dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We calculate two metrics, namely WA for weighted accuracy (overall accuracy) and UA for unweighted accuracy (average of the recall). We perform 10-fold cross validation on each dataset. So, the presented results are the mean WA and mean UA along with their standard deviations in parentheses. The results in Table  1  are for the dataset EMO-DB with four emotions (happiness, anger, neutral and sadness). Table  2  presents the results with all the seven emotions. The fundamental frequency f0 curve can also be considered as a functional data object, so we decide to compare the results with or without this feature adding to MFCC features. The lines corresponding to MFCC+ Pitch represent the method that adds the fundamental frequency curve. Different sizes have been considered, we present the two relevant sizes, namely 60, which indicates that the array of MFCCs has been resized to 60 × 60 and 80 to 80 × 80.
We have chosen a cubic B-spline basis (commonly used in FDA, see  [9] ) to decompose the functional features. Let us comment on these three tables of results. The best reshaping size is 60 for all the seven emotions in EMO-DB with a gain of 2% comparing with the size 80. In the case of the four emotions, there is no important gain in favor of one method. The best UA is for 60 and the best WA is for 80, but the results are similar. Concerning the result on IEMOCAP database (Table  3 ), a pre-result study has been made to select the size and it is 80 × 80, which has the best result. The fact that the mean duration of speeches in IEMOCAP database is higher than the one in EMO-DB database is an argument in favor of a higher reshaping size for the IEMOCAP database than for EMO-DB database.
Effects of the Resizing Method In order to compare multivariate functional objects, the ranges of each functional variables of the object must correspond. It is then natural to resize all the MFCCs to the same size. The chosen number of MFCCs is an important parameter of our procedure. An insufficient number of MFCC leads to a loss of information and accuracy. And the other hand, too many MFCCs do not add relevant information and increase the computation time. The other parameter we must take in account is the duration of the audio sample. The mean duration time in the IEMOCAP database is higher than in the EMO-DB database. An idea with a very high duration audio sample would be to cut in chunks of the audio file and to process them with our approach. Comparing the size on the results with the EMO-DB database, it is not clear which number of MFCCs must be chosen between 60 and 80.
Comparison with Other Methods It is important to note that previous functional methods, which solely consider Pitch as a functional feature, do not achieve the same performance results as our approach. In order to assess the effectiveness of our method, we compared our results with state-ofthe-art (SOTA) deep learning methods, namely  [22]  for the IEMOCAP database and  [4]  for the EMO-DB database, which were the top-performing methods in 2021 for these two databases. In the case of the EMO-DB database, the article by  [4]  achieved an overall accuracy of 97. 7%, whereas our approach achieved 85. 8%. This indicates that our method, although slightly lower in accuracy, still demonstrates competitive performance compared to the SOTA approach of  [4]. Similarly, when comparing our results with the findings of  [22]  on the IEMOCAP database, their weighted accuracy (WA) and unweighted accuracy (UA) were both reported as 83.
Conclusions and Discussion In conclusion, our approach of treating Mel-Frequency Cepstral Coefficients (MFCC) as functional data objects offers a promising solution for speech emotion recognition tasks. The methodology we propose not only demonstrates high efficiency, but also proves to be well-suited for implementation on small devices, such as Field-Programmable Gate Arrays (FPGAs). Compared to deep learning methods, our approach significantly reduces the learning time required for emotion recognition, making it particularly suitable for scenarios where new data becomes available at different intervals. This capability is valuable in applications where real-time or near real-time emotion recognition is essential, such as interactive systems or online platforms. Additionally, the reduced learning time also contributes to resource efficiency, making our method more practical and cost-effective for deployment on devices with limited computational capabilities.
7%)] varied between 36. 2% on the BTA04 and 63. 3% on the BTA10 (Supplementary Fig. S2 ). Pairwise Fst values obtained with Genepop corroborated the genetic proximity within AFI populations (0. 016 < Fst < 0. 044) and between these latter and GUA (0. 049 < Fst < 0. 062) while Fst estimates between GUA and AMASI breeds were almost two times greater (Fst > 0. 1). Moderate values of Fst are observed between GUA and EUT breeds with a South European origin [the Spanish breeds (IBER), Blonde d' Aquitaine (BAQ), Limousin (LIM) and Charolais (CHA)] (0. 0691 < Fst < 0. 0799) (Supplementary Table  S5 ). We have examined our data with the TreeMix software which allows for modelling both population splits and gene flow between populations. Ten migration events were sequentially added to the phylogenetic tree which explained 99.
S5 ). Furthermore, these five regions overlapped with one of the top 1% windows showing the highest genetic differentiation with EUT and AFT breeds (Supplementary Tables  S8  and S9 ). We also tested for signals of selection using the composite likelihood ratio implemented in SweeD software to detect patterns of site frequency spectrum in the GUA population. We found that all five candidate regions had the highest CLR values in their respective chromosomes (Supplementary Fig. S6 ). Genes that fell within the peak area of the aforementioned five candidate regions in addition to the outlier window on the BTA04 (at position 113-113. 5 Mb) detected with the iHS test were considered as relevant candidates (Table  1 ). On chromosome 2, the highest signal is 73. 5 Kb downstream of EIF4E2 gene. On chromosome 5, the two variants with the highest p value fell within GRIP1, a gene that facilitates the anti-inflammatory effects of glucocorticoids in vivo  16.
Sample selection and genome sequencing. Sequence alignment and genotype calling. Genotype data were generated following the 1000 Bull Genomes Project Run 8 guideline  50. Quality control of raw sequence reads was performed using the fastQC software v. 0. 11. 7 (http:// www. bioin forma tics. bbsrc. ac. uk/ proje cts/ fastqc/). Trimmomatic-0. 36  51  was used to remove Illumina adapter sequences, low-quality bases and artefact sequences. Filtered sequences were then mapped against the bovine reference genome (ARS-UCD1. 2) using the Burrows-Wheeler Alignment tool (bwa mem v. 0. 7. 17)  52  with default parameters. The resulting SAM files were then converted to BAM format, sorted, and indexed using SAMtools  53. Potential PCR duplicates were removed using the MarkDuplicates tool from Picard version 1.
88 (http:// broad insti tute. github. io/ picard). Only properly reads with a mapping quality of at least 30 were kept. Prior to variant discovery, local realignment was performed using two GATK (Genome Analysis Toolkit) version-3. 8-1-0-gf15c1c3ef modules, RealignerTargetCreator and IndelRealigner. GVCF files were then created for each sample using the GATK HaplotypeCaller module. Single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) were subsequently called for all samples using the GATK GenotypeGVCFs tool. VCFtools v. 0. 1. 17 software was used to filter out indels, multiallelic SNPs and variants that (1) displayed a minor allele frequency (MAF) < 0. 1; (2) have missing genotype rates > 0. 1; (3) did not pass Hardy-Weinberg equilibrium (HWE) test (p < 0. 01) and (  4 ) have a quality score (-minQ option) below 100.
The selected variants were subsequently annotated using the SnpEff annotation software  54. Data merging and relatedness check. We downloaded publicly available genomes of of 41 individuals including 12 AFI, 10 AFT, 7 Australian-American indicine (AMI), 4 Asian indicine (ASI), 4 European taurine breeds from Spain (IBER) and 4 LIMO individuals (representative of Criollo (CRIO) cattle) (Supplementary Table  S13 ). We followed the same procedure aforementioned in sequence alignment and variant calling. Finally, we included in the data set, genotyping data belonging to 50 individuals from six French breeds provided by the 1000 Bull Genomes Project (Supplementary Table  S13 ). We applied the same marker selection thresholds (maf = 0. 1, missing genotype rate = 0. 1, p value HWE = 0. 01 and minQ = 100) to build these two databases which resulted in 16,360,962 and 8,955,346 high-quality SNPs for the public and the 1000 Bull Genomes genotyping data, respectively.
All three databases were combined into a single one containing 5,316,956 common snps. Nucleotide diversity (Π) at a whole-genome scale was estimated in sliding windows of 1 Mb using VCFtools considering the following breed groupings: AFI (N = 11) and AFT (N = 7) and the each of the following breeds separately: Aubrac (AUB; N = 8), Charolais (CHA; N = 7), Holstein (HOL; N = 6), Limousin (LIM; N = 7), Montbeliarde (MON; N = 6). In Π computation, we selected 10 GUA individuals among the least related. This was done to have sample groups with similar size (for a matter of comparison). We further performed an LD-based SNP pruning using PLINK  55  with the "-indep-pairwise 20 4 0. 6" option which resulted in 1,064,342 SNPs spread over all autosomal chromosomes used in population structure analyses.
Average inter-marked distance was 2. 3 Kb ± 4. 3 Kb (Supplementary Table  S14 ). To overcome the effect of closely related animals, we performed a relatedness test between individuals within each population using PLINK. The software calculates a variable called PI-HAT reflecting extended haplotypes shared between distantly related individuals. We excluded one individual from each pair of individuals with a PI-HAT value > 0. 05 which is a value roughly corresponding to 3rd-degree relationships. In total, after relatedness filtering, 99 individuals including 19 GUA animals, were available for the different analyses (Supplementary Table  S13 ). Population structure and genetic relationship analyses. We performed a principle component analysis (PCA) using the adegenet R package  56. Furthermore, genetic structure was inferred from SNP data in ADMIXTURE 1.
3 software 57. We ran ADMIXTURE for values of K from 3 through 5. DISTRUCT software  58  was used to graphically display ancestry within each individual. Global averages of pairwise population differentiation measured by fixation index (Fst) was estimated using Genepop 4. 6 software 59 for populations represented by more than one animal. The four Spanish individuals were considered as one population as well as the four Colombian animals. Next, we performed local ancestry assignment across the GUA genome using the Efficient Local Ancestry Inference (ELAI) algorithm  60  under a three-way admixture model using AMASI and AFI populations as proxies for indicine ancestry, Muturu and Ndama as proxies for AFT ancestry, the French and the Spanish breeds as proxies for EUT ancestry. ELAI uses a two-layer hidden Markov model to detect the structure of haplotypes for unrelated individuals.
Identification of selection signatures. Prior to selection signature analysis, we performed a more stringent LD-based pruning with the '-indep-pairwise 20 4 0. 4' option of PLINK which resulted in 433,408 SNPs. Integrated haplotype score (iHS)  15  , Rsb  22  and cross population extended haplotype homozygosity (XP-EHH)  21  scans were performed using the rehh package  62. In iHS computation, the information on the ancestral and derived allele state is needed for each SNP because this statistic is based on the ratio of the extended haplotype homozygosity (EHH) associated to each allele. In our analysis, the ancestral allele was inferred as the most common allele within our dataset. iHS scores for each SNP were transformed into two-sided p values: piHS = -log10[1 -2|Φ(iHS) -0. 5|]. Rsb and XP-EHH analyses were performed for each of the three pairwise comparisons: GUA versus AFT, GUA versus EUT (French, Spanish and Criollo breeds) and GUA versus IND (AMASI and AFI populations).
Haplotype phasing was performed using fastPHASE 1. 4  63. Since fastPHASE is based on haplotype clusters, whose size should be set a priori, we used the toolkit implemented in imputeqc R package  64  to estimate the optimal number of haplotype clusters (K) needed for haplotype phasing. Imputeqc package has been designed to assess the imputation quality and/or to choose the model parameters for imputation. In the present study, we found that K = 20 provided the best imputation quality (for 5% of masked data). Therefore, we used this value to run fastPHASE. Considering that Rsb and XP-EHH values are normally distributed, a Z-test was applied to identify significant SNPs under selection. Two-sided p values were derived as pRsb = -log10[1 -2| Φ(Rsb) -0. 5|] and pXP-EHH = -log10[1 -2|Φ(XP-EHH) -0.
Acknowledgements The authors wish to express their sincere gratitude to  Didier Boichard , G2B team leader, for allowing access to the raw sequence data of French samples (part of the 1000 Bull Genomes Project). The first author is financially supported by  FEDER AGROECODIV. The sequencing of the samples from Guadeloupe is financed by  SELGEN TROCADERO. Sequencing data of the Columbian samples were provided by IMAGE ( H2020  project  677353 ). The authors would like to thank  AGROSAVIA , The  Colombian Corporation for Agricultural Research  for providing the Colombian samples.
M edical videos are important for the diagnosis and classification of many neurological disorders. 1  Paroxysmal disorders, in which symptoms and signs occur intermittently, are particularly dependent on the use of video recordings for diagnosis. Epilepsy is a common and disabling chronic neurologic condition 2 characterized by paroxysmal seizures (usually lasting seconds-minutes), which are defined as transient occurrence of signs or symptoms because of abnormal excessive or synchronous neuronal activity in the brain. 3  Seizures can be captured on video recording, most often in the hospital setting accompanied by concomitant electroencephalography (EEG) monitoring. However, increasingly, seizures are being captured on smartphones or other home video devices, which has been groundbreaking in allowing clinicians to view video data of paroxysmal events (even those that occur only rarely), filmed by the patient and their family in an ecological setting.
An AI-based face-swapping approach as a means of protecting patient privacy in medical data sharing was recently tested in a clinical movement disorder research study,  14  but has not to date been investigated for epileptic seizure videos. Here, we hypothesized that different AIbased facial transformation (source facial data) models would show different performances in terms of achieving deidentification while preserving clinical features of interest in epileptic seizure video recordings (target facial data). We studied 3 different AI models of facial transformation applied to hospitalcaptured seizure videos, to investigate the following: (1) the feasibility of applying facial deidentification to seizure videos; (2) the degree of facial deidentification achieved with different models; and (3) the preservation of facial seizure semiologic information.
Material and Participants A data set of seizure videos was compiled from the epilepsy monitoring unit, Department of Neurology, Taipei Veterans General Hospital, Neurological Institute, Taipei, Taiwan. Patients were retrospectively selected from those who had undergone elective video-EEG using standard clinical methodology for assessment of seizures between August 1, 2020 and February, 28 2023. Ethics approval was granted by the institutional review board (IRB number: 2022-07041BC) and patients provided informed consent. Videos of epileptic seizures displaying any facial changes relevant to the clinical analysis of seizure semiology (eg, facial contraction, emotional expression, and eye deviation) of satisfactory technical quality were selected. These faces of patients are the target faces.
Clinical Data Set The final data set was composed of 26 videorecorded seizures expressing facial semiology from 16 patients (7 males and 9 females, age range at recording 9-47 years, median of 25. 5 years). These patients all had drug-resistant focal epilepsy (ie, persistence of seizures despite adequate trials of at least 2 antiseizure medications,  6  and seizures arising from part of 1 cerebral hemisphere). 3  The cerebral seizure onset zone (as determined by the epilepsy team based on the results of the video-EEG and supporting data, including neuroimaging) was frontal lobe in 6 patients, temporal lobe in 7, occipital lobe in 1, frontotemporal region in 1, and temporoparietal region in 1. Five patients eventually underwent resective brain operation, 2 had radiofrequency thermocoagulation, and 4 had vagal nerve stimulation for treatment of epilepsy after presurgical evaluation.
Video Frame Preparation The 26 video frames (1 representative facial image per seizure) presented to clinicians for analysis were chosen to include a selection of clinically relevant and clearly visible facial seizure manifestations, for example, eye gaze toward one side, grimacing, smiling, pouting, facial contraction, and lip pursing (see Supplementary Table, available online at https://www. mcpdigitalhealth. org/). Seizure data were annotated by 5 expert clinicians to allow correct selection of relevant video frames from the ictal period. A single representative frame showing the facial semiology was then prepared for each seizure. Using open-source models, video facial transformation was carried out for each selected seizure frame.
Both aspects were scored using a 3-point scale as follows: 3¼good, 2¼fair, and 1¼poor. Results were collated and compared between observers, with a mean facial deidentification (FD) score and mean facial semiology preservation (FSP) score calculated per observer and per tested model, based on scores for that observer across all seizures. Statistical analysis of inter-observer agreement was performed using Krippendorf's a. All statistical analyses of clinicians' scores were performed using R software. The analysis of the ratings is available on Open Science Framework at https://osf. io/rxf9e/?view_only¼f7c 656112aba4944b029681423e4b556, and an analysis of gender congruency effect, that is, effect of gender of swapped face compared with patient face in clinicians' scores of deidentification and semiology preservation.
Observations and data reduction 2. 1. New data 2. 1. 1. CHEOPS CHEOPS  (Benz et al. 2021 ) initially observed ν 2 Lup in its early science programme (CH_PR100041), which was part of the guaranteed time observations (GTO), from April to July 2020. ν 2 Lup d was first detected in transit on 9 June 2020  (Delrez et al. 2021). The first window of opportunity for a confirmation was in late April 2021. We scheduled a new CHEOPS visit from 24 April to 28 April 2021 to cover both the 3σ window on the transit-timing uncertainty and the transit of the planet Hill sphere, as part of the GTO programme CH_PR100031. The visit represents 56 CHEOPS orbits (3. 85 days). A total of 4 720 images were obtained; each image is a 200×200-pixel subarray resulting from the co-addition, performed on board, of 26 exposures of 1.
Archival data To derive the strongest constraints on the system parameters, we also included the data previously published in Udry et al. (  2019 ),  Kane et al. (2020), and Delrez et al. (2021)  in our combined analysis. These data sets are (i) the 246 radial velocities obtained between 27 May 2004 and 4 August 2017 with the HARPS spectrograph on ESO 3. 6 m telescope (La Silla, Chile), (ii) the TESS 2-min-cadence photometry obtained during sector 12 of its primary mission (21 May-18 June 2019), covering two transits of planet b and one transit of planet c, and (iii) the six CHEOPS visits obtained between 4 April and 6 July 2020 and covering in total four transits of planet b (one of which is partial), three transits of planet c, and one partial transit of planet d. We refer to the studies cited above for more details about these data and their reduction.
Combined data analysis We performed a combined analysis of the whole data set using the juliet python package  (Espinoza et al. 2019)   (b) Each CHEOPS subarray image results from the on-board co-addition of 26 exposures of 1. 7 s. ( c) After binning the 20 s exposures to the 2 min cadence. R ⋆ (R ⊙ ) 1. 054 ± 0. 014 ρ ⋆ (ρ ⊙ ) 0. 746 ± 0. 041 v sin i (km s -1 ) <1 age t ⋆ (Gyr) 11. 7 +2. 1 -2. 3 on several publicly available tools such as batman  (Kreidberg 2015)  for the modelling of transits, radvel  (Fulton et al. 2018)  for radial velocities, celerite (Foreman-Mackey et al. 2017) for Gaussian processes (GPs), and dynesty (Speagle 2020) for estimating Bayesian posteriors and evidence via dynamic nested sampling. Our analysis is very similar to the one presented in  Delrez et al. (2021)  and simply incorporating the new CHEOPS and TESS light curves.
Alternative photometric extractions In addition to the standard DRP reduction of CHEOPS data (Sect. 2. 1. 1; Hoyer et al. 2020) , which uses aperture photometry, we ran an alternative and independent pipeline based on point spread function (PSF) fitting, called the PSF Imagette Photometric Extraction pipeline (PIPE 4 ; see  Morris et al. 2021; Szabó et al. 2021; Deline et al. 2022; Brandeker et al. 2022). We ran PIPE on the CHEOPS stacked subarray images (the same input as for the DRP) as well as on the imagettes, which are small stamps centred on the target, extracted on-board directly out of the unstacked subarray images. While the subarray images result from the on-board co-addition of 26 individual 1. 7 s exposures (performed to save bandwidth), the imagettes are only stacked in groups of two, allowing us to retrieve a sampling close to the actual observing cadence.
We find that the PSF PCA method  (Wilson et al. 2022 ) removes a subtle flux ramp at the beginning of the dataset. Satisfied that we constructed a noise model that can account for as much of the systematic flux variation as possible, we fit the data with this model simultaneously with either N = 0, 1, and 2 transit models in order to determine the true inclusion probabilities (TIP;  Hara et al. 2021). Therefore, we can statistically verify the presence of one or two transits in the CHEOPS light curves. For the first transit model, we took priors on ν 2 Lup d from the results of our global analysis (see Table. 3), whereas for the second transit model, we assumed a similar orbital period and impact parameter as ν 2 Lup d, and left uniform priors on the transit depth and centre time. An example result from the N = 1 fit using the DRP fluxes is shown in Fig.
Acknowledgements. We thank the anonymous referee for their time spent reviewing the manuscript. CHEOPS is a  European Space Agency  ( ESA ) mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden  and the  United Kingdom. The Swiss participation in CHEOPS has been supported by the  Swiss Space Office in the framework of PRODEX  and the  Activités Nationales Complémentaires  and the  Universities of Bern and Geneva  as well as the  NCCR PlanetS  and the  Swiss National Science Foundation. The MOC activities have been supported by  ESA  contract  4000124370. This work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606.