dataesr/scientific-paragraphs-categorization huggingface dataset:
5 for all visits. This can be explained by the final step of the PheNorm algorithm which is a mixture model on the predicted sum of main ICD and main CUI. As our dataset is largely imbalanced, with many more negative patients than positive ones, both normal distributions of the mixture model are concentrated on the negative class. Because they are close to each other, the probability of belonging to each class (positive or negative phenotype) is really close. However, as shown on figure  3 , those probabilities are not constant and still give good prediction performance according to AUROC and AUPRC, in spite of being poorly interpretable. Other methods do not have this problem, as they mainly learn on binary silver standard. Table  3  details the performances of the algorithm in the train and test sets compared to other methods and with different hyperparameters (half-life,  and pseudo-labels).
More elements justifying pseudo-labels are available in the supplementary material. As expected, for TB the advantage of PheVis is less important because the information is accumulated over a shorter time period, still it performs well especially in term of AUROC compared to other unsupervised methods. Supervised methods worked significantly better with accumulated variables supporting the importance of taking into account past history to predict actual phenotype. Among the other unsupervised methods, we can denote that PheNorm and the random forest POLAR seem to be the best methods, however, as shown on figure  2  PheNorm has serious calibration problem on unbalanced datasets. XPRESS failed to reach convergence in our setting possibly because it uses lasso penalization instead of elasticnet as in SAFE or PheVis, or ridge as in ANCHOR or POLAR.
Patient 3 and 4 have tuberculosis. For patient 3, that information is still trailing behind after the patient is cured for PheVis, partly because of the lack of advanced natural language processing tool hindering the distinction between past and actual disease history. PheNorm predictions are close to 0. 5 because the mixture model fails to learn meaningful probabilities in this extremely imbalanced setting. XPRESS fails to estimate any probability higher than 0 for TB because of convergence failure and RA probabilities are either almost 0 or 1 providing a binary interpretation of the disease status rather than a continuous one. Both PL_RF and Anchor are too volatile to provide a trusted interpretation of their output probability.
[13]  Our application setting is different from the other methods original paper, mainly because there is intrapatient correlation between visits phenotype. This is not accounted for in Phenorm, XPRESS or POLAR where the learning is at patient level, nor in ANCHOR because it learns on acute diseases. [10] [11] [12] [13]  As in POLAR or ANCHOR, our dataset is largely unbalanced towards the negative class (e. g 1. 1% epilepsy prevalence in POLAR, 2. 0% septic shock prevalence in ANCHOR) contrary to PheNorm (lowest disease prevalence was RA with 22. 5%). This unbalanced setting seems to favor unsupervised learning with silver standard (PheVis, POLAR, ANCHOR, XPRESS) in terms of calibration. In terms of performance, PheVis performed better for both diseases on both AUROC and AUPRC. Random forest POLAR and PheNorm have close performances.
As those three methods rely on different approaches, future developments might be able to leverage and combine each of their strengths. These phenotyping algorithms are highly sensitive to the input features, which emphasizes the need for finer natural language processing tools able to perform semantic analysis. The use of other features such as biological test results or treatment should also be considered, as they should be highly predictive of the phenotype, but further works is needed to define how they could be integrated into the silver-standard surrogate strategy used in PheVis. Also, instead of providing raw variables as input of the algorithm, PheVis could benefit from embedding or tensor factorization approaches that might provide more informative variables but would also increase the complexity for non-expert users.
[25] [26] [27]  Our performance evaluation is made against an imperfect gold standard, mainly due to the lack of large annotated patient reference sets. For TB, the gold standard was manually curated, while for RA, we used a highly specific form but which might lack sensitivity: interestingly, upon manual inspection it appeared that PheVis was able to accurately recover RA patients visits of 5 patients who were not treated in the Rheumatology department of the University Hospital of Bordeaux and thus had no record of this specific form, resulting in a failure of the gold standard. Such phenomena might underestimate the algorithm performance. PheVis can provide a probability for a large set of diseases and medical conditions with little effort. The performances might vary depending on the disease of interest, the database quality and the EHR language but were better than state-of-the-art method in our study.
used by rheumatologists at the University Hospital of Bordeaux in usual RA care, for tuberculosis we manually reviewed patients with at least one mention of tuberculosis treatment while other patients were considered not having the disease. Latent tuberculosis was labelled as tuberculosis negative because, even if the bacterium is the same, symptoms, diagnosis and treatment are different. Patients were included in the study cohort if i) they had been hospitalized at the University Hospital of Bordeaux at least once since 2010 and ii) if they had either one primary or secondary ICD code of RA (M05 or M06), or one biological measurement of Anti-Citrullinated Peptide Antibody. The cohort was split into training and test datasets at the patient level with a 70% to 30% ratio. The cohort is described in Table1, highlighting the discrepancy between ICD, CUI and goldstandard justifying the need for automated phenotyping algorithms.
The latter held the black cylinder in the middle (Fig. 1B ). After being attached to its tether, each bee was placed on a small (49 mm diameter) Styrofoam ball for familiarization to a provisory set-up and provided with 5 μl of 1. 5 M sucrose solution. Each bee was held for 3 h in this provisory setup, which was kept in the dark and without visual stimulations. Virtual reality set-up. The bee was then moved to the VR setup to be trained and tested in a 3D visual environment. To establish this environment, we used a custom software developed using the Unity engine (version 2018. 3. 11f1), open-source code available at https:// github. com/G-Lafon/ BeeVR. The software updated the position of the bee within the VR every 0. 017 s. The VR apparatus consisted of a spherical Styrofoam ball, which acted as a treadmill onto which a stationary bee walked while perceiving an artificial visual landscape displayed in front of it on a semi-circular screen (Fig.
Four experimental conditions were defined (see Table  1 ). In the 'Transparent Condition' (N = 24), no background was provided and the VR display contained only the two cuboids on an empty dark background (Fig. 1E ). The residual light from the empty background had a dominant wavelength of 449 nm and an irradiance of 38 µW/cm 2. In the 'Vertical Grating-Optic Flow Condition' (N = 17), the walls of the virtual arena were covered by a vertical grating made of black (RGB: 0, 0, 0; irradiance: 45 μW/cm 2 ; dominant wavelength 628 nm) and red bars (RGB: 255, 0, 0; irradiance: 1130 μW/cm 2 ; dominant wavelength 628 nm), each subtending a visual angle of 6° (Fig. 1F ). Moving forward increased this visual angle by 0. 18°/cm. In the 'Vertical Grating-No Optic Flow Condition' (N = 17), the same grating made of black and red bars was used but the VR software moved it in synchrony with the bee's gaze so that no motion cues could be derived from the background.
Training was balanced in terms of color contingencies (i. e. blue and green equally rewarded across bees) based on a random assignment by the VR software. If the bee reached the CS+ within an area of 3 cm in the virtual environment (i. e. the chosen cuboid subtended a horizontal visual angle of 53°) and centered it in its front, the screen was locked on that image for 8 s (Fig. 2B ). This allowed the delivery of sucrose solution in case of a correct choice, or of quinine or NaCl in case of an incorrect choice. Solutions were delivered for 3 s by the experimenter who sat behind the bee and used a toothpick to this end. The toothpick contacted first the antennae and then the mouthparts while the screen was locked on the visual image fixated by the bee. Each training trial lasted until the bee chose one of both stimuli or until a maximum of 60 s (no choice).
Data were bootstrapped to plot the proportion of bees in each category with their corresponding 95% confidence interval. Performances were analysed using generalized mixed linear models (GLMM) with a binomial error structure-logit-link function (glmer function of R package lme4)  39. The independent variables (fixed factors) were the experimental group (Condition), the trial number (Trial; Experiments 2 and 3), the choice category (Choice) and the color of the CS+ when applicable (Color: Blue or Green). Bee ID was included as a random factor to account for the repeated-measure design. Several models were run by testing interactions between factors and by dropping each factor subsequently to select the model with the highest explanatory power (i. e. the lowest AIC value). P values for each factor or interaction were obtained by comparing models.
In addition, we analyzed the latency to make a choice starting from the beginning of a trial to the moment in which a choice (either for the CS+ or the CS-) was recorded. NC data were excluded from the latency analysis. The analysis of these continuous variables was done using a linear mixed model (lmer function) in which the individual identity (Bee ID) was a random factor and the experimental condition (Condition) and trial number (Trial) were fixed factors. For each experimental condition, we represented the bees' cumulative trajectories (CS+ choosing and CS-choosing bees) in terms of heat maps, which show the cumulative coordinates occupied by the bees either during the ten training trials or during the non-reinforced test to which they were subjected. Coordinates were binned into 1 cm 2.
Results Experiment 1: choosing the red intensity for the achromatic black and red background. In a first experiment, we determined the spontaneous phototactic responses of bees towards a red cuboid (dominant wavelength 628 nm) varying in intensity. Using different groups of bees, we tested three different intensities to define the one that would be sufficient to induce phototactic attraction: Red 10 (RGB: 26, 0, 0; irradiance: 13 μW/ cm 2 ), Red 50 (RGB: 128, 0, 0; irradiance: 140 μW/cm 2 ) and Red 100 (RGB: 255, 0, 0; irradiance: 1130 μW/ cm 2 ). Each bee was tested along four consecutive tests with the same intensity. The model that best fitted the data included an interaction between the red intensity and the bees' choice (Choice*Intensity: χ 2 = 65. 48, df: 4, p < 0. 001). There was no significant effect of the test sequence on the bees' choices (Test: χ 2 = 0.
11, df:15, p < 0. 001) but no effect of the nature of the CS+ was found (blue or green: χ 2 = 0. 000, df:1, p = 1). For each background condition, data were thus represented as a CS+ vs. a CS-discrimination irrespective of color identity. In the heat maps, the rewarded cuboid is represented on the left side although its side was randomized along the training sequence. When no grating was present in the background and the colored cuboids were displayed against a dark homogeneous background ('Transparent Condition'; Fig. 4A ), bees learned to respond more to the CS+ than to the CS-. The interaction between trial number and bee choices was significant (χ 2 = 7. 99, df:2, p = 0. 02). In the course of the 10 conditioning trials, the percentages of bees responding to the CS+ and that of bees responding to the CS-evolved differently (z = 2.
We recorded the percentage of bees correctly choosing the CS+, the CS-or not choosing (NC). There was no significant effect of the ventral optic flow on test performances when the variable considered was the choice made by the bees (Condition: χ 2 = 0, df:1, p = 1). Thus, the results of both groups of bees Coordinates were binned into 1 cm 2. Warmer colors depict locations more frequently occupied (see color bar). The highest frequency is cut down to 10% of the maximum on the color bar. While the side of the rewarded stimulus was randomized along conditioning trials, it was placed arbitrarily on the right in the heat maps. (B) Color discrimination learning in the absence of ventral motion cues on the treadmill (N = 29). Left: Acquisition curves as in (A). Right: Heat map as in (A). (C) Data pooled for the two optic-flow conditions and segregated according to the situation in which the CS+ color was Blue while the CS-color was Green.
Acknowledgements We thank two anonymous reviewers for useful comments and suggestions on previous versions of this article. This work was supported by an  ERC   Advanced Grant  ('Cognibrains') to M. G, who also thanks the  Institut Universitaire de France (IUF) , the  CNRS  and the  University Paul Sabatier/Fondation Catalyses  for support. S. R. H. was supported by a post-doctoral fellowship of the  Fyssen Foundation.
The map was performed with Matlab R 2018a  20  (https ://fr. mathw orks. com/produ cts/new_produ cts/ relea se201 8a. html). the two areas. Positive anomalies were higher in the north, reaching 5 m s -1. In comparison, negative anomalies were around 2 m s -1 in the north and south of Cap Blanc (Fig. 3 ). Sea surface chlorophyll-a concentration (SSC) showed the opposite pattern, with a significant increase in the north sCCLME and a very significant decrease in the south (Fig. 2 , Table  1 ). Low SSC positive (+ 2 mg m 3 ) and negative (< -1 mg m 3 ) anomalies were recorded in the north of Cap Blanc. In comparison, in the south, high positive (+ 9 mg m 3 ) and negative (-4 mg m 3 ) anomalies were recorded. Barycenter displacement analysis. No significant trends were detected for the barycenter of the acoustic density (expressed in Nautical Area Scattering Coefficient (NASC or s A , m 2 nmi -2 )), which was used as a proxy of pelagic abundance 'PA' , for both groups in the acoustic surveys (i.
At a higher trophic level, the bulk of pelagic biomass is represented by small pelagic fishes (e. g. , S. aurita, S. maderensis), which are also key for safeguarding food security in West Africa. Acoustics and environmental data processing. Acoustic data. Fisheries acoustic surveys were performed annually in November or December (upwelling season) from 1995 to 2004, and again in 2006, 2011, and 2015 (Fig. 1 ), on-board the research vessel R/V Dr. Fridtjof Nansen (DFN). The way in which total acoustic density is distributed divides the study area into two parts: north and south, separated by Cap Blanc. These surveys were conducted using a 38 kHz echosounder at depths ranging from 10 to 500 m isobaths. We deployed an ES38-B transceiver, towed at a depth of 5. 5 m, with the following settings: absorption coefficient of 8.
7 dB km -1 , pulse length of 1. 024 ms -1 , and maximum used transmission power of 2000 W  70. The echo sounder was calibrated following classic calibration procedures  71. Acoustic data were processed with an offset of 10 m below the sea surface to avoid integrating air bubbles during post processing. Environmental data. To obtain a homogenized overview of the area, environmental data were collected from satellite remote sensing products: upwelling wind (UW), sea surface chlorophyll-a concentration (SSC), and sea surface temperature (SST). UW is the upwelling-favorable meridian wind speed at the sea surface, and is used as an upwelling index  3. UW was extracted from the daily CCMP (Cross-Calibrated MultiPlatform) wind product V2. 0 at a 0. 25° spatial resolution (available at http://www.
remss. com/measu remen ts/ccmp/) from 1988 to 2017. SSC is an adequate proxy of primary productivity models on which biomass is directly based  32. SSC was collected daily from the SeaWIFS (1997-2002) and AQUA-MODIS sensor (2003-2018) (available at https ://ocean color. gsfc. nasa. gov/). SST data were extracted from daily day-time series of the pathfinder AVHRR dataset version 5. 2, from 1982 to 2018, at 4 km resolution (available at https ://www. nodc. noaa. gov/Satel liteD ata/pathfi nder 4km53 /). The spatial averages of satellite data were computed using a fixed distance from the coast of 100 km. The monthly average of all data (UW, SSC, and SST) were calculated, and the spatial averages were computed for the sCCLME area. Annual climatological anomalies were estimated to elucidate fluctuations of the different environmental variables and support the trend analysis.
The barycenter is the average position of acoustic density for 1 year, weighted by the biomass of each zone. For instance, if "B ai " represents the biomass in zone "Z i " for year "a" and "A i " the coordinates of the center of "Z i ", the coordinates of the barycenter "C a " of the biomass would fit the equation below as: And the coordinates in latitude "X a " and longitude "Y a " of C a are: All statistical analyses were completed in a free software environment for statistical computing and graphics  81. Figures and maps were also performed with R and Matlab R2018. Spatial and temporal variability of the acoustic abundance time-series were investigated for: "zooplankton, " "pelagic fish, " and "total. " The statistical significance of the annual position (latitude and longitude) of changes to the barycenter and PA trend were tested using Spearman tests, which are considered valid for testing the significance of trends  82.
Acknowledgements This work was completed within the AWA " Ecosystem Approach  to the management of fisheries and the marine environment in West African waters" project funded by the  IRD and BMBF  (grant  01DG12073E  and  01DG12073B ), http://www. awa. ird. fr(SRFC:  Sub Regional Fisheries Commission ) and the  PREFACE  project funded by the  European Commission 's  Seventh Framework Program  ( 2007-2013 ) under Grant Agreement number  653521 , https ://prefa ce. b. uib. no/ and ended within  TriAtlas European project  (grant agreement number  817578 ). We thank the  Nansen  project ( FAO/IMR ),  Dr. Jens-Otto Krakstad , and  IMR Norway as FAO  and West African colleagues involved in data collection.
The only prerequisite were some standard architecture and data structure classes. The focus was on applied formal methods, and we chose to build it around Frama-C. The version of the class described here was taught in Fall 2019 to 37 bachelor students, most of them in their fourth year in the Computer Science program or the Applied Computer Science program. At INSA (Institut National des Sciences Appliquées) Centre Val de Loire, this course was taught to students in their fourth year. INSA is a network of French engineering schools. Each school offers five year programs from which students graduate with a Master's degree. The students who followed this course majored in Security and Computer Technologies. These students have a strong background in programming using different languages but were not exposed to theoretical courses covering formal methods.
We focused on utilizing and presenting, in this order, the RTE, WP and EVA plug-ins as well. Project: Contiki's memb module As demonstration of the effectiveness and usability of code specification and verification, we proposed a project that aims to specify and verify the memb module of the Contiki OS. The subject of this project was inspired from the work presented in  [12]. Contiki is an operating system for networked, memory-constrained systems with a focus on lowpower wireless Internet of Things devices. Uses for Contiki include systems for street lighting, sound monitoring for smart cities, radiation monitoring, and alarms. It is open-source software. Contiki was developed in 2002, the main focus was on enabling communication in the most constrained devices, with no particular attention given to security.
As it matured and as commercial applications arose, communication security was added at different layers, via standard protocols such as IPsec or DTLS. The security of the software itself, however, did not receive much attention. The memb module is Contiki's main memory management module. To avoid fragmentation in longlasting systems, Contiki does not use dynamic allocation. Memory is pre-allocated in blocks on a perfeature basis, and the memb module helps the management of such blocks. It offers a simple API, enabling to initialize a memb store, to allocate a block, to free a block, to check if a pointer refers to a block inside the store and to count the number of allocated blocks. The memb. c file consists in about 100 lines of C code but is one of the most critical elements of Contiki, as the kernel and many modules rely on it.
The Contiki code base involves a total of 56 instances of memb. Not all are included in a given Contiki firmware, but a subset is included depending on the application and configuration. memb is used for instance for HTTP, CoAP, IPv6 routes, the MAC protocol TSCH, packet queues, etc. The goal of the project was to formally specify -in ACSL -and verify -using Frama-C/WPthe memb module. The API studied in this project is extremely close to the actual Contiki-NG API, but the provided implementation differs for some functions, to make the verification easier for students. Elements to guide them through the different phases were detailed in the document they received at the beginning of each phase. The files that were provided to students were: logic defs. h containing userdefined predicates and logic functions; lemmas.
2012) , and performed a quantitative analysis of sgRNA transcription. They revealed that N RNA was the most abundantly expressed transcript, followed by S, 7a, 3a 8, M, E, 6, and 7b RNAs  (Kim et al. 2020). Finkel et al. used  two approaches to calculate the abundance of sgRNAs in NGS data from SARS-CoV-2 infected cell cultures  (Finkel et al. 2021). The first was the STAR-based assessment of the relative abundances of RNA reads spanning leader-body junctions for the canonical sgRNAs. The second approach used deconvolution of RNA densities. In the deconvolution or "decumulation" approach, the RNA expression of each ORF is calculated by subtracting the RNA-read density upstream of the ORF region (inter-TRS region)  (Irigoyen et al. 2016 ). Finkel et al. 's study has described the N transcript as the most abundant followed by M, ORF7a and ORF3a.
Validation of noncanonical sgRNA produced inside ORF1ab We used the RT-qPCR approach in order to test the accuracy of our sgDI-detector algorithm. We validated the noncanonical ORF U3dc4 that has its body TRS sequence located in ORF1ab. ORF U3dc4 was less present in our data than the majority of other noncanonical ORFs. Of note, U3dc4 was previously detected in the  Kim et al. (2020)  study by the STAR approach, but to our knowledge was never FIGURE 3. sgDI-tector detects most canonical sgRNAs in all replicates with a high number of counts. (Left panels) Deletion DVGs distribution across the last 10 kb positions of the SARS-CoV-2 genome (GISAID ID: EPI_ISL_414631). Each blue or orange bar corresponds to a deletion, the position of the bar being the starting point of the "body" part of the junction (called RI position in sgDI-tector).