dataesr/scientific-paragraphs-categorization huggingface dataset:
effort. Note that we only record annual reproductive effort. Where data are collected at sub-annual timesteps, this means that reproduction must be aggregated to annual units (e. g. April-March). /creativeco mmons. org/licenses/by/4. 0/forfurther details). Data can be accessed via Github: https://github. com/JJFoest/MASTREEplus, Dryad: https://doi. org/10. 5061/dryad. 18931zd02,or via the MASTREE+ Siny App. Publications using the RENECOFOR data (Reference = RENECOFOR_2020) are requested to acknowledge the RENECOFOR network, and send copies of publications to manuel. nicolas@onf. fr. Publications using the Lopé data (Reference = Bush_2021) are requested to cite the original data set (http://hdl. handle. net/11667/152),acknowledge The National Parks Agency of Gabon (ANPN) and the University of Stirling, and send copies of any resulting publications to science@parcsgabon.
ga and k. a. abernethy@stir. ac. uk. We thank MarkGreen and Esther Dale. This study was funded by the UK Natural Environment Research Council grant no. NE/ S007857/1 to AHP, AJT and PAT. JJF was supported a PhD studentship under Natural Environment Research Council grant number NE/S00713X/1. Original data collection was supported by many funders, including the Natural Sciences and Engineering Research Council of Canada (SB, AM, BD, JL) the National Science Foundation (AM, BD), the Austrian Climate Research Program (ACRP) of the 'Klima-und Energiefonds' (9th Call, project: MoreSeedsAdapt-KR16AC0K13339), the Brazilian Agricultural Research Corporation (EMBRAPA) through the Kamukaia III Project 'Appreciation of non-timber forest products in the Amazon' (grant number SEG 12. 13. 07. 007. 00. 00), the W.
DATA AVA I L A B I L I T Y S TAT E M E N T The data that supports the findings of this study are available in the supplementary material of this article, and are openly available via Github: https://github. com/JJFoe st/MASTR EEplus, Dryad: https:// doi. org/10. 5061/dryad. 18931 zd02, or via the MASTREE+ Data Explorer: https://mastr eeplus. shiny apps. io/mastr eeplus.
Fernández-Martínez, M. , Pearse, I. , Sardans, J. , Sayol, F. , Koenig, W. D. , LaMontagne, J. M. , Bogdziewicz, M. , Collalti, A. , Hacket-Pain, A. , Vacchiano, G. , Espelta, J. M. , Peñuelas, J. , & Janssens, I. A. (2019). Nutrient scarcity as a selective pressure for mast seeding. Nature Plants, 5(12), 1222. https://doi. org/10. 1038/s4147 7-019-0549-y Fernandez-Pascual, E. (2021). SylvanSeeds, a seed germination database for temperate deciduous forests. Journal of Vegetation Science, 32, e12960. https://doi. org/10. 1111/jvs. 12960.
Salguero-Gomez, R. , Jones, O. R. , Archer, C. R. , Buckley, Y. M. , Che-Castaldo, J. , Caswell, H. , Hodgson, D. , Scheuerlein, A. , Conde, D. A. , Brinks, E. , de Buhr, H. , Farack, C. , Gottschalk, F. Hartmann, A. , Henning, A. , Hoppe, G. , Römer, G. , Runge, J. , Ruoff, T. , … Vaupel, J. W. (2015). The COMPADRE Plant Matrix Database: An open online repository for plant demography. Journal of Ecology, 103(1), 202-218. https://doi. org/10. 1111/1365-2745. 12334.
Examples of population-level time-series of reproductive effort from MASTREE+. For five diverse plant species, data from several local populations are plotted to illustrate the range of spatiotemporal variation in reproduction that is typical in long-lived plants. Note that axis scales and units vary between plots HACKET-PAIN ET Al. | 3071 Carya floridana Year 1980 1990 2021). Reflecting a bias in sampling to temperate forests, woody Empetrum nigrum Year seeds/m 2 1980 1990 2000 0 350 seeds/individual 1970 0 50 Year g/m 2 1985 1995 2005 0 8 species from the genera Quercus (60 species), Nothofagus (10), Pinus (25), Abies (13), Acer (13) and Eucalyptus (15) are highly rep-resented, but other well-represented genera include Acacia (11), F I G U R E 1 Pinus sylvestris Shorea (9) and Chionochloa seeds/individual 1995 0 400 Fagus sylvatica Year 2005 2015 flowers/individual 1990 0 Chionochloa macra Year 1995 2000 2005 and 82 species overlap with the seed germination database SylvanSeeds (Fernandez-Pascual,.
Identification of the original compiler of the data. AHP, Andrew Hacket-Pain; ES, Eliane Schermer; JVM, Jose Moris; XTT, Tingting Xue; TC, Thomas Caignard; DV, Davide Vecchio; DA, Davide Ascoli; IP, Ian Pearse; JL, Jalene LaMontagne; JVD, Joep van Dormolen Date_entry Date of data entry into MASTREE+ in the format yyyy-mm-dd Notes on the location of the data within the original source, such as page or figure number Variable Description ID_enterer Note on data location updated over time, so users are encouraged to check for the lat- est version of the data set on GitHub (https://github. com/JJFoe st/MASTR EEplus) and via associated updates to the MASTREE+ Data Explorer. The MASTREE+ Data Explorer allows users to explore the MASTREE+ data set and provides an alternative for downloading the data set, including user-defined subsets thereof.
Szafer Institute of Botany of the Polish Academy of Sciences and the Polish National Science Foundation Potential contributors of data are encouraged to search the lat-est version of the data set to check whether the data are already in-cluded in MASTREE+, either by downloading the latest version from GitHub, Dryad (Section 5) or via the MASTREE+ Data Explorer. If the data are not already included, potential contributors are encour-aged to contact the corresponding author to discuss arrangements for sharing data. The minimum data requirements are included in MASTREE+ is published under a CC-BY-4. 0 licence, which ena-bles users to copy and redistribute, adapt and modify the data set in any medium or format and for any purpose, including commer-cial. You must give appropriate credit by citing this publication, provide a link to the license and indicate if changes were made (see https:/ACK N OWLED G EM ENTS 6.
Programmable arrays of hundreds of Rydberg atoms have recently enabled the exploration of remarkable phenomena in many-body quantum physics. In addition, the development of high-fidelity quantum gates are making them promising architectures for the implementation of quantum circuits. We present here Pulser, an open-source Python library for programming neutralatom devices at the pulse level. The lowlevel nature of Pulser makes it a versatile framework for quantum control both in the digital and analog settings. The library also contains simulation routines for studying and exploring the outcome of pulse sequences for small systems.
In fact, in neutral-atom optical traps, the connectivity of the QPU can be reprogrammed at every single run. This opportunity opens up new avenues for exploration on these devices but also requires providing the user with a lower-level description of the physical setup. We here introduce Pulser  [20] , an open-source software library for designing pulse sequences for neutral-atom QPUs. The main goal of this library is to serve as an interface between experienced users and neutral-atom quantum hardware. Using Pulser, users can control all the relevant physical parameters of a pulse-level quantum program. This program can subsequently be sent to QPUs via external tools and executed on the hardware after a device-specific compilation step. Providing users with a pulse-level access to QPUs will allow them to explore advanced pulseshaping techniques facilitating the design of new quantum gate protocols, the implementation of optimal control and error mitigation techniques, or the exploration of mixed digital-analog algorithms  [21].
Exposing the low-level details of the hardware will also enable algorithm developers to design software procedures while keeping the particularities of the hardware in mind. Besides providing an interface to a QPU, Pulser includes a built-in emulator that faithfully reproduces the hardware behavior. This is intended to make the design of experiments easier for both the theoretician and the experimentalist. Pulser is an open-source Python library licensed under the Apache License 2. 0 and intended for the community of quantum scientists and engineers working on neutral-atom devices. The development and use of open-source software has enabled the flexible and efficient simulation of quantum systems  [22, 23] , the connection of quantum processors to the cloud  [24, 25]  and their use to launch jobs running quantum hardware.
Code Architecture The main components of Pulser are: (i) The Device, which consists of a series of specifications that characterize the hardware, including the chosen Rydberg level, the ranges of the amplitudes and frequencies of the lasers, the minimal and maximal distance between the atoms, and the different channels that can be declared (see (iii) below). (ii) The Register stores the information about the coordinates of the atoms and their re- (iv ) Waveforms are the basic building blocks of a pulse. They can have custom or predetermined shapes, like a ramp waveform or a Blackman waveform, all of them indicating the specific profile of the waveform and its duration. (v ) Pulses consist of waveforms for the amplitude and the detuning. They can be further shifted by a phase. Once a Pulse is constructed it has to be added to the sequence indicating which atom(s) are targeted and what channel will implement it.
Simulation The primary goal of Pulser is to serve as a frontend framework for designing pulse sequences for neutral-atom quantum processors. However, it is also possible to emulate the output of a sequence locally on a classical device through the Simulation class. Pulser's emulator relies on the open-source package QuTiP. Incorporating an emulator in Pulser is valuable for several reasons. First, it enables users to carefully design their pulse sequences and understand their effects before sending the instructions to a real device. It also enables them to gain intuition with simple setups, and explore new possible applications on limited-size systems. Coming back to our example, we use the emulator on our sequence by calling: 51 from pulser. simulation import Simulation 52 sim = Simulation(seq) 53 res = sim.
Moreover, they support basic arithmetic operations and, when they are of size> 1, iteration and item accessing. The notion of parametrized sequence can become very handy with real world constraints such as those we find in cloud-based platforms. First, the bandwidth allocation of a program, variational or not, is greatly reduced thanks to this factorization. A user only needs to send the parametrized sequence that describes his program once, and then each new iteration only requires the associated set of parameters. Thanks to the high flexibility of Pulser, users can have a very fine-grained control over the waveforms that define their pulses. This results in needing larger objects to define these pulses, which makes such a factorization even more useful. In addition, one area of improvement for cold atom platforms is the large calibration times for atom register configuration changes.
Classical optimization and QAOA results The parametrized sequence above can then be used in conjunction with a classical optimizer in order to determine a set of good values for t_list and s_list. When running the full QAOA procedure in closed loop, the optimizer is responsible for iteratively selecting the next set of parameters to be tested. The parametrized sequence can then be updated in Pulser and the new program sent externally to the quantum hardware. The procedure can also be emulated locally: to try out the algorithm, we applied the nongradient method Nelder-Mead for a few dozen function evaluations, initializing the parameters in a convenient point (the implementation can be found at Pulser's online documentation). This is already sufficient in order to find some acceptable parameters t and s.
In this paper, we introduced Pulser, an opensource Python library allowing users to pilot such powerful devices at the pulse level. This tool will enable the exploration of new avenues in physics and quantum information science requiring an advanced control of the system. After their creation in Pulser, pulse sequences can be translated and read by an arbitrary waveform generator for implementation on a quantum processor. The architecture of the software makes it possible to use as an interface for any neutral-atom QPU as the specifics of the back-end can be encapsulated by creating new Device objects. Pulser currently enables the control of laser pulses during the quantum information processing phase, but it could be extended in the future to include other controls either in the processing phase, during the assembly of the register or the measurement phase.
Acknowledgments We thank  Lucas Beguin ,  Julien Brémont ,  Antoine Browaeys ,  Mauro d'Arcangelo ,  Eric Giguere ,  Christophe Jurczak ,  Thierry Lahaye ,  Boxi Li ,  Georges-Olivier Reymond ,  Pascal Scholl ,  Adrien Signoles , and  William J. Zeng  for useful discussions.
Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models.
Introduction Recommender systems have been widely studied in requirements engineering (RE)  [34, 46, 69] , and several diverse applications of this paradigm have been proposed in the literature. These include stakeholder recommendation for requirements discussions  [19] , refactoring recommendation based on feature requests  [67]  and bid management  [34]. One typical application scenario of recommender systems in RE is related to requirements retrieval  [23, 50]. Specifically, when a new requirement is proposed, the requirements analyst looks for reuse opportunities and compares the new proposal with existing requirements in order to adapt their previously developed models and implementations  [56, 83]. This can be supported by content-based recommender systems  [60] , which, given a new requirement, return the most similar ones in a historical database of product releases, together with the associated artifacts.
To study the relationship between requirements similarity and software similarity in this setting, we consider 254 real-world requirements related to two power propulsion control (PPC) projects. We consider different state-of-the-art language models to semantically represent the requirements and support similarity computation, namely the traditional tf-idf, the Jaccard Similarity Index (JSI)  [61] , and the more advanced Doc2Vec  [58] , FastText  [14] , Bidirectional Encoder Representations from Transformers (BERT)  [30] , and the Universal Sentence Encoder (USE)  [20]. Our choice of language models covers representative seminal models from lexical approaches (JSI) to information retrieval (tf-idf), and to word2vec-based (Doc2Vec, FastText) and Deep Learning (DL)-based models (BERT, and USE).
We complement this quantitative analysis with a focus group involving participants from two teams at the company, i. e. , the PPC team and the Train Control and Management System (TCMS) team. Specifically, the data from the quantitative analysis are used to trigger discussion around the topic of requirements-based software reuse. Our results show that, in our context, on average, the deep learning-based BERT model with pre-processing is the one that leads to the highest correlation with the software similarity, computed with JPLag  [75]. Furthermore, we show that the correlation between requirements similarity and code similarity is moderately high for BERT and tf-idf with pre-processing. FastText, USE and JSI without pre-processing also show a moderately high correlation. This provides some evidence that similar implementations realize similar requirements in the context of the considered case study.
In addition, it also addresses the problem of limited annotated data that affect the performance of RNNs used by Guo et al. [45] , thanks to the transfer learning paradigm  [70]. Change Impact Analysis. Change impact analysis (CIA) consists in estimating the consequences of a certain change in one or more artifacts produced during the software process, including requirements, in terms of refactoring effort for other artifacts. This can be based on novel requirements, identified bugs, or other sources of change  [13]. Representative works in this field are those by Arora et al. [9]  and Borg et al. [16]. Arora et al. [9]  use the SEMILAR (SEMantic simILARity) toolkit  [80]  to experiment with different similarity metrics and select the best combinations to support inter-requirements CIA. Their work suggests that the best metrics are the Levhenstein distance  [61] , a syntactic metric, combined with Path  [80] , a semantic one.
Borg et al. [16]  reuse previous CIA information coming from an issue tracking system. This is used to build a graph that links artifacts-e. g. , requirements, test cases-based on their previous changes identified by the issue tracking system. Given a novel issue, similar issues are detected in the knowledge base, and artifacts potentially impacted by the change are retrieved. Issue similarity is evaluated by means of the Lucene library  [47]  with the traditional LSI, which was used also by previous studies in the field (e. g. , ImpactMiner from Gethers et al. [43] ). CIA is a task that heavily relies on trace links, as one change in a software artifact needs to be propagated on the related ones, and trace links can be a relevant source to channel the ripple-effect  [13]. Aung et al. [10]  present a recent literature review on automatic trace links recovery for the purpose of CIA.
In line with works focused on traceability previously surveyed by Borg et al. [15] , the authors confirm that the most common language models to support similarity computation are vector space with tf-idf and LSI, Jensen & Shannon models (JSMs)  [5] , and Latent Dirichlet Allocation (LDA)  [52, 71]. The study also shows that CIA mostly focuses on relations between textual artifacts (e. g. , requirements, issues, features) and source code, followed by interrequirements relations. Recommender Systems. One of the seminal contributions on recommender systems in RE is the work by Natt och Dag et al. [23] , where the tf-idf language model and cosine similarity are used to support retrieval of previous requirements on a large industrial dataset. The authors developed a tool called ReqSimile, which reaches a recall of around 50% for the top-10 requirements.
The syntactical similarity is calculated based on sub-tree comparison techniques between the Abstract Syntax Trees (ASTs) extracted from code pieces. Narasimhan et al. [65]  use the CDT Eclipse plugin to extract ASTs from the C/ C++ source code. Then, they rely on the robust tree edit distance algorithm (RTED) proposed by Pawlik et al. [72]  to identify syntactical similar code clones that can be merged into a more reusable piece of code using the abstraction pattern. To understand variability in android families, Shatnawi et al. [84]  rely on ASTs to identify code clones allowing to analyze the commonality and variability between android applications of the same family. Based on their approach, a parameterized tool is provided to identify code clones at different levels of abstraction in which practitioners can configure different software similarity metrics.
The semantic similarity is used to identify code clones that have different textual and syntactical representations but have similar behavior when the corresponding programs are executed, i. e. , Type 4 code cloning. Statically, this type of similarity can be calculated using the control and data-flow analysis of program dependency graph reverse-engineered from code pieces  [90]. In our empirical study, to measure the software similarity between the code pieces implementing similar requirements, we rely on JPLag  [75]  because it is based on a syntactical-based similarity metric that is compatible with the type of similarity realized in our case studies, it is freely and publicly available and can be executed on local machines which allow us to comply with our confidentiality agreement with the company.
The effectiveness of the similarity computed with cosine is heavily dependent on the choice of the language model used for computing feature vectors. In this paper, we use six language models for similarity computation among requirements. The selected language models are Jaccard Similarity Index (JSI), Term Frequency Inverse Document Frequency (tf-idf), Doc2Vec, FastText, Bidirectional Encoder Representations from Transformers (BERT), and Universal Sentence Encoder (USE). Note that JSI is not a language model but rather a string-level similarity metric. However, we refer to it as a language model in the remainder of this paper for the simplicity of reporting. These six language models are selected as representative models from the following seminal categories. -Lexical. This is a basic category, which considers the terms solely as they appear in the text of the sentences (i.
e. , requirements in our case). To include a representative lexical similarity metric, we include JSI due to its promising performance in software clustering  [25]. -IR-based language models. This category includes measures that consider lexical aspects of requirements in relation to the lexical aspects of other requirements in a repository, as typical in an IR scenario. We include the traditional tf-idf as it is the most widely used IR-based language model in RE  [94]. -word2vec-based language models. This category includes all language models that enrich terms representation with semantics based on techniques inspired by the word2vec algorithm. To represent the prominent word2vec-based language models, we include the widely used Doc2Vec and FastText. -DL-based language models. DL-based language models provide a contextual representation of expressions utilizing deep learning architectures.
For example, words like "simple" and "easy" would result in similar vectors. This helps in inferring feature vectors of fixed length for a variable length of requirements. FastText is another model based on word2vec, where instead of learning word vectors directly, it utilizes the character level n-grams. For example, the word "run" would be divided into n-grams such as "ru," "run," "un". Such a model is useful for cases where shorter words are used. In addition, FastText also understands suffixes (such as verb ending) and prefixes (such as unhappy, where un is the prefix) better because it utilizes character-level information. Bidirectional Encoder Representations from Transformers (BERT) is a recent breakthrough in language understanding researches. It is a bi-directional model based on the Transformer encoder architecture that also considers positional and contextual information of words.
Study design This section outlines the research method used to obtain the results. This work can be regarded as an exploratory case study  [79] , oriented to understand the relationship between requirements and their associated software in the specific context of a railway company. We designed this study following the guidelines of Runeson et al. [79]  for conducting and reporting case studies. The study is designed to collect both quantitative and qualitative data to answer the research questions. Quantitative data are collected from two safety-critical projects at Alstom. To collect qualitative insights, we designed a focus group session involving Alstom engineers.