dataesr/scientific-paragraphs-categorization huggingface dataset:
To demonstrate that our tool can work across several graphs, we created a small graph extracted from DBpedia, containing images and geolocations for entities mentioned in the Nobel Dataset, which represents 2 234 triple statements. In a second step we adapted the system to make it work on the French National Library's dataset. Experts at the Bnf extracted a sample that they considered representative, containing 32 106 950 statements. Then we tried the tool on several other datasets from the Linked Data cloud. Qualitative Evaluation. We are currently running a series of qualitative evaluations, with users representative of each of our persona categories. We demonstrate the tool, and ask users to perform discovery tasks, using a think-aloud protocol. We also collect their feedback in a semi-structured interview.
RESULTS User Perspective. Though the evaluation of S-Paths is still running, we can already share some observations. Lay users were able to use the tool without knowing about graph databases or what a path in a graph is. However, they were bothered in the navigation by the limitations due to Linked Data: they expected the interface to be more responsive, and complained about not getting previews and rollovers for selections. On the contrary, experts easily understood that such limitations are due to the data's structure and distributed nature, and expressed enthusiasm for the possibility to get overviews. All of them were fully focused on the navigation and 20 minutes appeared very short to them. They would have wanded more time to practice with the tool, and to discover the data. We asked data reusers to explore a dataset as they would have at the start of a hackathon.
Our first participant told us that the tool could automatically do most of the work he would usually do when discovering a new dataset. "When you engage in a hackathon, what you are looking for is irregularity in the data, and this tool finds them and points them out". This user noticed that the map showing the co-laureates' affiliation showed surprisingly very few entities in the USA, which revealed that most of the laureates affiliated with US institutions win the prize as a single laureate. This example is particularly interesting, since it would otherwise have demanded an important analysis work from an expert to reach this conclusion, and maybe he would never have had the idea to plot a map with co-laureates only. S-Paths, following the paths, and thus being able to relate geolocations from DBpedia with places across the two graphs, made this starightforward.
A side-effect of the systematic use of aggregation is that it gives the impression of presenting the whole set even when the properties displayed only partially cover that set. Although we clearly indicate coverage in the the top-right corner of the interface (see Figure  1 ), and even though the tool favors well covered properties, a user pointed out that this should maybe be even more visible. Scalability and adaptability. So far we tested S-Paths with 7 datasets of various sizes and characteristics: Nobel 2 , Data BnF 3 , ELI 4 , RISM 5 , John Peel Sessions 6 , Amsterdam Museum 7 , Linked Movie DB 8. Two of them, ELI and RISM did not feature any rdf:type statement. Since such statements are the starting point for our tool, we had to generate them in a small adjacent graph according to the model.
A limitation of the tool is that it needs to find enough properties in a readable dimensions on the whole set of entities to offer views. At the moment, given the views available and their configuration, this means either dates, numbers, geolocations in any dimensions, or text or URIs with a number of unique values at the end of the path lower than 100. Several classes of resources in the John Peel Sessions dataset did not match this condition. This could be overcome by designing views that group text values and URIs by their first characters(s), thus enabling aggregation for any path. The view would be rated low, because this is not the most meaningful aggregation, but it could serve as an entry point when no other is available.
FUTURE WORK S-Paths, by putting a resource in the context of other similar resources, aims at presenting Linked Data in a manner that is understandable by humans. Continuing with this approach, and taking one more step back, my second research question is: could we identify, analyze and display elements constituting a (pseudo) structure to give an overview of a RDF dataset ? Indeed, talking with experts during workshops and more informal meetings, we realized that they had difficulties gaining a general understanding of their datasets. In particular, data producers themselves do not seem to have a clear idea about the number of entities in their dataset that are linked to another dataset. Detecting precise links between datasets is not a simple task. If the rest of the analysis can be performed on each SPARQL endpoint independently, links require to query at least two SPARQL endpoints at a time.
The support for federated queries and the availability of other endpoints being uncertain parameters, link detection might have 2 http://www. nobelprize. org/about/linked-data-examples/ 3 http://api. bnf. fr/dumps-de-databnffrwearecurrentlyworkingona10percentsample 4 http://data. public. lu/fr/datasets/legilux-journal-officiel-du-grand-duche-de-luxembourg/ 5 https://old. datahub. io/dataset/rism 6 http://raimond. me. uk/resources/peel. tar. gz 7 https://bitbucket. org/biktorrr/amlod/downloads/ 8 https://old. datahub. io/dataset/linkedmdb to be piloted from a central server. We are currently working on these specific questions. Finding a way to efficiently detect links between datasets would also be of interest for S-Paths, since it would allow to turn it into a full linked data browser. For now, it is limited to the exploration of multiple graphs hosted behind a single endpoint.
Supplementary materials include: (1) verification examples and implementation of our framework in F ⋆ (https://github. com/FStarLang/FStar/tree/dm4all/examples/dm4all); (  2 ) verification examples and a formalization in Coq (https://gitlab. inria. fr/kmaillar/dijkstra-monads-for-all); (3) an online appendix with further technical details (https://arxiv. org/abs/1903. 01237).
In fact, we can embed their work into ours, since their notion of weakest precondition gives rise to an effect observation. Generic reasoning about computational monads dates back to  Moggi's [1989]  seminal work, who proposes an embedding of his computational metalanguage into higher-order logic. Pitts & Moggi's evaluation logic  [Moggi 1995; Pitts 1991 ] later introduces modalities to reason about the result(s) of computations, but not about the computational context. Plotkin and Pretnar [2008]  propose a generic logic for algebraic effects that encompasses Moggi's computational λ-calculus, evaluation logic, and Hennesy-Milner logic, but does not extend to Hoare-style reasoning for state. Simpson and Voorneveld [2018]  and  Matache and Staton [2019]  explore logics for algebraic effects by specifying the effectful behaviour of algebraic operations using a collection of effectspecific modalities instead of equations.
ACKNOWLEDGMENTS We thank  Nikhil Swamy  and the anonymous reviewers for their feedback. This work was, in part, supported by the  ERC  under  ERC Starting Grant   SECOMP (715753 ). Guido Martínez ' work was done, in part, during an internship at  Inria Paris  funded by the  Microsoft Research-Inria Joint Centre. This material is based upon work supported by the  Air Force Office of Scientific Research  under award number  FA9550-17-1-0326.
In this paper, we propose an end-to-end trainable regression approach for human pose estimation from still images. We use the proposed Soft-argmax function to convert feature maps directly to joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two very challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods and comparable results to the state-of-the-art detection based approaches.
Experiments We evaluate the proposed method on the very challenging MPII Human Pose  [1]  and Leeds Sports Poses (LSP)  [23]  datasets. The MPII dataset contains 25K images collected from YouTube videos, including around 28K annotated poses for training and 15K poses for testing. The annotated poses have 16 body joints, some of them are not present and others are occluded but can be predicted by the context. The LSP dataset is composed by 2000 annotated poses with up to 14 joint locations. The images were gethered from Flickr with sports people. The details about training the model and achieved accuracy results are given as follows.
For the MPII dataset, we train the network for 120 epochs. The learning rate begins at 10 -3 and decreases by a factor of 0. 4 when accuracy on validation plateaus. We use the same validation split as proposed in  [40]. On the LSP dataset, we start from the model trained on MPII and fine-tuned it for more 70 epochs, beginning with learning rate 2 • 10 -5 and using the same decrease procedure. The full training of our network takes three days on the relatively outdated NVIDIA GPU Tesla K20 with 5GB of memory. Data augmentation. We used standard data augmentation on both MPII and LSP datasets. Input RGB images were cropped and centered on the main subject with a squared bounding box, keeping the people scale (when provided), then resized to 256 × 256 pixels. We perform random rotations (±40 • ) and random rescaling from 0.
Results LSP dataset. We evaluate our method on the LSP dataset using two metrics, the "Percentage of Correct Parts" (PCP) and the "Probability of Correct Keypoint" (PCK) measures, as well as two different evaluation protocols, "Observer-Centric" (OC) and "Person-Centric" (PC), resulting in four different evaluation settings. Our results compared to the state-of-the-art results on the LSP dataset using OC annotations are present in Table  1  (PCK measure) and Table  2  (PCP measure). In both cases, we overcome the best scores by a significant margin, specially with respect to the lower leg and the ankles, on which we increase the results of Pishchulin et al. [33]  by 6. 3% and 4. 6%, respectively. To the best of our knowledge, we are the sole regression method to report results using this evaluation settings.
Using the PC annotations on LSP, we achieved the best results among regression based approaches and the second general score, as presented in Tables  3  and 4. On the PCK measure, we outperform the results reported by Carreira et   al. [6]  (CVPR 2016), which is the only regression method reported on this setup, by 18. 0%. MPII dataset. On the MPII dataset, we evaluate our method using the "Single person" challenge  [1]. The scores were computed by the providers of the dataset, since the test labels are not publicly available. As shown in Table  5 , we reached a test score of 91. 2%, which is only 0. 7% lower then the best result using detection based method, and 4. 8% higher than the second score using regression. Taking into account the competitiveness of the MPII Human Pose challenge 2 , our score represents a very significant improvement over regression based approaches and a promising result compared to detection based methods.
Major, trace element and isotopic measurements were only carried out on samples from lava flows, dykes and sills which were relatively fresh based on their macroscopic and microscopic features, and from which zeolite-and carbonate-bearing vesicles were removed by handpicking. The corresponding analytical database includes 5 samples from V1, 53 from V2, 28 from V3 and 5 from V4 lava flows, together with 47 intrusive (sills, dikes) samples (Tables  1, 2, 3  and 4 ). Most of the pillow fragments collected from V1 are altered (Loss on Ignition higher than 1. 9 wt%), and a number of compoundbraided basaltic flows (pahoehoe lobes and lava tube piles, see below), especially from V2, are too rich in secondary minerals (zeolites and carbonates) to have retained their primary magmatic features. Such samples were primarily collected to investigate these post-magmatic mineral associations found as vesicle fillings and vein/fracture deposits (57 samples from V2 and V3).
, 2008; Thirlwall et al. , 2004; Peate et al. , 2010). The presence of distinct Icelandic isotopic components has also been described in Eastern Greenland  (Barker et al. , 2006). Nevertheless, in that region, the different signatures appear in separate areas (Milne, Rømer and Skraeterne), located hundreds of kilometers away from each other. In the present study, we report distinct signatures on samples all coming from a restricted segment (<40 km along the coast) and covering a time span shorter than 8 m. y. Such diversity can therefore hardly be imagined as the result of the tapping of distinct parts of an Iceland type mantle plume. Instead, we suggest that the Sk3 signature in our dataset corresponds to a trend toward the C component of  Hanan and Graham (1996). This component has been recognized as being widely disseminated within the upper mantle feeding the Arctic segments of the mid-Atlantic ridges and is therefore believed to correspond to an ubiquitous ingredient of the North Atlantic upper mantle  (Blichert-Toft et al.
Acknowledgments This study is part of the  Petrobras-UBO Volcabasin program. The field work was carried out during a five-week expedition in the summer of 2012 and benefited from the logistic support of the research vessel Porsild from the  Arctic Station in Godhavn (Greenland)  and the  French polar yacht Vagabond. Philippe Nonnotte's help was especially appreciated before and during the fieldwork in Greenland. We also thank the.
Speech materials and recording The speech materials consisted of 37 (near-)minimal pairs of lexical words with a voicing contrast for the plosive onsets /p-b, t-d, k-g/, plus 4 words with /m/ onset as a reference for analyses, making a total of 78 words. 1  Within each word pair, words of high and similar degrees of familiarity were used, based on the NTT database  (Amano & Kondo, 2000). Each word contained 2 to 3 syllables and 2 to 5 moras. The target C was either in word-initial position, that is, the onset of the first syllable, or in word-medial position, that is, the onset of the second syllable. The wordlist contained 38 accentless words and 40 initial-accent words (see Section 1. 2. 2 for a description of pitch-accent in Tokyo Japanese). In an accentless word, the initial mora had an L tone, and a non-initial mora had an H tone, while in an initial-accent word, the initial mora had an H tone, and a non-initial mora had an L tone.
Measurements and analyses 282 tokens (5% of the entire dataset), were produced with a pitch-accent pattern deviant from the prescriptions in the database, or with reading errors, and were excluded from all analyses. The data were segmented and annotated manually with Praat  (Boersma & Weenink, 1992 -2017). We measured voicing-related properties for voiced vs. voiceless plosives, which included VOT and voicing-ratio, as well as closure duration. We also measured f0 on the vowel following plosive and /m/ onsets. Voice onset time (VOT). VOT was measured in POST-PAUSAL position (WI_CITATION and WI_CARRIER-FOCUS). For phonetically voiceless plosives (i. e. , without voice lead), VOT intervals were marked between the onset of the release burst and the onset of the following vowel. The first ascending zero-crossing point on the waveform at F1 onset was determined as the vowel onset.
The vowel was divided into 50 equal time intervals, and the mean f0 of each time interval was measured. In the case of diphthongs, the boundary between the two vowels being generally unclear, the first half of the diphthong was measured. Vowels followed by a nasal coda were generally easy to separate from the nasal coda by inspection of the waveform and the spectrogram. We visualized individual plots of raw f0 curves for each token by each speaker and excluded 91 tokens (i. e. , 1. 6% of the entire dataset), which manifested visible deviant f0 curves that were caused by detection errors (mainly octave errors) by Praat. The raw f0 values were then normalized using a within-speaker z-score transformation so that between-speaker variations were minimized. For analyses of the VOT-f0 correlation, in order to make the f0 difference more comparable between speakers, the f0 values were converted from hertz to semitones relative to each speaker's mean f0.
Statistical methods. Statistical models were constructed using functions in the lme4 package  (Bates, Maechler, Bolker, & Walker, 2017)  in R (R Core  Team, 2017). Generalized linear mixed models (GLMM) were built to fit to binary data using the glmer function. Linear mixedeffects models (LME) were built to fit to continuous data using the lmer function. All models included random intercepts for speaker and item and by-speaker random slopes for each predictor provided that they converged and improved the fitness of the model as evaluated by AIC. Predictors specified with levels were included as factor variables; otherwise, they were included as numerical variables. Likelihood-ratio comparisons, using the anova function in R, were conducted to assess the effect of each predictor and will be reported when relevant.
Since we were especially interested in the contrasts between levels (e. g. , between different conditions, places of articulation, and vowels) as well as the interactions between factors, we will mainly report post-hoc pairwise comparisons of the GLMM and LME models using the emmeans package  (Lenth, 2018)  in R, with p-values adjusted using the Tukey method. Sex and the interactions between sex and all the other factors were included as predictors in all the models, and all the results will be presented separately for male and female speakers. In some models, interactions involving more than two factors were included as predictors when the interaction was of interest and improved the fitness of the model.
Phonologically voiced plosives are often prevoiced in INTERSONORANT position (WI_CARRIER, WM_CITATION, WM_CARRIER). However, the percentage of fully voiced tokens in WI_CARRIER in Tokyo Japanese is 49. 9%, lowered than 62. 5% in German, commonly categorized as an -aspirating‖ language, and 97. 5% in Russian, commonly categorized as a -true voicing‖ language (see Section 3. 1). This outcome fits well with (B2), expected for a typical -aspirating‖ language: (B2) In intersonorant word-initial position, voicing is, at most, passively maintained during the closure of voiced plosives, that is, a low percentage of voiced plosives are fully voiced. Phonologically voiceless plosives are moderately aspirated in word-initial position, indicated by medium-lag VOTs. In word-medial position, they are generally unaspirated, indicated by shortlag VOTs.
Acknowledgements This study was supported by a  JSPS   Postdoctoral Fellowship  awarded to the first author, and was funded by  JSPS   Grant-in-Aid  No. 17F17006. We wish to thank the editor,  Taehong Cho , and three anonymous reviewers for their invaluable suggestions, which greatly improved this paper. The interpretation of the results benefited from numerous discussions with  Martine Mazaudon. We are grateful to her as well as  Marc Brunelle ,  Pierre Hallé ,  James Kirby ,  Mafuyu Kitahara , and  Douglas Whalen , for very constructive comments on earlier drafts; to  Boyd Michailovsky  for proofreading the manuscript; and to  Takeki Kamiyama  for suggestions on some details of this study. None of them necessarily agrees on all the points reviewed or.
Often, they also try to move the workload to power efficient machines, to lower the cost and environmental impact of the data centres. One problem is that machines can range to up to tens of thousands (e. g. , OVH, a European leader in the domain, have 150,000 servers in 12 data centres 1 ), and services up to millions (e. g. , VMware ESX accepts up to 320 VMs per host). At this scale, any instance of the reassignment problem becomes a challenge to the existing heuristics/solvers and finding the 'best' (re-) assignment an illusion. Another problem is that, as we mentioned in the previous paragraph, managers have different perspectives on what is a 'good' solution, and ranking all the solutions according to a single utility function (e. g. , minimising energy consumption) is probably not relevant.
Experimental Setups The ROADEF challenge (  2012 ) is particularly suited to our needs, as it is rather realistic (proposed by Google) and it is quite comprehensive: a lot of resources for the machines/processes while many papers in the area only consider two (namely, RAM and CPU), reasonably high number of machines and processes, complex dependencies and constraints on the services and processes which make the assignments not straightforward. The ROADEF dataset distinguishes three categories of instances (a 1 are considered 'easy', a 2 'medium' and b 'hard'). In this paper, we pick up 14 instances (see Table  1 ), leaving out only the biggest ones (as they require a large execution time). We have added variables α m and β m to each machine m ∈ M, and γ l for every location l ∈ L in order to include electricity consumption.
3% MUP) (n = 24) and an MIS 2 Late Upper Paleolithic one (n = 21); the former sample is entirely European, but the latter one includes one southwest Asian specimen (Ohalo 2). Additional data are included for small samples of late Middle Pleistocene (MIS 6) archaic humans (n = 5) and an early Late Pleistocene (MIS 5) Middle Paleolithic modern humans (MPMH; n = 4). The three primary samples are all male biased (for the sexable specimens) (Table  2 ), although they are not different from each other in sex proportions (χ 2 p = 0. 976). They are also biased towards young adults plus older adolescents versus older adults (Table  2 ), as are Late Pleistocene mortality profiles in general  [48]. However, the three samples are not significantly different from each other if the adolescents and young adults are pooled (χ 2 p = 0.
941). Therefore, although EAE, as a degenerative process, generally increases in frequency and/or severity with age and/or exposure  [30, 34, 43, [49] [50] [51] , the age distributions should not affect the comparisons across these Late Pleistocene samples. To provide a recent human reference framework, presence/absence data are presented for 120 recent human samples (S2 Table ). As originally proposed by Kennedy  [18] , they are separated by latitude into those <30˚, those between 30˚and 45˚, and those >45˚(north and 1 The male and female counts include both pelvically and craniofacially sexed remains. 2 Given that the adolescent remains are all late adolescent and routinely considered as adult, and that the included cranial/temporal remains from the large Krapina sample cannot be aged to adolescent versus young adult, the adolescent and young adult (YA) samples are pooled in the counts.
Acknowledgments We thank  V. S. Sparacello  for providing EAE observations on Late Upper Paleolithic crania,  H. Rougier  and A. S ˇefčakovafor photographs of fossil specimens, and  R. Schmitz  and  J. -J. Hublin  for CT scans of crania (see SI Acknowledgments for details). Portions of the data collection have been funded by the  Universitede Bordeaux, Washington University , and the  Agence Nationale de la Recherche.