dataesr/scientific-paragraphs-categorization huggingface dataset:
llr and  min llr are primary metrics of the 2020 VoicePrivacy challenge  (Tomashenko et al. , 2020a,b)  for assessing biometric privacy, alongside the equal-error rate (EER). Both, EER and  min llr , relate to the convex hull of the receiver operating characteristic  (Brümmer and De Villiers, 2011). If  llr > 1, scores are badly calibrated (better to use a coin toss instead of the particular classifier); if  llr = 1, a classifier performs as good as a coin toss, and if  llr < 1, scores are referred to be calibrated (scores are perfectly-calibrated if  llr =  min llr ). In brief, to ensure  ( tar |) =  ( tar |lr) (LLR idempotence), oracle score calibration maps the empirical posterior  ( tar |) to the accurate (oracle) posterior  ( tar |lr). An (oracle) LLR is derived by removing the empirical database prior ̇π = | tar | | tar |+| non | from the oracle posterior  (Brümmer and De Villiers, 2011; Brümmer and Preez, 2013) : log(lr) = logit(𝑃 (𝜃 tar |lr)) -logit( ̇π).
Extension of the ZEBRA framework: Score calibration One strategy for the privacy preserver is to reduce the discrimination between the target and impostor score distributions. An alternative is to make score calibration impossible for the attacker. The latter calibrates scores to obtain the necessary knowledge in order to make the ideal choices regardless of its classifier's discrimination power. If scores are badly calibrated such that the ECE profile (red) fits or exceeds the black one, the attacker is better off tossing a coin. There are two commonly used calibration methods: linear and isotonic regression  (Brümmer and De Villiers, 2011). Linear regression 7  is more robust but less accurate, whereas isotonic regression is sensitive to changes but results in oracle calibration. 8 Conventionally, training and testing a calibrationeither] method on the same dataset is considered cheating.
Let  be an unprotected dataset of evidence,   () be a safeguard running that manipulates  at a time instant , ( ′ ) scores computed from a set of (protected or unprotected) evidences  ′ and we use the notation  or ( ′ ) when scores are oracle calibrated. Let also   (|) be a function to calibrate scores  ( lin for linear and  iso for isotonic regression) 9 which is trained on the score set . We Fig. 8. Examples of ZEBRA plots to illustrate the calibration distortion. The black profile is perfect privacy. The blue profile is for privacy disclosure on an instance  0 () of the safeguard and is thus ECE profile with oracle calibrated scores. The gray profile is the same but on another instance  1 (). For these two, ( ECE ,   , tag) is provided in the legend. Orange and violet profiles are actual ECE plots on  1 () where scores have been calibrated with respectively a linear ( lin ) and a isotonic ( iso ) regression learnt on the other instance  0 ().
For these two, [ ECE ;  llr (  )] is provided legend. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) simulate an adversary observing two protected datasets of same origin (without knowing) who wants to yield calibrated scores   :  𝜑 = 𝑐 𝜑 ((𝑓 1 ()) | (𝑓 0 ())). (15) Then, the expected calibration distortion  ECE (|  ) is computed simply as: 𝐶 ECE (𝛩| 𝜑 ) = 𝐷 ECE (𝛩| 𝜑 ). (16) The difference to the above  ECE (|) is that   is not derived through oracle score calibration. The scores of   are desired by an adversary to be well-calibrated but one purpose of a safeguard is to countermeasure that. This is evaluated by the expected calibration distortion which should be as low as possible: equal to zero is good, negative infinity is best.
Set System In our experiments, for B2r, the McAdams coefficient is sampled, from a speaker to another, uniformly between 0. 7 and 0. 9  (Patino et al. , 2020). All experiments were performed using the trial part of the challenge enrollment and test sets. These are the LibriSpeech 10 testclean dataset and a subset of the VCTK  (Veaux et al. , 2019)  dataset. Each are split into male and female partitions. A separate partition of the VCTK dataset, denoted with the _c abbreviation, contains utterances of identical linguistic content (the same spoken sentence). Further details concerning the use of each database can be found in  Tomashenko et al. (2020b,a). Scores are obtained by comparing segments using the x-vector speaker embedding with the probabilistic linear discriminant analysis (PLDA)  (Snyder et al.
, 2018)  trained on the LibriSpeech train-clean-360 dataset. Scores are oracle calibrated using the Pool Adjacent Violators algorithm  (Brümmer and Preez, 2013). Results on each of the baselines are presented in Table  2. It reports the metrics based on the voice similarity matrices: the de-identification (DeID) and the gain of voice distinctiveness ( VD ); as well as ZEBRA's outputs: the expected ( ECE ) and worst-case privacy disclosure (  ) and the corresponding categorical tag (Tag). Both frameworks agree that B1 gives good protection relatively to B2 and B2r. Indeed, it leads to a DeID close to 100% and a low  ECE for all sets. However, protection is to the detriment of voice distinctiveness (low  VD ), making this system less suitable for applications such as teleconferencing or speaker diarization.
B2 and B2r better preserve voice distinctiveness but provide a lower protection as their  VD values are closer to zero ( VD = 0 means that the voice distinctiveness remains globally the same, no lose, no gain) and DeID are lower. A relatively weak protection for B2 and B2r is also shown by the high   values and the categorical tags. This is not surprising, since the expected privacy disclosure ( ECE ) for B2 and B2r are also higher. However, even if it is not shown by the presented results, one can meet a system which performs globally better than an another one but may leave some segments with a worst protection. and its categorical tag are necessary to detect such a scenario. Voice similarity matrices are illustrated in Fig. 9  for three of the six datasets. The better de-identification provided by B1 is evident from the lack of a distinctive diagonal and the uniformity of the  OP matrices (upper-right and lower-left) which indicate good protection for each speaker.
For B2 and B2r systems, the distinctive diagonal indicates inferior de-identification performance and the non-uniformity of  OP matrices' diagonal values relatively to the off-diagonal values indicates that speakers do not receive the same level of protection. The lack of a distinctive diagonal for  PP matrices (lower-right) confirms that B1 fails to preserve the voice distinctiveness, while B2 and B2r systems fair better. PP matrices also expose some imbalances level of voice distinctiveness. PP matrices for B2r show off-diagonal values which are, mostly, either high (red) or low (yellow) meaning that the system results in a group of voices that are well distinguishable and a group in which voices look alike as discussed in Section 4. 3. Fig. 10  shows a visualisation of ZEBRA's ECE profiles for three of the six datasets.
Comparison of the frameworks' findings By normalising  diag ( OP ) (in Eq. (  9 )), DeID gives a percentage of de-identification reached by a system in relation to the initial level of privacy. Applying the same normalisation strategies to  (OP)  ECE and  min(OP) llr allows to better compare these metrics 11 : llr represent all a percentage of de-identification, 100% means full or perfect de-identification whereas 0% means no improvement of the average level of privacy. Table  3  reports these three measures for the three baselines. While both frameworks again show that B1 outperforms both B2 and B2r systems, there are inconsistencies in the ranking of B2 and B2r. (OP/OO) ECE and  min(OP/OO) llr rankings for each dataset are consistent 12 but DeID gives different findings. It will now be shown how to measure a gain of voice distinctiveness from  ECE and also from  min llr in order to compare with  VD.
2004 , Skagen and Adams 2012 , Samplonius et al. 2018) , little is known about large-scale synchrony in their trait values (but see  Olin et al. 2020). Likewise, there is a lack of understanding of how spatial synchrony in traits is influenced by environmental conditions or scales up to spatial synchrony in population abundances. Laying date, clutch size, and fledgling number are the focus of some of the most extensive long-term individual-based studies, with multiple decades of data collected over multiple continents  (Culina et al. 2021) , making them ideal for studying spatial synchrony. Here, we use a unique collection of data from 86 long-term (i. e. , at least 9 years) monitored populations of blue tits (Cyanistes caeruleus), great tits (Parus major) and pied flycatchers (Ficedula hypoleuca) at 44 different study sites across Europe to study spatial synchrony in trait values across populations.
We extracted daily mean temperatures (ºC) and daily precipitation (mm) from the corresponding 0. 1º x 0. 1º grid cell in the E-OBS gridded dataset version 20. 0e  (Cornes et al. 2018). The E-OBS dataset did not include data for 3 of 44 (7%) study sites. For Askainen and Cambridgeshire, we used data from the nearest neighboring grid cells in the E-OBS dataset, which were, respectively, 6 km and 2 km from the study sites. For Vlieland, an island population in the Netherlands, no neighboring grid cells were available. Temperature data were used from grid cells corresponding to the neighboring island of Texel (18 km from the study site), which strongly correlated (Pearson's r = 0. 999) with data from a Royal Dutch Meteorological Institute (KNMI) weather station on Vlieland (available for 1996-2017).
Climatic variables For both climatic variables, we calculated annual values as the mean in February-May (i. e. , the period before and during breeding), because this period is the most crucial to the species and traits studied here (e. g. ,  Both et al. 2004 , Visser et al. 2006). This is further confirmed by other studies using climate window analyses  (Samplonius et al. 2018 , Bailey et al. 2022) , a statistical approach that identifies and quantifies weather signals and their critical time window on trait values (van de Pol and Bailey 2019). In addition to the two local climatic variables, we used the North Atlantic Oscillation (NAO) index as a climatic variable on a larger, regional scale. We extracted daily data on the   (www. cpc. ncep. noaa. gov). Analyses involving the NAO-index are available in Appendix S2.
Acknowledgements 19399170, ja, Downloaded from https://esajournals. onlinelibrary. wiley. com/doi/10. 1002/ecy. 3908by  NTNU Norwegian University Of Science & Technology/Library ,  Wiley Online Library on  [ 07/11/2022 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License This work could not have been carried out without the long-term effort and dedication of all the fieldworkers and landowners to data collection. We thank  Jaime Potti  for his feedback to the manuscript and his fundamental contribution to the data collection for the La Hiruela study system. This work was funded by the  Research Council of Norway  ( 223257 ). MDB thanks  Natural England  for their support to the monitoring.
ME and RP were funded by  University of Antwerp and FWO Flanders. JAK was funded by the  Norwegian Environment Agency. BK was funded by the  Max Planck Society. JM-P was funded by  ARAID  and the  Spanish Ministry of Education and Science  ( PID2019-104835GB-I00 ). DC was supported by the  Hungarian Academy of Sciences  (MTA  Premium Postdoctoral Research Program ; ID:  2019-353 ). JCS was funded by project  CGL-2020 PID2020-114907GB-C21. SMD was funded by the  DECRA fellowship  ( DE180100202 ). EB was funded by the  Ministry of Science and Innovation  (project  PID2021-122171NB-I00 ). The Gotland field work was supported by the  Swedish Research Council , the  CNRS , and the  Polish National Science Centre  ( UMO-2015/18/E/NZ8/00505 ,  UMO-2012/07/D/NZ8/01317 ). The Pirio and Rouvière field work was funded by the  OSU-OREME.
Four archetypes of baseline T2D We analyzed the baseline visit data from the newly diagnosed T2D cohort of the IMI DIRECT study, comprising of 726 participants (41% female) with complete data for 32 anthropometric, clinical, and biochemical phenotypes. All participants were diagnosed within two years before recruitment, were on lifestyle and/ or metformin treatment only, and had HbA 1c < 60. 0 mmol/mol (<7. 6%) within previous the three months. Mean age at baseline visit was 62 years, mean BMI was 30. 4 kg/m 2 , and mean HbA 1c was 46. 4 mmol/mol (Table  1 ). Instead of applying a hard-clustering approach to impose discrete clusters, we used the soft-clustering method of archetypes  11, 12  to uncover the baseline T2D etiological processes that contribute to disease heterogeneity. The archetype analysis identifies extreme observations within a multivariate dataset and subsequently represents individuals in the phenotype spectrum as convex combinations of these extreme observations.
A B C Figure  1. Archetype stability as evaluated using the following approaches (A) Minimized residual sum of squares (RSS) for k number of archetypes ranging from 1 to 10 in a scree plot was assessed first. The screen plot was based on RSS from the best model out of 100 restarts of the archetype algorithm for each k and showed a plateau in the drop in intra-cluster variance at k4 or k5. (B) Stability of the k archetypes was then assessed by a randomized subsampling of 90% of the original dataset repeated 100 times and compared to the original subgroups using the adjusted Rand index. Simultaneously, we evaluated the stability of the archetypes at archetype membership cutoffs ranging from 0 to 1 in intervals of 0. 05. The most stable solution was k2, irrespective of membership threshold, followed by k4, which reached a median adjusted Rand index > 0.
DISCUSSION The phenotypic landscape of individuals newly diagnosed with T2D forms a continuum of degrees of dysfunction within biological processes contributing to the complex etiology. Despite the common notion that the disease presentation at diagnosis is highly heterogeneous, it has been challenging to identify specific T2D endotypes with clearly defined boundaries. Therefore, instead of partitioning the dataset into discrete hard clusters as attempted before by others,  5, 7, 21  we applied a soft-clustering method to detect the archetypes that capture the continuous combinations of dysfunction in the underlying etiological processes for individuals. This approach identified the combinatorial effect of five etiological processes: insulin secretion (archetype A), obesity (archetypes B, C, and D), insulin resistance (archetypes C and D), dyslipidemia (archetypes C and D), and reduced b cell glucose sensitivity (archetype D).
We acknowledge that the size of the group with mixed etiology was large compared with the individuals at the extreme. The soft-clustering approach showed that there is phenotypic variation among individuals, which has some structure, but that it is best resolved in the archetype extremes. We explore the molecular, physiological, and phenotypic variation to provide insight into the heterogeneity of type 2 diabetes, but we do not think one should try to over-interpret the data to infer clinical utility. Nevertheless, the detailed and harmonized longitudinal phenotyping of the IMI DIRECT cohort, in combination with a method that capture the continuous combination of etiological processes, helps redefine the disease mechanisms for which the identified omics signatures serve as proxies. Better understanding of these signatures' contribution to the T2D phenotypes will be imperative to apply them to stratify patients and guide future treatment decisions.
Disease progression Diabetes progression was assessed by change in HbA 1c over time using individual HbA 1c slopes. HbA 1c concentrations were measured at baseline and 9, 18, 27 and 36 months after start of the study. HbA 1c trajectories were described with a conditional linear e2 Cell Reports Medicine 3, 100477, January 18, 2022 Article mixed-effect model. 32 The conditional approach employs a linear transformation of the data and produces a longitudinal and a crosssectional component, which are orthogonal. The transformation makes modeling of the longitudinal component, which is relevant for HbA 1c trajectories, independent of the cross-sectional effects, which are potential confounders and were not considered. In particular, the approach eliminates possible spurious correlations between the longitudinal parameters and baseline HbA 1c , which may arise if baseline HbA 1c is not accurately modeled.
Archetype soft-clustering All variables were rank-normally transformed and residualized for XX/XY genotype and recruitment center at each time-point using linear regression analysis. We performed soft-clustering using the R package 'archetypes',  11, 12  with 'robustArchetypes' function, and selected the combination of baseline archetypes which best minimized the residual sum of squares over 100 iterations of the algorithm. We evaluated the number of archetypes that best fitted this dataset by multiple approaches. First, we assessed the minimized residual sum of squares (RSS) for k number of archetypes ranging from 1 to 10 in a scree plot (Figure  1A ). Then, we assessed the stability of the k archetypes by a randomized subsampling of 90% of the original dataset repeated 100 times (Figure  1B ) using the adjusted Rand index.
Simultaneously, we evaluated the stability of the archetypes at archetype membership thresholds from 0. 0 to 1. 0 in intervals of 0. 05 (Figures  1B  and 1C ). Together these analyses showed that four archetypes had the lowest RSS while showing high stability after randomization. The subgroup stability increased with increasing membership threshold and plateaued at 0. 6: we therefore used this threshold to define extreme archetype-scores. All individuals with memberships < 0. 6 for all of the archetypes were assigned to the 'mixed etiology' group (i. e. , no membership to any extreme archetype, n = 472). We performed a post hoc Silhouette analysis with a standard distance matrix for the four extreme achetype groups and the mixed etiology group using the factoextra package in R. This analysis showed that the four extreme archetype groups look well clustered, and that the individuals in the mixed etiology group do not form a homogeneous cluster (Figure  S1 ).
Parameter pruning To see if there was a smalled set of clustering parameters that could recover most of the information in the full set of phenotypes we pruned the clustering input phenotypes by pairwise Pearson correlation. We constructed pruned datasets by removing parameters correlated at a progressively lower correlation coefficient, starting at 0. 8 and ending at 0. 2. This removed 10 to 28 of the original 32 parameters. We then ran archetype clustering for each pruned dataset and evaluated the cosine similarity between the original archetypes and the archetypes at each pruning. We next identified archetype similarity > 0. 8 at each pruning and searched for a subset where this cut-off was fulfilled for all four original archetypes. This resulted in a dataset that was pruned at a correlation coefficient cut-off at 0.
Genotyping quality control was then performed to provide high-quality genotype data for downstream analyses using the following criteria: call rate < 99%; deviation from Hardy-Weinberg equilibrium (exact p < 0. 001); variants not mapped to human genome build GRCh37; and variants with duplicate chromosome positions. We performed an additional quality control step to identify plausible sample swaps and/or sample labeling errors utilizing available RNaseq data on genotyped samples. For each sample with both genotype and RNaseq data availables, we identified the best matching expression-genotype pair using expression profile and genotypes (see transcriptomics section below). When mismatches between genotype and expression samples were identified, we examined the reported and (genotype) derived gender and traced back the samples through each step involved in their acquisition, extraction, and genotyping and re-mapped the samples to correct identifiers.
Sequences generation For the study of transcriptomic profiles samples of mRNA from whole blood samples were processed for RNA-sequencing. Concentration of mRNA per samples was assessed using the Qubit2. 0 from Invitrogen. The quality of the samples was then assessed using the TapeStation Software (A. 01. 04) with an RNA Screen Tape from Agilent to check the mRNA quality on gel and samples were discarded due to low mRNA quality. The remaining samples were processed and sequencing libraries were prepared. Quality of the libraries was evaluated using Qubit and TapeStation using DNA1000 Screen Tape. One sample was discarded after library preparation due to low quality. The remaining samples were placed in Flow cell PE using the cBOT system from Illumina. The samples were then sequenced on the Illumina HiSec2000 platform using 49 bp paired-end reads.
Read mapping and exon quantifications The 49-bp sequenced paired-end reads were mapped to the GRCh37 reference genome  36  with GEMTools 1. 7. 1. 37 Exon quantifications were calculated for all elements annotated on GENCODE v19. 38 All overlapping exons of a gene were merged into metaexons with identifier of type ENSG000001. 1_exon. start. pos_exon. end. pos. Read counts over these elements used paired-end reads if their both ends have a quality score > = 150, a total mismatch % 5 (5 mismatches max in 2x49pb) and if they are in proper orientation. We filtered transcripts from genes that were not protein coding, lincRNA or processed transcripts if they overlap in the opposite strand with protein coding genes and lincRNA genes. For split reads, we counted the exon overlap of each split fragment, and added counts per read as 1/(number of overlapping bases per exon).
Untargeted Metabolomics Plasma samples were stored at À80 C prior to analysis at Helmholtz Zentrum M€ unchen, Germany. On the day of extraction, samples were thawed on ice, were randomized, and were distributed into 25 batches for the T2D cohort. A hundred mL of the plasma were pipetted into a 2 mL 96-well plate. In addition to samples from this study, a pooled human reference plasma sample (Seralab, West Sussex, UK) was extracted in the same way as samples of the study and placed on 7 wells of each batch. These samples served as technical replicates throughout the dataset to assess process variability. Besides those samples, 100 mL of water was extracted as samples of the study and placed in 6 wells of each 96-well plate to serve as process blanks. Protein was precipitated and the metabolites in the plasma samples were extracted with 475 mL methanol, containing four recovery standard compounds to monitor the extraction efficiency.
Full scan mass spectra (80 -1000 m/z) and data dependent MS/MS scans with dynamic exclusion were recorded in turns. Metabolites were annotated by curation of the LC-MS/MS data against proprietary Metabolon's chemical database library (Metabolon, Inc. , Durham, NC, USA) based on retention index, precursor mass and MS/MS spectra. In this study, 544 metabolites, 341 compounds of known identity (named biochemical) and 203 compounds of unknown structural identity (unnamed biochemical) were identified. The unknown chemicals are indicated by a letter X followed by a number as the compound identifier.