dataesr/scientific-paragraphs-categorization huggingface dataset:
Shapelet selection Section 4. 3 illustrated cluster explanation using GS-SCE and the potential of fewer shapelets yielding equivalent results to all learnt shapelets. Beside providing insights into clustering results, this demonstrated further possibilities for the explanation. We further illustrate this with 10 datasets in transductive clustering. Table  1  shows the clustering score obtained using shapelet subsets that give an 80% cumulative information gain. For the majority of the datasets, reducing the dimensionality results in more than 0. 7 NMI compared to clustering in the overall space, despite only retaining around 2% of the shapelets. However, the removed shapelets contain necessary information to preserve the original DTW distances. Fig. 8  shows an example of distance maps obtained using DTW similarity (Fig.
8a ), CDPS space (Fig. 8b ), and reduced CDPS space (Fig. 8c ) for the Plane dataset. It can be seen that DTW and CDPS produce similar distance maps, but reduced CDPS has is very different as it loses DTW approximation (yet retains cluster separation). Thus, one can either use all shapelets to retain a DTW approximation while respecting constraints or opt for fewer shapelets for speed and simplicity.
Strengths and limitations Unlike other transformation approaches for time series, CDPS integrates the expert to guide it toward their expectation. When a CDPS representation is given as an input to any downstream algorithm, the results achieved will inherently include the expert knowledge as shown in Section 4. In addition to this, the proposed shapelet cluster  explanation enables a user to choose the best shapelets in a successive manner, such that new information is added with each shapelet, and obtain comparable clustering results to that of the full shapelet space. In contrast, CDPS can be limited by DTW's accuracy. For some datasets the performance of DTW can be poor depending on the dataset domain, type, and task, and since CDPS approximates DTW it can reflect this. Depending on the weight and number of constraints provided, this can be compensated for to a certain degree.
B1. Effect of α and γ It is common that only a small fraction of the dataset is under constraint, therefore their influence on the learning process is relatively small. The parameters α and γ weight the relevance of these must-link and cannot-link constraints (respectively). To test the stability of the learning process to different values of these parameters, a grid search was performed over the range 0, 0. 5, 1, 1. 5, 2, 2. 5 (with 10 repetitions of one set of constraints for each pair of values). Both univariate and multivariate datasets from the UCR archive are chosen at random. Fig. B. 11 presents these results, in which the clustering scores are presented for each pair of values (note that brighter colour equals better performance). It can be observed in some datasets (Fish, ECG200, BME, for example) that higher parameter values increase performance, in others these parameters do not have a large effect (other datasets) As such, taking α = γ = 2.
15⋅L i (since shapelet length is directly related to the block number). Fig. B. 12 shows the clustering scores of S max vs scale. Note that brighter colour means better performance. It can first be observed that for the majority of datasets the clustering performance is often the same for multiple parameter combinations. This implies that we can choose a smaller multiplier for the shapelets per block as well as a smaller value for the number of shapelet blocks and still guarantee a similar clustering score. For instance, clustering on Epilepsy produced similar results for (4,1) and (4,8), but since (4,1) offers less complexity, it would be preferred. In addition, we can see that for some datasets, the performance degrades as the number of shapelets increase (whether in terms of S smax or scale), as seen in the cases of BME, RacketSports, and EGC200.
Acknowledgements This work was supported by the  HIATUS  and  HERELLES  ( ANR-18-CE23-0025 ,  ANR-20-CE23-0022 )  ANR  projects. We thank the  Nvidia Corporation , the  Centre de Calcul de l'UniversitȨ de Strasbourg , and HPC resources of  IDRIS  under the allocation 2021- A0111011872  made by  GENCI.
) Definition 7. 7 (Information Gain) Measures the reduction of entropy, i. e. information gained, in a dataset after being partitioned into distinct groups based on a splitting criteria (e. g. a threshold). The Information Gain (IG) T with split ζ is defined such that IG(T , ζ) = E(T ) -E(T )| ζ , where E(T )| ζ is the entropy after the split. Suppose D is partitioned into two sets T l and T r based on the split ζ,.
84 0. 28 0. 21 0. 32 0. 16 Wine 0. 01 0. 0 0. 01 0. 01 0. 02 0. 01 0. 02 0. 02 WordSynonyms 0. 49 0. 42 0. 45 0. 41 0. 47 0. 41 0. 49 0. 41 Worms 0. 16 0. 09 0. 1 0. 07 0. 11 0. 07 0. 13 0. 08 WormsTwoClass 0. 03 0. 01 0. 02 0. 02 0. 03 0. 02 0. 04 0. 03 Yoga 0. 01 0. 01 0. 0 0. 03 0. 0 0. 0 0. 0 0. 0 Table H. 6 Inductive NMI results for CDPS on the UCR archive. Dataset 0% 5% 15% 25% LDPS DCC CDPS DCC CDPS DCC CDPS DCC ACSF1 0. 57 0. 31 0. 54 0. 35 0. 55 0. 39 0. 56 0. 35 Adiac 0. 73 0. 6 0. 73 0. 59 0. 73 0. 6 0. 73 0. 6 ArrowHead 0. 25 0. 46 0. 36 0. 48 0. 37 0. 47 0. 42 0. 45 ArticularyWordRecognition 0. 89 0 0. 89 0. 89 0. 88 0. 89 0. 88 0. 88 AtrialFibrillation 0. 16 0 0. 17 0. 28 0. 17 0. 25 0. 18 0. 3 BME 0. 52 0. 3 0. 46 0. 35 0. 42 0. 43 0. 47 0. 42 BasicMotions 0. 88 0 0. 87 0. 34 0.
| Data compilation We followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol for data compilation, meta-analysis and reporting of results  (Moher et al. , 2009 , Page et al. , 2021; see Appendix S1). First, we searched Web of Science™ and Scopus until 19 April 2020 for publications in peer-reviewed international scientific journals using key search terms in the title or the abstract. In addition, we searched Google Scholar using the same terms in the full text of scientific publications, and restricting our selection to the first 200 references found. The terms 'centre/ center-periphery', 'central-marginal', 'abundant centre/center' and 'latitudinal cline' were introduced in combination with performance related terms including 'fecundity', 'performance', 'survival', 'recruitment' and 'population growth rate' (see full details and search strings in Appendix S1).
Three filters were then applied to the initial subset of papers. First, we only considered studies reporting field data from natural populations, including control populations of transplant experiments if these were measured at their home sites and met all other criteria. Second, we only considered studies with at least two central and two peripheral populations (i. e. true replicates). Third, we only considered papers that provided sufficiently explicit and clear information on our criteria for the definition of central and peripheral range parts relative to the global range of the target species (and not only parts of it; see below). This filtering procedure resulted in a total of 51 publications, with 113 centre-periphery contrasts of 54 species. The workflow and output of our compilation and selection process are described in detail in Figure  S1.
In the second case, we selected the three most central and the three most marginal populations along the gradient (rarely more if several populations were located closely together). We extracted quantitative data for our target metrics either manually from text and tables or from figures with Dagra digitizing software version 2. 0. 12 (Blue Leaf Software, 2016). We recorded mean values for each individual population, and then calculated the average performance, sample size and resulting standard deviation for central, high-and low-latitude margins  (Pulido et al. , 2022).
| Meta-analytical models Our dataset had a hierarchical structure as some primary papers contained several case studies. We accounted for this potential non-independence of cases by estimating model heterogeneity from multiple sources: (a) among true effect sizes, (b) among CP comparisons stemming from the same primary papers (by computing the variance-covariance matrix among all effect sizes), and (c) among groups of moderators. This was done using multi-level error meta-analysis with the rma. mv function of the R package metafor v. 2. 0-0 (R Core  Team, 2021; Viechtbauer, 2010). Primary paper identity was declared as a random factor and individual CP comparisons were nested as random factors within primary papers. We estimated variance components for primary papers (σ 1 2 ) and case studies (σ 2 2 ) together with intra-class correlations (ρ), that is, correlations between true effect sizes from the same study [such that ρ = σ 1 2 /(σ 1 2 + σ 2 2 )].
| Climate data We gathered the geographical coordinates of all populations included in the meta-analysis from the primary papers (n = 705 populations;   et al. , 2003)  for marine species. For terrestrial taxa, we also extracted average annual precipitation at each site, again from CRU TS 4. 04. We could not match climate data for two fish species  (Heibo et al. , 2005; Power et al. , 2005;  Appendix A) and hence excluded these species from the analyses. The final dataset for the climatic analysis contained 683 populations from 52 species (37 terrestrial, 15 marine) and 109 margin-centre comparisons (Appendix S1, Figure  S5 ). We then aggregated populations to calculate average temperature and precipitation for each combination of study, species, performance variable, and region (either central, HLM or LLM).
| DISCUSS ION Overall, our results show that populations from the centre of the range tend to outperform those residing at the range margins, and that this difference is considerably more pronounced at lowlatitude margins. Such latitudinal asymmetry is predicted when the   (Hargreaves et al. , 2014; Normand et al. , 2011). To evaluate the potential role of such long-term colonization lags, we thoroughly searched the literature (including the BIOSHIFTS database;  Lenoir et al. , 2020)  for published evidence on ongoing range shifts of the 54 species included in our meta-analysis. We detected published evidence for a total of 24 ongoing range shifts concerning 15 of our species. Most of these shifts (79%) were polewards and hence in accordance with the range-wide asymmetry in population performance that we are reporting here (see Table  2 ).
Operando wide-field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, we harness the power of unsupervised machine learning (ML) algorithms to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH -fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM).
Introduction [4] [5]  Image analysis is typically done using a supervised method, which involves manually labeling training data. [6] However, this approach can be costly and time-consuming and can introduce bias from the training data. An alternative approach, which is less commonly used but gaining popularity, is to apply unsupervised ML methods that do not require manual input. [7, 8] The majority of the practical examples in science of unsupervised image analysis often come from biology and related bioinformatics fields,  [9] [10] [11] [12]  while other fields are barely considered and still waited to be explored. This work showcases a practical application of unsupervised ML in surface science, leveraging the analysis of optical datasets from an operando reflection-based microscope (RM).
The ability to predict material reactivity through such methods can have significant socio-economic implications for improving material durability and increasing energy efficiency. [13] [30]  RM has high temporal and spatial resolution, allowing for measurements with a resolution of approximately 200 nm and acquisition rates up to kHz in a wide field (mm) range. [14, 25] However, the generation of large optical datasets also poses significant challenges in processing feature-rich optical images. 33] [34]  While these methods are automated, they can be subject to human bias and may require finetuning of empirical parameters. In this study, we propose an efficient unsupervised machine learning framework for fully automated analysis of reflective videos obtained from the operando monitoring of the local electrochemical degradation by aqueous solution of Al alloy of 6061 series as a model system.
This provides a qualitative chemical explanation of the observed pattern of surface film evolution for particles in category 2. Note that in contrast to our previous work where the interparticle communication was observed,  [38]  in this work, the averaged distance between particles was increased from ca. 2 µm to ca. 6 µm that should be responsible in the transposition of inter-to intraparticle communication, observed herein (SI-4). In order to test the hypothesis of the influence of particle size on the pattern of surface evolution, a finite element model (FEM) was developed using COMSOL Multiphysics 5. 5 software. The model considered circular disks of varying sizes generating identical normalized fluxes of OH -while the bulk pH was equal to 7. Additionally, the model assumed that the particles were covered with an Al(OH)3 film that could either precipitate or dissolve as a function of local pH.
Conclusion In this study, we adapted unsupervised ML algorithms to analyze the reactivity patterns from wide-field reflection-based optical microscopy of individual Si-rich particles during the immersion of Al6061 alloy interface in 5 mM NaCl. The analysis uncovered two distinct patterns: (1) homogeneous precipitation of a surface film over the particles and (2) the presence of an area in the particle center where the surface film was dissolving. The largest particles consistently displayed pattern 2, which was attributed to higher pH values in the particle center as a result of overlapping generated OH -diffusion fields within a single particle. This phenomenon, known as intraparticle chemical communication, is sensitive to bulk pH and was less pronounced after pH acidification. The experimental results of unsupervised ML clustering were rationalized through FEM simulations, highlighting the synergy of the two approaches in extracting knowledge from data-rich experimental datasets.
Ex-situ scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX) surface analysis of identical locations Identical locations of optical images were retrieved in secondary electron images during SEM analysis with the aid of grid marked on the Al6061 surface. SEM and EDX analyses were performed on a Gemini SEM 360 from Zeiss, with an acceleration voltage of 10 kV and 5 kV correspondingly, and 60 µm aperture window. Spectra and elemental mappings were processed using the AZtec software. The alignment to a pixel precision of optical and SEM/EDX images were performed with the automated feature-based algorithms implemented in OpenCV library as described in our previous work. [8].
SI-3 Unsupervised machine learning (ML) for data extraction We utilized unsupervised ML algorithms throughout the process of data extraction. The utilization of unsupervised algorithms minimized the number of necessary empirical parameters, resulting in a fully automated, unbiased approach to data extraction. The technical details of the procedure, depicted schematically in Figures  2  and 4  in the main text, are described below. The code for each step as well as the original data have been made available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 8017645). Step 1, Figure  2.
Conversely, in Figure  SI3 -5, particles 3, 22, 34, and 39 could be categorized as 1 instead of 2. These minor deviations are a result of the statistical nature of the applied unsupervised ML procedure, indicating that certain instances may deviate from the overall pattern, but the overall trend is still accurately identified. The clustering of the data in Figure  4 , in which bulk pH was acidified and two images of the same particles were taken, before and after acidification, was conducted using a similar process to the one described above, with the exception of constructing the correlation matrix. In the following section, we will provide a comprehensive description of this procedure. Step 1, Figure  4 : Correlation matrix In this dataset, we have two images taken from the same location instead of just one.
Long-term costs, survival and disutilities were based on published literature. Unit costs were derived from national databases and discounted at 3% annually from a Swedish healthcare perspective. Results One-month treatment with ticagrelor plus aspirin resulted in 12 fewer strokes, 4 additional major bleeds and cost savings of €95 000 per 1000 patients versus aspirin from a Swedish healthcare perspective. This translated into increased quality-adjusted life-years (0. 04) and reduced societal costs (-€1358) per patient over a lifetime horizon. Key drivers of cost-effectiveness were number of patients experiencing subsequent disabling stroke and degree of disability. Findings were robust over a range of input assumptions. Conclusion One month of treatment with ticagrelor plus aspirin is likely to improve outcomes and reduce costs versus aspirin in mild-to-moderate AIS or high-risk TIA.
4  These findings clearly highlight an urgent need for cost-effective treatment strategies to reduce disability and care costs and prevent the premature mortality associated with stroke. 5  Aspirin has been the mainstay of treatment for secondary prevention of stroke. [6] [7] [8]  However, there is growing interest in the role of antiplatelet drugs, including ticagrelor based on findings from the SOCRATES and THALES trials. SOCRATES (NCT01994720) included 13 199 patients with high-risk TIA (ABCD 2 score ≥4 and/or ipsilateral stenosis) or acute ischaemic stroke (AIS; National Institutes of Health Stroke Scale (NIHSS) ≤5); most patients had NIHSS ≤3 (67. 4%) and mean age was 65. 9 years. In this trial, occurrence of the primary composite endpoint of stroke, myocardial infarction or death within 90 days was lower with ticagrelor versus aspirin (6.
7% vs 7. 5%; HR 0. 89; 95% CI 0. 78 to 1. 01; p=0. 07), as was major bleeding (HR 0. 83; 95% CI 0. 52 to 1. 34; p=0. 45). 9 THALES (NCT03354429) subsequently evaluated ticagrelor plus aspirin versus aspirin for the prevention of the composite of stroke or death within 30 days (primary endpoint) in 11 016 patients with high-risk TIA (ABCD 2 score ≥6 and/or ipsilateral stenosis) or AIS (NIHSS ≤5); most patients also had NIHSS ≤3 (60. 6%) and mean age was 65. 2 years. 10  Compared with aspirin, ticagrelor plus aspirin significantly reduced the risk of the composite of stroke and death (6. 6% vs 5. 5%; HR 0. 83; 95% CI 0. 71 to 0. 96; p=0. 02) but increased the risk of major bleeding (0. 5% vs 0. 1%; HR 3. 99; 95% CI 1. 74 to 9. 14; p=0. 001). A subanalysis of 3312 patients from THALES with moderate AIS (NIHSS 4-5) showed that ticagrelor plus aspirin also non-significantly reduced stroke or death versus aspirin in this high-risk group (7.
Data sources Transition probabilities for NDS and DS Base-case transitions for NDS and DS during months 1 and 2 were based on the 30 day duration of THALES (shown in online supplemental table 3A). 10  Transition from the no subsequent stroke state from month 2 onward was derived from published data. 19  Transition probabilities were calculated with the assumption that the ratio of NDS to DS were the same as that observed for the first subsequent stroke event in THALES. No other transitions were introduced since, in SOCRATES, the only other randomised controlled trial of ticagrelor versus aspirin in patients who had a stroke, 6199 (94%) patients in the ticagrelor group and 6160 (93%) patients in the aspirin group did not have a subsequent stroke over a longer 90-day duration. Transition probabilities for mortality rates Transition probabilities for mortality rates were chosen as close to both trials as possible, based on the month of occurrence; when not possible, published HRs were used.
Following these principles, mortality rates were derived from THALES for months 1 and 2, 10 and from SOCRATES  9  for transitions from the no subsequent stroke state from month 2 onward; mortality following a subsequent initial stroke from month 2 onward was adjusted for NDS and DS using published HRs. 17  Base-case mortality rates are listed in online supplemental table 3B). 9 10 17 19  Utilities Utilities used in the model are summarised in online supplemental table 4. 9  10 20 21  EuroQol-5 Dimension Open access (EQ-5D) data collected in THALES  10  and SOCRATES  9  were used to generate utility values by applying the UK EQ-5D tariff published by Dolan. 21  This approach was used to calculate utility values at months 0, 1 and 3. The utility at month 2 was obtained by linear interpolation.
Resource use and cost data Unit costs for drug acquisition were calculated from published sources,  24  and dosing was aligned with THALES. 10  All costs were valued in 2021 Euros. The cost per month for ticagrelor and aspirin was €67. 57 and €2. 13, respectively. 24  The mean length of stay for each complication was derived from SOCRATES. Medical resource costs (hospitalisations and outpatient costs for stroke) were based on published DRG codes. 25  The mean cost per hospitalisation for an NDS and DS event was calculated to be €6623 and €12 502, respectively, using costs from the Swedish National Board of Health and Welfare (Socialstyrelsen) (online supplemental table 5). 9 10 24-26  Outpatient care costs in the first 3 months were also based on published DRG codes, with the resource use for each health state being based on SOCRATES.
The model did not adjust for the long-term natural history of stroke; however, 30-year survival curves were estimated using the model outcomes plotted against observational data to visually assess plausibility between datasets (see online supplemental figure  1 ). Most of these limitations could be alleviated by further evidence generation, especially regarding long-term stroke risk, and long-term difference between patients with more than one NDS or DS. Despite these limitations, the results of the model are very robust, as tested through sensitivity analyses.
Acknowledgements Editorial assistance was provided by  Sam Phillips  and  Jean-Etienne Poirrier  ( Parexel ) and was funded by  AstraZeneca. Contributors Concept and design: AT and CM. Data acquisition and analysis: AT, RJ, CM, KR, HD, PL, MK, MO and SJ. Data interpretation: AT, RJ, CM, KR, HD, PL, MK, AH, MO and SJ. Manuscript writing: all authors. Final approval of manuscript: all authors. Guarantor: AT Funding  AstraZeneca  funded the  THALES  trial and the cost-effectiveness analysis of this study.
Acknowledgments We thank  Dr Mara Baldry  and  Charlotte Costa  for assistance in animal experiments,  Dr Frédéric Wallet  from  Lille Hospital  for 15A and 24F clinical isolates,  Dr Loic Coutte  for Il17a -/-mice,  Mrs Delphine Cayet  for LafB-specific ELISA, and  Dr Guillaume Lefebvre  and  Dr Julie Démaret  for discussion on pneumonia patients. We thank  Dr. Jingwei Xu  for his assistance on RoseTTAFold prediction of LafB 3D structure. We also thank  UNIL's Metabolomics and Genomics Technologies Facilty ,  UAR2014-US41-PLBS ,  BioImaging Center Lille, Flow Cytometry Core Facility and PLETHA animal facility. This work was supported by an  ERC  consolidator grant  771534  and the  Swiss National Science Foundation  (grants  310030_192517 ,  310030_200792 ,  51NF40_180541  and  IZSEZ0_213879  to J.