dataesr/scientific-paragraphs-categorization huggingface dataset:
It is developed in C/C++ and the project's main aim is to provide implementations for real-time applications. This results in fast implementations with a comparatively high barrier of entry for code study and modification. The library provides interfaces for several high-level programming languages, including Python through the NumPy-array data-type for images. The Python interface is essentially a one-to-one copy of the underlying C/C++ API, and thus image processing pipelines have to follow an imperative programming style. In contrast, scikitimage provides a Pythonic interface with the option to follow an imperative or functional approach. Beyond that, OpenCV's image processing module is traditionally limited to 2-dimensional imagery. The choice of image processing package depends on several factors, including speed, code quality and correctness, community support, ecosystem, feature richness, and users' ability to contribute.
Background CMR is an established method for the diagnosis and assessment of myocardial infarction in routine exams. In addition, there has been a growing interest in using infarct size as a surrogate endpoint for clinical trials assessing the efficacy of acute MI reperfusion treatments and MI size is reported to be a stronger predictor of outcome than LVEF and LV volumes  [1]. Various methods have been proposed for infarct size (IS) measurement without reference standard method and side-by-side comparison among them. The goal was to develop a user-friendly cardiac segmentation toolbox implemented as a plugin in the open-source OsiriX software with a set a commonly proposed semi-automatic algorithms.
Methods Contour segmentation was implemented using a fast automated segmentation framework (B-spline Explicit Active Surfaces  [2] ) that could be corrected or manually performed. Several semiautomatic methods were imple-  mented from simple image intensity threshold based on 1) histogram, or 2) equal to × standard deviation (xSD) above the mean of remote normal myocardial intensity 3) full-width at half-maximum (FWHM) with 2D/3D and maximum or region based variants. More sophisticated algorithms were also implemented including 4) the Gaussian Mixture Model (GMM) and 5) the method proposed by Hsu that assign a weighting to each voxel depending on its image intensity or use a combination of methods along with regional feature analysis. Performance of methods was 1) evaluated for precision and accuracy on a realistic phantom based on real datasets with varying contrast to noise ratio (CNR) between infarct aera and normal myocardium and 2) compared in-vivo on DE-CMR images in post-AMI patients.
In fact, we develop a new system that uses the wind energy and the suspension dynamics to produce power. First, we present an experimental P308 car with a number of sensors in order to measure the wind velocity and the road profiles  [5]. The wind turbine was designed with CAO software. For road slop estimation, a classical Luenberger observer is used. Then, the ground-wheel contact creates generalized forces that can be spread to the suspension as kinetic energy. A rack and pinion system which converts linear motion into rotational one is used to produce power. Both wind turbines and suspension power form a global connected system called "DynaPower".
Conclusion In this paper, a new system called "Dynapower" is proposed. The philosophy of this concept is to exploit the dynamics of the vehicle and its environment to produce power. First, a new wind turbines system is designed with CAD software. A slope angle observer is studied in order to use the high kinetic energy when vehicle moves along its descent path. An instrumented P308 car is utilized to make some experiments. A first wind turbines device attached to the roof bars is developed. Then, a rack and pinion mechanism is proposed to convert suspension dynamics into electric energy. As a perspective work, we will develop an embedded board to control "DynaPower" system and to define optimal management of stored power. Moreover, we will exploit other motions to produce more energy.
Acknowledgments I would like to thank  Joao Caetano ,  Nikolay Gromov ,  Vladimir Kazakov ,  Ivan Kostov ,  Didina Serban ,  Pedro Vieira  and  Konstantin Zarembo  for interesting discussions on threepoint correlation functions in N =4 SYM. I especially thank  Vladimir Kazakov , Gregory Korchemsky  and  Pedro Vieira  for valuable comments concerning this manuscript. I am especially grateful to  Gregory Korchemsky  for generously sharing with me his knowledge on the subject in question. I thank  Perimeter Institute in Waterloo, Canada, Kavli IPMU  in Tokyo, as well as  Yukawa Institute for Theoretical Physics in Kyoto  for their hospitality. I thank  ERC grant  and  "Project Unification of Fundamental Forces and Applications " (UNIFY, Number  269217 ) for support.
The project has several national and international partners, including Mons University, Belgium, The Réseau Européen des Associations de Langues (REAL), Lingua e nuova didattica (LEND), Italy and the private company Totemis. As we mentioned in the introduction, it is obligatory in France to study a foreign language at university for all students; by far the largest demand is for English, but the Maison des Langues et des Cultures at Université Stendhal offers courses in many languages (including Polish) 6. The languages concerned by Innovalangues are initially English and Italian, followed by Spanish, German, Chinese and Japanese. Blended learning is at the heart of the project, with the development of a digital ecosystem, built around a CLMS (Content and Learning Management System), Claroline Connect© 7.
Innovalangues has a team of full-time IT developers who are working with the Claroline developers to produce a set of plug-ins conceived by the various teams on the Innovalangues project, according to their specific demands and in accordance with the principles of agile development. The project currently comprises seven teams all of which have been tasked with providing innovative approaches to the problem of helping the students in the learning situations described above to achieve B2 level (their names are often acronyms in French which we have chosen to paraphrase / translate):  THEMPPO (oral production and prosody)  COCA (oral comprehension -"exerciser" and learning support)  CASSIS (application design in support of social interaction)  Parcours (conception of model learning paths)  ColOr (collaborative oral expression practice)  SELF (formative and diagnostic assessments for languages)  GAMER (serious gaming techniques applied to language learning)  Approches créatives (creative approaches for languages, e.
Contextualization based on the Social Web will be complemented by exploring topic-centered, event-centered and entity-centered processes for content appraisal and acquisition, as well as for rich preservation. In this paper, we give an overview of the crawler architecture implemented within the ARCOMEM project that addresses the requirements mentioned above. The paper goes beyond our initial work in  [5]  by providing more details about the different phases, the data management and analysis modules. It furthermore describes the implementation choices that lead to the final system. The ARCOMEM system has been published as open source  [6]. The remainder of the paper is structured as follows. The next section will present two example use cases and the derived requirements for the Web crawlers.
Crawling Level At this level, the system decides on and fetches the relevant Web objects, as these are initially defined by the archivists and are later refined by both the archivists and the online processing modules. The crawling level includes, besides the traditional crawler and its decision modules, some important data cleaning, annotation and extraction steps (we explain this in more detail in Section 5. 5). The Web objects (i. e. , the important data objects existing in a page, excluding ads, code, etc. ) are stored in the ARCOMEM storage together with the raw downloaded content.
Offline Processing Level At this level, most of the basic processing over the data takes place. The offline, fully-featured versions of the entity, topics, opinions and events (ETOE) analysis and the analysis of the social contents operate over the cleansed data from the crawl, which are stored in the ARCOMEM storage. These processing tools perform linguistic, machine learning and natural language processing (NLP) methods in order to provide a rich set of metadata annotations that are interlinked with the original data. The respective annotations are stored back in the ARCOMEM storage and are available for further processing, text and data mining. After all of the relevant processing has taken place, the Web pages to be archived and preserved are selected in a semi-automatic way. For the selection, the user defines a lower threshold of the relevance score for the documents to be preserved.
ARCOMEM Data Model One of the first tasks to be addressed in ARCOMEM was the identification of the concepts that are relevant for knowledge capturing, crawling, and preservation. After a requirements analysis, a structured domain representation was created in OWL (Web Ontology Language)  [9]  that reflects the informational needs of ARCOMEM. Then, from the perspective of data interoperability, access and reuse we have concentrated on embedding the ARCOMEM conceptual vocabulary in the wider semantic Web context. We have done this in several ways. First, we have ensured interoperability with the Open Archival Information System  [10]  (OAIS, ISO 14721). OAIS constitutes a conceptual framework for long term digital information preservation and access. As such, it provides a central point of entry into OAIS related standardization, and a basis for comparing the data models of digital information preserved by archives.
g. , Linked Open Descriptions of Events (LODE)  [13] , Event-Model-F  [12]. The central concepts in ARCOMEM's configuration are InformationObject, InformationRealization and CrawlingConcept. InformationObject is the class that subsumes all ARCOMEM's information types: entities, events, opinions, and topics. Multilingual instances of this class are classified according to the language they belong to. InformationRealization captures the concrete instantiations of these information objects in the form of multimedia Web object such as texts and images. CrawlingConcept describes required aspects of the crawling workflow. Figure  4  illustrates the resulting data model in the form of a simplified UML notation in the form of classes and their relations. Open arrows represent subClassOf relations, whereas closed arrows indicate any other type of relation.
Moreover, as some decisions are made at runtime (e. g. , during the online processing), queries need to be resolved in near-real-time. Even for complex analytics tasks, high throughput is important, since they may trigger a large number of queries and, thus, take hours or even days to complete. To cover the functional requirements, we have designed and implemented a storage module consisting of two components: the object store and the knowledge base. Both of them rely on distributed solutions that combine the MapReduce  [15]  programming model and NoSQL databases to cover the non-functional requirements. MapReduce is an ideal paradigm for harnessing scale-out architectures to build huge indices on distributed storage, while NoSQL databases, based on shared-nothing architectures, offer scalability and availability at a low cost.
The ARCOMEM Object Store The object store is responsible for storing and serving the raw content harvested by the crawler. It relies on Apache HBase  [16] , a NoSQL database written in Java and modeled after Google's BigTable  [17]. Essentially, each table is a sparse map storing values in cells defined by a combination of a row and a column key. In order to provide high availability of data, HBase keeps its data in a distributed filesystem called Hadoop Distributed File System (HDFS)  [18]. This approach also diminishes the impact of a node failure on the overall performance. A sample row of the object store is shown in Figure  5. The URL of the fetched Web object, whether it is text, image or video, will serve as the row key (www. cnn. com in our case), while the actual binary content will be stored under "content".
The ARCOMEM Knowledge Base The task of the knowledge base is to handle the data that derives from the annotation of the Web objects, as performed by the online, as well as the offline processing modules. Such annotations are described using the ARCOMEM data model (see Section 4. 1) that interlinks ETOEs and points to actual content residing in the object store. Thus, the problem translates to building an efficient processing engine that appropriately indexes and stores RDF triples and offers SPARQL querying capabilities, while maintaining the scalability and high-performance characteristics. Existing solutions fail to simultaneously offer both query expressiveness and scalability/performance. Thus, we have designed, H 2 RDF  [19, 20] , a distributed triple index solution, following a hybrid approach that combines the power and expressiveness of an RDF engine with the performance and scalability of NoSQL and MapReduce.
Content Analysis In order for archivists to judge the content of a particular Web document and to decide whether it should be archived, they need to be able to assess this content. The aim of the content analysis module is the extraction and detection of informational elements, called ETOEs (entities, topics, opinions and events) from Web pages (see Section 3). The ETOE extraction takes place in the offline phase and processes a collection of Web pages. The results of the offline ETOE extractions are used to: (1) get a better understanding of the crawl specification; and (2) populate the ARCOMEM knowledge base with structured data about ETOEs and their occurrence in Web objects. In the online phase, single documents will be analyzed to determine their relevance to the crawl specification. A crawl campaign is described by a crawl specification given by the archivist.
GATE is a framework for language engineering applications, which supports efficient and robust text processing. GATE uses natural language processing (NLP)-based techniques to assist the knowledge acquisition process, applying automated linguistic analysis to create text annotations and conceptual knowledge from textual resources. Documents are analyzed by linguistic pre-processing (tokenisation, language detection, sentence splitting, part of speech tagging, morphological analysis and verb and noun phrase chunking). This is then followed by the extraction of both named entities using ANNIE (a Nearly-New Information Extraction System)  [22]  and term candidates using TermRaider  [23]. Both tools use rule-based heuristics, while TermRaider applies statistical measures in order to determine termhood.
Data Enrichment and Consolidation As the content analysis extracts structured data from unstructured resources, such as text and images, the generated data is highly heterogeneous. This is due to the data being generated by different components and during independent processing cycles. For instance, during one particular cycle, the text analysis component might detect an entity from the term "Ireland", while during later cycles, entities based on the term "Republic of Ireland" or the German term "Irland" might be extracted. These would all be classified as entities of type arco:Location and correctly stored in the ARCOMEM knowledge base as separate entities described according to the ARCOMEM data model. Data enrichment and consolidation in ARCOMEM follow two aims: (1) to enrich existing entities with related publicly-available knowledge; and (2) to identify data correlations within the Web archives created by ARCOMEM by aligning extracted entities with reference datasets.
Both (1) and (  2 ) exploit publicly-available data from the Linked Open Data (LOD) cloud, which offers a vast amount of data of both a domain-specific and domain-independent nature. To achieve the described aims, the enrichment approach first identifies correlating enrichments from reference datasets, which are associated with the respective entities, and, secondly, uses these shared enrichments to identify correlating entities in the ARCOMEM knowledge base. In particular, the current enrichment approach uses DBpedia  [24]  and Freebase  [25]  as reference datasets, though this approach does not depend on any particular dataset and is applicable to additional and more domain-specific datasets, e. g. , event-specific ones. DBpedia and Freebase are particularly well-suited for the enrichment of Web crawls, due to their vast size, the availability of disambiguation techniques, which can utilise the variety of multilingual labels available in both datasets for individual data items and the level of inter-connectedness of both datasets, allowing the retrieval of a wealth of related information for particular items.
These approaches can be used to cluster closely-related entities and the Web objects that they originate from in ARCOMEM Web archives. Furthermore, enrichments can provide an additional hierarchical view on the extracted entities and, thus, archived Web objects. Both DBpedia and Freebase possess rich category structures that can be interconnected with the archived Web objects using reference entities. For example, in DBpedia "Ireland" is classified as a region in Northern and Western Europe. This classification can help in finding other entities located in these regions within the Web archive, as well as the archived Web objects related to these entities. In addition to hierarchical structures natively provided by the LOD datasets, further hierarchies can be obtained by mapping the schemas of the reference datasets used by the enrichment process to external ontologies.
For illustration, we present the results of the analysis of the cultural dimensions of a set of tweets crawled by the ARCOMEM system for the period November 1-11, 2012, for a set of target keywords related to the U. S. Elections 2012. Figure  7(a)  shows the distributions of the languages for tweets and for users on the U. S. elections crawl. Without surprise, the distributions exhibit a power-law indicating as the dominant language mainly English. Interestingly, the user engagement (Figure  7.
, who suggest that classifying the sentiment expressed about a target keyword may be enhanced by also classifying the sentiment expressed for other related keywords  [41]. Consequently, we argue that it is important to be able to explore the context of terms, to create datasets and to perform complex analysis tasks based on term associations. Currently, there is a gap between this need and the functionality that online social network APIs offer. In the Twitter dynamics module, we designed and implemented a model and a number of query operators  [42]  that enable users to explore the context of keywords in data obtained from online social networks and microblogging services. The model represents terms as nodes and associations between terms as directed edges annotated with the association weight and the corresponding time span.
We expand on this architecture in the ARCOMEM project, adding new functionalities to the crawling system, as shown in the bottom part of Figure  9. As already noted, the traditional crawl definition file is replaced by an intelligent crawl definition, which allows the specification of relevance scores and the referencing of the particular kinds of Web applications and ETOEs that define the scope of the archiving task. Queue management functions similarly as in a traditional architecture, but the page fetching module is replaced by some more elaborate resource fetching component that is able to retrieve resources that are not simply accessible by a simple HTTP GET request, but only by other means: a succession of such requests, a POST request, the use of an API (which is a very common situation on Social Web networks) or the selection of individual Web objects inside a Web page (e.
g. , blog posts, individual comments, etc. ). Each content item obtained by the crawler is stored in the ARCOMEM object store for use by analysis modules and archivist tools. Traditional processing chain of a Web crawler compared to the ARCOMEM approach. After a resource (for instance, a Web page) is fetched, an application-aware helper module is used in place of the usual link extraction function, in order to identify the Web application that is currently being crawled, decide on and categorize crawling actions (e. g. , URL fetching, using an API) that can be performed on this particular Web application and the kind of Web objects that can be extracted. This is a critical phase for using clues from the Social Web to crawl content, because, depending on the kind of Web application that is being crawled (traditional static Web site, Web forum managed by some content management system, wiki, social networking sites, such as Twitter or Facebook), the kind of relevant crawling actions and Web objects to be extracted vary dramatically.
Implementation The ARCOMEM system has been implemented as described in the previous sections. To achieve storage scalability and benefit from distributed processing, we used the HBase and Hadoop combination. Both the online and offline analyses require running analysis modules in map-reduce jobs over the Web data stored in HBase and to write outputs in either HBase along the Web data or as triples in the ARCOMEM knowledge base (see Section 4. 2). We created the ARCOMEM framework to ease these tasks. Since Hadoop and HBase are written in Java and as it turned out to be an appropriate language for the analysis modules, we wrote the ARCOMEM framework in Java, too. The framework features a flexible configuration system that allows one to describe the dependencies between processes. Any analysis can be split into as many map-reduce and local processes as needed, and the framework will run them in an order compatible with the dependency graph.
The dependencies between different analysis modules can be expressed in the same way. When dealing with Web data, robustness is a prime concern, since a wide variety of content will be encountered. We tried to isolate the code dealing with Web content as much as we could: we run certain processes in a separate thread and limit the time and memory available to them. When a resource exceeds these limits, we mark it as problematic in HBase and skip it on subsequent runs. As a last resort, when a whole task fails repeatedly, it is skipped (we make it return immediately with a success status), and the rest of the job is allowed to proceed. We initially studied the use of a trigger on Put operations to run the online analysis as we imported Web content into HBase. Since it requires running analysis code directly in the region servers of Hadoop, we preferred to use a map-reduce job after import, trading extra latency for better isolation.
NutchWAX  [61]  is based on Apache Nutch  [62] -an open source Web-search software project that supports URL-and keyword-based access. It adapts Nutch by searching against Web archives rather than crawling the Web. The Portuguese Web Archive information retrieval system  [63]  adapted NutchWAX to support versions of the same URL across time and to improve the ranking of results. A URL-based access to Web archives is provided by Wayback  [64]. Wayback is an open source implementation of the Internet Archive Wayback Machine. It allows browsing the history of a page or domain over time. Overall, the possibilities to explore Web archives are limited to basic functionalities. The ARCOMEM enriched Web archives allow accessing and browsing by a number of different facets. With the availability of long-term Web archives, increasing interest in the temporal analysis of these collections can be observed.