dataesr/scientific-paragraphs-categorization huggingface dataset:
The best possible point in each plot is at coordinates (1, 1), but this is usually unattainable. The gray dashed line has slope -1 and, as such, points on that line have the same tradeoff between accuracy and fairness. The lines showed in each plot pass through the point closest to (1, 1) under the L 1 metric. These points are, thus, the best performers under the assumption that fairness and accuracy are equally important. We can see that, with the exception of the plot concerning the COMPAS dataset, the points ( ★ ) representing FairSwiRL +RF are always in the upper half of the plots. Higher values of 1-SAD mean that the debiasing component of FairSwiRL is working as expected. In the SYNTHETIC dataset we can notice that the points representing FairSwiRL +RF are the closest to the ones of the random forest trained on perfectly debiased data (PD+RF), which is theoretically perfect as far as fairness is concerned.
The comparisons with FairSSL ( ), FESF ( ) and VFAE ( ▶ ) are also favorable. Except for the CARD dataset, FairSwiRL lies on the optimal tradeoff line. In CARD, where the best results are attained by FESF, FairSwiRL has a better fairness, but the lower MCC leads the FESF model to prevail in terms of the linear trade-off we are assuming here. This is a typical case of accuracy-fairness dilemma: higher 1-SAD implies also lower predictive power when the sensitive attribute and target variable are correlated. On the COMPAS dataset we have a mixed situation, while the best points are attained by Fair-SwiRL, we can see that for some experiments (specifically, those with fewer labeled examples) it attains worse performances than the competitors. Overall, we would not judge this experiment as a clear win for FairSwiRL, but we still maintain that it is a competitive approach also in this case.
Given the peculiarity of COMPAS, additional experiments on this dataset are presented and discussed in Appendix 2. As far as more general trends are concerned, we observe that more labeled instances (warmer colors in Fig. 6 ) lead all methodologies to more accurate, but less fair results. This result, in our view, justifies further future employment of semi-supervised techniques in fair classification: a small amount of labeled data does not impact fairness negatively. Beyond the linear tradeoff discussed above, we also experiment in an hypothetical context in which fairness is paramount and performance may be pursued only when fairness is already guaranteed. To model this situation, we repeated the experiments recording the discounted MCC metric: DisMCC = MCC y ⋅ e -SAD , where MCC y is the MCC computed on the target variable.
FairSwiRL +RF compared to other competitors when the number of labeled instances is very low In this experiment the setting is similar to the previous one. The only difference is the number of labeled instances that does not change from 100 to 2000 but from 10 to 100, thus leading to a more challenging scenario with very few labeled instances. As before, we consider FairSwiRL +RF as our candidate combination and PD+RF, FESF, FairSSL, VFAE as competitors. Before looking at the data, it is worth reporting that, given the low number of labeled instances, FESF and FairSSL fail their training procedure in several runs and on different datasets because, at a certain point, the training set becomes empty: FESF involves a down sampling procedure while FairSSL uses situation testing which also reduces the number of data points.
We still report the average value of successful runs in the plots whenever possible in order to enable a comparison. The results of this experiment are reported in the right column of Fig. 6. The plot setting is identical to the one reported in Sect. 6. 3, the only difference being in the number of labeled examples. As in the previous case, we can see that, except for the COMPAS dataset, the points ( ★ ) representing FairSwiRL +RF are always in the upper half of the plots. This means that the debiasing component of FairSwiRL is working as expected also when the number of labeled instances is very low. In particular, in COMPAS, FairSwiRL +RF seems to have the best classification performances while still remaining near the grey dashed line. In the case of the SYNTHETIC dataset FairSwiRL +RF is the only model that has almost the same level of 1-SAD reached by theoretically optimal PD+RF.
In ADULT, both FairSwiRL +RF and FESF reach the threshold line: the former gives more importance to the fairness, while the latter is more optimized for the classification task. In BANK, every method reaches a good fairness but none of them display solid classification performances. We posit that, given the extremely low number of labeled instances we considered, the classification models learned on this dataset are not too different from random guessing. In CARD, FairSwiRL +RF remains very competitive by reaching high level of fairness while maintaining also a good performance on the classification task. To complete the analysis, we report, in Table  3 , the performances of the algorithms in terms of the average rank computed by using the DisMCC metric with = 30. The value of is arbitrary chosen at the beginning and kept fixed during all experiments.
Coherently with the observations made for the plots in the second column of Fig. 6 , FairSwiRL +RF outperforms the competing methods in SYNTHETIC, ADULT, BANK and CARD. In the remaining dataset (COMPAS), FESF performs better. It is worth pointing out that, while FairSwiRL +RF results as the best performer in most datasets, VFAE (the other fair representation learning strategy) behaves poorly in this extreme setting in which only a very low number of labeled instances are available.
FairSwiRL +RF compared to other competitors when labeled instances are very biased In this experiment, we assess the behaviors of FairSwiRL +RF and competitor methods in an extreme and difficult setting: for each dataset we cherry-pick a set of 100 labeled instances where the SAD value computed on the target variable is exactly 1 (maximum bias). As mentioned in Sect. 6. 2, our general setup is to select a fixed number of unlabeled and labeled instances for each experimental run. Therefore, we are able to construct a maximum-bias setup by only selecting instances with positive outcomes ( Y = 1 ) for the privi- leged group ( S = 1 ). Symmetrically, we include instances with negative outcomes ( Y = 0 ) for the underprivileged group ( S = 0). In Table  4  we report the average SAD values (lower is better) computed on the predictions provided by different methods.
We also report the values of [ Ŷ | S = 0] and [ Ŷ | S = 1] within the parentheses. We can observe that, in this extreme setting, the CARD dataset is problematic for every method: FairSSL failed the training process, FairSwiRL +RF and FESF predict Ŷ = 0 for almost every instance of the test set, and VFAE provide very biased predictions. In Having made sure of the fact that the representations learned by FairSwiRL are as unbiased as possible also in this extreme setting, let's consider the target variable prediction performance in Table  5. In this table we can observe that the representations learned by FairSwiRL , while remaining as much unbiased as possible, still provide useful and not random predictions in almost every dataset.
2010). The overall goal of this study was to investigate the relationships between wind conditions and seabird migratory movements at the scale of the North Atlantic Ocean. Our study utilizes a long-term, multi-colony and multi-species tracking dataset developed under the SEATRACK project  (Strøm et al. 2021). We selected as study models two pelagic seabirds that are long-range migrants but with different flight capacity, the black-legged kittiwake Rissa tridactyla (hereafter kittiwake) and the Atlantic puffin Fratercula arctica (hereafter puffin). Both species use flapping (as opposed to glide-flapping or soaring) flight, but differ in morphology (wing area, wingspan, general body shape), wing loading, and flight speed  (Pennycuick 1987). While kittiwakes are surface feeders, puffins are wing-propelled divers.
Bird tracking data We used data collected by the SEATRACK project between 2014 and 2020. In total, 794 kittiwakes and 425 puffins from 18 colonies (Supporting information) were equipped with geolocators attached on a ring, and recaptured in following breeding seasons. Models of geolocators were c65, c65_super, f100 and w65 produced by Migrate Technology, mk18 produced by BAS, and mk4083 and mk4093 from Biotrack (Supporting information). All of these models recorded light and saltwater immersion data. Details on the logger types, number of deployments per colony and frequency of data acquisition are presented in the Supporting information. The median number of annual tracks per individual (a track being defined as data recorded between two successive breeding seasons) was two (range 1-6 tracks per individual, Supporting information).
We then followed the method of  Amélineau et al. (2021)  to identify migratory segments and exclude stationary periods during the remaining months for each track. Based on the excluded periods, seasons were defined as follows for each species: autumn contained data from August to November for kittiwakes, and from July to November for puffins; spring contained data from December to April for kittiwakes, and from February to May for puffins. For each migratory segment, we calculated the detour by dividing the total length of the migratory segment (cumulative sum of the distances between successive locations) by the shortest distance between the departure and arrival locations. For each individual, the overall migratory distance was calculated as the cumulative distance between the centroids of successive stationary periods (the first and last stationary periods being the breeding season at the colony) and represents the whole non-breeding season.
Distances were calculated as great circle distances using the function distGeo from the 'geosphere' package  (Hijmans 2021). For each colony and species, the winter distribution (Fig. 2 ) was calculated as the 50% utilisation distribution kernel calculated on the December positions with the 'adehabitatHR' package  (Calenge 2006) , using a smoothing factor (h) of 200km, a grid with 50 × 50 km cells and a Lambert azimuthal equal area coordinate system. Wet/dry sensors of the geolocators were used to calculate the proportion of time spent wet per 24 h, which gives an approximation of the proportion of time spent on the water and in the air.
Wind data We used wind from the ERA5 model  (Hersbach et al. 2020 ), a reanalysis model from the European Centre for mediumrange weather forecast (ECMWF). Data were obtained from the climate data store (https://cds. climate. copernicus. eu/). Wind data of the ERA5 model are available at 10 m (surface winds), 100 m, and higher altitudes. We chose winds at 10 m as previous studies found a median flight altitude of 16 m (range 0-81 m) for kittiwakes  (Borkenhagen et al. 2018) , and < 10 m for puffins  (Johnston et al. 2014 ). We extracted the hourly u (east) and v (north) components of surface wind (10 m height) on a 0. 25° grid over the study area. We then calculated the daily mean u and v wind components. To get wind conditions encountered along the track, we calculated the midpoint between two successive locations, and extracted the daily mean wind at this midpoint.
Statistical analyses All analyses were performed in R ver. 4. 1. 2 (www. r-project. org). We investigated the variation in wind support between seasons and species. We worked at the scale of an annual track, with one median value per track and per season. We fitted linear mixed-effects models with the lmer function of the 'lme4' package  (Bates et al. 2015). To take the lack of independence in our data into account (repeated measurements per individual, per colony and per year), we included a random effect of year, and a random effect of individual nested in colony  (Zuur et al. 2009). We considered all combinations of covariates. We then used AIC (Akaike's information criterion) for model selection. If the difference in AIC values between two models was < 2, the models had equal statistical support and in case of nested models, the simplest was preferred  (Burnham and Anderson 2002).
We detected no pattern in the distribution of model residuals indicating that the normality and homoscedasticity assumptions were met. The 'MuMin' package was used to generate the marginal R 2 (variance explained by the fixed effects) and the conditional R 2 (variance explained by the fixed and random effects)  (Bartoń 2022). Unless otherwise mentioned, results are presented as median and 95% confidence interval. The presented maps have a plate carrée projection.
Funding -The  SEATRACK  project (www. seapop. no/en/seatrack) was funded by the  Norwegian Ministry for Climate and the Environment , the  Norwegian Ministry of Foreign Affairs  and the  Norwegian Oil and Gas Association  along with eight oil companies. The  SEAPOP program  (www. seapop. no, grant no. 192141 ) supported the fieldwork in Norway (including Svalbard and  Jan Mayen ). Kittiwake fieldwork in the Kongsfjord was supported by the  French Polar Institute (IPEV , prog. 330 to  O. Chastel ). The work on the Isle of May was also supported by the  Natural Environment Research Council  (Award  NE/R016429/1  as part of the  UK-SCaPE programme delivering   National Capability ). Permits -All bird handling was done according to the national guidelines and under the necessary field work permits for each colony.
Cohorts We analyzed three RNAseq AML cohorts. Beat-AML  (Tyner et al. , 2018) ,  Leucegene (BCLQ, 2019) , and Beat-AML2  (Bottomly et al. , 2022)  cohorts with  462, 437, and 213 samples (phs001657. v1. p1, GSE49642, and phs001657. v2. p1 accession IDs), respectively. From these cohorts, the analyzed samples are Bone Marrow and Peripheral Blood sample types. For each sample, we have information about the presence or absence of NPM1 mutation that allows us to investigate the difference between these two conditions and provide a selection of k-mers. Table  1  presents an overview of information from these AML cohorts. Additionally, we used five wild-type cohorts, totalizing 132 healthy samples, which in this paper, we treat as a single cohort, the healthy cohort that includes CD34+ cells (37 samples), purified monocytes (50 samples) and mononuclear cells (45 samples).
We sought to understand the behavior of the selected k-mers in healthy samples. For that, we searched the k-mers expression in the healthy cohort, considering that the healthy cohort does not have an NPM1 mutation. We interpreted that the k-mers expression needs, at least, to be close to zero to have a link to AML conditions. More information about the five wild-type cohorts can be found in the supplementary material. To complete the analysis, we analysed at the leukemia cell level. We used 34 RNAseq samples from mature and immature blasts cells obtained from 17 AML patients (https://www. ncbi. nlm. nih. gov/bioproject/PRJEB54896 accession ID). We analyze the k-mers expression to have a closer understanding of the genes. For this task, we searched the selected k-mers from the 3 AML cohorts.
Generating and selecting the k-mers We checked the quality of the raw downloaded data using fastQC version 0. 11. 9  (Andrews et al. , 2010)  and MultiQC version 1. 9  (Ewels et al. , 2016). As a complementary quality control, we checked sequencing protocol information and contamination with KmerExplor  (Riquier et al. , 2021) ). Then, we used Kmtricks to build a k-mer count matrix for each cohort, totaling five matrices (three AML, one healthy, and one mature and immature blasts cells). Kmtricks  (Lemane et al. , 2022)  is a tool to count kmers efficiently in large datasets and produce a k-mer count matrix across multiple samples. For example, the Beat-AML cohort has 7 terabytes of fastq files, Kmtricks reduced to a k-mer count compressed matrix of 78 gigabytes. To generate the matrices, in the AML cohorts, we considered that the k-mer needs to have a minimum abundance of 4 (which means, the k-mer has to be found at least 4 times in the sample) and be present in at least 5% of the cohort samples to be counted.
In the healthy and mature and immature blasts cells cohorts, we only considered that the minimum abundance was 2. The k-mer size applied was the tool default, size 31nt. The parameters used in Kmtricks for each matrix can be found in the supplementary material. After building the matrices, we implemented a C++ code to select the kmers with significant differences between NPM1 mutated and non-mutated samples, based on the coefficient of variation of these two conditions. Due to the k-mers expression variation across the cohorts, for each AML cohort we selected manually the coefficient of variation based on the gene investigated. For that, we analyzed the k-mers coefficient of variation of NPM1 across the AML cohorts. We designed all the specific and unique kmers of the NPM1 gene with Kmerator  (Riquier et al.
Classifier models To analyze if the selected k-mers are distinct between NPM1 mutated and non-mutated patients, we applied six of the most used Machine Learning algorithms to built the models and predict the conditions. For this task, considering that often the highest performing model are the least explainable and the most explainable are the least accurate  (Gunning et al. , 2021) , we used two less complex models: K-nearest neighbors (KNN) and Logistic Regression (LR); and four complex models: Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB). We implemented the algorithms using Scikitlearn package  (Pedregosa et al. , 2011)  in Python, applying for each model a grid search and a stratified cross-validation with 5-folds. We build models with normalized and non-normalized features.
The k-mers count are considered the features, and the samples are the instances. To understand how the k-mers can generalize between the cohorts, we constructed three settings to train and test the models, as shown in Figure  2 , training on one cohort and testing on another cohort. The Setting A, where the models were trained on Beat-AML and tested on Leucegene. Setting B, where the models were trained on Leucegene and tested on Beat-AML2. Setting C, where the models were trained on Beat-AML and tested on Beat-AML2. The models were evaluated by area under the receiver operating characteristic (AUC) for each setting. Additionally, we evaluated the models by Accuracy, Kappa, F1-Score, Precision, and Recall metrics. Reproducible scripts can be found in https://osf. io/4s9tc/.
Mapping and Annotation Our main objective was to identify the influence of the NPM1 mutation on k-mers and, consequently, on genes. To identify the genes belonging to the k-mers, we applied STAR 2. 7. 8a  (Dobin and Gingeras, 2015)  to map the k-mers to a reference human genome, GRCh38 assembly. After, we used Samtools 1. 11  (Homer et al. , 2009)  to generate flexible alignment formats, SAM, BED, and BAM files. Next, we implemented a script in R using the Ensembl REST API Yates et al. , 2015 to request the genes annotation for each k-mer using the SAM and BAM files. Also, we generate BAM files for 27 samples: 9 NPM1 mutated and 9 NPM1 non-mutated (for every 9 samples, we selected 3 from Beat-AML, 3 from Leucegene, and 3 from Beat-AML 2), and 9 healthy samples (random selected). The 27 BAM files and the BED file from the k-mers were visualized using Integrative Genomics Viewer (IGV) version 2.
The good performance of SVM is due to the basic parameters of the SVM model, such as the C value, being able to treat the imbalance classes. In contrast, the XGB model needs specific parameters for imbalanced classes. The parameters and reproducible scripts for each model can be seen at https://osf. io/4s9tc/. The results presented here belong to normalized models, performing better than non-normalized models. However, the results for nonnormalized models only differ in 1% or 2% and can be found in the supplementary material.
Even though we can not assume the suppression of these genes is linked with the presence or absence of NPM1 mutation, we can presume that these genes are linked with AML conditions. We then analyzed the gene expression of the 13 genes in a dataset of 34 samples corresponding to purified mature and immature blasts separated with specific surface markers from 17 AML patients. The expression analysis, Figure  7 , shows that the 13 genes are expressed in the AML patients with a significant difference between mature and immature leukemia cells. Figure  7. A shows a tSNE (t-Distributed Stochastic Neighbour Embedding) analysis where the genes were able to group the mature and immature leukemia cells in different clusters. A P P B A A L C B A A L C _ A S 1 B A A L C _ A S 2 B C L 2 L 1 4 C L E C 9 A L IN C 0 0 8 6 5 L R P 6 M A R K 2 P 1 6 M T C O 3 P 1 2 P T P R M S 1 0 0 A 9 S D K 2 S E T B P 1 T R H Expression.
Conclusion Publicly available human RNA-seq datasets are precious resources for biomedical research and the analysis of existing datasets can be used to find a new information and search specific transcriptional events across patient cohorts. This required efficient tools and pipelines. As shown in our results, our pipeline, using k-mers, performed large datasets analyses corresponding to 1112 RNAseq samples. To predict and extract biological information, we constructed six classifier models with K-nearest neighbors (KNN), Logistic Regression (LR), Neural Network (NN), Random Forest.
To tackle this, we designed a method to create a Generic ANN, by transforming motion tracking data recorded from previous participants into a training dataset adapted to the current participant. Recordings produced in the Initial Acquisition phase of the 10 participants of Exp1 were concatenated into a single dataset. However, this dataset would not be appropriate "as is" to train a Generic ANN because the relationship between hand locations and postures depends on arm segment dimensions that differ between participants. Indeed, as illustrated Supplementary Fig. 1b , similar arm postures give rise to different hand locations depending on arm morphology, such that hand positions need to be "remapped" to the current participant's arm before being fed as training data to a Generic ANN. This was achieved using forward kinematics solving with the 7-DoF model underlying the Virtual Arm calibrated for the current participant.
The dataset adapted to the current participant contained the remapped hand locations as well as the original arm angular configurations from Exp1, and was used to train the Generic ANN to perform the same task as the Own ANN: predict 5 distal DoFs from 2 proximal joint angles and 5 spatial parameters expressing the hand location. For the sake of fair comparison, this network's structure (i. e. , layer arrangement and number of neurons) was identical to that of the Own ANN. However, the training dataset included data from 10 participants instead of just 1, thus containing approximately 10 times more samples than data used to train an Own ANN. Given this, the epoch number was reduced to 10 to minimize computation time, and a momentum parameter was introduced to further prevent overfitting. An offline analysis indicated that a learning rate of 1.
Acknowledgments The authors would like to thank  Émilie Doat  and  Léa Haefflinger  for their help during the experiments,  Gerald E Loeb  for interactions on an earlier version of the control proposed here and feedback on this manuscript, and all participants who took part in this study. This work was supported by the  CNRS  interdisciplinary project  RoBioVis , and the  ANR-DGA-ASTRID  grant  CoBioPro  ( ANR-20-ASTR-0012-1 ).