dataesr/scientific-paragraphs-categorization huggingface dataset:
Typically, o20 NR localizations were obtained in a background image (10. 9 Â 10. 9 mm area) and were not considered as clusters using the same DBSCAN parameters used for data in Figs  2  and 3. The sPAINT analysis macro (see Supporting code, Supplementary Data 4) with beta parameters (determined in step 1-1 x and y) was run using clustered localizations file to retrieve the spectral component of the image. Step 3. sPAINT Rendering. sPAINT rendering was performed using a homemade written plugin for ImageJ (Supplementary Data 5). Each localization was rendered using a numerical erf-based approximation of a discretized 2D Gaussian (integral normalized to unity) with widths in x and y-directions depending on localization precision  51, 52. The spectral information at each pixel was calculated as the average of wavelengths observed at this particular pixel weighted by their respective localization intensities and pixels were finally falsely jet-coloured as an overlay over the localization density image.
For analysis of experimental data, an ImageJ plugin and example sPAINT raw data of LUVs are provided  48. Instrument stability. The achievable spatial localization precision, s, of the instrument was measured by imaging diffraction-limited TetraSpeck beads (0. 1 mm, T7279, Invitrogen) using a 532 nm excitation laser. 2,000 frames at 10 ms per frame were collected at a range of excitation powers and localized using the PeakFit plugin for ImageJ from the GDSC SMLM package. Beads localized in at least half the total number of recorded frames were analysed. TetraSpeck 'orange' peak centres, at B580 nm, were fitted and used to determine the spectral localization precision s. The same analysis was also use to evaluate the localization precision for the LUV data (Supplementary Fig. 6 ). Precision analysis was conducted using MATLAB software and custom written code.
Acknowledgements This work was supported by grants from the  MRC  (D. K. , MR/ K015850/1 ),  EPSRC ,  Royal Society   University Research Fellowship  (S. F. L. ,  UF120277 ) and the  Augustus Newman Foundation  (M. H. H. ). We acknowledge the  Cambridge Advanced Imaging Centre  for the  TEM images and Christ's College  (M. H. H. ). We thank the members of the  Klenerman and Lee research groups  for their input and discussion, in particular we would like to thank  Franziska Kundel ,  Jason Sang  and  Dr Janet Kumita  for their helpful contributions.
In recent years, new methods for computational RNA design have been developed and applied to various problems in synthetic biology and nanotechnology. Lately, there is considerable interest in incorporating essential biological information when solving the inverse RNA folding problem. Correspondingly, RNAfbinv aims at including biologically meaningful constraints and is the only program to-date that performs a fragment-based design of RNA sequences. In doing so it allows the design of sequences that do not necessarily exactly fold into the target, as long as the overall coarse-grained tree graph shape is preserved. Augmented by the weighted sampling algorithm of incaRNAtion, our web server called incaRNAfbinv implements the method devised in RNAfbinv and offers an interactive environment for the inverse folding of RNA using a fragment-based design approach.
This recent use was also worked out for IRESlike structural subdomain identification in  (14). It has potential to advance the field described in  (15)  for conserved RNAs in general. Thus, computational RNA design is of increasing biological importance. Since the first program for solving the inverse RNA folding problem (or RNA design) called RNAinverse was put forth in  (12) , several other programs were developed. The approach to solve it by stochastic optimization relies on the solution of the direct problem using software available in RNA folding prediction web servers, e. g. the RNAfold server  (16)  or mfold  (17) , by performing energy minimization with thermodynamic parameters  (18). Initially, a seed sequence is chosen, after which a local search strategy is used to mutate the seed and apply repeatedly the direct problem of RNA folding prediction by energy minimization.
This extension, when offering a fragment selection to the user, is called 'fragment-based' design because it is based on a user-selected secondary structure motif (the fragment) that possesses a functional role and is therefore inserted as a 'fragment-based' constraint to the design problem. The shape of the RNA can be represented as a treegraph  (33)  that groups together a family of RNA secondary structures, all belonging to the same coarse grained graphical representation. The aforementioned extension led to a unique inverse RNA folding program called RNAfbinv (32) that to the best of our knowledge is more general in scope than any existing program in its shape-based approach. In that regard a shape-based approach is more general than a structurebased approach by allowing more designed sequences as solutions to the design problem, although other generalizations like pseudoknot inclusion that can be found in several other programs have not yet been implemented in our program.
In parallel, controlling nucleotide distribution in RNA design problems was addressed in our presented web server in a unique way by a weighted sampling approach  (34). The approach is of general importance for the future of inverse RNA folding because instead of a random start performing a local search, the initial sequence for performing the iterative procedure of solving the inverse problem is carefully picked by using global considerations in a guided manner in search space. The program for the weighted sampling approach called incaRNAation has so far only been exemplified in  (34)  for RNA design. The incaRNAfbinv web server described herein is a merge between RNAfbinv (32) and incaRNAtion  (34). It offers sequence design solutions that to the best of our knowledge are not available in neither the most recently published programs for RNA design, namely antaRNA  (26)  and RNAiFold  (27) , nor any other such program that was devised since the seminal program called RNAinverse from the Vienna RNA package  (12)  was put forth.
It should be noted that incaRNAfbinv relies on other programs aside of RNAinverse that are available in the Vienna RNA package such as RNAfold that solves a direct problem at each iteration and RNAdistance. Moreover, pseudoknots have not yet been implemented in our designed program, as well as some experimental constraints, such as avoiding transcription slippage in the case of consecutive G nucleotides. In the future, it will be desired to add these features and others of experimental type to our program, as more experimental results with designed sequences obtained from our program are accumulated. In the following sections, the incaRNAfbinv web server and its method are described.
WEB SERVER The incaRNAfbinv web server is available at http://www. cs. bgu. ac. il/incaRNAfbinv. It runs on a Unix Lenovo system x3650 M5 server with Dual Intel(R) Xeon(R) CPU E5-2620 v3 2. 4GHz processors containing six logical cores and 15MB L3 cache each. The backend is written in Java EE and run on Tomcat 8. It dispatches design tasks responsible for running incaRNAtion  (34)  and RNAfbinv  (32). Every design task runs on up to four cores depending on load. The server runs up to ten simultaneous design tasks while the rest wait in a queue. The frontend is designed using the Bootstrap css framework. Web pages are generated using JSP and JSTL. They utilize JavaScript, Jquery, JSON and ajax.
The RNAfbinv program that implements this type of sequence design, as described in  (32) , relies on programs from the Vienna RNA package such as RNAfold, RNAinverse, RNAdistance  (12). Most of the constraints are inserted to the objective function in an additive manner with proper weights. This raises compatibility issues with rigid constraints like sequence constraints, which could also be inserted to the objective function in future work although at present they are left as rigid constraints for simplicity. For any user-provided target structure S , it attempts to find sequences whose MFE folding S minimizes the following objective function: f (S, S ) = 10 3 × target motif exists(S) + 10 2 × neutrality Sneutrality S + 10 2 × tree edit dist shapiro(S , S) + 1 × | G S -G S | + 10 -2 × base pair dist dotBracket(S , S).
The base-pair distance is evaluated by the RNAdistance program from the Vienna RNA package. The minimum free energy G is for measuring thermodynamic stability in kcal/mol, therefore a unity weight is assigned. All distances between secondary structures are calculated using RNAdistance in the Vienna RNA package (12) (supporting both the coarsegrain tree graphs called the Shapiro representation  (33) , and the dot bracket representation of the secondary structure). For the tree edit distance between Shapiro representations, a relatively large weight of 100 is chosen for shape preservation, while for the base pair distance in the last term, a very small weight of 10 -2 is assigned. This last term is the one used in the original RNAinverse (12) for preserving the exact secondary structure and its purpose is to protect the solutions from being over-dominated by shape.
The remainder of this article is structured as follows: First, Section 2 provides a brief overview of acoustic analyses in the fields of psychology, phonetics, acoustics, and engineering which are the basis of the recommendation proposed in this article; next, in Section 3 we give a detailed description of the acoustic parameters contained in the recommended parameter set and the implementation thereof. The parameter set is extensively evaluated on six well-known affective speech databases and the classification performance is compared to all high-dimensional brute-forced sets of the INTERSPEECH Challenges on Emotion and Paralinguistics from 2009 to 2013 in Section 4. Final remarks on the parameters recommended in this article and the classification performance relative to other established sets as well as a discussion on the direction of future research in this field are given in Section 5.
BASELINE EVALUATION The proposed minimalistic parameter set and the extended set are both evaluated for the task of automatic recognition in binary arousal and binary valence dimensions. The original labels (mixed various categories and continuous dimensional) of six standard databases of affective speech were mapped to binary dimensional labels (Arousal/Valence), as described in Section 4. 2 in order to enable a fair comparison of performances on these databases. The original labels (cf. Section 4. 1 for details on the databases) are: Levels of Interest (TUM AVIC database), acted speech emotions in the Geneva Multimodal Emotion Portrayals (GEMEP) corpus and the German Berlin Emotional Speech database (EMO-DB), emotions portrayed in the singing voice of professional opera singers (GeSiE), valence in childrens' speech from the FAU AIBO corpus  [42]  as used for the INTERSPEECH 2009 Emotion Challenge  [43] , as well as real-life emotions from German talk-show recordings (Vera-am-Mittag corpus (VAM)).
Berlin Emotional Speech Database A very well known and widely used set to test the effectiveness of automatic emotion classification is the Berlin Emotion Speech Database, also commonly known as EMO-DB. It was introduced by  [48]. It contains sentences spoken in the emotion categories anger, boredom, disgust, fear, joy, neutrality, and sadness. The linguistic content is predefined by ten German short sentences, which are emotionally neutral, such as "Der Lappen liegt auf dem Eisschrank" (The cloth is lying on the fridge. ). Ten (five of them female) professional actors speak 10 sentences in each of the seven emotional states. While the whole set contains over 700 utterances, in a listening test only 494 phrases are labelled as a minimum 60 percent naturally sounding and a minimum 80 percent identifiable (with respect to the emotion) by 20 people.
Geneva Singing Voice Emotion Database This database of singing emotional speech was first introduced by  [50]. Here, an extended set of the database is used (abbreviated as GeSiE). Compared to the original set which contains three singers, additional recordings of five professional opera singers have been added following the same protocol. In total the recordings present are from five male and three female singers. The singers sung three different phrases and tone scales in ten emotion categories: neutral (no expression), panic/fear, passionate love, tense arousal, animated joy, triumphant pride, anger, sadness, tenderness, calm/serenity, condescension. Every recording session was recorded in one continuous stream without pause. The recordings were afterwards manually split into the phrase and scale parts.
Vera-Am-Mittag The Vera-Am-Mittag corpus  [51]  consists of videos extracted from the German TV show "Vera am Mittag". In this show, the host (Vera) moderates discussions between the guests, e. g. , by using questions to guide the discussion. The database contains 947 emotionally rich, spontaneous speech utterances sampled from 47 talk show guests. The discussions were authentic and not scripted and due to the nature of the show and the selection of guests these discussions rather quite affective and contain a large variety of highly emotional states. The topics discussed in the show were mostly personal issues, like friendship crises, fatherhood questions, or love affairs. At the time of the recording of the TV show, the subjects were not aware that the recordings were ever going to be analysed in scientific studies.
Experimental Protocol All experiments, except those on AIBO, are performed using the Leave-One-Speaker(Group)-Out (LOSO) cross-validation. Note, that for FAU AIBO, due to the nature of the original five class labels, only a mapping to binary valence is feasible. Thereby, if the number of speakers in the corpus is smaller or equal to eight (only for GeSiE), data from each speaker is seen as one cross-validation fold. For more than eight speakers, the speaker IDs are arranged randomly into eight speaker groups and the data is partitioned into eight folds according to this grouping. The cross-validation is then performed by training eight different models, each on data from seven folds, leaving out the first fold for testing for the first model, the second fold for testing for the second model, and so on.
Results We compare the results obtained with the proposed minimalistic parameter sets with large state-of-the-art bruteforced parameter sets from the series of Interspeech Challenges on Emotion in 2009  [43]  (InterSp09), Age and Gender as well as level of interest in 2010  [36]  (InterSp10), Speaker States in 2011  [44]  (InterSp11), Speaker Traits in 2012  [45]  (InterSp12), and Computational Paralinguistics in 2013 and 2014  [12] ,  [37]  (ComParE). Table  2  shows the summarised results obtained for binary arousal and binary valence classification. In order to eliminate all variables except the parameter set, the results are averaged over five databases (all, except FAU AIBO) and the highest nine SVM complexity settings, starting at C ¼ 0:0025. The decision to average only over the higher complexity settings was taken because at complexities lower than this threshold, performance drops significantly for the smaller feature sets, which biases the averaging.
A high efficiency of the GeMAPS sets is shown by the average results. The eGeMAPS set performs best for arousal, reaching almost 80 percent UAR, while it is third best for valence (close behind the two largest sets-ComParE and the Interspeech 2012 speaker trait set). When looking at individual results (Table  3 ), i. e. , when selecting the best C value for each feature set and database, the GeMAPS sets are outperformed for the classification of categories always by the large ComParE or Interspeech 2012 sets, and are outperformed in many cases by the Interspeech 2009-2011 sets for binary arousal and valence classification. More detailed results are given in plots in the Appendix (Section 6. 2). The eGeMAPS set gives the best result for  4. Best result for FAU AIBO obtained with downsampling (not upsampling) because the computational complexity of upsampling with high dimensional parameter sets in relation to the expected accuracy gain was too high.
binary arousal classification on the GEMEP database and for binary valence classification on the GeSiE database. However, it can be concluded that the eGeMAPS set is always superior or equal to the GeMAPS set, which is an indication that the additional parameters (MFCC and spectral flux in particular) are important. This is in particular the case for valence where the average difference between GeMAPS and eGeMAPS is larger, suggesting the importance of those parameters for acoustic valence. Yet, also for valence, the difference between the GeMAPS sets and the large Interspeech Challenge sets (esp. ComParE with its 6 373 parameters) is large compared with arousal (except for the databases GeSiE and VAM-again, the latter not being representative for valence; GeSiE contains sung speech, which is different in nature).
Again, this suggests that for valence further important parameters must be identified in future work, starting with a deep parameter analysis of the Com-ParE set, for example. Although slightly behind the large-scale parameter sets on average, overall, the GeMAPS sets show remarkably comparable performance given their minimalistic size of less than 2 percent of the largest (ComParE) set. In future studies it should be investigated, whether the proposed minimalistic sets are able to obtain better generalisation in cross-database classification experiments.
Not all parameters which have been found to be relevant or correlated to certain phenomena can be reliably extracted automatically though. For example a vowel-based formant analysis requires a reliable automatic vowel detection and classification system. Thus, with GeMAPS, only those parameters which can be extracted reliably and without supervision in clean acoustic conditions have been included. The validation experiments were restricted to binary classification experiments in order to allow for best comparability across databases. The performance in regression tasks might differ. Although we can believe, that due to the solid theoretical foundations of the selected features, the set will yield good performance also for regression tasks, this should be investigated in follow up work. Another potential danger of automatic extraction of a standard parameter set is that the link to production phenomena may be neglected.
Detailed Results This section shows detailed results in plots which compare all investigated acoustic parameter sets for each database over a range of SVM complexity constants. For details on the experimental set-up, please refer to Section 4. 3. Results for the TUM-AVIC database are shown in Fig. 1 , for EMO-DB in Fig. 2 , for GEMEP in Fig. 3 , for GeSiE in Fig. 4 , and for the VAM database in Fig. 5. It can be seen that the proper tuning of classifier parameters (e. g. , SVM complexity) is more crucial for the smaller sets, and generally higher complexities are preferred. On the GEMEP and Geneva GeSiE sets the GeMAPS sets are outperformed by the larger, brute-force sets such as the ComParE or IS11 set, but on more naturalistic databases, such as TUM-AVIC and VAM, the results are on par with the larger sets, at a fraction of the dimensionality.
ACKNOWLEDGMENTS The authors would like to thank  Tanja B€ anziger ,  Pascal Belin , and  Olivier Lartillot  for their helpful contributions and inspiring discussions at the  Geneva Bridge Meeting , which started our joint effort to create this parameter set recommendation. This research was supported by an  RC Advanced Grant  in the European Community's  seventh Framework Programme  under grant agreement  230331-PROPEREMO  (Production and perception of emotion: an affective sciences approach) to  Klaus Scherer  and by the  National Center of Competence in Research (NCCR) Affective Sciences  financed by the  Swiss National Science Foundation  ( 51NF40-104897 ) and hosted by the  University of Geneva.
In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologies and application start to be mature, becoming more realistic about how Big Data can be useful for organizations. NoSQL data stores are becoming widely used to handle Big Data; these databases operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema. But, there is still a need for a conceptual model to define how data will be structured in the database. In this paper, we show how to store Big Data within NoSQL systems. For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from a conceptual model that describes a set of complex objects, we propose transformation rules formalized with QVT to generate a column-oriented NoSQL model.
Big Data bring many attractive opportunities to knowledge management  (Fredriksson, 2015). Simultaneously, to extract knowledge from Big Data, we have to face a lot of challenges mainly related to Big Data storage and process; our focus in this paper is only on Big Data storage. Using relational databases proves to be inadequate for all applications, particularly ones involving large volumes of data  (Abello, 2015). As a result, a new kind of databases has appeared, known as "NoSQL" data stores, that are able to handle Big Data with high performance  (Angadi, 2013). The key feature of NoSQL databases is that they are schema-less, meaning that data can be inserted in the database without upfront schema definition. This feature enables applications to quickly and easily modify data without rewriting tables when new data are encountered.
Nevertheless, there is still a need for a semantic data model to define how data will be structured and related in the database  (Daniel, 2016) ; it is generally accepted that UML meets this requirement  (Abello, 2015). Therefore, the purpose of this paper is to present how to store Big Data in NoSQL databases. For this, we propose a MDAbased approach that transforms an UML conceptual model describing Big Data into a NoSQL model. The rest of the paper is structured as follows. Section 2, we motivate our work using a case study taken from the healthcare field. Section 3 reviews previous work on models transformation. Section 4 shows our MDA-based approach that aims to map an UML conceptual model into NoSQL model. Section 5 details our experiments. Finally, section 6 ends up with the conclusion and future work.
Objective Our purpose is to implement a conceptual model describing Big Data into NoSQL database. There are four basic types of NoSQL databases: key-value, document-oriented, column-oriented and graphoriented. In this paper, we choose to focus on column-oriented NoSQL model. This model is considered to be the most efficient in terms of performance, for multi-criteria access queries (vertical data organization with columns-families)  (Abadi, 2012 ). An overview of our approach is illustrated in figure  1. Starting from a conceptual model, we propose a transformation process to automatically generate a NoSQL logical model. We introduce this logical-level model between conceptual level (business description) and physical level (technical description) to limit the impacts related to technical details of NoSQL platforms.
Indeed, there are many column-oriented NoSQL databases available, such as HBase, Cassandra, Accumulo, etc. Each one has its own technical aspects. For example, for the medical program, several column-oriented NoSQL databases can be used in the same interestablishment software architecture; each establishment may have a specific column-oriented database. To overcome this situation, we propose a logical model independent of a particular NoSQL platform and can be easily implemented in several columnoriented databases independently of their specific technical details.