dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion BINoculars unlocks the full power of two-dimensional detectors and helps visualize surface diffraction patterns to an extent that was previously only achievable with low-energy electron diffraction in ultra-high vacuum conditions, or by increasing the X-ray energy and sacrificing k-space resolution and dynamic range  (Gustafson et al. , 2014) , or by taking an impractical amount of time. In addition, BINoculars simplifies structure determination by providing a quick, easy and accurate method to integrate crystal truncation rods. BINoculars is open source under the terms of the GNU General Public Licence (http://www. gnu. org/licenses/ gpl. html) and is available online (http:// binoculars. uithetblauw. nl/; https://github. com/id03/binoculars). Reflectivity scan of (PbSe) 4+ (TiSe 2 ) 4 , a telluride misfit layer compound  (Moore et al.
This paper introduces NEMWEL, a system that performs Never-Ending Mul-tiWord Expressions Learning. Instead of using a static corpus and classifier, NEMWEL applies supervised learning on automatically crawled news texts. Moreover, it uses its own results to periodically retrain the classifier, bootstrapping on its own results. In addition to a detailed description of the system's architecture and its modules, we report the results of a manual evaluation. It shows that NEMWEL is capable of learning new expressions over time with improved precision.
By doing so, NEMWEL tries to resemble the way human beings learn. We have developed a prototype that implements this idea. To the best of our knowledge, this is the first attempt to build MWE lexicons using a never-ending learning approach. We have manually evaluated the extracted MWEs and we show that the precision of the learner seems to increase with time. The remainder of this paper is structured as follows: we discuss related work on MWE extraction (Section 2) and never-ending learning methods (Section 3). Then, we present the architecture and detail the modules in NEMWEL (Section 4). Finaly, we present the results of automatic and manual evaluation in Brazilian Portuguese (Section 5) and ideas for future work (Section 6).
However, in related work, candidates are not extracted from web texts, but from traditional corpora. Differently from previous corpus-based or web-based learning approaches, our goal is not to build one static MWE lexicon. Instead, we propose to build a system that continuously learns new expressions from the web. It populates and enriches the lexicon with new MWEs every day. Our proposal is to employ bootstrapping on a traditional supervised machine learning setting, enriched with new features and dynamically crawled corpora. At any given time, a snapshot of the database will include the current MWE lexicon, which can be exported, evaluated and used to retrain the classifier. To the best of our knowledge, this is the first time never-ending learning is applied to MWE lexicon discovery.
Extractor After collecting and cleaning the texts, the Extractor annotates the tokens in each text with its surface form, part-of-speech tag and lemma. To Mais de 100 famílias de baixa renda ocuparam casas de um conjunto habitacional, em Paulínia (SP), na madrugada desta quarta-feira (19). More than 100 low-income families occupied houses of a housing development in Paulinia (SP) in the early hours of this  Wednesday (19). do so, we used the TreeTagger  (Schmid, 1994)  with a model trained for Portuguese 5. Tagging the corpus is required because we evaluate our learner using nominal MWEs, thus we need to be able to identify nouns and their complements. The TreeTagger was chosen because it is free, easy to use and fast, enabling us to quickly process large amounts of crawled texts. The same excerpt of Figure  1  processed by the Extractor is shown in Figure  2.
The sequences of tagged tokens in the crawled texts are processed by the mwetoolkit  (Ramisch, 2015) , which is the core of our Extractor and Processor modules. In the Extractor, a list of MWE candidates is obtained by matching a multilevel regular-expression pattern (Figure  3 ) against the tagged corpus. Figure  4  shows an example of MWE candidate extracted from our example sentence, using the pattern of Figure  3. The pattern is based on intuitive noun phrase descriptions, but it also captures more candidates, that are not necessarily nominal compounds. Further filters must be applied to remove regular noun phrases and keep only nominal MWEs.
Processor In this module, the mwetoolkit calculates some association measures that will be used by the Promoter in the next step. These measures are calculated based on the number of occurrences of the MWE candidate and of the words that SENT. <patterns> <pat> <w pos="NOM"/> <pat repeat="{1,3}"/> <either> <pat> <w pos="PRP*" lemma="de"/> <w pos="NOM"/> </pat> <pat> <w pos="ADJ"/> </pat> </either> </pat> </pat> </patterns> Figure  3 : List of part-of-speech sequences describing nominal multiword expressions in Brazilian Portuguese. They correspond to a noun followed by 1 to 3 complements, which can be either an adjective or a prepositional phrase introduced by de. <cand candid="684"> <ngram> <w lemma="conjunto"> <freq name="g1" value="10"/> <freq name="plnbr" value="3005"/> </w> <w lemma="habitacional"> <freq name="g1" value="3"/> <freq name="plnbr" value="359"/> </w> <freq name="g1" value="3"/> <freq name"plnbr" value="86"/> </ngram>.
Features The next module, the Promoter, uses supervised training performed using the 17 features defined below. • Association measures -measure of the strength of the association between the frequency of an n-gram and the frequency of each word that forms the n-gram. In our experiments, four measures were used: normalized frequency, Student's t score, point-wise mutual information and Dice's coefficient. All of these measures were calculated by the mwetoolkit in the two corpora: G1 and PLN-BR. Thus, in total, we have eight features based on association measures. • Translatability -measure based on the non-translatability property of true MWEs. First, we estimate the probability of a content word w 7  to be translated into a word x in English (en) and then back to Portuguese (pt), using a bilingual weighted lexicon: T (w) = x P pt→en (w, x) × P en→pt (x, w) Two new features were proposed based on this probability: translatability_mult = n i=1 T (w i ) translatability_mean = 1 n n i=1 T (w i ) Figure  5  shows an example of these features for the candidate expression taxa de juros (interest rate).
Promoter The last module, the Promoter, analyses the MWE candidates and promotes to beliefs the ones with the best scores. Beliefs are candidates that were classified as true MWEs in a previous iteration of the learner. The Promoter applies a classification model trained using Weka  (Hall et al. , 2009)  as a wrapper and LibSVM  (Chang and Lin, 2011)  as the core. The result is a support vector machine that distinguishes true MWEs from ordinary noun phrases. As training data, it uses previously annotated instances. The Promoter is generated based on examples that were already classified, either manually, for the Promoter-0, or manually+automatically, for the Promoters built in subsequent iterations. SVM was the chosen classifier because it has presented good performance on diverse NLP tasks such as text categorization  (Sassano, 2003) , sentiment analysis  (Mullen and Collier, 2004)  and named entity recognition  (Li et al.
The decay in recall from 65. 5% to 52. 3% from the second to the third generation seems to be related to overfitting. Another possible explanation for this decay is that only the candidate MWEs annotated as true by both annotators were taking into account. Furthermore, since the dataset is unbalanced, the classifier  may tend to classify new candidates always as non MWEs. New experiments will be carried out to investigate this decay. Table  2  shows some examples of MWE candidates extracted by NEMWEL.
Conclusions From the results presented in this paper, it is possible to conclude that the never-ending learning approach can be applied to the automatic extraction of MWEs. Although with just a few iterations (15), it was already possible to see that NEMWEL is able to improve its learning based on previously learned knowledge, with an increase of 10 percentage points in precision. The next steps of this work include running NEMWEL for a long period, ideally 24 hours per day, continuously. It is also our intention to expand NEMWEL to be able to learn other MWEs, from other sources and for different languages, such as English, maybe following a multilingual extraction process. Finally, some new features can be added such as the one that tests the substitutability of a MWE candidate, i. e. , the non-replacement of words that form the MWE candidate by synonyms.
Cardiac electrical asynchrony occurs as a result of cardiac pacing or conduction disorders such as left bundle-branch block (LBBB). Electrically asynchronous activation causes myocardial contraction heterogeneity that can be detrimental for cardiac function. Computational models provide a tool for understanding pathological consequences of dyssynchronous contraction. Simulations of mechanical dyssynchrony within the heart are typically performed using the finite element method, whose computational intensity may present an obstacle to clinical deployment of patient-specific models. We present an alternative based on the CircAdapt lumped-parameter model of the heart and circulatory system, called the MultiPatch module. Cardiac walls are subdivided into an arbitrary number of patches of homogeneous tissue.
Local myofibre strain in the patient simulation shows qualitative agreement with circumferential strain patterns observed in the patient using tagged MRI. We conclude that the MultiPatch module produces realistic regional deformation patterns in the asynchronous heart and that activation time is more important than tissue location within a wall for determining myocardial deformation. The CircAdapt model is therefore capable of fast and realistic simulations of dyssynchronous myocardial deformation embedded within the closed-loop cardiovascular system.
CircAdapt allows rapid simulation of cardiac pump function and cardiovascular system dynamics for both research and educational purposes (www. circadapt. org). The model uses a highly simplified ventricular geometry where the cardiac walls are represented by thickwalled spherical shells consisting of myofibres. In the original CircAdapt model, mechanical properties and load were assumed to be evenly distributed within each wall. In the present study we introduce the MultiPatch module, allowing simulation of the effects of an uneven distribution of mechanical properties in the cardiac walls within the CircAdapt framework. Advantages of this approach over FE implementations include an extremely low computational effort, and a smooth haemodynamic incorporation of the heart within the entire circulation.
The MultiPatch Module In the basic CircAdapt model, wall area determines wall tension. In a cardiac wall, consisting of n different patches, the total area of the patches adds up to the given total wall area. The wall area is the area of the surface that divides the wall volume in half (see Materials and Methods). The MultiPatch module is designed 1) to calculate wall tension; and 2) to determine the area assigned to the different patches. To solve for the unknown areas of n patches we use one equation, saying that the sum of all patch areas equals the given total wall area. Furthermore we use a set of n-1 equations, saying that wall tension in all patches is the same. Thus, in total, n equations are used to solve the area of n patches. From the area and the known constitutive equations of each patch, myofibre strains and stresses are calculated.
MultiPatch Allows Fast Simulation of Asynchronous Activation Simulation of cardiovascular mechanics using the MultiPatch module requires a considerably reduced computational effort compared to FE implementations. Speed of the Matlab implementation can be assessed using CircAdapt files provided as an online supplement that perform all simulations in the present study (. txt files). FE-based models of cardiac mechanics presently run significantly slower than real-time and generally require access to supercomputing facilities. The speed of simulation in CircAdapt allows assessment of the role of factors that require simulation of multiple full cardiac cycles, such as homeostatic control and tissue adaptation  [24, 28]. Real time simulation on regular desktop computers when implemented in C++ enables use of CircAdapt in a teaching environment  [42].
A future application of the Mul-tiPatch module is integration within the CircAdapt Simulator (freely available through www. circadapt. org) to allow education of clinical trainees in the haemodynamic consequences of myocardial scar and conduction disorders, and in the interpretation of regional deformation patterns in the failing heart. Our study focusses on the heart. However, the theory and methods could also be applied to computer models of other contractile organs such as the gastro-intestinal tract or uterus to allow faster simulation  [43, 44].
Patient-Specific Simulation of Ventricular Asynchrony In the current study, we have established that the MultiPatch module in CircAdapt enables simulation of realistic deformation patterns in a patient-specific manner, based on a series of relatively simple parameter changes in the model (Fig  5 ) and invasively recorded activation maps. Combined with the speed at which simulations can be performed in CircAdapt, this provides the opportunity for patient-specific simulations of mechanical dyssynchrony and haemodynamics that can be performed on a personal computer in a bedside context. We have focussed on the effects of asynchronous mechanical activation on deformation in this study, but the model is also capable of simulating regional wall hypocontractility and/or stiffening, consistent with acute or chronic regional myocardial infarction or fibrosis  [30].
Conclusions The MultiPatch module integrated within the CircAdapt model of the heart and circulation produces realistic simulations of local mechanical deformation, work distribution, and global haemodynamic pump function of the dyssynchronous heart. The success of the model in predicting fibre strain patterns suggests that once timing of activation of a region of tissue is known, the location of the tissue is not important for determining of myocardial deformation. We have demonstrated the potential of this model for use in patient-specific simulation of dyssynchronous heart failure, based on clinical recordings of the LV endocardial activation sequence. As a future perspective, comparing patient-specific simulations from the MultiPatch module against recorded myocardial deformation could be used to determine whether the tissue substrate underlying electro-mechanical dyssynchrony in a patient is amenable to CRT.
CSPAMM images were processed using the software Osirix v3. 9. 4 (Osirix Fondation, Geneva, Switzerland). Strain computation was performed with the Sine Wave Modeling method  [47]  using the inTag Osirix plugin (CREATIS-INSA, Lyon, France). Circumferential strain curves were computed for each of the 16 left ventricular segments, according to the standard AHA segmentation, excluding the apical segment  [27].
A 0,w and dT w /dA w do not change between TriSeg iterations and so need only be calculated once per time step. Once the TriSeg solution is found, transmural pressure p trans can be calculated by Laplace's law (Eq 4) using T w and C m. In this way, mechanical behaviour of the total wall is described in relation to the properties of its composing patches. After wall tension has been determined, the true areas A p,j of the separate patches are calculated using T w , dT p,j /dA p,j , and A p0,j in Eq  (5). The fibre stress σ f,j and fibre natural strain ε f,j are then calculated for each patch, using the patch equivalent of Eq (1) and the sarcomere module (online supplement, section 'The CircAdapt sarcomere contraction model'). Different mechanical properties and activation times may be assigned to each patch and so can result in differences in fibre stress and strain between patches, despite having the same wall tension and curvature.
Calculating Patch Stiffness and Unloaded Area We now explain how A p0,j and dT p,j / dAp,j are calculated for use in Eqs 6 and 7. In the CircAdapt sarcomere module, sarcomere length L s is described as follows, L s;j ¼ L si;j þ L se;j ; ð8Þ where L se,j is the length of the series elastic element and L si,j is the intrinsic sarcomere length (online supplement, section 'The CircAdapt sarcomere contraction model'). L se,j is proportional to the active stress generated by the tissue. Assuming that area is proportional to the square of sarcomere length, L s,j is related to patch area A p,j by A p;j ¼ L s;j L sRef ;j ! 2 A pRef ;j : ð9Þ A pRef,j is the reference area of patch j when the sarcomere length is L sRef,j (i. e. 2μm, see  Fig 6). In order to calculate A p0,j , we use Eq 5. Since A w , and hence A p,j , can change during the Tri-Seg iteration, L s is unknown.
We therefore use the intrinsic sarcomere length L si with Eq 9 to compute a first estimate of the patch area, A pi,j. A pi,j corresponds to the area of the patch without the influence of active stress. The first estimate of natural fibre strain in the patch is ε fi;j ¼ ln L si;j L sRef ;j ! : ð10Þ The strain ε fi,j is used by the CircAdapt sarcomere module to calculate the fibre stress σ fi,j and fibre stiffness dσ fi,j / dε fi arising from L si,j. σ fi,j is a component of the fibre passive stress. The stiffness dσ fi,j / dε fi additionally depends on the contractile state of the tissue in the patch since the contractile and passive elements of the sarcomere module are arranged in parallel (online supplement, Eq. 21). Hence dσ fi,j / dε fi is dependent on the timing of patch activation. To calculate the unloaded area A p0 , we must subtract the effects of passive stress due to L si from A pi,j.
Baseline Simulation of the Human Heart and Circulation The CircAdapt model was used to obtain a simulation that represents the healthy human cardiovascular system. A resting cardiac output of 5. 1 l/min and heart rate of 70bpm, and an exercise condition of three times cardiac output and doubled heart rate, were used to adapt tissue volumes and areas in the cardiac walls and large blood vessels as described previously  [24, 28]. Mean arterial pressure of 92 mmHg was maintained at both rest and exercise. The resulting baseline human simulation was used as the basis for all subsequent simulations. All parameters used in the reference simulation may be found in the PRef Matlab structure that is part of the code provided in the online supplement (. txt files).
Comparing Simulated and Experimental / Clinical Data Strain for comparison to MRI data is calculated by taking the model sarcomere length at a reference time point, L s (t 0 ), for each patch, then computing the strain in each patch E j as E j ¼ L s;j L s;j ðt 0 Þ À 1 ð15Þ E j is non-dimensional. For the canine simulations, the reference time is the time of first ventricular activation. For the patient simulation, the reference time is 100ms before aortic valve opening.
JW and JL were funded through a personal grant within the framework of the  Dr E. Dekker program  of the  Dutch Heart Foundation  to JL(https:// www. hartstichting. nl,2012T010). TA, FWP, and TD received no specific funding for this work. ND, PB, HC, and SP were supported by the  French Government ,  Agence National de la Recherche au titre du programme Investissements d'Avenir  (http:// www. agence-nationale-recherche. fr, ANR-10-IAHU-04 ). The funders had no role in study design, data.
, 2011; van Dooren et al. , 2012; Danne et al. , 2013)  (Appendix 2 and Supplementary file 4). Both chromerids and apicomplexans encode modular multi-domain fatty acid synthase I (FASI)/polyketide synthase enzymes and single-domain FASII components (Figure  2 -figure supplement 2A,B). Treatment of Chromera with a FASII inhibitor triclosan showed decreased production of long chain fatty acids (Figure  2 -figure supplement 2C and Appendix 2), suggesting that Chromera synthesizes short-chain saturated fatty acids using the FASI pathway, which are then elongated using the FASII pathway. This was previously demonstrated in Toxoplasma, an apicomplexan that possesses both FASI and FASII  (Mazumdar and Striepen, 2007). Likely, the proto-apicomplexan ancestor was a phototrophic alga harboring characteristic metabolic features previously found only in apicomplexan parasites, especially with regard to plastid-associated metabolic functions (see above and other examples in Appendix 2)  (Ko řen ý et al.
, 2004a; Anantharaman et al. , 2007). Stage III was accompanied by gains of those encoding DBD proteins, mostly apiAP2 proteins (Figure  3  and Figure  3 -figure supplement 1A,B), suggesting extensive regulatory changes mediated by apiAP2 proteins during lineage differentiation. We note that losses of other canonical DBD proteins, for example, proteins with HSF_DNA-bind (Pfam: PF00447) domain during transition to apicomplexan ancestor (Stage II) and proteins with Tub (Pfam: PF01167) domain along the piroplasm lineage, contribute to further dominance of apiAP2 among the DBD proteins (Figure  3 -figure supplement 1C). Stage II was accompanied by over-represented gains of various cytoskeletal components, including alveolins, those of the actomyosin complex (e. g. , myosins) and glideosome-associated proteins with multiple membrane spans 1 and 3 (GAPM1 and GAPM3), suggesting that the molecular machinery powering gliding motility, which is essential for host cell invasion arose during evolution to apicomplexans  (Frenal et al.
, 2010)  (Figure  3 , Figure  3 -figure supplement 2, and Appendix 4). Gene gains during Stage I were overrepresented by proteins with 'RBD abundant in Apicomplexans' (RAP, Pfam: PF08373) (  Lee and Hong, 2004) , many of which were conserved as one-to-one orthologs across descending lineages, suggesting development of evolutionarily conserved functions before apicomplexans and chromerids diverged (Figure  3 , and Figure  3 -figure supplement 3). Chromerid genomes encode many orthologs of apicomplexan cytoskeletal proteins (Appendix 4), including GAPM2, a member of an important protein family for apicomplexan cytoskeletal structure and gliding motility  (Bullen et al. , 2009) , and the IMC sub-compartment protein family (ISP), implicated in establishing apical polarity and coordinating the unique cell cycle of apicomplexans  (Poulin et al.
Data access Sequencing data have been deposited in the European Bioinformatics Institute under the European Nucleotide Archive (ENA) sample accession number ERP006228 for C. velia and ERP006229 for V. brassicaformis for all DNA-and RNA-seq experiments. The assembly and the annotations were submitted under accession numbers CDMZ01000001-CDMZ01005953 for C. velia and CDMY01000001-CDMY01001064 for V. brassicaformis. Some of the Vitrella DNA-seq experiments were done at Broad Institute and are deposited at Short Read Archive under accession numbers SRX152523 and SRX152525. The annotations and assemblies can be viewed and queried in EupathDB (http://cryptodb. org/cryptodb/).