dataesr/scientific-paragraphs-categorization huggingface dataset:
As model-independent analyses of larger data samples of these decays by the LHCb collaboration  [83] [84] [85]  have revealed large CP violation effects that vary significantly across the DP, there is strong motivation for updated amplitude analyses. Understanding the origin of these CP violation effects requires related modes to also be studied. The Laura ++ package has also been used by the BaBar collaboration for a time-dependent DP analysis of B 0 → K 0 S π + π -decays  [44] , as well as for an amplitude analysis of B + → K 0 S π + π 0 decays  [86]. In the latter, the modelling of the large background contribution, as well as of the smearing of the DP position due to the limited resolution of the neutral pion momentum (self cross-feed), is particularly important. In addition, correlations between the DP position and the variables that are used to discriminate signal decays from background contributions are taken into account as described in App.
Speed It is essential for the Laura ++ amplitude analysis package to run quickly, since otherwise the large data samples available in modern experiments can lead to unmanageably long execution time. In this subsection some performance benchmarks are provided. More specifically, a selection of the examples that are provided with the package (several of which are based on analyses presented in the previous subsection) are run out of the box on the same machine (an Intel Core i5-3570 3. 4 GHz quad-core CPU with 8 Gbytes of RAM). In each case, timings for both generation of 50 toy datasets and for fitting those same 50 datasets are provided in Table  3. The fitting times are averaged over 20 fits with randomised starting parameters. The scenario demonstrated in each example is as follows: • GenFit3pi.
Decay-time-dependent fits As mentioned in Sec. 1, the evolution of DP structure with decay time of a B or D meson can be of interest to study CP violation effects. For example, studies of B 0 → π + π -π 0 and Dπ + π -decays are of interest to measure the angles α and β of the CKM Unitarity Triangle with low theoretical uncertainty. Studies of B 0 → K 0 S π + π -and K 0 S K + K -decays enable determinations of β that are not as clean, but are potentially sensitive to effects of physics beyond the Standard Model. Also, the D 0 → K 0 S π + π -channel appears the most sensitive to possible CP violation effects associated with charm mixing. Many other channels are potentially of interest. The Laura ++ package has been used for decay-time-dependent DP analysis, for example, in Refs. [44, 50]. However, this implementation was experiment-specific and therefore unsuitable for use in the more general case.
Interface to EvtGen The EvtGen package  [96]  is designed to predominantly simulate the decays of b-and c-hadrons. It is important in experimental particle physics to produce simulated data samples that are realistic and match the true data samples. Typically in three-body decays the simulated events are produced flat in the DP, without resonant contributions. It would be beneficial if Laura ++ could be used directly to provide realistic DP distributions for simulated samples of three-body decays.
Summary The Laura ++ package provides a flexible and optimised framework for Dalitz-plot analysis. While it can be used for the decay of any stable spin-zero particle to any final state containing three stable spin-zero particles, it has until now been most widely used for decays of B or D mesons to three pseudoscalars. Features included in Laura ++ allow the physics of such decays to be probed in detail, including studies of the resonances appearing in the contributing partial waves, and investigations of CP -violating effects. Use of the Laura ++ software has resulted in numerous publications to date, with many more expected in future with the increasingly large data samples available at LHCb, Belle II and other experiments.
Acknowledgements The Laura ++ package has been under development for many years, with support principally from the Science and Technology Facilities Council (United Kingdom) and by the European Research Council under FP7. Individual authors have received support from Marie Sk lodowska-Curie Actions and from the University of Warwick. We thank the Laura ++ user community for feedback, bug reports, testing and other contributions to improve the package, in particular Louis Henry, Adlene Hicheur, Patricia Magalhães, Jussara Miranda, Sian Morgan and Charlotte Wallace. We also acknowledge productive discussions regarding aspects of Dalitz plot analysis with many members of the BaBar, Belle and LHCb collaborations, notably Eli Ben-Haim, Alex Bondar, Jeremy Dalseno, Bill Dunwoodie, Brian Meadows and Anton Poluektov.
The system has a modular design. Patient samples are added to disease-specific, single use, disposable cartridges, containing all PCR reagents, buffers, primers and probes, and these are then loaded into one of the system's modules. The test cartridge also contains an internal control to verify adequate processing of the target sequence and the system monitors the presence of inhibitors in the PCR assay to avoid a false-negative result. Test results (detected or not detected), as well as assay details including sample adequacy are accessed from a computer connected to the system. Results can be stored and viewed at the convenience of staff at any time after the test is completed. Trained health care workers or lab technicians are required to operate the device. Both CAP/CTM HIV-1 and GeneXpert HIV-1Qual assays were conducted according to manufacturer's recommendations.
During the study period, the GeneXpert HIV-1 Qual Assay was performed on six different GeneXpert instruments by 10 trained laboratory technologists. The field-based laboratory technologists had formal training provided by the manufacturer on use of the POC, where they participated in a three day training program by the manufacturers before the field evaluation began. For sample analysis, one spot of the DBS was placed into the sample reagent buffer tube and incubated in the thermomixer for 15 min at 56˚C then rotated at 500rpm; 1ml of the liquid was transferred to the cartridge and closed, finally the cartridge was loaded in the POC instrument and result read. The test took approximately 90 minutes to process. The result was considered valid when the cartridge built-in controls were passed by the instrument.
When there was a quality control failure due to sample or instrument errors, an error code was provided and the data was recorded by the laboratory technologist and a repeat testing of sample with POC assay error was performed. All study samples were tested on both the Standard of Care (SOC) and POC platforms, and staff running both platforms were blinded to the test results. We repeated any sample associated with a discordant SOC/POC result, some of them necessitated further tests with additional samples. During analysis, these repeated tests were classified as second runs whereas the initial runs were classified as first runs. POC results were not given back to the study participants since the test was not approved locally and therefore was considered as experimental, therefore POC testing did not influence the clinical management of the participants and health care providers offered routine care as per usual practice.
Analysis All POC results were entered into a Microsoft excel database (Microsoft Corporation, Redmond, WA, USA). Data was analysed using STATA version 13 for Windows. Participant characteristics were presented by use of frequencies and percentages for categorical data, median and interquartile range for continuous data. Sensitivity and Specificity (with 95% Confidence intervals) of GeneXpert HIV QUAL EID POC were compared to Roche CAP/CTM HIV-1 qualitative PCR which was taken as the reference testing method.
The study staff operating the POC identified a few advantages of the device, including the use of DBS, as a sample type with limited sample volume, to run the test, limited specimen preparation for DBS, the device was hands-on with limited user engagement, and the ability to test at least 4 samples simultaneously on the different modules with detailed results printout and stored electronically within the POC device. However they also noted a few difficulties in interpreting the graph for the EID test result, a few cases of increased temperatures within the POC devices, a longer run time per test and frequent power fluctuations although batteries were available as power back-up. Overally the staff reported that the GeneXpert was a high performing device that was easy and feasible to use within the field setting.
Acknowledgments The authors would like to thank the study participants and their families for their participation and collaboration. We are also grateful to the  Homabay and Ndhiwa communities  for their support and the  Epicentre  field team and  KEMRI CGHR laboratory  for their commitment to conduct this study. Special mention goes to  Dr. Victor Mudhune ,  Gloria Omollo ,  Rebecca Achieng ' and  Laura Oyiengo  for their continuous support in the implementation of this study.
If sufficient information on the observation process is recorded, then biases in the observation process can be addressed during analysis. Uneven detectability of organisms. All biodiversity data collection projects suffer from the challenge of uneven species detectability  (Johnston et al. 2014). This issue involves accounting for both false positives -misidentifications of observed organisms, and false negatives-failures to report species that were present. Some false positives can be filtered out as anomalies that fall outside the norm of occurrence for a species at a particular time or space. However, false positives can also be misidentifications of expected species. An example of this challenge is identification of Purple Finch and House Finch (Figure  4 ), which are very similar in appearance.
Structured surveys often use participants who are paid technicians or experienced volunteers. Unstructured projects typically have few barriers to participation resulting in more substantial variability in participant skills in detecting and identifying species. Regardless of whether a project engages skilled participants or is open to anyone, a key feature of data collection and management should be the association of participant identifiers with the observations that each participant collected, which allow analysts to monitor the relative skills of participants and their activity. For example, eBird indexes. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted May 18, 2018.
This problem can be partially mitigated via increased rates of participation and adjusting weights of observations made from high-density locations. Regardless of how a location is selected, knowledge of precise locations of observations allows for connections to be made with data on environmental conditions, which are now often assembled from remote-sensed datasets. We recommend that the locations where data collection events occur are identified at high spatial resolutions, and that the projects recommend locations where increased observer effort would benefit the project. 4. When are the observations collected? Species detectability can change within a day or seasonally as behavior of animals change or plants alter their appearance. For example, daily flight patterns of butterflies are determined by local weather conditions.
Recording of the sampling process (e. g. , date, time, and duration of observations) allows for a subset of data to be extracted for specific times of year of interest and for control of the amount of effort made during a data collection event. We recommend that the timing of data collection be matched to the goals of the project, and that the date, start time, and duration of the sampling process is recorded. 5. Who is making the observations? Structured monitoring programs often rely on trained technicians for data collection, or have dedicated, long-standing participants, while most unstructured citizen science programs do not restrict participation and have a range of participants from those who are very dedicated to others who submit data only occasionally. While participants can develop enormous expertise in gathering information through participation in citizen science projects  (Kelling et al.
Biodiversity analysis with unstructured data from eBird. Overview: eBird engages more than 400,000 volunteers to report bird observations based on how bird watchers typically observe birds, i. e. , units of data collection are "checklists" of zero or more species including a count of individuals of each observed species  (Kelling et al. 2015b ). As of April 2018, 28 million "complete checklists" containing 461 million observations of bird species had been submitted to eBird since the project began in 2002. In order for the eBird observation process to be clearly articulated, the six questions that describe how observations are made must be clearly described. 1. Why is eBird being conducted? eBird collects data that will be used to estimate the distribution, abundance, and trends of bird populations by taking advantage of the global network of bird enthusiasts who submit their observations to a central data repository  (Sullivan et al.
2014). 2. What is observed? eBird gathers a complete list of all bird species observed along with counts of individuals of each species during a data collection event. 3. Where are the observations collected? eBird participants can select where they make their observations. All locations are georeferenced either through mapping tools provided on the eBird website, or, more accurately, through the GPS system available on mobile phones and used by the freely available eBird App. 4. When are the observations collected? eBird allows observers to record observations at any time of day or year. 5. Who is making the observations? Anyone can participate in eBird. However, individuals must register and login to eBird whenever they submit a list of birds. The eBird database links all observations to the registered individual.
6. How are the observations collected? The location, date, and start time are recorded for all collection events. Prior to submission of species observations, participants must also record whether they are submitting a list of all the birds they detected and identified, which allows analysts to infer the absence of species not reported. Additionally, participants are encouraged to record search effort information -the duration and the distance traveled during the data collection event. Together, all of this information comprises a "complete checklist," which is the foundation for much of the analysis of eBird data. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted May 18, 2018.
; https://doi. org/10. 1101/326314 doi: bioRxiv preprint Translating eBird checklists into useful biodiversity information describing patterns of species occurrence and abundance in space and time are done through Species Distribution or Niche models (SDMs). These statistical models estimate the distribution or abundance of a species by estimating relationships between the observed patterns of species occurrence and data describing the processes that give rise to these observations  (Franklin 2009). The data describing this process includes environmental data, often collected via remote sensing, which describe important ecological processes, e. g. , habitat selection, which determine the environments most likely to be occupied by a species, and data that describe important aspects of the observational process, like the information contained in eBird complete checklists.
When the goal is ecological inference, the effects of the observation process are sources of bias. If these sources of bias cannot be controlled during data collection, then data describing these biases are essential to account for them during analysis (Figure  1 ). SDMs provide the analytical framework where ecological signal can be separated from observational noise. Data from eBird complete checklists in combination with remote sensing data are used to create a series of biodiversity indicator data products for a species. These include rangewide, seasonal relative abundance estimates (Figure  6 ), weekly relative abundance estimates (Figure  7 ), and weekly estimates of the relative importance of land and water cover classes to species occurrence (Figure  8 ). The range-wide, seasonal relative abundance estimates are meant to show the population of each species across its entire distribution.
2017) , and identified seasonal resources needed for supporting bird populations during critical stages of their life history  (Johnston et al. 2015 , Reynolds et al. 2017). Overall, the data contained within eBird complete checklists allow analysts to account for a large proportion of the variation in the observation process. The information on protocol, distance traveled, start time, duration, location, observer expertise, and total number of observers all help us improve our ability to correct for these known sources of variation in detectability. Furthermore, the availability of complete checklists provides a critical source of information on species not detected. Accounting for variation in the observation process gives us greater ability to produce more accurate estimates of the ecological process (Figure  1 ).
For example, the birding community has responded well to gaming techniques such as leader boards, tools that allow individuals to manage their personal records, and features that allow an individual to explore the patterns of bird occurrences. Over the next several years, technical advances will improve the potential for an increase in the quantity and quality of citizen science data collected around the world. Already more than 60% of all eBird data are being submitted via mobile apps, which dramatically improve the accuracy of the location and distance an observer walks while observing birds. Developments in the fields of Artificial Intelligence and Augmented Reality, combined with increasingly efficient and powerful mobile technologies, will vastly improve the ability of citizen scientists to detect and identify organisms in the field.
Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and questionanswering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-grams scores to generate more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state-of-theart for evaluations led on news dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.
, 2015). Boudin and Morin (2013)  developed a reranking method measuring the relevance of a candidate compression using keyphrases, obtained with the TextRank algorithm  (Mihalcea and Tarau, 2004) , and the length of the sentence. Another reranking strategy was proposed by  Luong et al. (2015). Their method ranks the sentences from the counts of unigrams occurring in every source sentence. ShafieiBavani et al. (2016)  also used a WG model; their approach consists of three main components: (i) a merging stage based on Multiword Expressions (MWE), (ii) a mapping strategy based on synonymy between words and (iii) a reranking step to identify the best compression candidates generated using a POS-based language model (POS-LM). Tzouridis et al. (2014)  proposed a structured learning-based approach.
Evaluation Datasets Various corpora have been developed for MSC and are composed of clusters of similar sentences from different source news in English, French, Spanish or Vietnamese languages. The corpora used by  Filippova (2010)  and  Boudin and Morin (2013)  contain clusters of at least 7 or 8 similar sentences, whereas the data of  McKeown et al. (2010)  and  Luong et al. (2015)  have clusters limited to pairs of sentences. McKeown et al. (2010)  collected 300 English sentence pairs taken from newswire clusters using Amazon's Mechanical Turk. Like this previous study,  Luong et al. (2015)  used the same experimental setup to accumulate 115 Vietnamese clusters with 2 sentences by group. Boudin and Morin (2013), McKeown et al. (2010)  and  Luong et al. (2015)  made their corpora publicly available but only the corpus of  Boudin and Morin (2013)  is more suited to multi-document summarization or question-answering because the documents to analyze are usually composed of many similar sentences.
The compression ratio can be modulated with this modeling, by selecting for example a higher number of keywords for the sentences considered essential for a summary. Automatic measures with ROUGE package were supplemented with a manual evaluation carried out by human judges in terms of informativeness and grammaticality. We showed that keywords and relevant 3-grams are important features to produce valuable compressed sentences; in particular, testing three different keyword selectors highlighted their role in producing relevant sentences. The paths selected with theses features generate results consistently improved in terms of informativeness while keeping up their grammaticality. There are several potential avenues of work. Following the system proposed by  ShafieiBavani et al. (2016) , language models over POS can be added as an additional score to the optimization criterion to improve grammaticality.
Building Information Modelling (BIM) technique has been widely utilized in heritage documentation and comes to a general term Historical/Heritage BIM (HBIM). The current HBIM project mostly employs the scan-to-BIM process to manually create the geometric model from the point cloud. This paper explains how it is possible to shape from the mesh geometry with reduced human involvement during the modelling process. Aiming at unbuilt heritage, two case studies are handled in this study, including a ruined Roman stone architectural and a severely damaged abbey. The pipeline consists of solid element modelling based on documentation data using Autodesk Revit, a common BIM platform, and the successive modelling from these geometric primitives using Autodesk Dynamo, a visual programming built-in plugin tool in Revit.
INTRODUCTION Building Information Modelling (BIM), as an initial instrument for the parametric design of new buildings, shows potentialities for applications to the documentation of architectural heritage. The general term, Historical/Heritage BIM (HBIM)  (Murphy et al. , 2009) , is mostly the as-built HBIM process finished by the well-known scan-to-BIM process  (Volk et al. , 2014). The point cloud is imported into the BIM software, and the solid building components can be created by existing BIM IFC classes and selfdefined structures. The priority of architectural heritage compared to general buildings lies in the possible access to documentation materials. Current HBIM projects combine the existing documentation and reality-based point clouds  (Fai and Rafeiro, 2014; Murphy et al. , 2013; Barazzetti et al.