the biomedical domain, the MTI (Medical Text Insdexer) tool  [4]  is one of the first attempts to index biomedical documents (MEDLINE citations) using controlled vocabularies. To map biomedical text to concepts from the Unified Medical Language System® (UMLS) Metathesaurus -a system that includes and unifiesmore than 160 biomedical terminologies -the MTI tool uses the well-known concept mapper MetaMap  [8]  and combines its results with the PubMed Related Citations algorithm  [10]. The combination of these methods results in a list of UMLS concepts which is then filtered and recommended to human experts for indexing citations.
Recently, the MTI was extended with various filtering techniques and ML algorithms in order to improve its performance  [11]. Ruch has designed a data independent hybrid system using MeSHfor automatically classifying biomedical texts  [12]. The first module is based on regular expressions to map texts to concepts while the second is based on a VSM  [7]  considering the vocabulary concepts as documents and documents as queries. Then, the rankers of the two components are merged to produce a final ranked list of concepts with their corresponding relevance scores. His results showed that this method achieved good performances, comparable to ML-based approaches. One limitation of this system is that it may return MeSH concepts which match partially the text  [1]. ML-based approaches are also proposed to deal with such a task.
Then, a learning-to-rank model  [14]  is used to compute relevance scores and consequently to rank candidate labels 2  collected from these document neighbours. In this work, the number of labels to classify a document is set to 25. Experiments on two small standard datasets (respectively 200 and 1000 documents) showed that it achieves better performances than the MTI tool. On the other hand, indexing biomedical documents in which each document of the dataset is assigned one or several categories (also called "labels") can be assimilated as a multi-label classification task. Multi-label classification (MLC) is increasingly studied and especially for text classification purposes  [15]. Several methods have been developed to deal with this task  [16]   [17]. They can be categorized into two main approaches  [15] : the problem transformation approach  [18]  and the algorithm adaptation approach  [17]   [19].
Spyromitros et al. [17]  propose a similar method, named BR-KNN (for Binary Relevance KNN), and two extensions of this method. The proposed approach is an adaptation of the kNN algorithm using a BR method which trains a binary classifier for each label. Confidence scores for each label are computed using the number of neighbours among the k neighbours that include this label. In  [20] , an experimental comparison of several multi-label learning methods is presented. In this work, different approaches were investigated using various evaluation measures and datasets from different application domains. In their experiments, authors showed that the best performing method is based on the Random Forest classifier  [21]. Other recent works address MLC with large number of labels  [22]. Indeed, in many applications, the number of labels used to categorize instances is generally very large.
Datasets The BioASQ organizers, within the 2014 edition, provided a collection of over 4 million documents constituted by only titles and abstracts of articles (called also citations), coming from specific scientific journals for the task 2a of this challenge  [6]. These documents, extracted from the MEDLINE database, are annotated by descriptors ofthe MeSH thesaurus. In addition, during the challenge, the organizers provided each week PubMed®citations not yet annotated which were used as test sets to evaluate the systems participating in the task 2a. Participants were asked to classify these test sets using descriptors of the MeSH thesaurus. The test sets have subsequently been annotated by PubMed® human indexers for evaluating the proposals of the participating systems.
First experiment For the kNN retrieval, we used a dataset consisting of all articles of this collection published since 2000 (2,268,724 documents). The motivation for this choice is to discard old documents which are not annotated by descriptors recently added to the MeSH thesaurus (the MeSH thesaurus is regularly updated). This dataset is thereafter extended to the entire collection. For training the classifiers we randomly selected 20. 000 articles out of those published since 2013; the citations of the training set are discarded from the former dataset. We assume this training set sufficient to capture relevant information for building the classifiers. Only the kNN-based approach was used for our participation to the challenge. To assess this method, five of the different test sets provided by the challenge organizers were used.
Second experiment For the second experiment, we first extracted all articles published since 2013 (133,770 documents) from the previous dataset provided by the challenge organizers. We then selected randomly 20,000 documents to be used for training the classifiers and one thousand for constituting the test set. The data used to train the classifiers werethen extended to 50,000 documents, sincewe believed it could improve the classification performances; using large training dataset should enable the classifiers to capture more information. The test collection was also increased to 2,000 documents. Like in the training dataset, each document in the test set was assigned a set of labels by PubMed® annotators. These manually assigned labels were thus used to evaluate the results of our different methods.
The value of(constant used in the strategy based on label scores comparison -strategy c-for optimizing N) also affects the classification performance. The lower the value of, the higher the precision is but the lower the recall is and vice versa. In these experiments, we setto 1. 6 which yields the best results using cross-validation techniques. Furthermore, we note that when the classifiers are trained on this extended dataset, they yield similar performances but the RF classifier slightly outperforms the others. Table  5  gives an example of labels suggested for classifying the document having the PMID 23044786 (Figure  2 ) with the kNN-based approach. In.
In our method, this sum is only considered as one feature among others for determining the confidence scores of labels. While the results of our method do not outperform the extended (and improved) MTI system  [11]  which is currently used by the NLMcurators, it getspromising results (0. 49 against 0. 56 of F-measure). A direct comparison with the method proposed in  [5]  is not simple since the authors used an older collection than the official datasets provided in the BioASQ challenge, which are recent and annotated with descriptors of the recentMeSHthesaurus (2014 version). Similarly to their experiments, when our method is evaluated on 1,000 randomly selected documents, it outperforms this method (0. 55 against 0. 50 for the Fmeasure). But a comparison with their recent results in the first challenge of BioASQ  [28]  where they integrated the MTI outputs, their system performs better than ours (Fmeasure of 0.
56 against 0. 49). Compared with the two approaches proposed in  [32] , one based on the MetaMap tool  [33] and another using IR techniques, our method gets better results (0. 49 against 0. 42 for the F-measure). Our approach outperforms also the hierarchical text categorization approach proposed in  [34]. As part of our participation in the challenge, the NB classifier is combined with the average size of labels assigned to the neighbours to determine relevant descriptors for a given document. In the second experiment, we note however that a combination of RF with the cut-off technique proposed in  [28]  yields better results  [35]. A more recent evaluation of our kNN-based approach using a large dataset (50,000 documents) for training the classifiers shows that it provides better performances, comparable to the best methods described in the literature(with an f-measure of 0.
The Hippocrates system presented in  [37] only relies on binary SVM (Support Vector Machine) classifiers and trains them on a large dataset (1. 5 million documents) in contrast to our basic kNN approach trained on 20,000 documents. Note that these three systems use binary classifiers for building a model for each label  [31]. These systemsrequire therefore considerable resources in terms of computation and storage compared to our kNN-based approach. For the kNN retrieval, we have investigated the cosine similarity which is widely used in IR. It would be interesting to combine this measure with domain knowledge resources, such as ontologies,to overcome the limitation of similarity computation based only on common words. The second method based on the ESA, meanwhile, yields very low performances comparable to basic methods using a simple correspondence between the text and the semantic resource inputs.
Example 3: Architecture Constraints from a Real-World Componentbased Application Let us now consider an example of the architecture description of a real-world component-based application, together with its architecture constraints. The modeled software system is a set of Eclipse plugins that provide the Java programming environment JDT (Java Development Tools) in the Eclipse SDK. The architecture of this set of plugins is depicted in Figure  11. It has been defined starting from the OSGi meta-data of these plugins (the exported and imported packages and required bundles). For reasons of simplicity, we have omitted some very simple component descriptors like JDT Annotation. In addition, we have hidden from component descriptions the interfaces required from the Eclipse Platform (required from the core plugins of Eclipse, like SWT -the Standard Widget Toolkit, or the Platform Runtime).
Implementation of the Constraint Component Model We have implemented the constraint component model as a DSML (Domain-Specific Modeling Language) using the UML profile mechanism. In the past, we have developed another implementation as a new metamodel in Eclipse using Ecore with EMF  [11]. A UML profile implementation is a better solution than an EMF-based one, because it provides a better integration in existing IDEs supporting UML modeling. All CLACS concepts have been designed as UML stereotypes that extend UML meta-classes such as: Component, Port and Connector. These stereotypes extend UML meta-classes having the same name, except ComponentDescriptor which extends Component, and ComponentInstance which extends Property. We have extended this basic UML profile with concepts related to architecture constraints.
Each one offers an interesting reuse schema in a given situation. But in our context of architecture constraint specification, in the situations we have encountered when reusing constraints, we have either added or removed constraint parts to/from existing constraints. We have not had to redefine a constraint part. We think thus that inheritance is not a pertinent solution for architecture constraint reuse. However, inheritance certainly makes sense in reusing specifications of architectural styles or patterns (which are different from architecture constraints). FScript 8  is a scripting language for the reconfiguration of Fractal  [31]  component-based architecture descriptions. It provides a way for writing actions (reconfigurations with side effects) and functions (architecture constraints without side effects).
It has a syntax for introspecting architecture descriptions (FPath) which is based on XPath. In FScript as in CLACS, we can write parameterized architecture constraints. In addition, the authors of FScript state that it is possible to call existing functions inside FPath requests. We assume more generally that we can call functions representing architecture constraints inside functions representing other architecture constraints for composing them. In contrast to CLACS, FScript's architecture constraints are defined in scripts outside architecture descriptions and are checked by external tools. This does not provide a unified paradigm/environment for software architects, where constraints are defined, reused and then checked with architecture descriptions. AADL  [32]  (Architecture Analysis and Design Language) is an ADL for describing (software and hardware) architectures of embedded and real-time systems.
Architecture Constraint Specification and Reuse at Implementation Stage In the literature, there is a great number of languages which has been proposed for defining architecture constraints on object-oriented programs  [36, 37, 38, 39, 40, 41, 42, 43, 44, 45]. Some of these languages refer to architecture constraints as conditions on structural dependencies. These languages vary from Prolog-like languages, like LogEn  [41]  (Logical Ensembles), LGA (Law Governed Architecture)  [42, 43] , Spine  [44]  or Vespucci  [45] , to object-oriented programming languages like CoffeeStrainer  [36]  for Java, CCEL (C++ Constraint Expression Language)  [37]  for C++, or DSLs like CDL (Category Description Language)  [38] , DCL (Dependency Constraint Language  [39] ) or SCL (Structural Constraint Language)  [40].
The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.
Introduction Imaging spectroscopy has been demonstrated to be a powerful tool for numerous environmental applications  [1] [2] [3]. So far, research has mainly been based on airborne hyperspectral image data with restricted spatial and temporal coverage, while spaceborne imaging spectroscopy has been strongly limited by data availability. Successful technology demonstrators include NASA's Hyperion launched in 2000  [4] , ESA's CHRIS launched in 2001  [5] , MERIS on Envisat operated from 2002 to 2012  [6] , and NASA's HICO on the international space station operated from 2009 to 2015  [7] , among a few others. The future German imaging spectroscopy mission EnMAP (Environmental Mapping and Analysis Program) will represent a milestone towards high-quality hyperspectral observations of terrestrial and aquatic ecosystems from space.
EnMAP will cover a 30 km swath with a ground sampling distance of 30 m and a total image data acquisition length of 5000 km per day  [9]. The scientific preparation of the mission includes an extensive science program dedicated to demonstrate the scientific potential of future EnMAP data, develop image processing tools for efficient data processing, and build an expert community to ensure exploitation of the full information content of EnMAP data once operational. In the framework of the EnMAP preparatory program, a large number of hyperspectral airborne flight campaigns are carried out to support scientific application development in a wide range of environments. The datasets are made freely available to the scientific community  [10]. Furthermore, preparatory activities involve the simulation of the entire image generation and processing chain using the EnMAP end-to-end scene simulator (EeteS)  [11].
EeteS comprises four main modules (atmospheric, spatial, spectral, and radiometric) to generate EnMAP-like data products. The simulated data facilitate the development of pre-processing algorithms as well as algorithms for the retrieval of surface information for various applications. As part of the scientific preparation, the EnMAP-Box, a free software toolbox that is specifically designed for handling future hyperspectral spaceborne data, is being developed  [12]. It provides basic processing and visualization functionalities as well as advanced approaches for image analysis. This special issue aims to give an overview of the EnMAP mission and ongoing research conducted in the scientific preparation of the mission to demonstrate the potential of future EnMAP data for a range of application areas, namely forestry, agriculture, inland and coastal waters, soils, and natural ecosystems.
Overview of Contributions Guanter et al. [8]  provide a comprehensive overview of the EnMAP imaging spectroscopy mission and its current status, including a brief description of the mission and instrument requirements, mission organization, components of the space and ground segment, the science program, as well as on-going preparatory activities. To study the potential of future spaceborne EnMAP image data for a range of applications, the contributions in this special issue make use of simulated future EnMAP data generated from airborne acquisitions. Furthermore, several studies in this issue focus on the comparison with other missions, such as Sentinel-2, and discuss their potential synergetic use. Managing and process-based modeling of forest ecosystems may strongly benefit from information gained from future spaceborne imaging spectroscopy missions such as EnMAP.
[17]  explored the usage of multi-date narrow-band spectral indices to estimate vegetation cover fractions by fitting boosted regression tree models, while Suess et al. [18]  and  Malec et al. [19]  studied the potential of sub-pixel cover fraction estimation using synthetic mixtures in an adapted support vector classification model parameterization and a MESMA approach, respectively. Future spaceborne imaging spectroscopy missions, such as EnMAP, will open up new opportunities for monitoring coastal and inland waters including the differentiation of phytoplankton taxonomic groups. Xi et al. [20]  tested the differentiation capabilities of phytoplankton taxonomic groups using remote sensing reflectance directly compared to absorption spectra derived from inversion models. Besides the application-based contributions, several articles deal with techniques for assessing and enhancing future EnMAP image data quality.
Bachmann et al. [21]  assessed the influence of the expected radiometric and spectral calibration stability on the uncertainty in the future EnMAP ground reflectance product. Cerra et al. [22]  proposed an unmixing-based denoising approach to correct future EnMAP image data, acquired under unfavorable illumination conditions and off-nadir viewing angles. To facilitate applications requiring higher spatial resolutions while maintaining the high spectral resolution, Yokoya et al  [23]  investigated the potential of fusion-based resolution enhancement using simulated EnMAP and Sentinel-2 data. In particular, they investigated the performance of a matrix factorization technique in comparison with two pan-sharpening techniques for improved mineral mapping capabilities. As part of the EnMAP scientific preparation program, image processing software is under development, aiming to provide free access to pre-processing and application-based imaging spectroscopy tools for a growing hyperspectral community.
This article studies, both theoretically and numerically, a nonlinear drift-diffusion equation describing a gas of fermions in the zero-temperature limit. The equation is considered on a bounded domain whose boundary is divided into an "insulating" part, where homogeneous Neumann conditions are imposed, and a "contact" part, where non-homogeneous Dirichlet data are assigned. The existence of stationary solutions for a suitable class of Dirichlet data is proven by assuming a simple domain configuration. The long-time behavior of the time-dependent solution, for more complex domain configurations, is investigated by means of numerical experiments.
Step 3. In the subsequent intervals of the form [y i , x i+1 ] or [x i , y i ], we repeat the same proofs of the steps 1 and 2 (respectively), with the only difference that U (y i ) = 0 instead of U (0) = u 0 > 0 (which implies that Step 1 has to be slightly modified by using a η > 0 such that V (y i ) -V (ξ) ≥ η for all ξ ∈ [y i + ǫ, x i+1ǫ]). Note that the limiting process c → 0 + selects one particular solution among the infinitely many solutions of the case c = 0, for which we have seen that there is not uniqueness (see section 3. 1). Definition 10. We define the critical values for the Dirichlet data u 0 > 0 and u 1 > 0 as follows: (24) u crit 0 = V M -V 0 , u crit 1 = V M -V 1 , where we put V 0 = V (0), V 1 = V (1) and V M = max x∈[0,1] V (x). If a boundary term is greater than the corresponding critical value, then it is said to be supercritical, otherwise it is said to be subcritical.
We can finally prove that the solution of the the Dirichlet problem (9) exists and is unique, provided that the Dirichlet data u 0 > 0 and u 1 > 0 are not both subcritical. Theorem 11. If u 0 > u crit 0 , then the Dirichlet problem (9) has a unique, strictly positive, solution u for all u 1 > 0. If u 1 > u crit 1 , then the Dirichlet problem (9) has a unique, strictly positive, solution u for all u 0 > 0. Proof. Consider first the case u 0 > u crit 0 and assume, temporarily, that c > 0. We know that the mapping c → u(1), obtained by solving the Cauchy problem (  18 ) is strictly monotone. Moreover, from Lemma 5, we have that lim c→+∞ u(1) ≥ lim c→+∞ v(1) = +∞, since it is easy to see that φ(x, u 0 , c, α) → +∞ as c → +∞, for all x > 0 and α ∈ R. Moreover, since u 0 > u crit 0 , from Definition 8 and Lemma 9 we have that lim c→0 + u(x) = U (x) = u 0 + V 0 -V (x) > 0, i.