dataesr/scientific-paragraphs-categorization huggingface dataset:
To improve the comparability of the retention measurements across tracers, the Centiloid method has been proposed  [24]. This method linearly scales the measurement of a particular tracer from 0 to 100 scale, where '0' represents the average uptake in young controls and '100' corresponds to the average uptake in typical AD patients at the dementia stage. To apply the Centiloid conversion, reference datasets, quantification pipelines, and regions of interest and reference are available publically from http://www. gaain. org/ centiloid-project. When expressed in Centiloids (CL), optimal thresholds for positivity against visual reads typically fall within the range between 25 and 35 CL  [25] [26] [27]. The applicability of CSF Aβ 42 determinations with enzyme-linked immunosorbent assays (ELISAs) has been limited by several preanalytical and analytical factors, resulting in lot-to-lot and between-laboratory variability.
Image processing All PET images were preprocessed following the Centiloid  [24]  pipeline using SPM12 (https://www. fil. ion. ucl. ac. uk/spm/software/spm12/). In brief, PET frames were coregistered. Averaged images were then coregistered to corresponding MRI scans. MRIs were then segmented and normalized to the MNI space together with PET images. We calculated the SUVr in MNI space using the target region provided in the GAAIN website (www. gaain. org) and the whole cerebellum as reference region. SUVr values were then transformed to the Centiloid scale as explained in Additional file 1: Supplementary methods.
Statistical analysis Demographic characteristics of both cohorts were first compared. T test for independent samples was used with continuous variables and χ 2 with dichotomous variables. To be able to directly compare Aβ 42 measures from both cohorts, we had to transform ADNI data to account for pre-analytical conditions  [30]. This transformation was only used to perform scatter plots but not to perform any other analysis, as each dataset had their own cut-offs for the CSF biomarkers (Additional file 1: Table  S1 ). Optimal Centiloid cut-offs were calculated as those that maximized the Youden's J Index (YI) or the overall percentage agreement (OPA; "accuracy"). YI statistic consists of the summation of the sensitivity and specificity  [36] , and the OPA reflects the percentage of cases with concordant binary classification with CSF and PET.
Both were calculated on the pooled ALFA+ and ADNI data as a function of Centiloid values for Aβ 42 , phosphorylated tau (pTau), total tau (tTau), and pTau/Aβ 42 and tTau/Aβ 42 ratios. For each CSF biomarker as binary outcome, optimal cut-offs were selected as those showing the maximum value of one of the statistics after minimally smoothing true-positive, true-negative, false-positive and false-negative curves using the 'smooth' function in Matlab (v2018b) with the 'lowess' method and a span value of 0. 1. On top of YI and OPA, we also calculated the positive percentage agreement (PPA, "sensitivity") and negative percentage agreement (NPA, "specificity") and the area under the curve (AUC) of the receiver operating characteristic (ROC) analysis. All 95% confidence intervals (95% CI) for all statistics were derived using bootstrapping methods (n = 5000).
Optimal CL cut-offs We computed the optimal CL cut-off values to differentiate individuals within the Alzheimer's continuum and controls using the AD CSF core biomarkers as a reference. We performed the analysis with CSF Aβ 42 alone, pTau/Aβ 42 and tTau/Aβ 42 ratios and also pTau and tTau alone. We studied the pooled data from ALFA+ and ADNI cohorts, using cohort-specific CSF thresholds. Table  2  summarizes the optimal CL values, using the pooled data from ALFA+ and ADNI cohorts, and associated statistical performance against CSF Aβ 42 , pTau/Aβ 42 , tTau/Aβ 42 , pTau and tTau. Figure  1  shows the associated ROC curves.
Almost all subjects with CSF Aβ 42 over 1000 pg/ml showed Centiloid values below 20, and only for CSF values < 1000 pg/ml, a linear association could be intuited. This nonlinear association makes goodness criteria (both the Youden's Index and the overall percentage agreement) to plateau between 10 and 40 CL (Fig. 2 ). Under these circumstances, to derive stable optimal cut-offs, it is critical to make use of a test sample comprising both sufficient concordant positive and negative cases as well as a good representation of individuals falling within intermediate amyloid ranges (10 < CL < 40 and 500 < CSF Aβ 42 < 1000 pg/ml). In this study, this was achieved by pooling the ADNI and ALFA+ datasets. The independent derivation of optimal CSF Aβ 42 cut-offs in the two cohorts confirmed this rationale.
By contrast, the ALFA+ population reflects a cohort of early amyloid accumulators at risk for cognitive impairment; therefore, a positive visual read may be reached when amyloid is still not at its peak. Indeed, the threshold of 12 CL is robust against variations in the cut-off for CSF positivity (Additional file 1: Table  S3 ) as well as if only the ALFA+ dataset is used for its derivation. Although 12 CL may initially be regarded as a low value for amyloid positivity, it matches recent reports of Centiloid cut-offs against postmortem measures of AD neuropathology showing that 12. 2 CL optimally detected Consortium to Establish a Registry for Alzheimer's Disease (CERAD) moderate-to-frequent neuritic plaques, whereas 24. 4 CL identified intermediate-to-high AD neuropathologic change (ADNC) differences  [40].
constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a singlecapture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.
Second, during the operational use of the authentication system, the template of users and their personal decision/adaption thresholds are updated; whereas, in the literature, only the template of users is generally updated when keystroke dynamics modality is dealt with. This new solution, compared to other approaches in the literature, leads to better results on significant and complex datasets used by the scientific community in the field. This paper is organized as follows: Section 2 presents the literature review on keystroke dynamics and its corresponding adaptation strategies. Section 3 describes the proposed methodology and the contributions of this paper. Section 4 details the experimental protocol and the used datasets. Section 5 shows and discusses the obtained experimental results.
When the reference contains an only one sample, σ C is a vector of fixed values (which are the standard deviation value of the one sample reference). µ C is the mean vector of the reference. After that, data normalization is carried out by dividing the characteristic vector by the standard deviation σ of the reference (to ensure a standard deviation of these features to 1). Equation (  2 ) depicts the normalization applied to the characteristic vactor. C(i) = C(i) σ C (i) (2) By applying the aberration correction and normalization steps to the 4 considered characteristics, the erroneous data are almost removed. Thus, we obtain a sample composed of four characteristic vectors containing the information necessary to model the users' keystroke dynamics.
Datasets For our experiments, we chose three datasets, among the most widely used in the literature, to validate the proposed approach: • GREYC 2009  [59] : This database was developed within the GREYC Laboratory. One hundred and thirty-three users participated in the creation of this database and typed the same password "greyc laboratory". Only 100 of them participated in five acquisition sessions during two months and provided 60 samples per user. These samples were focused on in our experiments. This database were chosen to compare our results with those of the experiments in  [21]. • CMU  [20] : This database includes data of 51 users. They typed the same password 400 times during eight acquisition sessions. The defined password was ". tie5Roanl". We opted for this database because it was frequently used in the literature.
Data stream generation To evaluate the performance of the proposed system and to follow its evolution, we divided the adaptation process into different sessions. For each session, we introduced eight new queries to the system. These data were divided into five genuine samples and three impostor ones for each adaptation session. First, 5 genuine queries are presented to the authentication process. They were presented according to the chronological order of the database safeguard. Subsequently, the three imposter queries were randomly introduced. The biometric data stream was then divided into 37. 5% (3/8) of impostor samples and 62. 5% (5/8) of genuine samples. The attack rate was higher than that generally used in keystroke dynamics studies  [33, 61]  (70% for genuine samples and 30% for impostor ones).
For both GREYC-2009 and GREYC-WEB databases, we had 60 samples for each user. These samples were divided into 12 sessions (5 genuine samples/session). As we used the first sample as initial reference, we presented in the last session only four genuine samples. The impostor attacks were randomly generated by the samples of other users of the database. For the CMU database, 400 samples per user are available. The system operates for 80 sessions.
This process was repeated for each adaptation session. Table  3  and Table  4  present the weight values obtained at the end of each adaptation session for GREYC-2009 and GREYC-WEB databases, respectively. We can notice that the Hamming and Euclidean distances (β s and γ s ) have in general the highest vote values. In fact, these two distances demonstrated also better performances than the others while testing each of them separately with the KNN classifier  [57].
Performance without adaptive thresholds We chose to draw the DET curve and calculate the EER as well as the AUC to show the system performance in relation to the reference without considering the adapted threshold criterion. Figure  5  depicts the EER and AUC values of each adaptation session for the three considered databases. Concerning the CMU database, as the number of sessions is quite high (80 sessions), we illustrate only the performances of every ten sessions. We can conclude that the results are slightly improved in each session. The performance improvements during the sliding window phase are much clearer than those of the growing window one. These performances are expected since the reference is not entirely defined at the beginning. The final result of the last session illustrates a statistically significant improvement.
The obtained performances (EER, AUC) in the last session are much improved compared to those of the first one, as shown in Figure  5. We compared the proposed method to different mechanisms of previous work. For GREYC-2009 database, the proposed method was compared to the average mechanism  [21] , which was applied to a reference initially composed of five samples and not exceeding 15. For that, we investigated various threshold types: global, individual. Once again, the proposed adaptation mechanism led to better performances. Table  8  shows the comparison of the obtained results on the GREYC-2009 database. In this paper, the considered performances of the average mechanism are those obtainned by  [21]. To highlight the impact of the proposed adaptation mechanism, we implemented the proposed method with different threshold types.
As depicted in Table  9 , we show the performance on the three datasets with different thresholds. To illustrate the advantages of the proposed adaptation approach, we applied other algorithms of the literature to the GREYC-WEB database. We firstly tested the growing window mechanism with a reference containing a single sample initially. The size of the reference increases up to 43 owing to the adaptation mechanism. Secondly, we applied the sliding window mechanism based on a 10-sized reference. Thirdly, we also tested the proposed double serial mechanism while the reference was initialized to 5 samples and its maximum size was fixed to 10. Finally, the double parallel mechanism was conducted us- ing two sub-references. One of them initially contained a single sample and it was adapted with the growing window mechanism.
Performance with adaptive thresholds The overall results of FMR, FNMR and accuracy concerning the three considered databases are shown in Table  10. These results are calculated over all adaptation sessions while considering the whole data of the databases. The best  We also compared the proposed method with some other work from the literature to analyze the impact of the number of samples used in the reference, especially in the training phase (See Table  11 ). For the CMU database, the best obtained result in  [33]  was 0. 670 accuracy, although the reference was obtained by 40 samples. The results achieved with the suggested method were much better with a unique sample as an initial reference template. We obtained 0. 794 accuracy.
Computation time As the proposed method is processing online, we are interested in the computation time of each phase. Table  12  presents the computation time of each phase for a single user and for all considered users of the GREYC 2009 database. Concerning the computation time of a unique user, the average computation time is considered. Timing is calculated on CPU with an Intel i7 processor with a speed of 2. 5 GHz and 8-Gb RAM. The adaptation phase is faster than the other steps of the process. All phases have a fast computing time except GA which operates in an offline way, so it does not affect the operating time of the proposed approach. The proposed method had the advantage of minimizing the computation time to create the reference that was very important for the online adaptation mechanism.
The experimental results showed that the obtained performance outperformed the other methods in the state of the art for the same databases and under the same test protocol conditions. Furthermore, the proposed method satisfied the suggested conditions in an industrial context. Indeed, a single sample was necessary during the enrollment step. It was a great advantage instead of asking users to type their password multiple times. Moreover, the experiment includes a novel authentication system based on the GA-KNN in addition to the adaptation strategy, thus it demonstrates competitive performances.
Actually, the growing window first serves to enlarge the users' galleries so as to capture more intraclass variability. When the maximum size of the reference is attained, the sliding window will take place and allow following the temporal variation in the users' keystroke dynamics. The proposed contribution is an interesting solution as it satisfies industrial needs (usable enrollment and good efficiency). We also evolved a GA-KNN verification method to achieve better performances during the whole adaptation session. Indeed, the weights from different distances for the KNN classifier, in addition to the GA optimization, are useful to minimize recognition errors. With regards to previous work, the suggested method shows a great performance improvement. As it has been applied on several databases, it has demonstrated competitive performances in each database.
Climate shifts are key drivers of ecosystem change. Despite the critical importance of Antarctica and the Southern Ocean for global climate, the extent of climate-driven ecological change in this region remains controversial. In particular, the biological effects of changing sea ice conditions are poorly understood. We hypothesize that rapid postglacial reductions in sea ice drove biological shifts across multiple widespread Southern Ocean species. We test for demographic shifts driven by climate events over recent millennia by analyzing population genomic datasets spanning 3 penguin genera (Eudyptes, Pygoscelis, and Aptenodytes). Demographic analyses for multiple species (macaroni/royal, eastern rockhopper, Adélie, gentoo, king, and emperor) currently inhabiting southern coastlines affected by heavy sea ice conditions during the Last Glacial Maximum (LGM) yielded genetic signatures of near-simultaneous population expansions associated with postglacial warming.
Significance We analyze population genomic datasets across 3 penguin genera to test for demographic shifts driven by historical climate events. Numerous species inhabiting coastlines affected by heavy sea ice during the Last Glacial Maximum show genomic signatures of near-simultaneous population expansions associated with postglacial warming, contrasting with stable or declining demographic histories inferred for species occupying consistently ice free habitats. Shallow population genomic structure detected within species distributed across the vast Southern Ocean likely provides further evidence for recent demographic shifts and recent genetic exchange among populations. Our results demonstrate dramatic, ecosystem-wide responses to climate change and highlight the potential for future biological shifts in the Southern Ocean as global warming continues.
Demographic reconstructions of effective population sizes (N e ) for 11 penguin species using CubSFS  (26) , SNAPP (SNP and AFLP package for phylogenetic analysis) (  27 ), Tajima's D  (28) , and Multidice (29) were based on 3,000 to 13,000 SNPs per species (SI Appendix, Tables  S1-S3 ). Macaroni and royal (Eudyptes chrysolophus chrysolophus/E. c. schlegeli) penguins were considered a single species based on structure/F ST (fixation index) analyses (see also ref. 18) , whereas Snares crested (E. robustus) and the northern rockhopper (E. moseleyi) penguins were excluded from some analyses due to their small sample sizes (Fig. 1  and SI Appendix, Tables  S4-S5 ). These analyses revealed comparable postglacial N e expansions for 6 southern species (macaroni/ royal, eastern rockhopper [E. filholi], Adélie [Pygoscelis adeliae], gentoo [P.
Ltd. (DArT-seq) in Canberra, Australia  (50). Each sample was processed following  (51)  and was sequenced across 3 lanes on an Illumina Hi-Seq 2500. Sequences were processed using in-house proprietary DArT analytical pipelines. We used DartR version 1. 1. 6 (52)  in R version 3. 5. 1 (R Core Team, 2018)  (53)  to filter the DArT-seq data for 10 separate Eudyptes datasets [based on previous systematic discussions  (17, 35)  and SI Appendix, Table  S5 ]. For these Eudyptes datasets, we filtered on reproducibility (t = 1) and filtered out monomorphic loci, loci with call rates <0. 95%, all individuals with call rates <0. 90%, all loci with trimmed sequence tags, and all loci that departed from Hardy-Weinberg equilibrium in any colony (P = 0. 05 following Bonferroni correction). We also obtained filtered RAD-seq datasets from an additional 5 penguin species generated and examined by refs.
S4 ), the Evanno method  (56)  in Structure version 2. 3. 4 (57) to estimate the most likely K (Fig. 1  and SI Appendix, Fig. S4 ), and DAPC using adegenet (SI Appendix, Fig. S4 ). We used the SNAPP tree set analyzer in BEAST version 2. 4. 7  (27, 58)  to investigate gene flow between closely related Eudyptes species (SI Appendix, Fig. S6 ) based on our results and systematic discussions of refs. 17 and 35. While SNAPP analyses were previously generated for the emperor, king, and gentoo datasets  (14, 33, 34) , we also undertook SNAPP analyses for the chinstrap and Adélie penguin datasets obtained from (14) (SI Appendix, Fig. S7 ). See SI Appendix for details. Testing for Demographic Expansions. We reconstructed population histories for 11 Eudyptes, Pygoscelis, and Aptenodytes species over the last 1,000,000 y by estimating the time and magnitude of demographic changes using 4 different approaches (northern rockhopper and Snares crested penguins were excluded from some analyses due to low sample size).
Specifically, we reconstructed the demographic histories using CubSFS (Fig. 2A  and SI Appendix, Figs. S2  and S3  and Table  S1 ), obtained Tajima's D (SI Appendix, Table  S2 ), identified the change in theta values as inferred by our previous SNAPP analyses (SI Appendix, Figs. S8  and S9 ), and tested for synchronous expansion using Multidice (Fig. 3B  and SI Appendix, Table  S3 ). As the Eudyptes and Pygoscelis/Aptenodytes datasets were obtained using different pipelines (DArT-seq versus RAD-seq), we applied further stringent filtering to ensure consistency between the datasets (SI Appendix). While previous studies reported shallow population genetic structure within most Pygoscelis and Aptenodytes species  (11, 14-16, 33, 34)  (Fig. 1 ), given the relatively shallow F ST values involved and reported K = 1  (14, 33, 34) , we consider this fine-scale structure likely to have evolved recently and broadly consistent with a scenario of high gene flow suitable for combined demographic analysis [with the exception of the gentoo penguin  (14) ].
To account for the deeper genetic structure observed in gentoo populations [e. g. , 4 distinct lineages (  14 )] (Fig. 1 ), we limited most subsequent analyses of this species to one lineage (Fig. 1 ) (see ref. 14). For each Pygoscelis, Eudyptes, and Aptenodytes VCF file, we projected the folded site frequency spectrum down to increase the number of segregating sites using EasySFS. We then adjusted the number of monomorphic sites in our site frequency spectrum to reflect the total number of monomorphic loci within each species following down projection. For all analyses, we assumed a generation time of 8 to 14 y (from ref. 42). For all models, we used a mutation rate of 2. 6 × 10 -7 per locus per generation  (12, 15). Data Availability. The raw data reported in this paper has been deposited in the NCBI Short Read Archive BioProject PRJNA589336 (https://www.
ACKNOWLEDGMENTS. We thank  C. Bazjak ,  A. -S. Coquel ,  N. Dehnhard ,  M. Fawcett ,  H. Irvine ,  K. Morrison , and  M. Nicolaus  for sample collection;  T. King ,  C. Mitchell , and  K. Trought  for laboratory assistance;  V. Chhatre ,  A. Georges ,  B. Gruber ,  F. Pinamartins ,  B. Roberts ,  A. Savary ,  J. Wood , and  A. Xue  for bioinformatics assistance; and  L. Beheregaray ,  C. Fraser ,  A. Kilian ,  M. Knapp ,  A. Ruzzante ,  A. Santure ,  P. Scofield ,  P. Sunnucks ,  J. Wilmshurst , and  J. Wood  for discussions. Work was carried out under  New Zealand (NZ) Department of Conservation Permits  ( OT-25557-DOA, IACUC-18958. 00 , 32202-FAU ,  35682-FAU , 37312-LND, 50437-DOA, 50436- FAU ,  50464-DOA , and  38882-RES ),  NZ Ministry of Primary Industries  ( 2015056535 ,  2016060908 ,  2017064905 ),  Permit to Possess Threatened Fauna for Scientific Purposes TFA 15086 ,  DPIWE Permit  to  Take Wildlife  for Scientific Purposes  FA05246 ,  Tristan Da Cunha Conservation Department, South African Department of Environmental Affairs, Government of South Georgia  and the  South Sandwich Islands Restricted Activity Permits  ( GSGSSI RAP 2018/018 , GSGSSI),  US NSF Department of Polar Programs ACA permits  ( ACA 2016-011 ,  ACA 2016-012 ),  Falkland Islands Government  ( R05/2009 ,  R014/2006 ), and  UK Antarctic Permits.