dataesr/scientific-paragraphs-categorization huggingface dataset:
Rule-based systems Among the rule-based systems dedicated to negation detection, NegEx  (Chapman et al. 2001) , which pioneered the area, is still popular. This system uses regular expressions to detect the cues and to identify medical terms in their scope. Later this system was adapted to other languages such as Swedish  (Velupillai, Dalianis and Kvist 2011)  and French  (Deléger and Grouin 2012). NegFinder  (Mutalik, Deshpande and Nadkarni 2001 ) is another pioneering system which combines a lexical analyzer, which uses regular expressions to generate a finite state machine, and a parser, which relies on a restricted subset of the non-contextual Look-Ahead Left Recursive grammar. NegFinder makes it possible to identify the concepts impacted by negation in medical texts when they are close to the linguistic units marking the negation.
Derived from NegEx, ConText  (Harkema et al. 2009 ) covers additional objectives. This system detects negation, temporality, as well as the subject concerned by this information in the clinical texts. It has been adapted to French  (Abdaoui et al. 2017). NegBio  (Peng et al. 2017 ) relies on rules defined from the universal dependency graph (UDG). The code for this system is available online. a.
In French, the two word embedding models are trained using the Skip-Gram algorithm, 100 dimensions, a window of five words left and right, a minimum count of five occurrences for each word, and negative sampling. The training data are composed of the French Wikipedia articles and biomedical data. The latter includes the ESSAI and CAS corpora, the French Medical Corpus from CRTT, e and the Corpus QUAERO Médical du français f  (Névéol et al. 2014 ). These models are trained using the Gensim g (Rehurek and Sojka 2010) python library. In Brazilian Portuguese, we use the pre-trained models available on the NILC (Núcleo Interinstitucional de Lingustica Computacional) website h  (Hartmann et al. 2017 ). These models were obtained using the Skip-Gram algorithm and 100 dimensions. In English, we use two fastText models.
The original fastText model: 1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and statmt. org news dataset (16B tokens); and our own model trained on Conan Doyle's novels, the assumption being that this domain-specific model will outperform models trained on generic data. The latter was trained with Gensim using CBOW, hierarchical softmax, and 100 dimensions for 100 epochs. For all languages, we also use randomly initialized vectors as input to our neural networks. In this case, the weights are initialized very close to zero, but randomly. Lemma and part-of-speech embeddings are randomly initialized as well. Then, those embeddings' weights are updated during training.
The created model is therefore simpler than the model obtained with the standard LSTM. In our experiments, we used a bidirectional recurrent neural network, which operates forward and backward, to detect both negation cues and scopes. The backward pass is particularly relevant for the scope detection because the negated words may occur before or after the cue. Implemented with Keras using TensorFlow  (Abadi et al. 2016)  as backend, our systems include French-language and Brazilian Portuguese-language versions of the bidirectional LSTM inspired by  Fancellu et al. (2016) , as well as a bidirectional GRU (BiGRU). Prediction is computed either by a softmax layer, which is the most common method, or by a CRF layer, which seems to be particularly suitable for sequence labeling. An overview of the BiRNN architecture is shown in Figure  7.
The scope detection task In all of the proposed scope detection experiments, the neural networks are trained only on sentences with negations. The base system takes an instance I(n, c, t) as its input, where each word is represented by: n vector with word-embedding, c vector with cue-embedding, t vector with postagembedding. Cue and PoS-tag vectors are randomly initialized. We use embedding layers of 100 dimensions. For each system, we use the same empirically defined hyperparameters given before. During training, the embeddings weights are updated. Each of our corpora has been randomly segmented into the training set (80%, 20% for validation) and the test set (20%). We train and evaluate our systems on the * SEM-2012 datasets to get comparable results in English as well.
Indeed, when computed on the * SEM-2012 data, our BiLSTM-CRF trained with FT-D gets a much higher F 1 than  Li and Lu (2018)  in terms of correctly labeled tokens (+1. 27 points); however, we only get slightly higher results for the exact scope match (+0. 41 points). Besides, in the medical domain, the returned scopes need to be as precise as possible and not to include medical concepts, which is more correctly managed by the CRF output layer. In both languages, we get better results on clinical data (clinical narratives and clinical cases) than on clinical trials. Overall, we get the best results on the CAS corpus with French clinical cases, with outstanding F 1 scores for the exact scope match. These results indicate that negative instances in clinical documents have simpler and more stable structures: typically, they contain less discontinuous cues and gaps in scopes.
Conclusion and future work The interest for automatic detection of negation in English with supervised machine learning has increased in the recent years. Yet, the lack of data for other languages and for specialized domains hampers the further development of such approaches. In this work, after presenting the difficulties related to this task and after a brief reminder of existing work, we presented new bodies of biomedical data in French and Brazilian Portuguese annotated with information on the negation (cues and their scope). Prior to the dissemination to the research community, the French and Brazilian clinical trial protocols corpora will be finalized through the integration of new data and the computation of the IAA. The French CAS corpus will be distributed as more automatically annotated sentences are corrected.
The Brazilian corpus with clinical narratives may prove to be more difficult to share with the community, as it contains data on real patients. Another contribution of our work is the exploitation of different types of word vector representations and recurrent neural networks for the automatic recognition of the cues and scope of negation. The experiments are conducted and evaluated in three languages: in English, which has independent reference data, and in French and Brazilian Portuguese, which have not experienced much work of this type, especially for the biomedical domain which contains specific negation phenomena. Our system yields state of the art results on * SEM-2012, which validates our approach. It also shows good performances on our corpora except for the Brazilian clinical protocols.
a https://github. com/ncbi-nlp/NegBio, last accessed in March, 9th 2020. https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S1351324920000352 Downloaded from https://www. cambridge. org/core. IP address: 88. 121. 52. 23, on 24 Nov 2020 at 09:26:33, subject to the Cambridge Core terms of use, available at.
i https://github. com/ffancellu/NegNN https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S1351324920000352 Downloaded from https://www. cambridge. org/core. IP address: 88. 121. 52. 23, on 24 Nov 2020 at 09:26:33, subject to the Cambridge Core terms of use, available at.
Alignment For the alignment, the software consists of a MPI wrapper to the BWA-MEM software such that the original code remains unchanged. The wrapper uses parallel IO and shared memory (Figure  1 ). It first parses the input FASTQ files in order to define chunks of equal size. The different chunks are actually represented as two offsets (start and end) from the original FASTQ file thus reducing the amount of information to store. The reference genome of your choice is loaded into the shared memory on each computer node. Then, each chunk is processed in parallel threads by the original BWA-MEM algorithm. The results are finally written thanks to a shared file pointer in a unique SAM file. For a human genome, the size of the reference genome file in the shared memory takes around 5 GB and the total memory used by a BWA-MEM thread is around 300 MB.
In this case, the total amount of memory used is around 1. 5 the size of the original SAM file if the file contains reads on all the chromosomes of the human genome. If the SAM file contains only one chromosome, then the total amount of memory required is 2. 5 the size of the original SAM file. To be efficient, the sorting requires a number of cores being a power of 2. We name our code for sorting mpiSORT  (Jarlier et al. , 2020b).
Implementation The code has been written in C programming language using MPI directives. mpiBWA encapsulates the original BWA-MEM version 7. 15. Two implementations for mpiBWA exist: the first outputs one SAM file with all the chromosomes, the second (named mpiBWAByChr) outputs one SAM file per chromosome. Moreover, mpiBWA comes along with mpiBWAIdx, which is responsible for creating a binary image of the reference genome. This image is subsequently loaded in the shared memory by mpiBWA. Then, every mpiBWA process on the same computing node will share the same genome reference in order to save memory usage. mpiBWAIdx does not need MPI to run.
Use cases The Figure  5  shows the scalability of both mpiBWA (with the mpiBWAByChr implementation) and mpiSORT with varying sample sizes (from 28X to 300X) using computation distributed over 128 cores. The output files from mpiBWAByChr have been used as input by mpiSORT. Both algorithms efficiently scale as the input data are bigger, the walltime to process the data being proportional to their input size. We assessed how much time was spent on pure computation versus IO (i. e. reading the input files and writing the output files): mpiBWA spent more than 95% of its walltime in computation while mpiSORT spent between 50% to 60% in IO. The walltime to analyze the biggest sample of 300X is less than 8h hours for the alignment and less than one hour for the sorting. The Figure  6  compares the performance of both mpiBWA (with the mpiBWAByChr implementation) and mpiSORT with varying number of cores and nodes with respect to the classical tools BWA-MEM version 7.
fa \ HCC1187C_R1_10K. fastq \ HCC1187C_R2_10K. fastq The total memory used during the alignment if approximately the size of the. map file plus the size of the SAM chunks loaded by each BWA tasks. A BWA thread takes around 300 MB of memory. mpiSORT From the results obtained with mpiBWAByChr, each SAM files can be sorted as follows: mpirun -n 4 mpiSORT -u chr1. sam ${HOME}/mpiSORTExample This command launches four MPI processes. As mpiSORT requires a the entire input SAM file to be loaded into the memory, the program is memory bounded. Therefore, a lot of attention has to be paid to define how many cores are needed to process the data. For example, let's assume that the computing cluster consists of nodes with 190 GB of RAM memory with 40 cores each (thus 4. 75 GB per core is available but this value can be rounded to the lower limit of 4.
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: bioinformatics and medical informatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Author Response 02 Oct 2020 Philippe Hupé, Institut Curie, Paris, France First, we would like to thank the second reviewer. We are very grateful for her time and very valuable comments that significantly helped to improve the article and the documentation of the tools.
Frédéric Jarlier and Philippe Hupé Detailed response to the reviewer2's comments > I do wish the authors would could also provide pseudo-code of their implementation, which will help readers to understand the process better. Indeed to facilitate the reading and understanding of both mpiBWA and mpiSORT algorithms, we added 2 figures with the pseudo-code. > Another minor point is that given the software's performance strongly relies on the underlying hardware architecture, and it would also be helpful for the authors to provide helper script(s) that help to calculate the number of cores to use on particular data input and computing infrastructure In order to help the user when writing submission scripts to a computing cluster, we added two scripts in the example folder of the github repository of mpiSORT.
> Otherwise, from which point is better to use the MPI version than the most common software. I. e. in case the MPI is 2 times slower but scales linearly, the execution time would be shorter when giving the MPI implementation at least 2 nodes. > The results of the scalability benchmark and the comparison with respect to the traditional tools demonstrate that both mpiBWA and mpiSORT perform efficiently on a single node and on multiple nodes. > This way, the potential users would know better the scenarios in which using this implementation becomes more interesting. > The results of the scalability benchmark show that the MPI implementation can be very versatile. Our MPI implementations can easily address the two typical scenarii: when time-to-delivery matters, the scalability allows the processing of the data very quickly using multiple cores and nodes.
Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed.
and sort the sequencing data, we developed software > align and sort sequencing data, we have developed software. > > Sorting section: by a pair of processors with performs a compare-split operation > by a pair of processors which performs a compare-split operation. >The typos have been corrected. Competing Interests:No competing interests were disclosed. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage •.
The authors declare that Institut Curie and Intel Corporation have a partnership. Grant information: This work was supported by  national infrastructure France Génomique  (contract  ANR-10-INBS-09 ) and  ABS4NGS  project ( ANR-11-BINF-0001 ) funded as part of  Investissements d'Avenir program  managed by the  Agence Nationale de la Recherche. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
You have mentioned there are similar approximations to this problem. However, you just mention them in the conclusions. Wouldn't it make sense to discuss similarities and differences with other implementations in the text? The documentation is available here: https://github. com/bioinfo-pf- curie/mpiSORT/blob/master/docs/README. md#benchmark ○https://github. com/bioinfo-pfcurie/mpiBWA/blob/master/docs/README. md#benchmark ○ >.
Services and Architectures Working Group (SA2) considers also the use of satellite as transport/backhaul link offering the interconnection of local access networks to 3GPP core network. [6]  Finally, terrestrial and satellite networks may use different or the same radio frequencies in the operation. In the latter case, it is possible for systems to coexist in the same band, and the operations of the integrated end-to-end satellite terrestrial network are managed by the spectrum management entity that governs the shared spectrum use for both systems. Spectrum sharing in satellite bands has been studied actively over the last decade,  [7] [8] [9] [10] [11]  and database-assisted systems have been found to provide a way to share the spectrum while proving sufficient certainties for the interference-free operation.
6-3. 8 GHz band. 23 here are also other database-assisted approaches implemented and trialed in recent years such as Citizens Broadband Radio Service (CBRS) system in 3. 55-3. 7 GHz band. 24 It includes standardized messaging protocols, which can be applied in LSA system as well. Finally, TV white space (TVWS) technology relies also on database-assisted approach. Information on implemented systems and coexistence measurements can be found in Murty et al. and Kalliovaara et al. 25, 26.
Real hardware tests with the BS controller, which Includes capabilities of adaptive power and frequency control, and Manages evacuation and frequency change times. LSA system functionalities were implemented in an end-to-end testbed used also for the live trials. The testbed architecture and UI are presented in Figure  2. The LSA system consists of an LR and an LC. The LR contains information about satellite Earth stations and IMT BSs characteristics as well as protection criteria and associated technical rules for sharing. The incumbent data are entered into the repository through a UI. In addition, some characteristics such as the location and characteristics of the licensed radio transmitters can also be uploaded from a national regulatory authority (NRA) license database, for example, as a CSV file.
| Trial and performance measurements The key performance indicators (KPIs) of the described testbed are shown in Table  2. We evaluated the testbed using the selected KPIs and assessed the scalability of the testbed. Regarding the registration time (KPI-1), the analysis in Finland showed that we were able to acquire up-to-date regulatory data regarding the use of certain frequency band and use that in the LSA system. The data include, for example, locations of IMT BSs and operational parameters. Obtaining the incumbent data typically differs between countries, incumbent types, and frequency bands. The process of handling the incumbent data from the incumbent notification, possibly processing it at the regulator and inserting it to the LSA system is taken into account in communicating the system performance to incumbents and licensees.
In some countries, the national regulator may not know all technical operational information required for the LSA implementation. In this study, we analyzed only the delay caused by the LSA system. The delay can be significant for two reasons: (a) the amount of incumbent data is huge requiring a long time for data transfer and insertion into the database or (b) the incumbent data need preprocessing, for example, if part of processing is done for all incumbent and geographic data before any requests come to the LSA system. Here, we did not have any preprocessing and the amount of data was not causing any significant delays. The incumbent data were entered through a web page. For an experienced user, entering the incumbent information took typically about 30-60 s.
| Scalability of the testbed The implemented LSA system can handle large amount of real and simulated BSs. In order to study the scalability of the testbed, we made performance measurements considering that the FSS Earth station in the C-band would be located in the Paris area. We registered up to 1000 BSs in the LSA system using the real network parameters obtained from the French regulatory authority public database 33 using locations of 2. 6 GHz BSs and converting them to 3. 5 GHz devices. The average results over multiple experiments regarding the registration of the BSs to the LSA system and the spectrum inquiry times are shown in Figures  7  and 8 , respectively. The registration of a single BS takes 275 ms. It is easy to register simultaneously multiple BSs using the same registration file where locations and operational parameters are stored in the system.
This is done with the protection zone computation in the system. The amount of GSM spectrum is 2 × 35 MHz (duplex) and could be completely reused by the satellite system. In our example, the interferer is a large multibeam geostationary Earth orbit (GEO) satellite with 121 beams providing voice and data communication using a Digital Video We modeled the satellite system and evaluated through simulation the maximum possible equivalent isotropic radiated power (EIRP) from the satellite (i. e. , downlink) that would not cause harmful interference to the cellular system (i. e. , respect the interference to noise [I/N] level below -6 dB). 34 The interferences at the mobile terminals and at the BSs were analyzed with Systems Tool Kit (STK). 35 We modeled terrestrial terminals with omni-directional antennas and BSs with directional antennas with 13 dBi gain in the boresight direction.
ACKNOWLEDGMENTS The work has been funded by the  European Space Agency ARTES  Project "ASCENT: Demonstrator for license assisted spectrum access satellite networks, contract no. 4000123000/18/NL/WE. "The authors would like to acknowledge fruitful discussions with the advisory board members of the project, that is, Eutelsat,  Intelsat, Avanti, Inmarsat , and  Nokia. The views expressed in the paper are those of the authors and do not reflect the official opinion of the  European Space Agency  or the advisory board.
I. INTRODUCTION F ISHER'S linear discriminant analysis (LDA) and principal component analysis (PCA) can be considered as two pillars of data analysis  [1]. Given a dataset with two classes, LDA finds a projection of the data points onto a one-dimensional space where the classes are well separated. When the two classes are Gaussian with equal covariances, LDA yields the optimal Bayes classifier. LDA is a supervised technique, whose performance depends heavily on the availability of correctly labelled data  [2]. On the other hand, PCA finds the linear projection best fitting the data in the least-squares sense, which is also the projection of maximum variance of the dataset. As a fully data-driven technique, PCA is unsupervised and does not require labelled samples  [3]. Spurred by the versatility of PCA, the last decade has witnessed a flurry of research into alternative criteria aimed at enhancing its capabilities or alleviating its limitations in various operating conditions.
Indeed, the optimization of a Rayleigh quotient as that of (  44 ) is equivalent to optimizing the numerator while constraining the denominator. The maxima and the minima of (  17 ) can be easily studied by plotting J(θ). The red line in Fig. 2  plots function J(θ) estimated from a realization of T = 200 data samples. The curve passes through a global maximum at θ = 0 degrees, which is consistent with the fact that the optimal direction resulting from Fisher's discriminant is parallel to the x 1 -axis as is easy to check (line joining the means; see Eqn. (  9 )). Next, the experiment is repeated, though this second time the objective function is estimated from only the first 50 samples of the previous 200-samples dataset. The corresponding objective function is shown in the blue line of Fig.