dataesr/scientific-paragraphs-categorization huggingface dataset:
We give a prototype implementation of our framework in λProlog  [41]. While we see it as a proof-of-concept, in so far as we do not pay any attention to efficiency, the implementation is, we argue, of some interest in several regards: as we detail in Section 4. 1, our prooftheoretic stance allows us to lift our analysis to meta-programming, in particular to model-checking meta-theory  [12] , where PBT has been used extensively, but with some difficulties  [33]. The trouble is dealing with binding signatures, by which we mean the encoding of language constructs sensitive to naming and scoping. Here λProlog's support for λ-tree syntax shines, allowing us to be competitive with specialized tools such as αCheck  [12]. Moreover, a hot topic in the functional PBT literature is (random) data generation under invariants  [15] , for example, generating well-typed λ-terms or complete runs of abstract machines  [30].
The reason for writing several clauses as one clause is to consider such clauses as a logical equivalence; viewing ":-" as an equivalence can support the building of proofs of negated atomic formulas. To complete that picture, Clark's completion needs the addition of some new axioms to describe both equality and inequality: in such an extended logic, negations (hopefully corresponding to negations-as-finite-failure) can be given actual proofs (in contrast to "failures to find proofs"). These two perspectives of Prolog programs have also been echoed in the proof theoretic analysis of logic programs. The foundations of the λProlog programming language  [41]  views the execution of logic programs as the search for sequent calculus proofs  [42]. In that development, the presentation of Prolog execution as SLDresolution (described in, for example,  [3] ) was replaced by using proof-search in the sequent calculus.
Certificate checking with expert predicates Figure  4  contains a simple proof system for Horn clause provability in which each inference rule is augmented with an additional premise involving an expert predicate, a certificate Ξ, and possibly continuations of certificates (Ξ ′ , Ξ 1 , Ξ 2 ) with extracted information from certificates (in the case of ∨ and ∃). The premise (A :-G) ∈ grnd (P) in the last inference rule of Figure  4  states the logic programming clause (A :-G) is a ground instance of some clause in a fixed program P. Although this proof system is a focused proof system, the richness of focusing is not apparent in this simplified setting: thus, we drop the adjective "focused" from this point forward. Figure  5  contains the λProlog implementation of the inference rules in Figure  4 : here the infix turnstile ⊢ symbol is replaced by the check predicate.
Notice that it is easy to show that no matter how the expert predicates are defined, if the goal check Cert B is provable in λProlog then B must be a sound consequence of the program clauses stored in the prog predicate (which provides a natural implementation of the premise (A :-G) ∈ grnd (P). As we mentioned in the introduction, the notion of proof certificates used here is taken from the general setting of foundational proof certificates  [14]. In our case here, an FPC is a collection of λProlog clauses that provide the remaining details not supplied in Figure  5 : that is, the exact set of constructors for the cert type as well as the exact specification of the six expert predicates listed in that figure. Figure  6  displays two such FPCs, both of which can be used to describe proofs for which we bound the number of occurrences of unfoldings in a proof.
PBT FOR METAPROGRAMMING Once a topic in computational logic is described as proof search in the sequent calculus, it is often possible to generalize that topic from a treatment of first-order (algebraic) terms to a treatment of terms containing bindings. For example, once Prolog was described as proof search in sequent calculus, there was a direct line to the development of a logic programming language, for example λProlog, that treated terms containing bindings  [41]. Similarly, once certain model checking and inductive theorem proving were described via the proof search paradigm, there were natural extensions of those tools to treat bindings in term structures: witness the Bedwyr model checker  [7]  and the Abella theorem prover  [6]. There are two reason why the proof search paradigm supports a natural treatment of binding within terms.
Lifting PBT to treat λ-tree syntax To showcase the ease with which we handle searching for counterexamples in binding signatures, we encode a simply-typed λcalculus augmented with constructors for integers and lists, following the PLT-Redex benchmark from http://docs. racket-lang. org/ redex/benchmark. html. The language is as follows: Types A, B ::= int | ilist | A → B Terms M ::= x | λx:A. M | M 1 M 2 | c | err Constants c ::= n | plus | nil | cons | hd | tl Values V ::= c | λx:A. M | plus V | cons V | cons V 1 V 2 The rules for the dynamic and static semantics are given in Figure  13 , where the latter assumes a signature Σ with the obvious type declarations for constants. Rules for plus are omitted for brevity. The encoding of the syntax in λProlog is pretty standard and also omitted: we declare constructors for terms, constants and types, while we carve out values via an appropriate predicate.
RELATED WORK The literature on PBT is too large to even try and sum up. We refer to  [12]  for a review with an emphasis to its interaction with proof assistants and specialized domains as programming language meta-theory. Within the confine of meta-theory model checking, a major player is PLT-Redex  [20] , an executable DSL for mechanizing semantic models built on top of DrRacket with support for random testing à la QuickCheck; its usefulness has been demonstrated in several impressive case studies  [33]. However, Redex has limited support for relational specifications and none whatsoever for binding signature. This is where αCheck  [12, 13]  comes in. That tool is built on top of the nominal logic programming language αProlog, a checker for relational specifications similar to those considered here.
Of course, our default uniform distribution corresponds to QuickCheck's oneOf combinator, while the weights table to frequency. The last few years have shown some interest in the (random) generation of data satisfying some invariants  [15] ; mostly, welltyped λ-terms, with an application to testing optimizing compilers  [22, 39, 48]. In particular, the most successful generator  [48]  consists of over 1500 lines of dense Haskell code hard-wired to generate a specific object language. Compare this to our 10 lines of readable clauses. We make no claim, at least not without trying first, about how successfully we could challenge a compiler, but we do want to remark how flexible our approach is. There also seems to be a connection with probabilistic logic programming, e. g. ,  [23] , although the inference mechanism is very different.
Model structure and basic assumptions HydroShoot is a static FSPM (with regards to plant structure) that takes plant shoot architecture, weather, and soil water conditions as inputs, and returns transpiration and net photosynthesis rates both of individual leaves and the whole plant at hourly time steps as outputs. It is conceived as a set of three modules which simulate water potential (hydraulic module), energy budget (energy module), and C 3 -type gas-exchange rates (exchange module). These three modules run jointly, having leaf xylem water potential and temperature at the leaf level as pivots (cf. implementation and numerical solution section). The formalisms used in each module are developed in the following sections.
Exchange module The exchange module computes the rates of net carbon assimilation and transpiration per unit surface area (respectively and ) for each individual leaf as a function of micrometeorological Eq. 5a does not account for stomatal sensitivity to soil water deficit ("remote" approach) or local leaf water potential ("local" approach). It is noteworthy that only the forced convective heat transfer is currently considered in HydroShoot since forced convection dominates free convection once wind speed exceeds roughly 0. 1  (Nobel 2005 ). This wind speed threshold is generally exceeded during diurnal hours. However, under low wind conditions heat transfer may be underestimated. Since the resolution of the last equation is highly time-consuming, we assumed the energy gain from the neighbouring leaves through thermal longwave as a lumped term whereby average leaf temperature is considered instead of individual leaves  (Dauzat et al.
Implementation and numerical resolution HydroShoot is developed using the Python programming language (Python Software Foundation The resolution of HydroShoot equations is performed by an iterative procedure that is schematized in Figure  2. Iterations have three levels. The first is in the hydraulic module and concerns calculating xylem water potential of plant segments in interaction with their hydraulic conductivity (interdependent processes, cf. equations 1 and 2). The second level is between the exchange and hydraulic modules in order to calculate jointly gas-exchanges rates and leaf water potential values (transpiration affects the hydraulic structure Eq. 1 while the latter affects stomatal aperture Eq. 5b). The third level is between the energy module and both exchange and hydraulic modules, so that at each time new.
7 ), internode diameter distribution (Figure  4b ), planting density (inter-and intra-row spacing of 3. 6 and 1. 0 respectively), soil type (Sand Loam) and initial collar water potential (-0. 6 ). The simulations were run using weather data extracted from the database of the weather Data on VSP grapevines (2009) were collected during a period of 4 days under well-watered and water deficit conditions. Water deficit was created by cutting off the irrigation system on the first day of the experiment (July 29 th ). Whole plant transpiration and net assimilation were monitored using open portable gas-exchange chambers (Perez Peña and Tarara, 2004). Temperature of individual leaves were monitored using thermocouples inserted into the primary veins of 10 fully developed individual leaves positioned on different heights from the top of the canopy to the inside, so that temperature gradient resulting from different irradiance conditions was captured.
Data on GDC grapevines (2012) were also collected during a 4 days experiment (starting on August 1 st ), but only under water deficit conditions. Only rate was monitored by measurements of sap flow installed on the two cordons of the GDC plants. Stomatal conductance and leaf water potential measurements were performed for a number of leaves on GDC grapevines during the experiment, but the exact position of leaves was not reported with measurements. For both VSP and GDC grapevines, shoot architecture was constructed based on digitisation data, using a grapevine-specific shoot architecture module following a Multiscale Tree Graph (MTG) approach  (Godin and Caraglio, 1998; Pradal et al. , 2008; Balduzzi et al. , 2017) , in which organs topological connections and geometry were associated to shoot architecture (Figure  5 ).
Complexity In order to explore the contribution of HydroShoot's hydraulic and energy modules components to the final simulation output, a sensitivity analysis was performed by plugging/unplugging each of these components and observing the resulting difference on simulated outputs. This procedure aims in fine at evaluating whether adding complexity to an FSPM would improve its performance in predicting gas-exchange dynamics at the plant-scale. The following simulation combinations are used: sim0: the reference (complete) version of HydroShoot having the ensemble of its components; sim1: stomatal conductance varies with (as described by Leuning, 1995 in Eq. 5a) regardless of leaf water potential; sim2: the hydraulic structure is disregarded (water potential of all leaves is forced equal to water potential at the collar) and stomatal conductance varies with collar water potential Eq.
http://www. python. org) in the OpenAlea platform (Pradal et al. , 2008; Pradal et al. , 2015). The code of the model can be freely accessed through its public depository (https://github. com/openalea/hydroshoot). It uses theMultiscale Tree Graph (MTG) method (Godin and Caraglio, 1998; Balduzzi et al. , 2017) as a central data-structure in order to allow indirect communication between the different models which favours modularity (Fournier et al. , 2010; Garin et al. , 2014). Each process has been implemented as a reusable component in OpenAlea and can be reused independently in other models and composed in various ways, provided that the other models are written in the Python language and provide the adequate inputs.
Acknowledgments This research was funded by the  European Community 's  Seventh Framework Program  ( FP7/2007-2013 ) under the grant agreement no. FP7-311775 , Project  INNOVINE. It was also partly funded by the  Environment and Agronomy department of the French National Institute for Agricultural Research (INRA). The authors greatly acknowledge  Mr. Gerardo Lopez  for his concise and constructive notes on this manuscript. They also thank  Dr. Junqi Zhu  for his valuable feedback on the gas-exchange code. The authors dedicate this work to their beloved colleague  Eric Lebon , who left us just before the closure of the  INNOVINE  project. Downloaded from https://academic. oup. com/insilicoplants/advance-article-abstract/doi/10. 1093/insilicoplants/diz007/5519776 by guest on 30 June 2019.
After studying the coding sheet, raters were instructed to mark the presence of Effort qualities that appeared in the movement at any time during the viewing periods. The distinctions between postural and gestural movements, body parts, and degrees of intensity was expressis verbis not considered. Raters viewed whole-body video footage of the patient and therapist without sound. They made naturalistic, real-time observations of movement sequences and were free to review the footage as much as desired. In this study, Davis discussed the result by pointing to the need for more intensive training, with a recommended training period of between 15 and 30 hours. Taken together, the abovementioned studies offer some limited support for the reliability of the concepts and components of LMA. However, from one study to another, a large variability in stimuli, data analysis and conclusions on inter-rater reliability seems to be present.
Stimulus material To conduct this experiment, we built a database of short video clips of a dancer performing two different gesture movements in a variety of different ways. The two base gestures are "knocking" and "giving directions" (Fig  4 ). The gestures were chosen to be relatively simple movements that can easily be varied along the LMA dimensions of Effort, Phrasing, Shape, and Space (Table  1 ). Moreover, these specimens are identified as one sequence of movement that represents an individual stimulus with a single variation. The set of "knocking" gestures contains a total of 25 videos (mean length 3. 91s): 1 neutral execution and 24 variations. The "giving directions" set comprises 20 videos (mean length 9. 50s) of the gesture being performed with 1 neutral execution and 19 variations.
The trade-off is that diversity of stimuli will decrease the power of the inferential statistics because we have fewer data points for each individual stimulus. Similarly, in the rating procedure we aim at balancing efficiency and ecological validity; while we want to efficiently collect as many ratings as possible, we want to make sure that the time spent on each rating is comparable to the amount of time experts routinely allocate. In order to ensure an exhaustive investigation of the reliability of the LMA system, we curated our database according to the categories. This will allow to assess the reliability of each LMA category in isolation. Conversely, using un-curated stimuli that come from everyday life would not allow an exhaustive assessment of LMA's reliability. Additionally, it would be more difficult to assess the ground truth of each stimulus with regards to the existing labels.
Finally, if CMAs observed un-curated stimuli, their LMA analysis would likely consist of more than 2 rounds of significant change. This would affect the experimental control over the study and jeopardize the statistical analysis through Krippendorff's α. It should also be noted that, in the present case, in contrast to most other publications on notation systems, we are not proposing a novel system, and demonstrating its advantages, but rather investigating the reliability of a widely used system. We therefore aim to assess the reliability of the system under optimal conditions rather than at its limits. The videos, as well as the associated motion capture data, is publicly accessible through the MoDa Open Source Movement Database (http://moda. movingstories. ca/).
Apparatus For the stimulus presentation and the annotation of the formalized LMA, we developed a custom video annotation tool (Fig  5 ). The tool was developed iteratively in close collaboration with four CMAs. A number of tools are available for annotating videos, both commercial (Mangold interact, https://www. mangold-international. com/en/products/software/behaviorresearch-with-mangold-interact), Noldus Observer XT, https://www. noldus. com/humanbehavior-research/products/the-observer-xt), and open source (ELAN, https://www. mpi. nl/ corpus/html/elan/index. html, Kinovea, https://www. kinovea. org/, Anvil  [55] ). However, most of these tools are geared towards annotation along a time line, and none of them support a decision-making process based on a directed graph. The novelty of our LMA video annotation tool is that the user is guided through a set of questions and answers that are based on the underlying directed graph representation of LMA.
The graph itself is stored in a database where one table maps the questions to possible answers, and a second table "A2Q" maps answers to follow-up questions. By changing the underlying database, the annotation tool can readily be adapted to support any directed graph type of decision process. The primary purpose of the LMA video annotation tool is to support the decision-making process during the LMA annotation process. To make the tool accessible to the wider LMA community, we have released the software under the GNU GENERAL PUBLIC LICENSE Version 3 on GitHub (https://github. com/bernuly/LMAVideoAnnotionTool).
g. "Yes-Effort-Light" and "Yes-Effort-Strong") this will result in difference ¼ 0:33 ¼ 1 À 2 3. Note that due to the constraint nature of the directed graph of the structure of questions and answers, we do not need to take into account where in the sequence the overlap occurred. Different approaches to combining round 1 and round 2 sequences. In the measurement procedure we used in our study, each participant rated the observed behavior twice, ordered by the assessed relevance of the change. Hence, our results comprise two answer sequences per participant per pair of videos. For each round, each participant had a choice between 27 different paths. We will refer to these two sequences as "R1" and "R2". We computed Krippendorff's α for four different combinations of data from R1 and R2. Firstly, we computed α only taking into account data from the first round of each participant ("R1").
This work was supported by:  Moving Stories ,  SSHRC Institutional Partnership Research Grant  (www. sshrc-crsh. gc. ca) to UB, SF, KS, KB, PP, TS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was also supported by  Movement + Meaning NEP, CANARIE, Industry Canada  (www. canarie. ca)to UB, PP, TS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These control samples after preprocessing steps yielded between 1 and 366 sequences per sample, with a mean of 98 and a median of 67. 5 sequences. The numbers of sequences obtained for the microdissected samples as well as the relative abundance for each taxon are reported in Table  S1A  in the supplemental material. Alpha diversity was estimated using the Chao1 index, which indicates the richness of bacterial communities based on the abundance of rare species belonging to each group of samples. The rarefaction plot indicates that the bacterial compositions of the samples from nontumoral colonic tissue from CRC patients has a higher richness than those from tumoral colonic tissues (Fig. S1A ). As mentioned in Materials and Methods, two databases were used to perform taxonomic assignment of each operational taxonomic unit (OTU): Greengenes  (35)  and HITdb  (36).
The comparative analyses between cancerous and noncancerous samples were performed using HITdb, as this database permits taxonomic analysis to the species level. The corresponding OTU assignments from phylum to species, obtained with HITdb, are reported in Table  S1B. Core microbiotas in normal colonic tissues. The study aimed to evaluate whether bacteria could be detected in human colonic crypts, as has previously been described for mice  (34). Using normal colonic biopsy specimens, bacteria were visualized inside the intestinal crypts by fluorescence in situ hybridization (FISH) technology utilizing a pan-bacterial probe (Fig. 1A ), as well as in the mucosa-associated region of the two right and seven left colon biopsy specimens (Fig. 1B ). These bacteria were then identified by 16S rRNA sequencing performed on LCM samples of the CAM and MAM regions, as illustrated in Fig.
py was used to cluster sequences using the UCLUST algorithm (84) into OTUs at 97% similarity; (ii) pick_rep_set. py was used to select a representatives set of OTUs, including a sequence representative of each OTU, corresponding to the centroid of the associated cluster; and (iii) assigne_taxonomy. py was used to perform taxonomy assignment for the representative set, using the UCLUST assignment method (default parameters) and the Greengenes 13. 8 16S rRNA gene sequence database  (35)  or the human intestinal 16S rRNA gene taxonomic database, HITdb  (36). To build the phylogenic tree of representative sets, the multiple alignment was generated using PyNAST (85) based on default similarity of 75%. The phylogenic tree was constructed using make_phylogeny. py with the tree_method_default parameter using the FastTree algorithm  (86).
The resulting phylogenic tree was further processed to calculate core diversity metrics, including ␤-diversity (based on weighted/unweighted UniFrac metrics  [87] ) and ␣-diversity (for different depths of rarefaction) measures. Moreover, we carried out taxonomic group diversity analysis and differential taxonomy abundance (scripts of QIIME). The intergroup high similarity and intragroup low similarity of microbiotas are assessed by determining ␤-diversity using PCoA (generated by QIIME using unweighted UniFrac metrics)  (83, 87). To test for significant differences in taxonomic abundances, we used the nonparametric Kruskal-Wallis test with the false-discovery rate correction implemented in QIIME and the zero-inflated Gaussian (ZIG) mixture model  (42). Differences were considered significant at a P of Ͻ0.