dataesr/scientific-paragraphs-categorization huggingface dataset:
Results obtained using the Positive Discrimination strategy Results obtained using the positive discrimination strategy are shown in Figure  7-(bottom). They clearly emphasize the spectacular effect of this strategy on the disparate impacts, which can be controlled by the data scientist. By adjusting the threshold, it is possible to adjust the levels of discriminations in the dataset, as in this example where the socially acceptable level of 0. 8 can be reached. In this case we see a decrease in the performance of the classifier, but yet being reasonable. These results should however be tempered for a main reason. Although the average error receives little changes, the number of false positive cases of women is clearly increased when introducing positive discrimination. In our tests more than half of the predictions that should have been false in the group S = 0 are even true.
A. 1. 1 Data preparation As discussed in the introduction of Section 2, the study has started with a detailed preprocessing of the raw data to give a more clear interpretation to further analyses. First, we noticed that the variable fnlwgt (Final sampling weight) has not a very clear meaning so it has been removed. For a complete description of such variable access the link http://web. cs. wpi. edu/ ~cs4341/C00/Projects/fnlwgt. We have also performed a basic and multidimensional exploration (MFCA) in order to represent the possible sources of bias in the data in https://github. com/wikistat/Fair-ML-4-Ethical-AI/blob/ master/AdultCensus/AdultCensus-R-biasDetection. ipynb. This exploration led to a deep cleaning of the data set and highlighted difficulties present on certain variables, raising the need to transform some of them before fitting any statistical model.
From the Central Limit Theorem, we have that Hence here confidence intervals play an important role. Actually the disparate impact is not statistically significantly lower than 0. 8, which entails that the discrimination of the decision rule of the German bank can not be shown, which promotes the use of a proper confidence interval. 2. COMPAS Recidivism data. A third data set is composed by the data of the controversial COMPAS score detailed in  Dieterich et al. (2016). The data is composed of 7214 offenders with personal variables observed over two years. A score predicts their level of dangerosity which determines whether they can be released while a variable points out if there has been recidivism. Hence Recidivism of offenders is predicted.
ConclusionsIn this paper, we provided a case-study of the use of machine learning technics for the prediction of the well-known Adult Income dataset. We focused on a specific fairness criterion, the statistical parity, which is measured through the Disparate Impact. This metric quantifies the difference of the behaviour of a classification rule applied for two subgroups of the population, the minority and the majority. Fairness is achieved when the algorithm behaves in the same way for both groups, hence when the sensitive variable does not play a significant role in the prediction. Main results are summarized in Figure8. In particular, we convey the following take-home messages: (1) Bias in the training data may lead to machine learning algorithms taking unfair decisions, but not always. While, in this widely studied data set, there was a clear increase of bias using the tested.
Materials We obtained the deidentified case list of suspected cholera cases in Centre during January 2015-September 2016 from the MSPP. The case list provided the   (15). All patient identifiers were removed previously. The mapping of rural localities in Haiti is incomplete  (6). We collected point global positioning system (GPS) coordinates for each locality from field visits by mobile response teams, authors' field visits, or satellite photos and from geographic repositories, including Haitian Institute of Statistics and Computer Science (http://ihsi. ht/publication_cd_atlas. htm), Index Mundi (https://www. indexmundi. com), OpenStreetMap (https://www. openstreetmap. org), Google Earth (https://earth. google. com), and Google Maps (https://www. google. fr/maps). We verified GPS coordinates against official data from Centre National d'Information Geospatiale (CNIGS), when available.
Further data validation was performed by using computerized participatory mapping techniques to integrate spatial information from local community stakeholders in each commune, either individually or in small groups. These stakeholders included local healthcare professionals from mobile response teams, WaSH technicians, and local government personnel. We checked and unified locality names and used satellite imagery-assisted visual mapping techniques to validate the estimated GPS coordinates. This process ensured coherence with local knowledge of the terrain and identified inconsistencies, which was vital for rural areas without road access. As part of a collaborative project financed by World Vision (https://www. worldvision. org), DINEPA and the nongovernmental partner organization Haiti Outreach (https://www.
haitioutreach. org) collected an inventory of water sources and provided GPS coordinates by visiting each water point and validating data with local partners (16) (Figure  2 , panel B). Water sources were classified as improved or unimproved according to the Joint Monitoring Program for Water Supply Sanitation and Hygiene  (17, 18). We obtained the location of rivers, roads, and altitude for Centre from CNIGS (Figure  2 , panel A). We obtained history of previous oral cholera vaccination campaigns by communal section from MSPP (Figure  2 , panel B). We geolocated markets during field investigations in Centre. We calculated Voronoi polygons from estimated point coordinates for each locality. These polygons, or proximity diagrams, consist of all locations closer to the locality point coordinate than any other point coordinate, providing estimated boundaries.
org). We performed multistep nonsupervised analysis to classify the localities according to their environmental and spatial characteristics. Hierarchical clustering on the principal components of a multiple correspondence analysis (MCA) was detailed previously  (19, 20) ; we used these data to classify neighborhoods in towns in Haiti  (12). The first step is an MCA, which is an exploratory method that considers the relationship between variables and reduces complex datasets into fewer dimensions  (21). We performed MCA by using the original categorical variables and the categorized continuous variables. Active variables included the presence of a market, urban or rural location, vaccination status, and area averaged: altitude, distance to a road, distance to an improved water source, distance to an unimproved water source, and distance to a river.
We accounted for the increasing population by using an offset of the log population and estimating standardized incidence ratios (SIRs) for each class. We considered p<0. 05 statistically significant. We used QGIS version 2. 14. 3 (QGIS Development Team, http://qgis. osgeo. org) as a geographic information system (GIS) for mapping. We performed all statistical analyses by using R version 3. 3. 0 (R Foundation for Statistical Computing, https://www. rproject. org). We used the FactoMineR package in R for classification analysis  (19)  and mgcv for GAMs, with generalized cross validation criteria for smoothing parameter estimations and the gam. check function to verify residual plots  (22, 25). All data remained anonymous with no patient identifiers, in accordance with national and international ethics guidance  (26).
The state of this system is recoverable if the level sensor or the outflow sensor is attacked, but not both. Fig. 3 (a, b ) shows that we can recover the state of the system from the outflow sensor, in case an attack is targeting the level sensor. The simulation is based on Matlab code, available on-line in a github repository. 5  Additional details about the simulation code and results are available in the appendix. Case 2: The state of the (1, 1)-resilient system, cf. Eq. (  13 ), is not recoverable if one sensor is attacked. Proof: When one sensor is attacked, there are no additional points of observations (Fig. 3 (c, d )).
Simulation of Case 2: The (1, 1)-resilient system has only four levels sensors (one per tank). When an adversary perpetrates an attack on these sensors, the state of the system is not recoverable. Fig. 3 (c ) shows the levels in each tank, when the system is not attacked. Fig. 3 (d)  shows the levels when an attack is perpetrated. Since there is no non-attacked sensor type implementing an injective function on its elements, the state is not recoverable. 5 Cf. https://github. com/mirrored-quadruple-tank/ Case 3: The state of the (1, 2)-resilient system, modeled by Eqs. (  13 ) and (  14 ), is recoverable in one step if, for i = 1, 2, 3, 4, only level sensors y[2i-1] or outflow sensor y[2i] is attacked, but not both (note, output column vector has format (y  [1] ,. , y [ n]) T ). Proof: It follows from the fact that sensors y[2i -1] and y[2i] are injective functions of state component x[i], for i = 1, 2, 3, 4 (note, state column vector has format (x[1],.
APPENDIX. A. SUPPLEMENTARY MATERIAL TO THE SIMULATIONS We report in this appendix the simulation of Case 3 (cf. Section V-B). An existing Matlab implementation of the quadruple-tank process for this case scenario (available online at https://github. com/karrocon/pcsmatlab), was adapted and complemented with Matlab and Simulink code, w. r. t. the resilience and adversary models presented in this paper. The resulting code is also available on-line, in our github repository (cf. https://github. com/mirrored-quadrupletank/). The simulation of the Case 3, as in the Cases 1, 2, 4, and 5 (already reported in the main body of this paper, cf. Section V-B), implements a proportional-integral (PI) controller based on the differential equations of the quadruple-tank scenario By Johansson in Ref. [12].
org/). The MLST and plasmid replicon types were determined using the MLST (version 2. 0. 4) and PlasmidFinder (version 2. 0. 1) software, respectively. Antimicrobial resistance was analyzed by ResFinder 4. 1 (https://cge. cbs. dtu. dk/services/ResFinder/) and CARD (https://card. mcmaster. ca/analyze/rgi). The constructed plasmids were annotated automatically by the RAST server using the RAST-tk scheme  [39] , followed by manual inspection and correction using the BLASTn and BLASTp tools (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The circular image and comparisons between other reported similar plasmids were performed using the BLAST Ring Image Generator (BRIG) tool (Figure  1 )  [40]. ing the CLC Genomic Workbench (version 20. 0. 4; CLC Bio, Aarhus, Denmark), and contigs with a minimum contig length of 800 nt were generated.
The resulting assembled sequences were uploaded to the Center for Genomic Epidemiology server (http://www. genomicepidemiology. org/). The MLST and plasmid replicon types were determined using the MLST (version 2. 0. 4) and PlasmidFinder (version 2. 0. 1) software, respectively. Antimicrobial resistance was analyzed by ResFinder 4. 1 (https://cge. cbs. dtu. dk/services/ResFinder/) and CARD (https://card. mcmaster. ca/analyze/rgi). The constructed plasmids were annotated automatically by the RAST server using the RAST-tk scheme  [39] , followed by manual inspection and correction using the BLASTn and BLASTp tools (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The circular image and comparisons between other reported similar plasmids were performed using the BLAST Ring Image Generator (BRIG) tool (Figure  1 )  [40].
Considering that several plasmid types were identified, seven representative mcr-1positive plasmids were entirely sequenced. WGS data analysis showed that the seven plasmids belonged to three types of plasmids, including IncX4 of size ranging from 31. 8 to 32. 1 kb (n = 3), IncI2 of size ranging 64. 2-67. 7 kb (n = 3), and IncHI2 of 195. 8 kb in size (n = 1). None of the mcr-1-positive IncI2 and IncX4 plasmids possessed additional resistance determinants, confirming the phenotypic observations made with the E. coli transconjugants, whereas multiple resistance elements were detected alongside mcr-1 in the pEGYMCR65 plasmid. The three sequenced IncX4 type plasmids (pEGYMCR8, pEGYMCR16, pEGYMCR60) were nearly identical, and showed typical plasmid backbones encompassing genes encoding proteins involved in replication, maintenance, and transfer.
Interestingly, detailed sequence analysis of the IncX4 plasmids showed that they were almost identical to the mcr-1-positive plasmid pCFSAN061769_01 (~97% query coverage and ~99. 9% sequence identity; GenBank accession no. CP042970. 1) identified in an E. coli recovered from cheese in Egypt, indicating that this plasmid type is circulating among different sources. The sequence of those IncI2 plasmids (pEGYMCR17, pEGYMCR23, pEGYMCR62) was almost identical to plasmid pEGYMCR-1, identified in a single E. coli isolate recovered from a meat product (beef sausage) in Egypt, and to plasmid pMCR-GN775 (accession no. KY471307) identified in an E. coli strain recovered from a gastrostomy tube site and rectum of a patient hospitalized in Canada who noticeably received previous healthcare in Egypt (Figure  1 )  [23, 69].
This basically underlines that the dissemination of the mcr-1 gene is at least partially linked to that of "epidemic" plasmids. The sequence of plasmid pEGYMCR65 was highly similar to that of the mcr-1-positive IncH12 plasmid, pCFS3292-1 (95% query coverage and 99. 99% sequence identity; GenBank accession no. CP026936. 2), identified from an E. coli of bovine origin recovered from animals presenting with diarrhea and mastitis in France. Also, plasmid pEGYMCR65 was highly similar to the mcr-1-positive plasmid pEGY1-MCR-1 (91% query coverage and 99. 99% sequence identity; NCBI Reference Sequence: NZ_CP023143. 1) identified in E. coli isolated from cheese in Egypt (Figure  1 ), that latter missing transposon Tn21 encoding mercury resistance genes (mer operon). Genetic environment of the mcr-1 gene: In a recent study, an intermediate circular form of the insertion sequence ISApl1 associated with mcr-1 was detected, suggesting that ISApl1 might play a driving role in the horizontal gene transfer of this resistance gene  [70].
Atomoxetine increases extracellular noradrenaline levels across the brain-including a three-fold increase in the prefrontal cortex-by inhibiting the presynaptic noradrenaline transporter  [89, 90]. Thus, for individuals with severe locus coeruleus degeneration, atomoxetine may help alleviate the dysfunctional modulation of prefrontal noradrenergic targets, and thereby help restore the precision of prefrontal representations of predictive signals. This study has limitations. First, although we collected a comprehensive dataset for each individual participant, incorporating various demographic, behavioural, and neuropharmacological measures, the total number of participants is modest. Our study design was therefore not well-suited for data-driven analyses  [91]  or predictive modelling  [92] , and may have been prone to type II error for frequentist statistical inferences on small effects.
[58]. Twenty-one age-, sex-and education-matched healthy controls were recruited from local volunteer panels, to provide normative data. They had no history of neurological or psychiatric disorders, and were not using psychoactive medications. One control participant and one participant with Parkinson's disease had excessive amounts of missing visuomotor task data (� 50% of trials) due to technical issues. After excluding these participants, the final sample consisted of 17 participants with Parkinson's disease and 20 controls. Demographic details and clinical characteristics are provided in Table  1  and S1 Text.
Software and equipment The visuomotor task was implemented in MATLAB R2018b using the Psychophysics Toolbox extensions version 3  [131] , and was displayed on a 12. 5-inch laptop screen (1920 × 1080 pixels). The force sensor had a sampling rate of 60 Hz and a measurement accuracy of ±9. 8 mN. The magnetisation transfer images were processed using the Advanced Normalization Tools version 2. 2. 0  [132]  and in-house MATLAB scripts  [57, 62]. All statistical analyses were implemented in R version 3. 6. 1  [133] ; detailed information about the specific R packages used for the analyses is provided in S6 Text. The hierarchical Bayesian modelling was implemented in Stan  [134]. A. Backward elimination of fixed effects in the linear mixed effects model predicting prior weighting. Values for predictors are standardised regression coefficients (ß).
Acknowledgments We thank the volunteers for their participation, staff at the  Wolfson Brain Imaging Centre and National Institute for Health and Care Research (NIHR) Cambridge Clinical Research Facility  for their help with data collection, and members of the  Cambridge Centre for Frontotemporal Dementia  and  Related Disorders  for valuable suggestions and discussions. The views expressed are those of the authors and not necessarily those of the  National Institute for Health and Care Research  or the  Department of Health and Social Care.
The study was primarily supported by the  Cambridge Centre for Parkinson-Plus  to JBR, RAB and CWG ( RG95450 ), with additional support from a  Parkinson's UK  research grant to JBR and  CO (K-1702 ; https://www. parkinsons. org. uk/), a  James S. McDonnell Foundation 21st Century Science Initiative Scholar Award in Understanding Human Cognition , to JBR (https://www. jsmf. org/)and an  Investigator Award  from the  Wellcome Trust  to JBR (220258; https://wellcome. org/). FHH was supported by a  Cambridge Trust Vice-Chancellor's Award and Fitzwilliam College  scholarship (https:// www. cambridgetrust. org/). NW was supported by an  Academic Clinical Fellowship  ( ACF-2019-14-013 ) from the  National Institute for Health and Care Research  (https://www. nihr. ac. uk/). CO was supported by a  Neil Hamilton   Fairley Fellowship  from the  Australian National Health and Medical Research Council  ( GNT1091310 ; https://www.
nhmrc. gov. au/). AGM was supported by the  Holt Fellowship  ( RG86564 ). NH was supported by the  Association of British Neurologists ,  Patrick Berthoud Charitable Trust  ( RG99368 ; https://www. theabn. org/). KAT was supported by the  British Academy  ( PF160048 ; https://www. thebritishacademy. ac. uk/) and the  Guarantors of Brain  (101149; https://guarantorsofbrain. org/). CHW-G was supported by an  RCUK/UKRI   Research Innovation Fellowship  awarded by the  UK Medical Research Council  ( MR/R007446/1 ; https:// mrc. ukri. org/). JBR was supported by intramural funding ( SUAG/051 G101400 ) and a research grant ( MR/P01271X/1 ) from the  UK Medical Research Council  (https://mrc. ukri. org/). This research was supported by the  NIHR Cambridge Biomedical Research Centre  ( BRC-1215-20014 ; https://cambridgebrc.
Bioinformatics Analysis Analyses were performed at the ABIMS Roscoff Galaxy facility (galaxy3. sb-roscoff. fr; accessed on 22 January 2019). Raw data are available through the NCBI Sequence Read Archive (SRA, BioProject PRJNA521849, BioSample SUB5166470). Read quality was assessed using the FastQC program (V0. 11. 5) (www. bioinformatics. babraham. ac. uk/ projects/fastqc; accessed on 22 January 2019). Raw reads were filtered with Trimmomatic V0. 36. 4  [29]  to remove Illumina adapters (for Truseq3) and reads with an average Q-value below 26 for 95% of their length. To characterize and quantify the transcriptome of each sample, the filtered reads were paired-mapped against a P. margaritifera draft genome  [30]  with TopHat (V1. 4. 0). Cufflinks (V2. 2. 1. 0) and Cuffmerge (V2. 2. 1. 0) were used to assemble and merge the transcriptome produced for each library, respectively  [31].
Transcriptome Functional Annotation The transcriptome produced was annotated by sequence comparison against worldwide databases. First, an initial annotation with PLASTX  [33]  was made against NR data base (e-value at 1 × 10 -3 )  [34]  and Uniprot-Swissprot (e-value at 1 × 10 -3 )  [35]. A protein domain search was then performed with InterProscan  [36]. Finally, Gene Ontology terms were assigned with Blast2GO  [37]. Scripts are provided in Supplementary Materials Table  S1.
Differential Molecular Function and Gene Expression To analyze our data, we followed a two-step strategy. The first step was transcriptomewide, considering the entire transcriptome for each color, and using RBGOA tool  [38]  to identify significantly over-, or under-represented molecular function. To weigh the analysis, the -Log(p-value) method was used to take into account the strength and significance of the regulation of each gene of the transcriptome (https://github. com/z0on/GO_MWU; accessed on 22 January 2019). The second step of the strategy was more targeted and aimed to identify candidate genes directly from significantly Differentially Expressed Genes (DEGs). For each approach, we performed the same pairwise comparisons (red vs. yellow, red vs. green, and yellow vs. green) using the DESeq2 R package (v.
3. 7)  [39]. The three color phenotypes were used at the same factor level (green individuals compared against yellow individuals compared against red individuals). The collective gene expression differences between phenotypes were examined with a Principal Component Analysis (PCA) from the tool set of the DESeq2 R package (v. 3. 7)  [39]. Gene expression differences between color phenotypes obtained with DEseq2, were considered significant below the 5% level (adjusted p-value (Padj) for multiple testing with the Benjamini-Hochberg procedure FDR < 0. 05). The online version of KAAS (http://www. genome. jp/tools/kaas/; accessed on 23 June 2018) was used to find the functional pathways in which significant DEGs were involved  [40]. Pathview (https://bioconductor. org/packages/release/bioc/html/ pathview.
Enzymatic Structure Analysis by Homology Modeling Three-dimensional homology modeling of the protein structures (longest ORF) was performed on the PDB (Protein Data Bank) file obtained with I-TASSER (https://zhanglab. ccmb. med. umich. edu/I-TASSER/; accessed on 30 March 2019) for the four PBGD sequences found in the DEGs analysis (named PBGD_1 to PBGD_D). The secondary structures are results from the I-TASSER analysis. Modeling was done with UCSF Chimera software  [41]. Hinge residues were determined with the HingProt server link in Song et al. 2009  [42]. Superimposition of our candidate proteins on a reference protein with a known structure was performed in SuperPose  [43]  (Version 1, http://wishart. biology. ualberta. ca/SuperPose/; accessed on 2 July 2019). Only the longest PBGD (A) is shown.
Raman Spectra Analysis Vibrational spectra of pearl oyster shell molecules were produced and analyzed by principal component analysis following the techniques described in Bonnier and Byrne, 2012  [44]  directly on raw spectra. Then, subtraction of the baseline and a lightweight smoothing (5 points) were added to the raw spectra data. To improve data accuracy, spectra from each of the three windows were mathematically calibrated against referential inorganic components found in molluscan shell. Thus, calcite at 703 cm -1 was used for the 380-880 mm window  [19] , aragonite at 1085 cm -1 was used for the 880-1380 window  [19] , and carbonate at 1547 cm -1 was used for the 1380-1880 window  [45]. The stat_peaks function from the ggspectra  [46]  R package (V. 0. 3. 5) was used to identify the exact peak position from the raw data to perform these calibrations.