dataesr/scientific-paragraphs-categorization huggingface dataset:
First of all, our plant identification system is now working on 2258 plant species (half of the whole French flora) whereas the MAED paper was limited to 127 species of French trees. This makes the system described and experimented in this new paper the widest automated plant identification system built anytime. Other contributions include: (i) an analysis of existing botanical datasets with regard to computer vision and machine learning issues, (ii) the description of the whole Pl@ntNet workflow and not only the visual search engine, (iii) a new fusion algorithm allowing to improve the performances of multi-image and multi-organ queries, (iv) much deeper and larger experiments, and (v) the results of our participation to the plant identification task of ImageCLEF 2011 & 2012 international benchmarks (only reported in unofficial CLEF working notes so far).
The scientific production of the research community working around the ImageNet dataset  (Deng et al. , 2009)  and the related benchmark is particularly important in this regard. ImageNet is actually the widest structured visual knowledge dataset ever built. It currently contains 14,197,122 images categorized according to a hierarchy of 21,841 English nouns. State-ofthe-art techniques experimented on this dataset achieve rather good recognition rates. Sánchez and Perronnin (2011)  reported a top-1 accuracy of 54. 3% on thousand categories (74. 3% top-5 accuracy). More recently,  Le et al. (2012)  reported a fair top-1 accuracy of 15. 8% on the huge dataset with 22 K object's categories. These promising performances on such a huge number of categories give hope in building effective plant identification systems on real-world floras with thousands or tens of thousands of species.
The big problem with data Building effective computer vision and machine learning techniques is unfortunately not the only side of the taxonomic gap problem. Whatever the technique used, the availability of rich and appropriate training data is actually as much challenging towards setting up powerful identification tools at large scales. Coming back to the ImageNet dataset, it is essential to notice that the average number of training images per category is in the range 600-1200. And this is actually several orders of magnitude richer than any existing plant images collection. Even Encyclopedia Of Life 6  (EoL), which is the world's largest data centralization effort concerning multimedia data for life on earth, does not have more than few images per organ of a given plant species. Thanks to the integration of hundreds of large expert collections built in the past, the global index is now approximating the 700 K images of plants, which is an outstanding number.
The long-tail problem In many datasets, the distribution of the number of images per species follows a long-tail distribution, with very few species well populated, and a vast majority of species with one or few images. A rather good average number of images per taxon can therefore be misleading regarding the real coverage of all species. Fig. 2  illustrates such distribution on the Bugwood 7  -ForestryImages 8  dataset, which includes 187 K images of about 18 K plants species of economic concern.
The heterogeneity problem Most datasets suffer from a high heterogeneity of the image contents in different aspects: heterogeneous acquisition protocols (outdoor photographs, studio photographs, scans), heterogeneous types of views (# organ views, # full plant views, landscape views, herbarium specimens, etc. ), heterogeneous tags (e. g. "leaf", "leaves", "foliage"). When the data structure is well defined and the metadata well populated, it is sometimes still possible to automatically extract some relevant visual training categories. But the whole data has to be split in many categories and the one-shot problem occurs again (very few images per species and type of view). This is for instance the case in the Morphbank 9  dataset (241,000 plant images). In the cases where the structure is weak and the metadata sparse, the problem is even worse, since all heterogeneous images have to be trained and/or indexed jointly, which usually leads to inconsistent search results.
The lack of diversity problem Some datasets such as the Swedish leaves dataset  (Söderkvist, 2001)  or the Smithsonian one  (Agarwal et al. , 2006)  have been built specifically for computer vision and machine learning usages. Thus, they do not suffer from the above-mentioned problems. They contain well populated, balanced and homogeneous image categories for few hundreds of species without any noise. A drawback, however, is their lack of diversity, which limits their generalization capacity. They were typically created by very few people collecting leaves in a limited period of time, in restricted areas and using exactly the same scanner and acquisition protocol. As a result, all leaves of a given species in such dataset are more likely to represent few individual plants at a given place and period of the year.
Crowdsourced annotation of web data The principle is to automatically crawl huge amount of images on the web and then to filter or validate them thanks to semi-automated crowdsourced applications such as Amazon Mechanical Turk (as discussed in Sorokin and Forsyth, 2008 for instance). This is actually the approach that was used to collect the 16 M images of ImageNet dataset  (Deng et al. , 2009). The 22 K English concepts of the WordNet ontology  (Miller, 1995)  were first used as text queries in popular web image search engines (e. g. Google Image). The raw search results were then filtered and consolidated by a crowdsourced interactive application where only images confirmed by at least two users were considered as valid. It is important to notice that each user was notified by a clear definition of the targeted category, with a short text and some illustrating images, before annotating any candidate image set.
Collecting data through a botany-oriented social network More concretely, the workflow presented in this paper relies on Tela Botanica 10  social network to collect relevant plant images and train our visual-based identification tool (presented in Section 4). Tela Botanica is the main French-speaking botany network linking 19 K registered members living worldwide in more than 70 countries. About 41% of the members declare being novice in botany, while 30% declare having a good practice and 7% to be experts. 53% of the members have a professional activity related to botany. The main activities of the network are organized around tens of collaborative projects animated by experts and dedicated to distinct botany issues (plant usages, distribution, identification, specific floras, etc. ). One of them was specifically launched in 2010 to collect the training images indexed by our content-based plant identification system.
The botanical target of the project has been gradually increased from leaf scans of French trees at the beginning to the whole French flora and multiple views of plants nowadays. The expected images are defined by an acquisition protocol that is disseminated to the members of the project through illustrated booklets containing full-text descriptions as well as positive and negative examples of the different view types (full plant, flower, leaf, fruit, bark, leaf scan). To reduce the long-tail distribution effect, several actions are conducted to orient the new contributions towards the expected species and view types. The members of the project are first asked to check themselves the state of the database through a dedicated web application allowing to view underrepresented taxa and view types.
Finally, the quality of each image with respect to a given protocol can also be entered by several users through a rating box. Based on all these metadata, several strategies can be used to select the images that should be part of the training dataset and indexed in our content-based identification tool. In the current version of the system, selected images are the ones belonging to species with at least five observed plants and that have a sufficient confidence score (basically, no conflict and at least one expert determination or two non-expert determinations). So far, we did not use the quality rating information but it could help in improving recognition performances in the future. The automatic crawling and indexing of the elected images by the identification system are done every night at 3:00 AM.
Collected data history and statistics Thanks to the new observations made by the social network project since 2010 and the semi-automated integration of pre-existing ones, the whole collection nowadays includes more than 70 K images from about 5 K plant species living in France area. Fig. 3  shows a history of the amount of collected data per view type. Subsets of this collection were provided periodically to the international community through the organization of the evaluation campaigns ImageCLEF (plant identification task). After keeping only the species with at least 5 observed plants from the whole collection, the number of species recognizable by the last version of our identification system is equal to 2258 species (detailed statistics of the number of images per view type in that dataset are provided in Table  3 ).
To the best of our knowledge, this is the widest plant identification tool built ever in terms of the number of species covered. Identification performances are studied and discussed in Section 5. 2. 4. Pl@ntNet-Identify: a multi-organ and interactive plant identification system Pl@ntNet-Identify is an interactive web application dedicated to content-based identification of plants using the social image data discussed previously as training set. It is composed of three main parts that are detailed in the next sub-sections: (Section 4. 1) an interactive web GUI on the client side, (Section 4. 2) a content-based visual search engine and (Section 4. 3) a multi-view fusion module on the server side.
Visual search engine Fig. 8  provides a global picture of the query processing chain of the visual search engine. Pictures belonging to a given plant view category are indexed and searched in separate visual indices (i. e. separate structures containing only the visual features extracted from the images of one category). This allows reducing confusion between pictures of different parts of the plant and therefore increases identification performances. At query stage, the N Q query pictures belonging to a query plant Q are searched separately in their respective visual index and the top-K most similar images are returned for each of them (K was learned by cross-validation on the training data and finally fixed to K = 20). Identification is then performed thanks to an instance-based classifier computed across all retrieved pictures (see Section 4.
The final score of a species s is thus given by f Q; s ð Þ¼ X V v¼1 w v Q ð Þ Ã f v Q ; s ð Þ ð3Þ where the weights w v (Q) are computed as w v Q ð Þ ¼ max s f v Q ; s ð Þ X v∈V max s f v Q; s ð Þ ð4Þ so as to reflect the discrimination power of each view type for query Q. The more distinctive an organ v is for that query plant, the more weight it is affected in the final decision score. Note that an alternative option that would consist in training the organ weights of each species from the whole dataset is not trivial because of the incompleteness of the observations. An observation does actually never contain an image for all types of view and the optimal weights depend on the composition of the query (flower + fruit, or leaf + fruit + bark, etc. ). The flower of a given species can for instance be rare in the observations and the training would affect a low weight to it.
Large-scale and multi-view performances evaluation This section reports the quantitative evaluation of our application on a snapshot of the social image collection, dated December 2012 (see Section 3. 4 for the data history). The total number of species with at least 5 individual plants at that time was 2258, about half the species in the French flora. More detailed statistics about the number of images, plants and species per view type are provided in Table  3. We evaluated the identification performances using a Leave-One-Out procedure (see  Romero et al. , 2007 for instance) , where one is not one picture but one set of individual plant from the same species, observed by the same photograph at roughly the same place and time. This prevents from any bias between the training and testing data: plants observed by the same photograph, during a short period and in a restricted area are actually more likely to be similar to each other (same lightening conditions and camera, similar neighboring trees, etc.
It can be seen that the performances of the leave-one-plant-out evaluation can be up to 4 times lower than the leave-one-image-out, but still, we believe that it is a more realistic test. It is not trivial to affirm which organ of the plant provides the best performances, since the taxonomic coverage is heterogeneous from one view type to another. Bark pictures provide the best raw performances with, for instance, the best id@5 image-out identification rate equals to 53%. But this only concerns 153 species in the dataset (only a fraction of plants actually have a bark, i. e. only trees). On the other side, flower pictures provide the widest taxonomic coverage with 1264 species having at least five representative plants in the dataset. The id@5 plant-out identification rate in that case is equal to 19%, which shows that the single-image visual search can already provide an effective filtering on thousands of species if they are sufficiently well represented in the training data.
The full plant view offers a similar taxonomic coverage as the flower view, but identification performances are lower, probably because this view is by definition more ambiguous. The fruit and the leaf views offer They are essentially complementary to the flower view because flowers are not visible during the whole year and flowers of some species are not informative or very hard to capture (e. g. too small). Table  4  shows the benefit of submitting several pictures of the same object of interest (i. e. the same type of view) rather than querying the system with a single image. Only the single-view level of our two-stage fusion algorithm is involved in that case (Section 4. 3). Multi-image queries were created by generating all possible combinations of images of the same type for each plant in the dataset.
Queries were then gathered by number of pictures and evaluated through the leave-one-plant-out procedure. The results show that the multi-image query strategy provides very high performance gains for all types of view. The best gain is obtained for the leaf view type for which the id@5 identification rate passed from 15% to 41%. Tables  5  and 6  show the benefit of combining different types of view in the query. Multi-view queries were created by generating all possible combinations of images for each plant in the dataset. Queries were then gathered by type of combination and evaluated through the leave-oneplant-out procedure. Table  5  presents the performances of image-pair queries compared to single-image queries. The bold numbers highlight all cases for which adding an image of a different type to the singleimage query improves identification performances.
Human-centered evaluation Beyond the raw identification performances presented above, we also achieved a human-centered evaluation to assess the utility and the ergonomy of visualization and interactive functionalities. This user trial was carried out in February 2012 so that the amount of data was smaller than the large-scale experiments in the previous section. The dataset actually contained about 10 K images and 127 tree species living in France area (see data history in Fig. 3 ). 10 non-expert users were asked to identify 14 plants randomly selected from two pools of about 200 multi-organ queries that were built outside the application. These photos were actually taken by a group of 24 people (not registered members), who participated in two one-day campaigns that we did organize at two different periods of the year.
This issue is evident by the failure of Amazon in 2011, 2012 and again in 2013. Moreover, HealthCare. gov deployed on Verizon's Terremark cloud service also went down in 2013. The failure of these services highlight the weakness in a centralized architecture. Such non-trivial issues often raises the question, "Why do organizations depend on a single data center?" 1. A si s evident today, the Internet is continuously evolving towards the Future Internet, which is a "federation of service and self-aware networks that provide built-in and integrated capabilities such as: service support, contextualization, mobility, security, reliability, robustness, and self-management of communication resources and services"  [1]. Hence, a centralized approach towards business process execution in the Future Internet is a slippery slope.
A request exiting from the lowermost level implies that an application has been composed for a user. The completion time, the waiting time, the request arrival rate and the queue size of each service node was tracked. Since, there is an absence of an orchestrator, therefore the tracking was done for every node. Based on the data collected, we discuss the behavior of MagEl in the next subsections. We have compared the proposed model with two similar techniques viz  [2]  and  [3]. The two techniques are the most closest in literature utilizing the waiting criteria in service composition. However, the two techniques focus on only a few parameters while selecting services(Queue Size, waiting time and latency only). This assumption is rather unrealistic, MagEl selects services based on attributes commonly found in existing works.
JHEP07(2014)112 In our paper, we calculate the residues from the maximal cut of integrals with doubled and tripled propagators. The degenerate residues are evaluated by our Mathematica package MathematicaM2, 1  which calls Macaulay2  [67]  to finish the computation of Gröbner bases. We demonstrate the multivariate residue computation explicitly by the one-loop box integral with double propagators. Then we show this method is general by two-loop examples. Related ideas were previously proposed to reduce one-loop integrals with generic powers of propagators  [54].
The Jacobian of {f 1 , f 2 , f 3 , f 2 4 } in α's is zero at both ξ 1 and ξ 2 , so direct computation does not work. We can use the transformation law (2. 8) to convert the denominators to a calculable form,      g 1 g 2 g 3 g 4      ≡      α 1 -1 α 2 -α 3 (α 3 + χ) 2 -α 4 (α 4 + χ) 2      = M      f 1 f 2 f 3 f 2 4      , (3. 11) where M is 4 × 4 matrix and all entries are polynomials in α's, and det M = - χ(1 + χ)(α 3 -α 4 )(α 3 + α 4 + 2χ) s 5 12. (3. 12) Hence, around either ξ 1 or ξ 2 , dα 1 ∧ dα 2 ∧ dα 3 ∧ dα 4 f 1 f 2 f 3 f 2 4 = dα 1 α 1 -1 dα 2 α 2 dα 3 α 3 (α 3 + χ) 2 det M dα 4 α 4 (α 4 + χ) 2. (3. 13) So the degenerate residue can be calculated by applying the univariate Cauchy's theorem four times. The explicit form of M is found by our package MathematicaM2.
Master integral projectors The hepta-cut contours are subject to consistency requirements in order to ensure that certain integral relations are preserved after pushing the real slice integrals into C 8. It is well known that the integrand can be parametrized in terms of four irreducible products, N = a 1 ,. ,a 4 c a 1 ,. ,a 4 (ℓ 1 • ω) a 1 (ℓ 2 • ω) a 2 (ℓ 1 • k 4 ) a 3 (ℓ 2 • k 1 ) a 4 , (4. 26) whose powers can be derived by renormalizability conditions and then multivariate polynomial division using the Gröbner basis method. The latter has been automated in the program BasisDet  [38]. Both the spurious and nonspurious part of the basis contains sixteen elements. At the level of integrated expressions, the amplitude is expressed as a linear combination of just two masters. Accordingly, we demand that all integral identities on which the reduction relies are respected.
Acknowledgments We are grateful to  Emil Bjerrum-Bohr ,  Simon Caron-Huot ,  Poul Henrik Damgaard ,  Tristan Dennen ,  Rijun Huang  and  David Kosower  for useful discussions. It is a pleasure to thank  Simon Badger  and  Hjalte Frellesvig  for comments on the manuscript in draft stage. Both authors acknowledge the organizers and participants of the workshop The  Geometry and Physics of Scattering Amplitudes at Simons Center for Geometry and Physics, Stony Brook University. We also express gratitude to  Institut de Physique Théorique, CEA Saclay , and in particular  David Kosower  for hospitality during this project. The work of YZ is supported by  Danish Council for Independent Research (FNU)  grant  11-107241.
Measuring the similarity of words is important in accurately representing and comparing documents, and thus improves the results of many natural language processing (NLP) tasks. The NLP community has proposed various measurements based on WordNet, a lexical database that contains relationships between many pairs of words. Recently, a number of techniques have been proposed to address software engineering issues such as code search and fault localization that require understanding natural language documents, and a measure of word similarity could improve their results. However, WordNet only contains information about words senses in general-purpose conversation, which often differ from word senses in a software-engineering context, and the softwarespecific word similarity resources that have been developed rely on data sources containing only a limited range of words and word uses.
INTRODUCTION A fundamental problem in natural language processing (NLP) is how to measure the similarity of words  [4]. Measuring word similarity is essential to the processing of more complex textual units, such as phrases, sentences, and complete documents. Thus, measuring the similarity of words has many applications in areas such as information retrieval or text categorization  [2, 7]. A number of studies have proposed techniques to measure word similarity  [1, 8, 14, 18]. Most of these techniques leverage WordNet  [13] , a wellknown lexical database. Relevance to Software Engineering. Recently, the software engineering community has proposed a number of tools that rely on textual software artifacts, leveraging information retrieval and document classification techniques  [9, 15]. The success of the use of word similarity in the NLP community, suggests that the text-based tools of the softwareengineering community could also benefit from this information.
For example, word similarity information could be used to expand a query with additional similar words to improve the accuracy of an information retrieval based solutions (e. g. , code search, bug localization, etc. ), cf. [5]. However, most software-engineering tools that have been proposed do not use word similarity, and instead consider words that are not identical to be unrelated. Thus, there is a need for a word similarity resource that is usable by software-engineering tools. Constructing such a resource leveraging WordNet is one option. However, WordNet contains the relationships between words considering general-purpose texts, such as newspapers, that do not reflect the software-specific meanings of many words, such as Python and Eclipse. To address this issue, we have built a software-specific word similarity database.