dataesr/scientific-paragraphs-categorization huggingface dataset:
Presentation of complex meaningful sentences, containing at least two clauses Simple sentence Presentation of simple meaningful sentences, with only one main clause Read jabberwocky Presentation of syntactically well-formed sentences whose content consist of meaningless, yet pronounceable, words Read words Presentation of list of words Read pseudowords Presentation of list of pseudowords Consonant strings Presentation of list of nonwords (i. e. consonant strings) TA B L E B. 1 2 Description of conditions from RSVP Language task. C | INTERSUBJECT VARIABILITY ACROSS CONTRASTS FROM THE IBC, ARCHI AND HCP DATASETS Intersubject variability of the ARCHI and HCP contrast maps was tested between different datasets. Figures C. 1 and C. 2 display the average of individual z-maps' correlations of a given contrast between every subject pair, within datasets.
They summarize how similar individual maps are per contrasts for each dataset, thus highlighting different levels of consistency of the functional signatures across individuals for these different datasets. Overall, both figures show that better levels of consistency were obtained for the IBC contrasts than for the ARCHI and HCP counterparts. shape outline tale vs. mental addition relational processing vs. visual matching punishment vs. reward mental motion vs. random motion 2-back vs. 0-back place image vs. any image tool image vs. any image face image vs. any image body image vs. any image left foot vs. any motion right foot vs. any motion left hand vs. any motion right hand vs. any motion tongue vs. any motionF I G U R E C. 1 Comparison of intersubject and intrasubject variability across contrasts between IBC and HCP datasets.
The bar chart represents the means of the distributions referring to the correlations of z-maps per contrast. (Left panel) Intersubject correlations were estimated from pairs of contrasts for all subjects pairs, whereas (Right panel) intrasubject correlations were estimated from all possible pairs of contrasts from "PA" and "AP" runs, within sessions. Bars in orange correspond to the correlations of contrasts obtained from the IBC dataset; bars in blue correspond to the correlations of contrasts obtained from the HCP dataset. Error bars represent the 95% CI.
Pinho et al. (2018)n C). They thus confirm that the IBC dataset hints at good levels of fSNR-in agreement with the results stated inPinho et al. (2018)-as well as an overall high data quality, including an adequate parametrization of the MRI acquisitions. mental motion vs. random motion false-belief story vs. mechanistic story false-belief tale vs. mechanistic tale expression intention vs. expression gender face trusty vs. face gender face image vs. shape outline punishment vs. reward inter-subject correlation intra-subject correlation tool image vs. any image place image vs. any image face image vs. any image body image vs. any image 2-back vs. 0-back read pseudowords vs. consonant strings read words vs. consonant strings read words vs. read pseudowords read sentence vs. read jabberwocky read sentence vs.
Remerciements Nous tenons à remercier  Jilong Tong , qui nous a autorisé à présenter ici son contre-exemple, décrit à la section 6,  Hélène Esnault  pour son intérêt pour notre travail au travers de nombreux échanges et  Matthieu Romagny  pour sa contribution à la preuve du corollaire 2. 2.
Marco Antei a reçu le soutien du projet  TOFIGROU  ( ANR-13-PDOC-0015-01 ). Michel Emsalem a reçu le soutien du Labex CEMPI  ( ANR-11-LABX-01 ). Carlo Gasbarri a reçu le soutien du projet  ANR-16-CE40-0008 , FOLIAGE.
A prominent barrier preventing widespread adoption of pharmacogenomics panel-based testing is the lack of evidence supporting this approach. Although a number of small, randomized and observational studies have indicated the potential benefits of pharmacogenomics panel-based testing  [14] [15] [16] [17] , a sufficiently powered prospective study assessing the clinical and cost-effectiveness of pre-emptive pharmacogenomics-testing is yet to be executed  [18]. Therefore, the Ubiquitous Pharmacogenomics Consortium initiated the PREemptive Pharmacogenomic testing for Preventing Adverse drug Reactions (PREPARE) study, (ClinicalTrials. gov: NCT03093818). The PREPARE study aims to quantify the collective clinical utility and cost-effectiveness of a panel of pharmacogenomics-markers to guide dose and drug selection in reducing the risk of clinically relevant adverse drug reactions (ADRs)  [19, 20] , and thereby providing clinical outcome data which has been cited as a contributing factor preventing uptake of pharmacogenomics  [21].
Primary endpoint collection The operationalization of data collection to support the severity and causality assessments required additional consideration because of to the broad range of anticipated ADRs. This was in context of the fact that extracting ADR data retrospectively from EMRs can be difficult. Prospective collection of data in PREPARE after assessment of the patient was therefore felt to be the most accurate method. To capture as much information as possible on the occurrence of ADRs, we use three sources and concurrently collect information to support severity and causality assessments. Information is obtained from patients during scheduled follow-up (both online surveys and interviews), clinician reports, and extraction from the EMR. Patient interviews consist of open-ended questions, designed to uncover any ADRs, which the patients may have endured.
On the other hand, a limitation of this methodology is the inability to uncover ADRs which are asymptomatic, such as citalopram-induced corrected QT-interval (QTc) prolongation, as ECGs are not routinely performed, potentially underestimating the occurrence of ADRs. However, once the ADR manifests symptomatically, such as Torsade de Pointes, it will contribute to the endpoint. To ensure systematic assessment of the defined composite endpoint, we utilized standardized methods for assessment of severity and causality; the Common Terminology Criteria for Adverse Events (CTCAE) and Liverpool Causality Assessment Tool (LCAT), respectively. In order to undertake severity assessment, using CTCAE, we require different types of data depending on the ADR reported. For example, to assess severity of a drug-induced dry mouth, we require data on the intervention required (grade 1: symptomatic without dietary alteration; grade 2: oral intake alterations; grade 3: tube feeding), whereas when assessing the severity of neutropenia, we require data on neutrophil counts (grade 3: ANC<1000/mm 3 ; grade 4: life-threatening consequences; grade 5: death).
As with the severity assessment, data to support the causality assessment is collected from direct questioning of patients after the report the adverse event during scheduled follow-up (e. g. Did you lower the dose or temporarily stop taking the index drug after having the side effect? Did this reduce the side effect?). In contrast to severity and causality assessment, there is no standardized method available for assessment of drug-genotype association. Therefore, to ensure unbiased assessment, an algorithm will be used to perform the analysis at study completion, to ensure consistent pharmacogenomics literature across assessments. This algorithm is being created by an expert panel, blinded to patient allocation. It is based on ADRs associated with certain genotypes in the literature underlying the DPWG (Table  1 ) and the ADRs reported in a drug's Summary of Product Characteristics.
Acknowledgements We would like to thank the  Scientific Advisory Board  ( Prof. Russ Altman ,  Prof. Michel Eichelbaum ,  David Haerry ,  Prof. Mark Ratain ,  Prof. Mary Relling , and  Prof. Dan Roden ) for their insights in the development of the study protocol and statistical analysis plan. The research leading to these results has received funding from the  European Community 's  Horizon 2020 Programme  under grant agreement No. 668353  ( Ubiquitous Pharmacogenomics ). M. S. is in part supported by the  Robert Bosch Stiftung, Stuttgart, Germany.
Acknowledgements We thank  Davide Fazio  for the discussions on the topics of the present paper. The following funding sources are gratefully acknowledged: MIUR, within the projects PRIN 2017: "Logic and cognition. Theory, experiments, and applications", CUP:  2013YP4N3 , and  PRIN 2017 : " Theory and applications of resource sensitive logics ", Prot. 20173WKCM5, CUP:  F74I19000720001 ;  Regione Autonoma della Sardegna  within the project " Per un'estensione semantica della Logica Computazionale Quantistica-Impatto teorico e ricadute implementative ", CUP:  RASSR40341.
2015). One of the reasons for the scarcity of studies is the technicality that these analyses entail. Although attempts are made in order to standardize the logs and develop specific data analysis tools  (Hao et al. 2016) , logs are often messy, unstructured, and full of "noise"-all of which leads traditional data analysis tools and techniques to work less well with these data. Process data, recorded as sequences of actions, can be likened to textual data and analyzed by making use of methodologies of natural language processing and text mining. Hao et al. (2015)  transform process data into a string of characters, encoding each action name contained in the logs as a single character. The authors use the Levenshtein distance, defined as the minimum number of single-character edits needed to convert one character string to another, in order to compare how far the students' activities in game/ scenario-based tasks are from the best performance.
Data-driven methods can be very helpful when trying to make sense of huge amounts of data and discover hidden patterns and relationships in these data. However, studies have shown that didactically meaningful findings are most likely yielded when data mining techniques are guided by theoretical principles allowing to describe subjects' skills  (Gobert et al. 2013 ). In the context of complex problem solving, studies have demonstrated how certain student behaviors yield better performances than others  (Greiff et al. 2015 (Greiff et al. , 2016)). Theoretical grounding has been essential for defining and identifying these strategic behaviors from the log data and verifying their implementation among different samples of students. CEDRE (Subject-related sample-based assessment cycle) is a sample-based large-scale assessment aiming to measure students' abilities in mathematics at the end of grade 9 every 5 years in France.
Therefore opportunities are given to students to engage more fully higher order skills such as problem solving, devising a strategy and carrying out mathematical thinking, which the CEDRE aims to capture. Problem solving strategies in complex mathematical tasks are targeted by the CEDRE framework since the first cycle  (MEN, DEPP 2017). Nevertheless, the way to capture student's strategies and processes in the paper-based assessment cycles used to be based on students' explanation of their answers and written arguments or sketches in response boxes. Communicating a mathematical answer being a mathematical competency in itself (OECD 2013), it does not directly indicate the actual strategy used by students to solve the task but only the way they were able to express it. Logging and analyzing process data can potentially lead to drawing a complete picture of how digital tools and interactions have been activated during the problem solving process.
Clustering using DBSCAN In parallel with this modeling, we have sought to segment our population to be able to describe the different strategies developed by the students. To do this, we first chose the DBSCAN algorithm to avoid the difficulty of determining a priori the number of clusters. The Fig. 4  shows the result of this grouping in the space created by the first two dimensions generated by a PCA on the data. The PCA results are not of importance here. They are simply used in order to allow a graphical projection of the clusters on a two-dimensional space. The limit of the DBSCAN is the difficulty of processing clusters with very different densities. The DBSCAN avoids this constraint by treating as outliers all values that would cause cluster densities to diverge too widely. As it is for this dataset, the success rate of the item is 47, 3%, and the 4 clusters have approximately the same size.
In this document we describe our method and the results obtained for comparing jaws of prehistoric humans. Our main goal was twofold: 1) establish a methodology for comparing the structure of 3D shapes of scans of jaws using geometric data analysis techniques, and 2) use this methodology for comparing and clustering individual objects according to their geometric similarity. Moreover, we also applied geometric modeling techniques to establish a "clean" version of the dataset, without significant artefacts present in the original data, such as large missing parts. We then applied our analysis techniques both on the original and the clean dataset in order to validate our comparison results. For our core task of shape comparison, we used recent state-of-the-art shape matching methods and we present similarity results using different comparison metrics.
Introduction and Context We are given ten 3D scans of jaws of prehistoric humans, shown in Fig. 1. Given this dataset, our main goal is to establish geometric similarity metrics for comparing different shapes in a robust and accurate manner. This is challenging for several reasons. First, we note that the dataset contains a lot of noise. For example, some jaws have some teeth while others do not. Furthermore, the shape "SH 5" has a very significant missing part. The shapes "Arago 89" and "El Sidron" have Figure  2 : Visualization of the correspondences between shape pairs. smaller but still significant parts missing. The shape "Arago 89" has very severe artefacts, potentially due to strong damage to the object itself. Second, the definition of a meaningful similarity metric is a challenging problem in itself, lacking a single clearly specified objective.
This is due to several factors: we are not given any ground truth annotations of shapes or parts that are either clearly similar or clearly dissimilar; further, there is no canonical deformation model between the shapes; finally these issues are significantly exacerbated by the partiality (missing parts) and artefacts in the data. Third, the dataset is very small, compared to commonly used datasets and benchmarks in geometric data analysis. This makes it very difficult to draw conclusions from individual observations, and completely precludes any use of learning-based methods. This limited size also has an adverse effect on the robustness of our results. For example, changing a single parameter can lead to significantly different clustering results. Finally, the very significant geometric variability, coupled with the small size of the dataset makes shape clustering particularly challenging.
Below we describe our general approach for dealing with these challenges and results obtained using several existing techniques. At the same time, we emphasize that these results are not conclusive, and have a rather suggestive nature. As we demonstrate, current geometric data analysis techniques are mature and can be adapted to deal with strong variability and used to define several robust and geometrically meaningful similarity metrics. However, the limited size of the dataset is a fundamental challenge that can only be overcome with more example shapes. Nevertheless, several interesting similarity results emerge from our analysis, as described below.
Methodology Our goal is to find the differences (or similarities) between the given ten jaws. Our general pipeline consists of the following steps: 1. For each pair of shapes, find correspondences between points on their surfaces, using a non-rigid shape matching method. 2. Compute the optimal rigid alignment of each pair of shapes, using the correspondences from step 1. Use this alignment to define the Rigid Alignment Measure for each pair of shapes. 3. Compute the optimal non-rigid deformation, trying to morph between each pair of shapes, using the correspondences from step 1. Use this deformation to define the Shape Distortion Measure for each pair of shapes. 4. For each of the two dissimilarity metric above, compute a hierarchical clustering, and a 2D embedding of the dataset, that preserves this metric as well as possible.
Shape Correspondences We used the method introduced in  [1]  to compute the point-wise correspondences between every pair of jaws. To avoid errors arising due to the missing data, we manually specified 22 landmark points on each shape (see Fig. 9  in the Appendix), which were used to guide the correspondence. In the Appendix we also present results computed without any landmarks on the cleaned dataset. Note that we use the manually specified landmarks only in this step of the pipeline for computing approximate correspondences. All other steps in our analysis pipeline are completely agnostic to these landmarks and operate on all points of the scans. Fig. 2  illustrates the computed correspondences two shape pairs. Specifically, if a vertex on the target shape corresponds to a vertex on the source shape (like the vertices connected by a line in the figure), they will be assigned the same color.
Rigid Alignment and Metric The shapes in the original dataset are not well aligned (e. g. , consider "H. sapiens" and "SH 5" shapes shown in Fig. 4 ), making it hard to compare their geometry. Therefore, we first rigidly align each pair of shapes using the standard Iterative Closest Point (ICP) method  [2]. ICP is an iterative method, which computes the optimal rotation and translation to align one shape to another given some initial correspondence. We use the correspondences obtained in the previous step as initialization to ICP. As an example, the position of "SH 5" after rigid alignment is shown in Fig. 4. Note that the shape of "SH 5" is not modified but only its position in space is changed. We provide the exact algorithm used for rigid alignment in the Appendix for completeness.
Similarly to ICP, this is also an iterative optimization technique, but crucially, it allows arbitrary shape deformations, and not only rigid motions. It also relies on initial correspondences between each pair of shapes, for which we use the maps computed in step 1 of our pipeline. The details of the exact method we use for non-rigid shape deformation are provided in the Appendix. Fig. 5  (right) shows an example of "SH 5" deformed into "H. sapiens". As can be seen from 5 (left), the right ramus and the alveolar part of "SH 5" are far from those of the "H. sapiens". After the non-rigid deformation, these two parts fit more closely. Of course, the non-rigid deformation induces a distortion between the original and the deformed shape. Our goal is to define a measure of this distortion in order to compare each pair of shapes in the dataset.
Note that a purely rigid deformation would induce zero error, since in that case the edge lengths would remain the same. Fig. 5  illustrates how SDM is defined. Specifically, we measure how much each edge gets deformed, and sum over all edges of the mesh to define the Shape Distortion Measure as the overall deformation metric. Since SDM is not symmetric and because a distance metric is supposed to be symmetric, we use the average of two distortion measures d to define the shape distance: Figure  6 : Average Rigid Alignment Measure on each shape. Intuitively, this highlights the regions on each shape that make it different or unique, compared to others in the dataset. D E (S 1 , S 2 ) = 1 2 SDM(S 1 , S 2 ) + SDM(S 2 , S 1 ). ( 3 ).
Global Dataset Analysis Given the measures of similarity defined above, we use the the pairwise dissimilarity between each pair of shapes for our global analysis. Specifically we analyze the dataset in three ways: 1. We visualize the average rigid alignment measure by color-coding them on the points of each shape. This intuitively highlights the points and regions that make each shape unique, compared to others in the dataset. 2. We compute a 2D visualization of the dataset, where each point corresponds to a particular shape. This helps to understand how different shapes relate to each other. 3. We compute a hierarchical cluster tree using our two dissimilarity metrics. This helps get a sense what pairs or groups of shapes are related. Note that methods 2. and 3. above depend on the choice of dissimilarity.
Average Shape Dissimilarity First, for each shape, we compute the average of its rigid alignment measure to the other nine shapes and visualize the error per vertex as shown in Fig. 6. The more highlighted a region is (i. e. , corresponding to the brighter red color), the farther it is from the other shapes in the dataset. Note that the rigid alignment measure is weighted by the correspondence accuracy. Therefore, for the regions that correspond to the missing part might not be highlighted since they are assigned with a small weight. Fig. 6  shows the average dissimilarity on all shapes in our dataset. We note several regions that are commonly highlighted: the mental protuberance, the condyle and the coronoid process. This corresponds to the fact that these regions have a lot of variability in the dataset.
Shape Relations and Clustering Our next goal is to analyze the relative global similarity of the shapes in the dataset. For this, we use the two metrics we defined (Rigid Alignment Measure and Shape Distortion Measure) first to establish a representation of the overall similarity of shapes in the dataset. Specifically, for each dissimilarity metric, we first compute a 10x10 matrix, that captures the dissimilarity for each pair of shapes. We use these distance matrices in two ways: first to obtain a 2D embedding using Multi-dimensional scaling (MDS)  [4] , which visually presents the dataset in two dimensions, while trying to respect the computed pairwise dissimiarity measures. Second, we build an evolutionary tree using a hierarchical clustering algorithm. Specifically, we assign each jaw a 2D-coordinate such that the Euclidean distance between two points in Fig.
Landmarks for correspondences Figure  9  (right) shows the landmark points we used for computing pairwise correspondences. Figure  9  (left) also shows the correspondences obtained without using landmarks via color-coding. Note that while generally reasonable, due to strong partiality, the correspondences without landmarks can have large distortion. In Section 4. 5 below 155 shows the results of our analysis pipeline without using any landmarks on the remodeled dataset, where artefacts and partiality are corrected.
Cycle consistency of computed correspondences We report the cycle consistency of the computed maps, which is a commonly-used measure of accuracy in the absence of ground truth correspondences. Specifically, given three shapes S i , S j , and S k , and the computed maps T i j , 160 T jk , and T ki , we compute the composite map T ki • T jk • T i j. This is a map from S i to itself. The cycle consistency error measures the difference between this composite map and the identity map. By averaging this metric over all possible choices of (k, j), we obtain an error defined on each shape S i. Following this idea, we computed and visualized the cycle consistency error on the complete dataset on each of the jaw in Fig. 10. The regions with large inconsistency across the dataset are colored orange. We can see that the inconsistent regions are mainly the teeth and the ramus of the jaws, which corresponds to regions that are commonly missing.